Technical Cooperation with the Philippines Department...

National REDD+ System Philippines Project

Deutsche Forstservice GmbH

Component 4 implemented

on behalf of Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH

Technical Cooperation with the Philippines

Department of Environment and Natural Resources (DENR)

National REDD+ System Philippines

Component 4:

Forest Land Use Planning and REDD+ Implementation in Selected Areas

PN 12.9022.0-001.00 / VN 81162755

Methodology and Results of the 2015 - 2016 Forest Resources Assessment

in the selected Project sites in Davao Oriental

August 2016

Ralph LENNERTZ


Deutsche Forstservice GmbH

Component 4 implemented

on behalf of Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH

Davao Oriental FRA Results i


TABLE OF CONTENT

TABLE OF CONTENT ............................................................................................................. i

ANNEXES ............................................................................................................................. iii

TABLES ................................................................................................................................ iv

FIGURES ............................................................................................................................... v

ACRONYMS ......................................................................................................................... vi

EXECUTIVE SUMMARY ........................................................................................................ 1

1. INTRODUCTION AND BACKGROUND .......................................................................... 2

1.1 National REDD+ System Philippines Project .......................................................... 2

1.2 Methodological Framework ..................................................................................... 3

1.3 Forest Resources Assessment Objectives, Scale and Scope ................................. 3

2. SOURCES OF INFORMATION ....................................................................................... 6

2.1 Forest Definition ..................................................................................................... 6

2.2 Forest Areas / Stratification .................................................................................... 6

2.3 Wood Specific Gravity ............................................................................................ 8

2.4 Soil Classes............................................................................................................ 8

3. INVENTORY DESIGN ................................................................................................... 10

3.1 Inventory Method .................................................................................................. 10

3.2 Areal Sampling Frame .......................................................................................... 10

3.3 Elements Sampled ............................................................................................... 10

3.4 Number of Sample Points ..................................................................................... 11

3.5 Distribution of Sample Points ................................................................................ 11

3.6 Configuration of Sampling Units ........................................................................... 11

3.6.1 Observations and measurements at and around the Sample Points ......... 14

3.6.2 Observations and measurements at and around the Satellite Centers ...... 14

3.7 Estimation Design ................................................................................................ 16

3.7.1 Tree volume equations and calculation of merchantable volume............... 16

3.7.2 Allometric equations and calculation of biomass ....................................... 16

3.7.3 Carbon fraction of dry matter ..................................................................... 19

3.7.4 Statistical parameters ................................................................................ 20

4. FIELD IMPLEMENTATION ............................................................................................ 21

4.1 Retrieval and Permanent Marking of Sampling Units ............................................ 21

4.1.1 Approach of Sample Points using GPS receivers ...................................... 21

4.1.2 Location of Sample Points and Satellite Centers using compass and distance tape or laser rangefinder ............................................................. 22

4.1.3 Permanent marking of Sample Points and Satellite Centers ..................... 22

Davao Oriental FRA Results ii


4.1.4 Inaccessible Sample Points and Satellite Centers ..................................... 22

4.2 Variables of Interest Assessed / Measured ........................................................... 24

4.2.1 Administrative location .............................................................................. 24

4.2.2 Actual coordinates..................................................................................... 24

4.2.3 Elevation ................................................................................................... 24

4.2.4 Slope......................................................................................................... 24

4.2.5 Slope orientation ....................................................................................... 24

4.2.6 Terrain ...................................................................................................... 24

4.2.7 Land classification ..................................................................................... 25

4.2.8 Land cover ................................................................................................ 25

4.2.9 Forest type ................................................................................................ 26

4.2.10 Tree crown cover ...................................................................................... 26

4.2.11 Plant diversity ............................................................................................ 26

4.2.12 Ground coverage classes by vegetation layers ......................................... 27

4.2.13 Ground coverage and average depth of litter ............................................ 27

4.2.14 Mid-diameter and length of lying dead wood sections ............................... 27

4.2.15 Observations / measurements on live trees and standing dead wood ....... 28

5. ORGANIZATIONAL ASPECTS ..................................................................................... 32

5.1 Inventory Instructions and Field Data Forms......................................................... 32

5.2 Inventory Teams ................................................................................................... 32

5.3 Inventory Equipment ............................................................................................. 33

5.4 Training ................................................................................................................ 33

5.5 Inventory Camps .................................................................................................. 34

5.6 Time and Costs of the Field Work ......................................................................... 34

5.7 Data Processing and Analysis .............................................................................. 36

6. QUALITY ASSURANCE / QUALITY CONTROL ............................................................ 37

6.1 Quality Assurance ................................................................................................ 37

6.2 Quality Control ...................................................................................................... 37

7. DETAILED RESULTS OF THE FOREST RESOURCES ASSESSMENT ...................... 39

7.1 Species Diversity .................................................................................................. 39

7.1.1 Species diversity of Closed Forests .......................................................... 39

7.1.2 Species diversity of Open Forests ............................................................. 41

7.2 Stand Composition ............................................................................................... 44

7.2.1 Stand composition of Closed Forests ........................................................ 44

7.2.2 Stand composition of Open Forests .......................................................... 46

7.3 Stand Structure .................................................................................................... 48

7.3.1 Stand structure of Closed Forests ............................................................. 48

7.3.2 Stand structure of Open Forests ............................................................... 54

7.4 Timber Stocks ...................................................................................................... 60

7.4.1 Timber stocks of Closed Forests ............................................................... 60

Davao Oriental FRA Results iii


7.4.2 Timber stocks of Open Forests ................................................................. 62

7.5 Carbon Stocks ...................................................................................................... 64

7.5.1 Carbon stocks of Closed Forests .............................................................. 64

7.5.2 Carbon stocks of Open Forests ................................................................. 65

8. UNCERTAINTY OF THE ESTIMATES .......................................................................... 66

8.1 Statistical Sampling Error ..................................................................................... 66

8.2 Poor Representativeness of the Sampling Network .............................................. 67

8.3 Measurement Errors ............................................................................................. 67

8.4 Data Encoding Errors ........................................................................................... 67

8.5 Estimation Design Uncertainties ........................................................................... 67

8.6 Overall Error Budget ............................................................................................. 68

9. REFERENCES .............................................................................................................. 69

ANNEXES

Appendix 1: List of Recorded Species

Appendix 2: List of Inventoried Sampling Units in Davao Oriental

Appendix 3: Field Data Forms

Appendix 4: Detailed Results - Closed Forests

Appendix 5: Detailed Results - Open Forests

Appendix 6: Statistical Parameters - Closed Forests

Appendix 7: Statistical Parameters - Open Forests

Davao Oriental FRA Results iv


TABLES

Table 1: IPCC Tier 1 Soil Organic Matter stocks of the Davao Oriental soil classes ........................................................................................................ 8

Table 2: 2010 forest strata areas inventoried .......................................................... 10

Table 3: Overview of plot sizes and observations / measurements made on live trees1 and dead wood ......................................................................... 15

Table 4: Time and costs the FRA field work in Davao Oriental ................................ 35

Table 5: Deviation of initial from control measurements .......................................... 38

Table 6: Deviation of encoded from field data ......................................................... 38

Table 7: Relative frequency, density and dominance, importance and rank of the 20 most "important" species in Closed Forests .................................... 40

Table 8: Threatened species in Closed Forests ...................................................... 41

Table 9: Relative frequency, density and dominance, importance and rank of the 20 most "important" species in Open Forests ...................................... 42

Table 10: Threatened species in Open Forests ........................................................ 43

Table 11: Stand composition (N/ha, G/ha, V/ha and AGB/ha) of Closed Forests ...... 44

Table 12: Stand composition (N/ha, G/ha, V/ha and AGB/ha) of Open Forests ........ 46

Table 13: Stand structure in terms of N/ha of Closed Forests ................................... 48

Table 14: Stand structure in terms of G/ha of Closed Forests ................................... 50

Table 15: Stand structure in terms of AGB/ha of Closed Forests .............................. 52

Table 16: Stand structure in terms of N/ha of Open Forests ..................................... 54

Table 17: Stand structure in terms of G/ha of Open Forests ..................................... 56

Table 18: Stand structure in terms of AGB/ha of Open Forests ................................. 58

Table 19: Merchantable volume in Closed Forests ................................................... 60

Table 20: Merchantable volume in Open Forests ...................................................... 62

Table 21: Carbon stocks of Closed Forests .............................................................. 64

Table 22: Carbon stocks of Open Forests ................................................................. 65

Table 23: Statistical sampling errors of the main variables of interest in Closed and Open Forests ..................................................................................... 66

Table 24: Overall error budget for V/ha ..................................................................... 68

Table 25: Overall error budget for AGB/ha ................................................................ 68

Davao Oriental FRA Results v


FIGURES

Figure 1: 2010 NAMRIA land cover of Caraga, Manay and Tarragona ....................... 7

Figure 2: 2013 BSWM FAO soil classes of Davao Oriental ........................................ 9

Figure 3: Distribution of the Sampling Units effectively (re-)measured in Caraga, Manay and Tarragona ................................................................. 12

Figure 4: Configuration of the sampling unit (cluster) ............................................... 13

Figure 5: Apple Map ................................................................................................. 21

Figure 6: Open Cycle Map with "Outdoors" base layer ............................................. 22

Figure 7: Re-location of inaccessible "satellites" ...................................................... 23

Figure 8: Measurements on lying dead wood sections ............................................. 28

Figure 9: DBH / DAB measurements ........................................................................ 30

Figure 10: Diameter estimates for inaccessible measurement points ......................... 31

Figure 11: N/ha, G/ha, V/ha and AGB/ha by number of species in Closed Forests ...................................................................................................... 41

Figure 12: N/ha, G/ha, V/ha and AGB/ha by number of species in Open Forests....... 43

Figure 13: Stand composition (N/ha, G/ha, V/ha and AGB/ha) of Closed Forests ...... 45

Figure 14: Stand composition (N/ha, G/ha, V/ha and AGB/ha) of Open Forests ........ 47

Figure 15: Stand structure in terms of N/ha of Closed Forests ................................... 49

Figure 16: Stand structure in terms of G/ha of Closed Forests ................................... 51

Figure 17: AGB/ha of Closed Forests by DBH / DAB threshold .................................. 52

Figure 18: Stand structure in terms of AGB/ha of Closed Forests .............................. 53

Figure 19: Stand structure in terms of N/ha of Open Forests ..................................... 55

Figure 20: Stand structure in terms of G/ha of Open Forests ..................................... 57

Figure 21: AGB/ha of Open Forests by DBH / DAB threshold .................................... 58

Figure 22: Stand structure in terms of AGB/ha of Open Forests ................................. 59

Figure 23: Merchantable volume in Closed Forests ................................................... 61

Figure 24: Merchantable volume in Open Forests ...................................................... 63

Figure 25: Carbon stocks of Closed Forests .............................................................. 64

Figure 26: Carbon stocks of Open Forests ................................................................. 65

Davao Oriental FRA Results vi


ACRONYMS

AD Ancestral Domain

AFOLU Agriculture, Forest and Other Land Use

AFP Armed Forces of the Philippines

AGB Above-Ground Biomass

ALOS Advanced Land Observing Satellite

a.s.l. above sea level

AVNIR Advanced Visible and Near Infrared Radiometer

BCEF Biomass Conversion and Expansion Factor

BGB Below-Ground Biomass

BMUB Bundesministerium für Umwelt, Naturschutz, Bau und Reaktorsicherheit (Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety)

BSWM Bureau of Soils and Water Management

C Carbon

CADT Certificate of Ancestral Domain Title

CBFM Community-Based Forest Management

CBFMA Community-Based Forest Management Agreement

CCC Climate Change Commission

CENRO Community Environment and Natural Resources Office(r)

CLUP Comprehensive Land Use Plan

CMA Co-Management Agreement

DAB Diameter Above Buttress

DBH Diameter at Breast Height

DEM Digital Elevation Model

DENR Department of Environment and Natural Resources

DFS Deutsche Forstservice GmbH

DOM Dead Organic Matter

DOSCST Davao Oriental State College of Science and Technology

Dref Reference Diameter

FAO Food and Agriculture Organization

FLUP Forest Land Use Planning

FMB Forest Management Bureau

FRA Forest Resources Assessment

FREL Forest Reference Emissions Level

FRL Forest Reference Level

GADM Global Administrative Areas

GHG Greenhouse Gas

GIS Geographic Information System

GIZ Deutsche Gesellschaft für Internationale Zusammenarbeit GmbH

GPG Good Practice Guidance

GPS Global Positioning System

HWSD Harmonized World Soil Database

ICC Indigenous Cultural Communities

IP Indigenous People

Davao Oriental FRA Results vii


IPCC Intergovernmental Panel on Climate Change

IUCN International Union for Conservation of Nature and Natural Resources

JDK Java Development Kit

JRE Java Runtime Environment

LB Living Biomass

LDW Lying Dead Wood

LGU Local Government Unit

LI Litter

LULUCF Land Use and Land-Use Change and Forestry

MAD Mean Absolute Deviation

MRV Measurement, Reporting and Verification

NAMRIA National Mapping and Resource Information Authority

NCIP National Commission on Indigenous People

NFRI National Forest Resources Inventory

NGP National Greening Program

NSCB National Statistical Coordination Board

NTFP Non-Timber Forest Product

ODBC Open Database Connectivity

OGC Open GeoSpatial Consortium

ORDBMS Object Relational Database Management System

PENRO Provincial Environment and Natural Resources Office(r)

PNRPS The Philippine National REDD-Plus Strategy

POI Point Of Interest

PSC Project Steering Committee

PSGC Philippine Standard Geographic Code

QA Quality Assurance

QC Quality Control

REDD+ Reducing Emissions from Deforestation and forest Degradation, and conservation, sustainable management of forests and enhancement of carbon stocks

RMSD Root Mean Square Deviation

SDW Standing Dead Wood

SINP Samar Island Natural Park

SLC Scan Line Corrector

SOM Soil Organic Matter

SOP Standard Operating Procedure

TLA Timber License Agreement

UNFCCC United Nations Framework Convention on Climate Change

UTM Universal Transverse Mercator

WGS World Geodetic System

WRB World Reference Base for soil resources

Davao Oriental FRA Results 1


EXECUTIVE SUMMARY

The present report describes the methodology and the results of the Forest Resources Assessment (FRA) conducted from 03 August 2015 until 14 March 2016 in the sites of the National REDD+ System Philippines Project in Davao Oriental selected for Forest Land Use Planning (FLUP) and the implementation of REDD+ eligible activities (Caraga, Manay and Tarragona). The methodology used is a refinement of the forest carbon baseline study carried out from 2011 to 2012 in Leyte in the framework of the Climate relevant Modernization of Forest Policy and Piloting of Reducing Emissions from Deforestation and Forest Degradation (REDD) Project. It takes into account evolving international standards and good practices with regards to forest carbon stock assessment for the estimation of Greenhouse Gas (GHG) emissions and removals in compliance with the latest (2006) Intergovernmental Panel on Climate Change (IPCC) guidelines for national GHG inventories. The FRA pursued the objectives of providing for the forests of the selected Project sites (i) stand and stock data estimates reflecting the forest resources conditions as well as (ii) carbon stock estimates for the key carbon pools:

Above-Ground Biomass (AGB) at Tier 3 level;

Below-Ground tree Biomass (BGB) at Tier 1 level;

Dead Organic Matter (DOM) at Tier 3 level;

Soil Organic Matter (SOM) at Tier 1 level;

of key forest strata according to the 2010 forest cover map prepared by the National Mapping and Resource Information Authority (NAMRIA). The report successively provides details about:

the background and purpose, including (i) a brief introduction to the Project, (ii) the methodological framework for which the FRA is to provide biomass / carbon stock estimates, and (iii) the FRA objectives, scale and scope (Chapter 1).

the sources of information used, notably regarding (i) the forest definition, (ii) the forest areas and their stratification, (iii) the carbon stocks in mangroves, (iv) the wood specific gravity, and (v) the soil classes (Chapter 2);

the inventory design, providing details about (i) the inventory method, (ii) the areal sampling frame, (iii) the elements sampled, (iv) the number of Sampling Units (SUs), their (v) distribution and (vi) configuration, and (vii) the estimation design (Chapter 3);

the field implementation, describing (i) the retrieval and permanent marking of the SUs and (ii) the variables of interest assessed / measured (Chapter 4);

the organizational aspects, covering (i) the inventory instructions and field data forms, (ii) the inventory team composition, (iii) the equipment used, (iv) the training of the inventory teams, (v) the use of inventory camps, (vi) the time and costs of the field work, and (vii) data processing and analysis (Chapter 5);

the quality assurance and quality control measures (Chapter 6);

the detailed results of the FRA (Chapter 7);

the uncertainties of the estimates (Chapter 8).

In the open and closed forests of Caraga, Manay and Tarragona, 44 plus 37 Sampling Units (SUs) have been retrieved in the field, permanently marked and measured. The inventory has sampled and identified 198 different species with a Diameter at Breast Height (DBH) / Diameter Above Buttress (DAB) ≥ 5.0 cm. The total forest carbon stock is estimated to amount to 7.26 million t C (on average 334 t C/ha) in closed forests, and to 3.89 million t C/ha (223 t C/ha) in open forests.



1. INTRODUCTION AND BACKGROUND

1.1 National REDD+ System Philippines Project

The German Federal Ministry for the Environment, Nature Conservation, Housing and Nuclear Safety (BMUB) funded National REDD+ System Philippines Project contributes to the overall goal that Department of Environment and Natural Resources (DENR), relevant government agencies, local government units and local communities / indigenous people in the Philippines use a national framework, based on internationally recognized ecological and social safeguards, to reduce Greenhouse Gas (GHG) emissions from deforestation and forest degradation and to achieve co-benefits (biodiversity conservation and livelihoods improvement). The Project supports the implementation of the Philippine National REDD-Plus Strategy (PNRPS) by assisting the process towards REDD+ Readiness. Considering the variability of the prevailing natural, cultural and institutional conditions throughout the Philippines, 3 replications (Project field sites) were deemed necessary to validate lessons learned in the field from activities contributing to the up-scaling of the implementation of the PNRPS. Following the geographical division into major island groups, one replication each is implemented in Luzon, the Visayas and Mindanao, respectively. Cities / Municipalities as territorial / jurisdictional units for FLUPs and Comprehensive Land Use Plans (CLUPs) were selected applying criteria regarding (i) forests and threats, (ii) priority watersheds, (iii) biodiversity conservation, (iv) poverty, (v) logistics, (vi) replicability, (vii) preparedness and commitment of the LGUs, and (viii) for one site at least priority areas of the National Commission on Indigenous People (NCIP). The following sites were retained:

in Albay (Luzon) the Municipalities of Ligao City and Oas;

in Eastern Samar (Visayas) the Municipalities of Borongan City and Maydolong; and

in Davao Oriental (Mindanao) the Municipalities of Caraga, Manay and Tarragona.

Project Component 4 shall achieve the following indicators:

Forest Land Use Plans (FLUPs) and co-management agreements with clear land tenure arrangements for local communities and Indigenous People (IP) groups and biodiversity conservation agreements with local actors are in place for at least three pilot areas covering a total forest area of at least 150,000 ha.

REDD+ eligible activities (avoided deforestation and degradation, reforestation, assisted natural regeneration, sustainable forest management) in at least three pilot areas for emissions reduction and CO2 removals have been implemented.

Moreover, Component 4 shall furnish substantial contributions to:

Forest Reference (Emissions) Levels (FR[E]Ls) for the three sites;

Concept for a REDD+ Measurement, Reporting and Verification (MRV) system;

Forest policy and regulatory frameworks related to Community-Based Forest Management (CBFM), tenure arrangements, Co-Management Agreements (CMAs), IP/ICC concerns, and biodiversity conservation;

Knowledge management and Project monitoring and reporting.

It is in support of the elaboration and pilot testing of FR(E)Ls and the MRV system that FRAs were carried out in Eastern Samar and Davao Oriental.



1.2 Methodological Framework

The 2006 Intergovernmental Panel on Climate Change (IPCC) guidelines for national GHG inventories propose two methods of calculating carbon stock changes in a given carbon pool for a given land-use category in the Agriculture, Forestry and Other Land Use (AFOLU) sector:

the "Gain - Loss Method", estimating the difference between increases (transfer from another carbon pool or increase of biomass [removal]) and decreases (transfer to another carbon pool or emissions) of the amount of carbon;

the "Stock Difference Method", estimating the change of carbon stocks through measurements at two (or more) points in time (which reflects the emissions and removals).

The "Stock Difference Method" is robust and transparent, particularly to monitor carbon stock changes from forest degradation, which, in the Philippines, is a GHG emission source key category (category "3 B 1 a Forest Land Remaining Forest Land") with a presumably higher emission level than deforestation (sub-category "3 B 2 b i Forest Land Converted to Cropland"). The "Stock Difference Method" requires two estimations: (i) forest area (preferably by strata that are correlated to carbon stocks) and (ii) carbon stock per unit area of forest. The forest area by strata has been mapped nationwide by the National Mapping and Resource Information Authority (NAMRIA), through visual classification of medium- to high- resolution multi spectral satellite data (116 ALOS AVNIR-2, 40 SPOT 5 and 29 LANDSAT 7 gap-filled Scan Line Corrector [SLC] off scenes covering the national territory) acquired mainly 2010. A new wall-to-wall mapping assessing the 2015 land cover is under way. The results, however, won't be available before 2017. Carbon stock per unit area of forest for the different strata must be determined using appropriate probabilistic (statistical) field sampling inventory methods. The adopted inventory methodology is a refinement of the forest carbon baseline study carried out from mid-2011 until end 2012 in Leyte in the framework of the BMUB funded, GIZ-assisted Climate relevant Modernization of Forest Policy and Piloting of Reducing Emissions from Deforestation and Forest Degradation (REDD) Project (SCHADE J. and R. LUDWIG, 2013), building on the experience gained during this inventory, and taking into account evolving international standards and good practices (see Chapter 9).

1.3 Forest Resources Assessment Objectives, Scale and Scope

The FRA conducted in Davao Oriental is expected to provide for the forests of the selected Project sites (Municipalities of Caraga, Manay and Tarragona) stand and stock data estimates reflecting the forest resources conditions such as

tree species variety,

stand density (N/ha),

basal area (G/ha), and

merchantable volume (V/ha).

stand composition (proportions of species / species groups in terms of N/ha, G/ha and V/ha), and

stand structure (distribution of N/ha, G/ha and V/ha by diameter classes).



In addition, the FRA shall also furnish forest carbon stock estimates for

key carbon pools (with definitions according to the 2006 IPCC guidelines for national GHG inventories)

o Living Biomass (LB), composed of

- Above-Ground Biomass (AGB), defined as follows: "All biomass of living vegetation, both woody and herbaceous, above the soil including stems, stumps, branches, bark, seeds, and foliage. In cases where forest understory is a relatively small component of the above-ground biomass carbon pool, it is acceptable for the methodologies and associated data used in some tiers to exclude it, provided the tiers are used in a consistent manner throughout the inventory time series."

- Below-Ground tree Biomass (BGB), defined as follows: "All biomass of live roots. Fine roots of less than (suggested) 2 mm diameter are often excluded because these often cannot be distinguished empirically from soil organic matter or litter."; and

o Dead Organic Matter (DOM), composed of

- Dead Wood (DW), defined as follows: "Includes all non-living woody biomass not contained in the litter, either standing, lying on the ground, or in the soil. Dead wood includes wood lying on the surface, dead roots, and stumps, larger than or equal to 10 cm in diameter (or the diameter specified by the country)." (for the forest resources assessments in the "National REDD+ System Philippines" Project sites, the inventory threshold / minimum diameter for dead wood is set to 5.0 cm), and

- Litter (LI), defined as follows: "Includes all non-living biomass with a size greater than the limit for soil organic matter (suggested 2 mm) and less than the minimum diameter chosen for dead wood (e.g. 10 cm), lying dead, in various states of decomposition above or within the mineral or organic soil. This includes the litter layer as usually defined in soil typologies. Live fine roots above the mineral or organic soil (of less than the minimum diameter limit chosen for below-ground biomass) are included in litter where they cannot be distinguished from it empirically." (for the forest resources assessments in the "National REDD+ System Philippines" Project sites, the inventory threshold / minimum diameter for dead wood is set to 5.0 cm);

if applicable disaggregated by species and diameter classes;

of key forest strata according to the 2010 forest cover map prepared by the National Mapping and Resource Information Authority (NAMRIA), distinguishing:

o Closed Forests (forests with a tree crown cover of more than 40%) and

o Open Forests (forests with a tree crown cover of more than 10% up to 40%);

at T0 (prior to the implementation of REDD+ eligible activities);

using permanent Sampling Units in view of the implementation of the "Stock Difference Method" for determining GHG emissions and removals.

Considering that more than 96% of the AGB biomass of tropical forest is found in trees with a Diameter at Breast Height (DBH) / Diameter Above Buttress (DAB) ≥ 10.0 cm (GILLESPIE A. et al., 1992; in the Philippines, LASCO et al., 2006, report from Surigao del Sur 98% of the AGB in trees with DBH / DAB ≥ 19.5 cm), the biomass of trees with a DBH / DAB < 5.0 cm and the non-tree biomass (except for bamboos and palms, which are also included in the Philippine forest definition, cf. Chapter 2.1) is not key and have not be included in the FRA / forest carbon stock estimates. BGB is not estimated directly, but calculated using the IPCC Tier 1 BGB to AGB ratio (R).



IPCC Tier 1 data are also be used to account for Soil Organic Matter (SOM) (IPCC, 2006: IPCC guidelines for national GHG inventories - AFOLU: Table 2.3), defined as follows: "Includes organic carbon in mineral soils to a specified depth chosen by the country and applied consistently through the time series. Live and dead fine roots and DOM within the soil, that are less than the minimum diameter limit (suggested 2 mm) for roots and DOM, are included with soil organic matter where they cannot be distinguished from it empirically. The default for soil depth is 30 cm.".



2. SOURCES OF INFORMATION

2.1 Forest Definition

DENR Memorandum Circular 2005-005 of 26 May 2005 defines forests as "Land with an area of more than 0.5 hectare and tree crown (or equivalent stocking level) of more than 10 percent. The trees should be able to reach a minimum height of 5 meters at maturity in situ. It consists either of closed forest formations where trees of various storeys and undergrowth cover a high portion of the ground or open forest formations with a continuous vegetation cover in which tree crown cover exceeds 10 percent. Young natural stands and all plantations established for forestry purposes, which have yet to reach a crown density of more than 10 percent or tree height of 5 meters are included under forest. These are normally forming part of the forest area which are temporarily unstocked as a result of human intervention or natural causes but which are expected to revert to forest. It includes forest nurseries and seed orchards that constitute an integral part of the forest; forest roads, cleared tracts, firebreaks and other small open areas; forest within protected areas; windbreaks and shelter belts of trees with an area of more than 0.5 hectare and width of more than 20 meters; plantation primarily used for forestry purposes, including rubber wood plantations. It also includes bamboo, palm and fern formations (except coconut and oil palm)."

2.2 Forest Areas / Stratification

The forest areas and their stratification are taken from the 2010 NAMRIA national forest cover map, released in 2013, where forests (and other land cover) have been classified through visual interpretation of medium- to high- resolution multi spectral satellite data (116 ALOS AVNIR-2, 40 SPOT 5 and 29 LANDSAT 7 gap-filled SLC off scenes covering the national territory, acquired mainly 2010), adopting a minimum mapping area of 0.5 ha in accordance with the 2005 DENR forest definition (see Chapter 2.1 above), distinguishing the following 3 forest strata:

Closed Forests: tree crown cover > 40%;

Open Forests: 10% < tree crown cover ≤ 40%;

Mangroves.

Tree plantations have not been mapped as a separate class, since the satellite data did not warrant their comprehensive and systematic identification. The documentation of the classification and its accuracy (confusion matrix) has not been published yet. Figure 1 shows an excerpt from this map, clipped to the Project sites (Caraga, Manay and Tarragona) in Davao Oriental.



Figure 1: 2010 NAMRIA land cover of Caraga, Manay and Tarragona



2.3 Wood Specific Gravity

Wood Specific Gravity (p, expressed in g / cm³ or t / m³), as required by the estimation of AGB using the equation developed by J. CHAVE et al. (CHAVE J. et al., 2014: Improved allometric models to estimate the aboveground biomass of tropical trees; see equation {5} in Chapter 3.7.2) has been looked up (and averaged whenever several gravities are available) by species / species group growing in South-East Asia from the following sources:

preferably in A. ZANNE et al. (ZANNE A. et al., 2009: Global wood density database);

else in G. REYES et al. (REYES G. et al., 1992: Wood densities of tropical tree species).

For species not found in any of the above cited sources, the average Wood Specific Gravity for tropical tree species in Asia of 0.57 g/cm³ published by S. BROWN (FAO, 1997: Estimating biomass and biomass change of tropical forests - A primer: Chapter 3.1.1) has been used. Appendix 1 provides a list of the species recorded by the inventory with the corresponding values of p.

2.4 Soil Classes

The World Reference Base (WRB) soil classes have been looked up from the 2013 FAO soil map of the Philippines prepared by BSWM, see Figure 2 (BSWM, 2013: Updating the Harmonized World Soil Database [HWSD]: Correlation of Philippine Soils into FAO's WRB for Soil Resources). Under the tropical wet (at elevations below 1,000 m) and montane (at elevations of 1,000 m and above) climate prevailing in the forested areas of Davao Oriental, these classes yield according to the 2006 IPCC Tier 1 data the Soil Organic Matter (SOM) C-stocks (IPCC, 2006: IPCC guidelines for national GHG inventories - AFOLU: Table 2.3) summarized in Table 1.

Table 1: IPCC Tier 1 Soil Organic Matter stocks of the Davao Oriental soil classes

Climate region FAO soil class Soil SOM

Tropical, wet Orthic Acrisol Low Activity Clay 60 t C/ha

Tropical, wet Orthic Luvisol High Activity Clay 44 t C/ha

Tropical, wet Vertic Luvisol High Activity Clay 44 t C/ha

Tropical, montane Orthic Acrisol Low Activity Clay 63 t C/ha

Tropical, montane Orthic Luvisol High Activity Clay 88 t C/ha

Tropical, montane Vertic Luvisol High Activity Clay 88 t C/ha



Figure 2: 2013 BSWM FAO soil classes of Davao Oriental



3. INVENTORY DESIGN

3.1 Inventory Method

The inventory adopted a stratified probabilistic (statistical) sampling. Conceptionally, the population from which the sample is drawn is not the biological population of trees1. Sampling is rather considered to be based on the selection of Sample Points, each with observations and measurements of single tree1-, stand- and site- variables of interest derived from associated Sampling Units (SUs, see Chapter 3.6). Since Sample Points are dimensionless, the population is infinite even in a limited area of interest ("infinite population approach"). Hence, the sampling frame required by the statistical theory cannot be defined through a list of all elements that can be drawn during sampling, but rather through the area (areal sampling frame) to be covered.

3.2 Areal Sampling Frame

The areal sampling frame of the FRA in Davao Oriental consists of the key forest strata (Closed Forests and Open Forests) of the selected Project sites (Caraga, Manay and Tarragona) (i) according to the 2010 NAMRIA national forest cover map (see Figure 1, and Table 2 below), (ii) referring in the absence of official / authoritative administrative boundaries to the municipal boundaries downloadable from the GADM database of Global Administrative Areas (see http://www.gadm.org/). In total, the areal sample frame measures 39 215 ha.

Table 2: 2010 forest strata areas inventoried

LGU Closed Forests Open Forests Mangroves Others Total Land Area

[ha] [ha] [ha] [ha] [ha]

Caraga 15,305 6,359 0 33,711 55,375

Manay 3,466 6,829 0 37,669 47,964

Tarragona 2,979 4,277 0 24,032 31,288

Total 21,750 17,465 0 95,412 134,627

3.3 Elements Sampled

The elements sampled to estimate the forest biomass and carbon stock consists of the following:

live trees1 with a DBH / DAB ≥ 5.0 cm;

dead wood, both standing and lying, down to a small end diameter of 5.0 cm (the smaller fractions are part of the litter);

litter.

For live trees1 and dead wood, the inventory threshold consistently amounts to 5.0 cm (in diameter).

1 including bamboos, palms, rattan and tree ferns.



3.4 Number of Sample Points

It was initially expected that 200 Sampling Units could be measured with the available budget, 150 thereof in the Municipalities with Project field activities (Caraga, Manay and Tarragona), and the remaining 50 in the other Municipalities of Davao Oriental (to serve as "zero-"plots [without Project-supported field activities]). Since the inventory could be implemented before the start of the REDD+ eligible field activities, the 50 Sampling Units outside the Municipalities with Project field activities were no longer needed. This was fortunate, since the available budget and time was barely sufficient to effectively measure 81 Sampling Units in the Municipalities with Project field activities.

3.5 Distribution of Sample Points

The 150 Sample Points were drawn at random without replacement from the 385 nodes of a quadratic grid with a side length of 1 km located within the areal sampling frame (Closed Forests and Open Forests according to the 2010 NAMRIA national forest cover map in the Municipalities of Caraga, Manay and Tarragona). The Sample Points were numbered consecutively in the sequence they were drawn, from "DAOM0001" to "DAOM0150" ("DAO" identifying the Province, "M" signifying "measurement", "C" signifying "control", i.e. the mandatory independent re-measurement of 10% of the Sampling Units for Quality Control). Figure 3 shows the grid and the distribution of the 81 effectively measured SUs in Caraga, Manay and Tarragona. Appendix 2 provides the list of these SUs with their Universal Transverse Mercator (UTM) and World Geodetic System (WGS) 84 geographic coordinates.

3.6 Configuration of Sampling Units

Each Sampling Unit consists of a cluster (offering the advantage of lowering the coefficient of variation between SUs) centered on the Sample Point, composed of the following elements (see Figure 4):

1 circular plot with 25 m radius centered on the Sample Point for the ocular assessment of the land cover, to serve as Remote Sensing (RS) training and validation data;

4 "satellites" with their centers at 40 m horizontal distance from the Sample Point in the 4 cardinal directions (North, East, South and West), each consisting of 2 concentric circular plots (featuring the best circumference : surface ratio, hence limiting the number of "boundary" trees, and moreover easy to establish even in steep terrain):

o 5 m radius plot (corresponding to an area of 0.0079 ha) for:

- the sampling of "small-sized" live trees1 (all species) with 5 cm ≤ DBH / DAB < 20 cm for the estimation of their contribution to the AGB and BGB (an average of 0.9 trees1 were actually sampled in these plots);

- the sampling of standing dead wood with DBH / DAB ≥ 5.0 cm for the estimation of their contribution to the DOM (an average of 0.1 standing dead wood were actually sampled in these plots);

- the sampling of lying dead wood down to a diameter of 5.0 cm for the estimation of their contribution to the DOM (an average of 0.3 pieces of lying dead wood were actually sampled in these plots;

- the sampling of litter for the estimation of its contribution to the DOM.

o 10 m radius plot (corresponding to an area of 0.0314 ha) for:

- the sampling of "big-sized" live trees1 (all species) with DBH / DAB ≥ 20 cm for the estimation of their contribution to the AGB and BGB (an average of 8.4 trees1 were actually sampled in these plots);



Figure 3: Distribution of the Sampling Units effectively (re-)measured in Caraga, Manay and Tarragona



Figure 4: Configuration of the sampling unit (cluster)

On average, 9.3 trees1 were sampled in each satellite, which comes close to the commonly recommended range of 12 to 20 trees in uneven-aged forests reputed to offer the best compromise in terms of sampling efficiency, considering the ratio between the "unproductive" time invested in retrieving SUs and the "productive" time measuring them. The entire cluster is inscribed in an area of 100 m x 100 m (1 ha). Statistically, one cluster constitutes one Sampling Unit. For the computation of the results per ha, the following blow-up factors are applicable:

parameters measured in the 10 m radius plots: 10 000 / (4 × π × 102) = 7.9577;

parameters measured in the 5 m radius plots: 10 000 / (4 × π × 52) = 31.8310.

The Sampling Units were marked permanently to be prepared for their periodic re-measurement.



3.6.1 Observations and measurements at and around the Sample Points

The following variables of interest were observed / measured at the Sample Points:

Administrative location: Province, City / Municipality and Barangay.

Actual coordinates.

Elevation.

Slope.

Slope orientation.

Terrain: 11 classes (plateau; summit / crest; upper slope; middle slope; lower slope; bench / terrace; valley; plain; narrow depression; water course; dunes).

Land classification: Legal status (forest land or alienable and disposable).

The following variables of interest were assessed within a radius of 25 m horizontal distance around the Sample Points:

Land cover: 12 classes (forest; marshland / swamp; fallow; shrubs; wooded grassland; grassland; annual crop; perennial crop; open / barren land; built-up area; fishpond; inland water).

Forest type: 10 types (dipterocarp old growth forest; dipterocarp residual forest; mossy forest; submarginal forest; closed pine forest; open pine forest; mangrove of growth forest; mangrove reproduction forest; native tree plantation forest; other plantation forest).

Tree crown cover: 3 classes (tree crown cover ≤ 10%; 10% < tree crown cover ≤ 40%); tree crown cover > 40%).

3.6.2 Observations and measurements at and around the Satellite Centers

The following variables of interest are observed / measured at the Satellite Centers (similar to the observations / measurements at the Sample Points):

Administrative location: Province, City / Municipality and Barangay.

Actual coordinates.

Elevation.

Slope.

Slope orientation.

Terrain: 11 classes (plateau; summit / crest; upper slope; middle slope; lower slope; bench / terrace; valley; plain; narrow depression; water course; dunes).

Land classification: Legal status (forest land or alienable and disposable).

The following variables of interest are observed / measured within a radius of 5 m horizontal distance around the Satellite Centers:

Plant diversity.

Ground coverage classes for six (6) vegetation layers according to height (< 50 cm; 50 cm ≤ height < 130 cm; 130 cm ≤ height < 200 cm; 2.0 m ≤ height < 4.0 m; 4.0 m ≤ height < 10.0 m; height > 10.0 m): 4 classes (none; coverage ≤ 10%; 10% < coverage ≤ 50%; coverage > 50%).

For each of the sampled "small-sized" live trees1 with 5 cm ≤ DBH / DAB < 20 cm: species, azimuth and horizontal distance (from the Satellite Center), and DBH / DAB.

For each of the sampled standing dead wood (including stumps) with DBH / DAB ≥ 5.0 cm: species, azimuth and horizontal distance (from the Satellite Center), DBH / DAB and merchantable height.



For each of the sampled lying dead wood sections (those portions that are within the 5 m horizontal distance radius plot) down to a diameter of 5.0 cm: mid-diameter and length.

Litter: ground coverage percentage plus average depth.

The following variables of interest are observed / measured within a radius of 10 m horizontal distance around the Satellite Centers:

Land cover: 12 classes (forest; marshland / swamp; fallow; shrubs; wooded grassland; grassland; annual crop; perennial crop; open / barren land; built-up area; fishpond; inland water).

Forest type: 10 types (dipterocarp old growth forest; dipterocarp residual forest; mossy forest; submarginal forest; closed pine forest; open pine forest; mangrove of growth forest; mangrove reproduction forest; native tree plantation forest; other plantation forest).

Tree crown cover: 3 classes (tree crown cover ≤ 10%; 10% < tree crown cover ≤ 40%); tree crown cover > 40%).

For each of the sampled "big-sized" live trees1 with DBH / DAB ≥ 20.0 cm: species, azimuth and horizontal distance (from the Satellite Center), DBH / DAB and merchantable height.

Table 3 summarizes the circular plot sizes and the observations / measurements made on live trees1 and dead wood.

Table 3: Overview of plot sizes and observations / measurements made on live trees1 and dead wood

Live Trees1 Dead Wood

"Small-Sized" "Big-Sized" Standing Lying

5 cm ≤ Dref* < 20 cm Dref* ≥ 20 cm Dref* ≥ 5 cm Dref* ≥ 5 cm

Plot radius 5.0 m 10.0 m 5.0 m 5.0 m

Species Species Species Species -

Azimuth Azimuth Azimuth Azimuth -

Hor. Distance Hor. Distance Hor. Distance Hor. Distance -

Diameter DBH / DAB DBH / DAB DBH / DAB Mid-Diameter

Height / Length - Merch. Height Merch. Height Section Length

* Dref of live trees1 and standing dead wood refers to DBH / DAB, Dref for lying dead wood refers to the small end diameter



3.7 Estimation Design

3.7.1 Tree volume equations and calculation of merchantable volume

The merchantable volume (V, expressed in cubic meter [m³] inside bark) of live trees and standing dead wood is calculated based on the diameter at breast height or above buttress (Dref) and the merchantable height (H) of sampled live trees and standing dead wood with a Dref ≥ 5.0 cm using the Philippine regional volume equations for dipterocarps and non-dipterocarps (DENR, 2012: FMB Technical Bulletin No. 3 - Measurement standards in the conduct of timber inventory):

𝑉 = 0.00005087 × 𝐷𝑟𝑒𝑓2 × 𝐻 dipterocarps, Eastern Mindanao

Equation {1}

𝑉 = 0.00004961 × 𝐷𝑟𝑒𝑓2 × 𝐻

non-dipterocarps, Eastern Mindanao

Equation {2}

with

V merchantable volume inside bark of Standing Dead Wood, in m³

Dref diameter at breast height (1.30 m) or above buttress (30 cm) of Standing Dead Wood, in cm

H merchantable height of Standing Dead Wood, in m

3.7.2 Allometric equations and calculation of biomass

In the absence of allometric equations specifically developed for the trees, bamboos, palms, rattan and tree ferns found in the tropical rainforests of the Philippines, the biomass (expressed in kg or t of dry matter [d.m.]) is calculated using the following available equations found in the literature and databases:

Above-Ground Biomass (AGB) of live trees:

Calculated based on the diameter at breast height or above buttress (Dref) of sampled live trees with a Dref ≥ 5.0 cm, using according to the preference of the user one of the two following equations:

o equation developed by J. CHAVE et al. (CHAVE J. et al., 2014: Improved allometric models to estimate the aboveground biomass of tropical trees), based on the destructive measurement of 4,004 trees, with 5.0 cm ≤ Dref ≤ 180.0 cm:

𝐴𝐺𝐵 = exp (−1.803 − 0.976 × 𝐸 + 0.976 × ln(𝑝) +2.673 × ln(𝐷𝑟𝑒𝑓) − 0.0299 × (ln(𝐷𝑟𝑒𝑓))2)

Equation {3}

with

- AGB oven-dry Above-Ground Biomass of live trees, in kg d.m.

- p wood specific gravity, in g / cm³; p by species or species groups (see Chapter 2.3)

- Dref diameter at breast height (1.30 m) or above buttress (30 cm), in cm

- E environmental variable measuring stress, defined as:

𝐸 = (0.178 × 𝑇𝑆 − 0.938 × 𝐶𝑊𝐷 − 6.61 × 𝑃𝑆) × 10−3 Equation {4}

with

- TS temperature seasonality, the standard deviation of the monthly mean temperature over a year, expressed in degrees Celsius multiplied by 100

- CWD climatological water deficit in mm per year, computed by summing the difference between monthly rainfall and monthly evapotranspiration, only when this difference is negative



- PS precipitation seasonality, the coefficient of variation in monthly rainfall values, expressed in percent of the mean value

A global gridded layer of E at 2.5 arc-minute resolution is available at http://chave.ups-tlse.fr/pantropical_allometry.htm#E and has been integrated into the FRA Database System Application used store, manage and analyze the inventory data (see Chapter 5.7); the values of E are extrapolated from the gridded layer based on the geographic coordinates of the Sampling Units (more precisely of the satellite center, cf. Chapter 3.6,).

o equation developed by S. BROWN (FAO, 1997: Estimating biomass and biomass change of tropical forests - A primer: Chapter 3.2.1) for moist climatic zones, based on the destructive measurement of 170 trees, with 5.0 cm ≤ Dref ≤ 148.0 cm:

𝐴𝐺𝐵 = exp (−2.134 + 2.530 × ln(𝐷𝑟𝑒𝑓)) (R² = 0.97) Equation {5}

with

- AGB oven-dry Above-Ground Biomass of live trees, in kg d.m.

- Dref diameter at breast height (1.30 m) or above buttress (30 cm), in cm

AGB of live bamboos:

Calculated based on the diameter at breast height (Dref) of sampled live bamboos with a Dref ≥ 5.0 cm, using the following equation, developed by R. PRIYADARSINI (1998, notably cited in ZEMEK O., 2009: Biomass and carbon stocks inventory of perennial vegetation in the Chieng Khoi watershed, NW Viet Nam), based on the destructive measurement of Dendrocalamus asper in Indonesia, with 3.0 cm ≤ Dref ≤ 7.0 cm:

𝐴𝐺𝐵 = 0.1312 × 𝐷𝑟𝑒𝑓2.2784 (R² = 0.95) Equation {6}

with

o AGB oven-dry Above-Ground Biomass of bamboos, in kg d.m.

o Dref diameter at breast height (1.30 m), in cm

AGB of live arborescent palms:

Calculated based on the diameter at breast height (Dref) of sampled live arborescent palms with a Dref ≥ 5.0 cm, using the following equations, developed by R. GOODMAN et al. (GOODMAN R. et al., 2013: Amazon palm biomass and allometry), based on the destructive measurement of 97 palms in Western Amazonia, with 6.0 cm ≤ Dref < 40.0 cm:

𝐴𝐺𝐵 = exp(−3.3488 + 2.7483 × ln(𝐷𝑟𝑒𝑓)) (R² = 0.80) Equation {7}

with

o AGB oven-dry Above-Ground Biomass of arborescent palms, in kg d.m.

o Dref diameter at breast height (1.30 m), in cm

Below-Ground Biomass (BGB) of live trees, bamboos and arborescent palms:

Calculated based on the AGB of sampled live trees (cf. Equations {3} or {5}), bamboos (cf. Equation {6}) and arborescent palms (cf. Equation {7}), using the BGB to AGB ratio (R) of the 2006 IPCC guidelines (IPCC, 2006: IPCC guidelines for national GHG inventories - AFOLU: Table 4.4):

𝐵𝐺𝐵 = 𝑅 × 𝐴𝐺𝐵 Equation {8}

with

o BGB oven-dry Below-Ground Biomass of live trees, bamboos and arborescent palms, in kg d.m.

o R BGB to AGB ratio: 0.37

o AGB oven-dry Above-Ground Biomass, in kg d.m.



Biomass of Standing Dead Wood (SDW):

Calculated in two steps:

a) Calculation of the merchantable volume (V) of sampled Standing Dead Wood based on the diameter at breast height or above buttress (Dref) and the merchantable height (H) of sampled dead wood with a Dref ≥ 5.0 cm using the Philippine regional volume equations for dipterocarps and non-dipterocarps, see Chapter 3.7.1.

b) Conversion of V into biomass (SDW) using the Biomass Conversion and Expansion Factor (BCEFs) of merchantable growing stock volume to AGB of the 2006 IPCC guidelines (IPCC, 2006: IPCC guidelines for national GHG inventories - AFOLU: Table 4.5), divided by 2 (THIELE T. et al., 2010: Monitoring, assessment and reporting for sustainable forest management in Pacific Island Countries: Chapter 4.3.2.2) to account for decay:

𝑆𝐷𝑊 = 𝑉 × 𝐵𝐶𝐸𝐹𝑠 / 2 Equation {9}

with

o SDW biomass of Standing Dead Wood, in t d.m.

o V merchantable volume inside bark of Standing Dead Wood, in m³

o BCEFs Biomass Conversion and Expansion Factor of merchantable growing stock volume to AGB for humid tropical natural forests, in t / m³, depending on the growing stock level:

9.0 t d.m. / m³ for V < 10 m³ / ha

4.0 t d.m. / m³ for 10 m³ / ha < V ≤ 20 m³ / ha

2.8 t d.m. / m³ for 20 m³ / ha < V ≤ 40 m³ / ha

2.05 t d.m. / m³ for 40 m³ / ha < V ≤ 60 m³ / ha

1.7 t d.m. / m³ for 60 m³ / ha < V ≤ 80 m³ / ha

1.5 t d.m. / m³ for 80 m³ / ha < V ≤ 120 m³ / ha

1.3 t d.m. / m³ for 120 m³ / ha < V ≤ 200 m³ / ha

0.95 t d.m. / m³ for V > 200 m³ / ha

Biomass of Lying (downed) Dead Wood (LDW):


a) Calculation of the volume (V) of sampled Lying Dead Wood sections up to a minimum diameter of 5.0 cm based on the mid-diameter (Dref) and the length (L) using the cylinder formula:

𝑉 = π × 𝐷𝑟𝑒𝑓² / 40,000 × 𝐿 Equation {10}

with

o V volume of Lying Dead Wood section, in m³

o Dref mid-diameter of Lying Dead Wood section, in cm

o L length of Lying Dead Wood section within the sample plot, in m

b) Conversion of V into biomass (LDW) using the average wood density for Asia (FAO, 1997: Estimating biomass and biomass change of tropical forests - A primer: Chapter 3.1.2) divided by 2 (THIELE T. et al., 2010: Monitoring, assessment and reporting for sustainable forest management in Pacific Island Countries: Chapter 4.3.2.2) to account for decay:

𝐿𝐷𝑊 = 𝑉 × 𝐷 / 2 Equation {11}

with

o SDW biomass of Lying Dead Wood, in t d.m.

o D average wood density for Asia: 0.57 t d.m. / m³



Biomass of Litter (LI):


a) Calculation of the volume (V) of sampled litter based on the ground coverage percentage (C) and the average depth (DPT) of the litter:

𝑉 = C × 𝐷𝑃𝑇 × 10,000 Equation {12}

with

o V volume of litter, in m³ / ha

o C ground coverage percentage of litter, in %

o DPT average depth of litter, in m

b) Conversion of V into biomass (LI) using the average density of litter (CHOJNACKY D. et al., 2009: Separating duff and litter for improved mass and carbon estimates: Table 2):

𝐿𝐼 = 𝑉 × 𝐷 Equation {13}

with

o LI biomass of litter, in kg d.m. / ha

o V volume of litter, in m³ / ha

o D average density of litter: 40 kg d.m. / m³

3.7.3 Carbon fraction of dry matter

The following Carbon Fractions (CF) of the 2006 IPCC guidelines are used for the calculation of the C equivalent of the various carbon pools calculated in terms of Dry Matter:

Carbon Fraction of Dry Matter for Living Biomass:

0.47 t C / t d.m. Equation {14}

according to the IPCC guidelines (IPCC, 2006: IPCC guidelines for national greenhouse gas inventories - Agriculture, forestry and other land uses: Table 4.3).

Carbon Fraction of Dry Matter for Dead Organic Matter:

0.37 t C / t d.m. Equation {15}

according to the IPCC guidelines (IPCC, 2006: IPCC guidelines for national greenhouse gas inventories - Agriculture, forestry and other land uses: Equation 2.19).



3.7.4 Statistical parameters

Assuming for simplicity's sake a random distribution of the Sampling Units, the estimated stratum and total means, variances, standard errors and margins of error are computed using the following formulas (cf. ZÖHRER F., 1980: Forstinventur: Ein Leitfaden für Studium und Praxis):

Stratum means

�̅�𝑗 =∑ 𝑦𝑖𝑗

𝑛𝑗𝑖=1

𝑛𝑗 Equation {16}

Stratum variances

𝑠𝑗2 =

∑ 𝑦𝑖𝑗2 − (∑ 𝑦𝑖𝑗

𝑛𝑗𝑖=1

)2

/ 𝑛𝑗

𝑛𝑗𝑖=1

𝑛𝑗−1 Equation {17}

Stratum standard errors

𝑆𝑗 =𝑠𝑗

√𝑛𝑗 Equation {18}

Stratum margins of error

𝐸𝑗 =𝑠𝑗

√𝑛𝑗× 𝑡𝑗 Equation {19}

Total mean

�̅� = ∑𝑛𝑗

𝑛× �̅�𝑗

𝑀𝑗=1 Equation {20}

Total variance

𝑠2 = ∑𝑛𝑗

𝑛× 𝑠𝑗

2𝑀𝑗=1 Equation {21}

Total standard error

𝑆 = √1

𝑛× (∑ 𝑃𝑗

𝑀𝑗=1 × 𝑠𝑗)

2 Equation {22}

Total margin of error

𝐸 = √1

𝑛× (∑ 𝑃𝑗

𝑀𝑗=1 × 𝐸𝑗)

2 Equation {23}

with

o 𝑦𝑖𝑗 variable (such as number of trees per ha, basal area per ha, volume per ha,

biomass per ha, etc.) of sampling unit i in stratum j;

o �̅�𝑗 arithmetic mean of variable 𝑦 in stratum j;

o �̅� total arithmetic mean of variable 𝑦;

o 𝑠𝑗2 variance of variable 𝑦 in stratum j;

o 𝑠2 total variance of variable 𝑦;

o 𝑆𝑗 standard error of the mean of variable 𝑦 in stratum j;

o 𝑆 total standard error of the mean of variable 𝑦;

o 𝐸𝑗 margin of error of the mean of variable 𝑦 in stratum j;

o 𝐸 total margin of error of variable 𝑦;

o 𝑀 number of strata;

o 𝑛𝑗 number of sampling units in stratum j;

o 𝑡𝑗 two-tailed Student t-value with 𝑛𝑗 degrees of freedom in stratum j;

o 𝑛 total number of sampling units;

o 𝑃𝑗 weight of stratum j.



4. FIELD IMPLEMENTATION

4.1 Retrieval and Permanent Marking of Sampling Units

4.1.1 Approach of Sample Points using GPS receivers

The Sample Points were accessed / retrieved on the basis of their geographic coordinates using handheld GPS stand-alone receivers. The Sample Points were uploaded from a computer as "Points of Interest (POIs)" rather than as "waypoints", using the "GARMIN POI loader" software (freeware downloadable from http://www8.garmin.com/support/ mappingsw.jsp). "POIs" offer the advantage that unlike "waypoints", they cannot be edited nor erased from the GPS receivers (unless connected to a computer and with the use of the aforementioned software). Good sources of information to study the approach of Sample Points are the following:

Satellite images in Google Maps (http://www.google.com/maps), Bing Maps (http://www.bing.com/maps) and Apple Maps (only available on Apple Mac OS and iPhone / iPad iOS operating systems), particularly where high resolution satellite data are available (see Figure 5), which was not the case for most of the upland areas in Davao Oriental; however, the images are regularly updated, and it is worthwhile to compare the different sources for best results;

Topographic maps in Open Cycle Map (http://www.opencyclemap.org) showing the "Outdoors" base layer, which is particularly useful for the appreciation of the relief (see Figure 6).

As much as possible, the approach of a targeted Sample Point was studied together with local helpers / guides, who are well versed with the terrain, existing trails, unsurmountable barriers and/or obstacles such as steep hills or waterlogged areas to be avoided.

Vicinities of SU No. DAOM0062, Barangay Tubaon, Tarragona; the activities of the Oro East Mining Inc. are visible near the lower right corner

Figure 5: Apple Map



Mouth of the Kalinawan river, Caraga

Figure 6: Open Cycle Map with "Outdoors" base layer

4.1.2 Location of Sample Points and Satellite Centers using compass and distance tape or laser rangefinder

Considering the limited positional precision of stand-alone GPS measurements / navigation (in practice ± 10 m, as evidenced by the virtual movement of an immobilized GPS receiver, which is a remarkable precision to come close to any point on the globe from whatever origin over considerable distances, but insufficient to measure distances of less than 100 m to 200 m, since the relative precision deteriorates to 10% - 5%), the location of Sample Points was determined covering the last 10 m to 15 m by compass and horizontal distance measurement (referring to the azimuth / bearing and distance to the Sample Point displayed by the GPS receiver once the distance to the destination was less than 15 m) using a distance tape or a ranging laser, in order to prevent bias (preference for easily accessible areas) when closing in on the Sample Point. The same applied to the location of the four (4) Satellite Centers of each Sampling Unit, situated at 40 m in the four (4) cardinal directions (North = 0°; East = 90°, South = 180 ; West = 270°) from the Sample Point. The azimuth / bearing was measured with the help of a handheld precision compass.

4.1.3 Permanent marking of Sample Points and Satellite Centers

The Sample Points and the 4 Satellite Centers of each SU were permanently marked with an iron rod (of at least 1 cm diameter and 50 cm length), forced at least 4/5 of its length into the ground, topped with a 50 cm bright-colored 1/2 " PVC pipe to facilitate the retrieval for Quality Control (QC) purposes (cf. Chapter 6.2).

4.1.4 Inaccessible Sample Points and Satellite Centers

In the rare event that one of the Satellite Centers turned out to be inaccessible, it was re-located at 80 m horizontal distance from the Sample Point in the next cardinal direction, turning clockwise (see Figure 7: if the Western Satellite Center is inaccessible, its center may be re-located at 80 m horizontal distance to the West + 90° = North from the Sample Point).



Figure 7: Re-location of inaccessible "satellites"

In the equally rare event that a Sample Point turned out to be inaccessible, the SU was abandoned. A replacement Sample Point was drawn at random from those nodes of the quadratic grid with a side length of 1 km (see Figure 3) located (i) in the same forest stratum and (ii) at a similar elevation as the inaccessible Sample Point. If one of the accessible Satellite Centers fell on an area whose "land cover" assessed in the field was other than "forest", it was not re-located, but observed / measured as is.



4.2 Variables of Interest Assessed / Measured

4.2.1 Administrative location

The administrative location, comprising at least the Region, Province and Municipality, and as much as possible the Barangay, was observed at and recorded for the Sample Points and all Satellite Centers. Hence, five such observations were recorded per SU (in some cases, a Sampling Unit may be crossed by an administrative boundary).

4.2.2 Actual coordinates

The actual UTM coordinates, comprising the Zone (in the Philippines 50 in Palawan, 52 in the Eastern-most portions of Mindanao, 51 elsewhere), the Northing in m and the Easting in m, were measured at and recorded for the Sample Points and all Satellite Centers. Hence, five coordinate measurements were performed per SU. The coordinates were read from the GPS stand-alone receiver, immobilized at the Sample Point or Satellite Center, using "averaging".

4.2.3 Elevation

The elevation in m above sea level was measured at and recorded for the Sample Points and all Satellite Centers. Hence, five elevation measurements were performed per SU. The elevation was read from the GPS stand-alone receiver.

4.2.4 Slope

The slope was measured at and recorded for the Sample Points and all Satellite Centers. Hence, five slope measurements were performed per SU. The slope corresponds to the average inclination in % measured with a handheld precision clinometer in two opposite directions along 10 m segments (oblique distance) of an imaginary straight line passing through the Sample Point / Satellite Center and following the steepest slope gradient (where water would run off).

4.2.5 Slope orientation

The slope orientation was measured at and recorded for the Sample Points and all Satellite Centers. Hence, five slope orientation measurements were performed per SU. The slope orientation corresponds to the azimuth / bearing in ° of the downhill direction of the imaginary straight line used for the measurement of the slope gradient, read from a handheld precision compass.

4.2.6 Terrain

The terrain / topography class was observed at and recorded for the Sample Points and all Satellite Centers. Hence, five terrain / topography classes assessments were performed per SU. The assessment through ocular inspection distinguished the 11 classes defined by FAO (FAO, 2012: National Forest Monitoring and Assessment - Manual for integrated field data collection. Version 3.0):

Plateau: Relatively flat (slope ≤ 5%); terrain of great extent and high elevation, above adjacent lowlands limited by an abrupt descent scarp on at least one side; may be dissected by deep valleys and deeply incised rivers.

Summit / crest: Crest of any kind or hilltop; can be sharp or rounded.



Upper slope: Upper slope of hillside (located on the upper 1/3 of the slope) (shoulder).

Middle slope: Middle slope of hillside (slope > 5%) (back slope).

Lower slope: Lower slope of hillside (foot slope).

Bench / terrace: Horizontal zone of average width over 30 m interposed in the valley side (slope < 15%) or a terrace over 6 m width.

Valley: Very wide, gently sloping depression with predominant extent in one direction commonly situated between two mountains or ranges of hills; the profile may be U- or V-shaped; includes river valley (formed by flowing water) or glacier valleys.

Plain: A large flat to very gently undulating area at a low elevation with reference to surroundings

Narrow depression: Enclosed depression or small, narrow valley or distinct crater (including ravine, gorges, gullies, canyons, etc.).

Water course: Permanent or temporary water course (river, etc.).

Dunes: Sandy hills developed through sand deposits from wind erosion / storms, often unstable and moving.

4.2.7 Land classification

The land classification (legal status) was observed at and recorded for the Sample Points and all Satellite Centers. Hence, five land classification assessments were performed per SU. The assessment through consultation of the latest available land classification map from DENR distinguished 2 classes:

Forest land.

Alienable and disposable.

4.2.8 Land cover

The land cover was observed at and recorded for the Sample Points within a radius of 25 m horizontal distance and all Satellites within a radius of 10 m horizontal distance from the centers. Hence, five land cover assessments were performed per SU. The assessment through ocular inspection distinguished forests (further classified according to their type, see Chapter 4.2.9) and the 11 non-forest land cover classes used in the 2010 NAMRIA national forest cover map:

Forest: Land with an area of more than 0.5 ha and trees able to reach a minimum height of 5 m in situ with a crown cover of more than 10% (see Chapter 2.1 for the detailed definition).

Marshland / swamp.

Fallow.

Shrubs.

Wooded grassland.

Grassland.

Annual crop.

Perennial crop.

Open / barren land.

Built-up area.

Fishpond.

Inland water.



4.2.9 Forest type

The forest type was observed at and recorded for the Sample Points within a radius of 25 m horizontal distance and all Satellites within a radius of 10 m horizontal distance from the centers. Hence, five forest type assessments were performed per SU. The assessment through ocular inspection distinguished the 8 natural forest types used in the conduct of the second National Forest Resources Inventory (1979 - 1988), plus 2 additional types for planted (man-made) forests:

Dipterocarp old growth forest: Tropical rain forest dominated by Dipterocarpaceae with traces of commercial logging.

Dipterocarp residual forest: Tropical rainforest dominated by Dipterocarpaceae after commercial logging.

Mossy forest: Tropical rainforests of the high elevations dominated by Podocarpaceae, Myrtaceae and Fagaceae with trees of medium height and short boled, covered with epiphytes.

Submarginal forest: Tropical rainforest dominated by Leguminosae and lesser utilized species, mainly restricted to shallow and excessively drained lime stone soils.

Closed Pine forest: Pure stands of Benguet or Minodoro Pine with crown cover > 30%.

Open Pine forest: Pure stands of Benguet or Minodoro Pine with 10% < crown cover ≤ 30%.

Mangrove old growth forest: Tidal forests dominated by Rhizophoraceae located on mud flats at the mouths of streams along the shore of protective bays, without traces of exploitation.

Mangrove reproduction forest: Tidal forests dominated by Rhizophoraceae and Verbenaceae dominated by Api-api (Avicennia officinalis) located on mud flats at the mouths of streams along the shore of protective bays, where utilization had been intensive and big trees had been removed.

Native tree plantation forest: Planted forest dominated by native rainforest species.

Other plantation forest: Planted forest dominated by non-native, often fast growing tree species.

4.2.10 Tree crown cover

The tree crown cover was observed at and recorded for the Sample Points within a radius of 25 m horizontal distance and all Satellites within a radius of 10 m horizontal distance from the centers. Hence, five tree crown cover assessments were performed per SU. The assessment through ocular inspection distinguished the 3 classes currently used by NAMRIA for forest cover mapping:

Non-forest: tree crown cover ≤ 10%).

Open forest: 10% < tree crown cover ≤ 40%.

Closed forest: Tree crown cover > 40%.

4.2.11 Plant diversity

The plant diversity was counted at and recorded for all Satellites within a radius of 5 m horizontal distance from the centers. Hence, four plant diversity counts were performed per SU. The inventory consisted of the counting of distinct higher plant species observed, even if not known by their local, official common or scientific names. To avoid repeated counting of the same species, the count was done by only one person, systematically collecting specimen of leaves from plants that can be reached from the ground.



4.2.12 Ground coverage classes by vegetation layers

Ground coverage classes for six (6) vegetation layers were observed and recorded for all Satellites within a radius of 5 m horizontal distance from the centers. Hence, four times six ground coverage classes assessments were performed per SU. For each of the following 6 vegetation layers:

Grass, herbs and mosses.

Tree regeneration, shrubs and plants with 50 cm ≤ height < 130 cm.

Tree regeneration, bushes and plants with 130 cm ≤ height < 200 cm.

Undergrowth of any kind with 2.0 m ≤ height < 4.0 m.

Lower trees and other plants with 4.0 m ≤ height < 10.0 m.

High trees with height > 10.0 m.

the following 4 ground coverage classes were assessed through ocular inspection:

None.

Coverage ≤ 10%.

10% < coverage ≤ 50%.

Coverage > 50%.

4.2.13 Ground coverage and average depth of litter

Litter, defined as all non-living biomass with a size > 2 mm and < 5.0 cm (i.e. the minimum diameter / inventory threshold for dead wood), lying dead, in various states of decomposition above or within the mineral or organic soil, was inventoried and recorded for all Satellites within a radius of 5 m horizontal distance from the centers through ocular estimates of

the ground coverage in %, and

the average depth in cm.

4.2.14 Mid-diameter and length of lying dead wood sections

Lying dead wood, defined as all non-living woody biomass lying on the ground with a diameter ≥ 5.0 cm (i.e. the inventory threshold for dead wood and live trees) not contained in the litter, was inventoried and recorded for all Satellites within a radius of 5 m horizontal distance from the centers. For each lying dead wood section within the 5 m radius plot (without considering those portions extending beyond the plot, see Figure 8, the following measurements were performed:

Mid-diameter: Mid-diameter outside bark in cm, rounded to 0.1 cm, of the dead wood section within the 5 m radius plot, without considering those portions (i) extending beyond the plot, or (ii) with a diameter < 5 cm. The mid-diameter was measured using a caliper or a diameter tape.

Length: Length in m, rounded to 0.1 m, of the dead wood section within the 5 m radius plot, without considering those portions (i) extending beyond the plot, or (ii) with a diameter < 5 cm. The length was measured using a distance tape.

If a lying dead wood section featured branches, these were measured separately. In total, 88 pieces of lying dead wood have been sampled.



Figure 8: Measurements on lying dead wood sections

4.2.15 Observations / measurements on live trees and standing dead wood

Live trees1 and standing dead wood with DBH / DAB ≥ 5.0 cm were inventoried and recorded for all Satellites within a radius of

5 m horizontal distance from the Satellite Centers for

o "small-sized" live trees1 (all species) with 5.0 cm ≤ DBH / DAB < 20.0 cm;

o standing dead wood with DBH / DAB ≥ 5.0 cm;

10 m horizontal distance from the Satellite Centers for "big-sized" live trees1 (all species) with DBH / DAB ≥ 20.0 cm.

For each of the sampled live trees1 and standing dead wood, (i) the species, (ii) azimuth and (iii) horizontal distance from the Satellite Center, (iv) DBH / DAB and for standing dead wood with DBH / DAB ≥ 5.0 cm as well live trees with DBH / DAB ≥ 20.0 cm (v) the merchantable height were observed / measured and recorded as described hereafter. In total, 3,286 live trees1 and 41 standing dead wood have been sampled.



4.2.15.1 Species

The species of each sampled live tree1 and, as much as possible, of each standing dead wood was recorded as identified by the team mates or the local guides / helpers, referring to the official common name or the scientific name. Local names are not suited to unequivocally identify a species, because they vary from dialect to dialect, and even from place to place. In cases where a tree1 could only be identified through its local name, the latter was recorded, as much as possible together with other information (such as digital pictures) that could facilitate the later identification of the species by its scientific name with the help of the taxonomy / dendrology expert, Assistant Professor John Glen P. SENIEL of the Davao Oriental State College of Science and Technology (DOSCST). Appendix 1 provides the list of species recorded by the inventory, including the official common names, the scientific family, genus and species names and the wood specific gravity.

4.2.15.2 Azimuth

The azimuth / bearing in ° of the center of each sampled live tree1 and standing dead wood at its basis / ground level was recorded as measured from the Satellite Center using a handheld precision compass.

4.2.15.3 Horizontal distance

The horizontal distance in m, rounded to 0.1 m, of the center of each sampled live tree1 and standing dead wood at its basis / ground level was recorded as measured from the Satellite Center using a distance tape or a laser rangefinder.

4.2.15.4 Diameter at breast height / above buttress

The diameter at breast height / above buttress outside bark in cm, rounded to 0.1 cm, of each sampled live tree1 and standing dead wood was recorded as measured using a diameter tape at the following measurement points (see also Figure 9):

in general at "breast height", i.e. 1.3 m above ground ("Diameter at Breast Height [DBH]") as measured from the uphill side of the stem;

for trees with prominent buttresses / basal flanges at breast height, the diameter is measured 30 cm above the end of the buttresses / flanges ("Diameter Above Buttress [DAB]");

for trees with bulges, swellings, depressions, branches or other abnormalities at breast height, the diameter is measured just below and above the abnormality at a point where it ceases to affect normal stem form, and computed as the average of the two measurements;

for stumps with a total height < 1.3 m at the section.

If a live tree / standing dead wood forks immediately above breast height, the diameter was measured below the swell resulting from the fork. If a live tree / standing dead wood forks below breast height, the stems were considered as separate trees / standing dead woods. On leaning live trees / standing dead woods, the "breast height" was determined along the axis of the stem.



Source: ZÖHRER F., 1980: Forstinventur: Ein Leitfaden für Studium und Praxis

Figure 9: DBH / DAB measurements

Whenever it proved impossible to measure the DBH / DAB with a diameter tape as described above (e.g. when the measurement point is inaccessible), it was approximated by comparison with a metric tape held horizontally at the base of the tree (see Figure 10).



Figure 10: Diameter estimates for inaccessible measurement points

4.2.15.5 Merchantable height

The merchantable height in m, rounded to 0.1 m, of each sampled live tree with DBH / DAB ≥ 20.0 cm and of each sampled standing dead wood with DBH / DAB ≥ 5.0 cm including stumps was recorded as measured using either a laser hypsometer or a handheld precision clinometer. Merchantable height of trees with DBH / DAB ≥ 35.0 cm is defined as the linear distance along the axis of the stem from the stump height to the top merchantability limit which is restricted by forks, large limbs, sweep, crook or decay, which make segments of the stem un-merchantable for saw logs. For trees with 15.0 cm ≤ DBH / DAB < 35.0 cm, the volume section is limited by a minimum top diameter inside bark which is fixed at 60% of DBH / DAB. By this definition, the measurement to the base of the tree has to be a measurement to the place where the felling cut would be applied, usually about 50 cm above ground, or above the buttresses. Limits for merchantability are the following:

Size of limbs and knots: The sum of diameters in any ¼ m segment ½ the diameter of the log at that point. Where limb and knot diameters exceed this limit, the merchantable height cannot extend through that point, unless there is a merchantable section of 3 m or more in length above that point.

Sweep: Sweep is a curvature in a tree section. Sweep is measured in centimeters of departure of the center line of the section from a straight line joining the centers of each end of the section. The departure is measured at the midpoint of the section containing the sweep. A simple rule for maximum sweep is that departure minus allowance for long taper cannot exceed ½ the small end diameter of the section. Merchantable length is terminated below a section with excessive sweep unless there is a merchantable section of 3 m or more in length above that section.

Crook: Crook is a more or less abrupt bending or angle in a tree section. Crook is measured in cm of maximum departure of the section center line from an extension of the center line of the straight portion of the log. The maximum departure cannot exceed ½ the small end diameter of the log. Excessive crook should terminate the merchantable length unless there is a merchantable section of 3 m or more in length above that section.



5. ORGANIZATIONAL ASPECTS

5.1 Inventory Instructions and Field Data Forms

A specific and detailed FRA field manual (LENNERTZ R., FIEL R. and MEGRASO C.P., 2014: Field Manual for the Forest Resources Assessments in Eastern Samar and Davao Oriental) explaining the use and care of the equipment, the configuration of the SUs as well as the orderly sequencing of the field operations to retrieve, establish, permanently mark and assess / measure the SUs was prepared to ensure that the field work follows Standard Operating Procedures (SOPs), minimizing operating errors and maximizing the homogeneity of the data acquisition. The data were recorded with pencils on sets of purposely designed paper field data forms (see Appendix 3). The latter were regularly collected by the Junior Advisor, Mr. Joise HONOR Jr., coordinating and supervising the field works, and taken to the office for electronic data encoding and processing.

5.2 Inventory Teams

Two (2) Teams carried out the FRA field work between 03 August 2015 and 14 March 2016 (with short breaks for All Saints' / Souls' Days as well as for Christmas and New Year) during a net assignment (including training) of 6.5 months (a limit imposed by the available budget). Each team was composed of the following:

Team Leader:

o Mr. Jose PALAÑA, B.Sc. Forestry (Central Mindanao University);

o Mr. Yolises POLEA, B.Sc. Forestry (Visayas State University);

Assistant:

o Mr. Christian Roquelo GONZALES, B.Sc. Forestry (Visayas State University);

o Mr. Val Jeason SOLANO, B.Sc. Forestry (Visayas State University);

Four Helpers, recruited locally, familiar with the area and preferably knowledgeable about tree species / forest products.

The Team Leaders were responsible for the security of the team, for the equipment entrusted to them, and for the work of their members. They directed the members, validated the data observed or measured by their Assistants, and completed the field data forms. The Assistants manipulated the equipment and carried out the observations and measurements. The Helpers advised on the retrieval of the sample points, carried the equipment, opened / brushed trails, access and sighting lines, marked the sample points and centers of the "satellites", helped the Assistants in carrying out the measurements, and marked the trees. One Control Team, composed of:

Junior Advisor Mr. Joise HONOR Jr., B.Sc. Forestry (Visayas State University),

Staff from DENR PENRO Davao oriental, DENR CENROs Manay and Mati City, and P-ENRO Davao Oriental,

Four Helpers,

re-measured for Quality Control purposes (see Chapter 6.2) 4 SUs (5% of the measured SUs) between 04 February 2016 and 02 June 2016.



5.3 Inventory Equipment

To carry out the field work, each team was equipped with the following:

One handheld IPX7 waterproof GPS receiver (GARMIN GPSMap 78 series) with proven sensitivity / ability to operate under difficult signal reception conditions (under tree cover), to retrieve the Sample Points and measure coordinates.

One handheld IPX7 waterproof precision compass (SUUNTO KB-14/360) graduated in degrees for the measurement of bearings / azimuth.

One handheld IP54 laser hypsometer (LASER TECHNOLOGY Inc. [LTI] TruPulse Laser 200 rangefinder or NIKON Forestry Pro rangefinder) for the measurement of tree heights using the trigonometric principle, hence capable of measuring distances and inclination angles. Regrettably, the LTI TruPulse Laser 200 hypsometer is not waterproof, and the NIKON Forestry Pro cannot measure distances of less than 10 m. Once to be replaced, it is recommended to consider the IP55 waterproof LTI TruPulse Laser 200X instead.

One handheld IPX7 waterproof precision clinometer (SUUNTO PM-5/360) as alternative to and backup for the laser hypsometer (a strategy that paid off when the LTI TruPulse laser hypsometers failed to work during and after heavy rain).

One fiberglass distance tape, 30 m, to measure distances.

One steel diameter tape, 5 m, to measure tree diameters. Upon request of the teams, the steel tapes were rapidly replaced with fiberglass tapes to lessen the risk of injuries from the sharp cutting edges of the steel tapes.

Per SU five iron rods (of at least 1 cm diameter and 50 cm length) to permanently mark the Sample Points and the Satellite Centers, forced at least 4/5 of its length into the ground, topped each with a 50 cm bright-colored 1/2 " PVC pipe to facilitate the retrieval for Quality Control (QC) purposes (cf. Chapter 6.2).

One hatchet to force the iron rods used to permanently mark the Sample Points and the Satellite Centers into the ground.

One first aid kit.

One backpack to carry the equipment.

Personal field work gear for the Team Leaders and Assistants (boots, rain coats, head lamps, sleeping bags, etc.)

Camping equipment (tents, mobile stoves, etc.).

5.4 Training

The teams were familiar with the inventory methodology thanks to their involvement in the similar FRA carried out in Eastern Samar from 01 December 2014 to 24 July 2015. Hence, their training in Davao Oriental was limited to a dendrology refresher course held from 05 - 07 August 2015, with the following program:

Day 1: Taxonomy and dendrology, common tree / bamboo / palm / rattan / tree fern species of Davao Oriental and their identification.

Day 2: Tree / bamboo / palm / rattan / tree fern species identification (on field).

Day 2: Tree / bamboo / palm / rattan / tree fern species identification (on field).

The taxonomy / dendrology subjects were administered by Assistant Professor John Glen P. SENIEL of the Davao Oriental State College of Science and Technology (DOSCST), who also continued during the implementation of the FRA assisting in the determination of species not known to the Team Mates, based on local names, digital pictures and/or specimen of samples forwarded to him.



5.5 Inventory Camps

Considering the location of the SUs to be inventoried and their accessibility, studied on the basis of all available information (see Chapter 4.1.1), the SUs were grouped into batches assigned to inventory camps strategically located, preferably in Barangays or Sitios. A reasonable compromise had to be found between (i) the number of SUs assigned to a specific inventory camp (ideally not less than the number of SUs that can be observed / measured in one field mission, and (ii) the distance from the inventory camp to the furthest SU. The output could have been higher if the teams had agreed to operate separately, since this would have reduced the average distance between the inventory camp and its assigned SUs. However, they insisted to camp together for safety reasons.

5.6 Time and Costs of the Field Work

Based on the experience gained in the implementation of the forest carbon baseline study from mid-2011 until end 2012 in Leyte in the framework of the BMUB funded, GIZ-assisted Climate relevant Modernization of Forest Policy and Piloting of Reducing Emissions from Deforestation and Forest Degradation (REDD) Project, it was initially expected that one Inventory Team working 2 x 12 days without break per month (to reduce the proportion of time spend in mobilization / de-mobilization) could establish and measure an average of 16 SUs per month. As a matter of fact, the average output turned out to be much lower: 6 to 7 SUs per month and per team, in total 81 SUs over the 6.5 months available. The factors that have contributed to the lower than expected output are the following:

remoteness and very difficult accessibility of the area to be inventoried (the furthest Sample Points are located 28.6 km [straight distance] from the sea shore / the only paved road):

o the Sample Points in the Northern portion are located up to 21 km (straight distance) from Barangay P.M. Sobrecarey, Caraga, accessible from the Barangay proper via Sitio Dangilas by an unpaved road (using habal-habal [motorbike]), and via Sitio Maglahus by trekking (for about 4 hours), respectively via Sitio Matabang of Barangay New Taokanga, Manay, by an unpaved road, followed by trekking in extremely steep and rugged terrain;

o the Sample Points in the Center portion are located up to 11 km (straight distance) from Barangay New Taocanga, Manay, accessible from the Barangay proper via Sitio Matabang by an unpaved road, respectively from Sitio Madsayap of Barangay Old Macopa, Manay, by an unpaved road, followed by trekking (for about 2 - 3 hours);

o the Sample Points in the Southern portion are located up to 8 km (straight distance) from Sitio Madian of Barangay Tubaon, Tarragona, accessible from the Barangay proper by an unpaved road, followed by trekking (for about 2 - 3 hours);

information of and coordination with local officials (Barangay Captains, Purok / Sitio Leaders), tribal chieftains and community members prior to the hiring of local helpers / guides and the conduct of the inventory activities, preventing the teams to swiftly proceed to the Sample Points or inventory camps;

unfavorable weather conditions in the forest area (frequent rains), hampering or stalling the measurement and data recording operations;

considerable distances to trek from safe camp sites to the Sample Points.



The costs of the field work are summarized in Table 4.

Table 4: Time and costs the FRA field work in Davao Oriental

Item Unit Quantity Cost / Total

Unit Cost

[PHP/Unit] [PHP]

Personnel Costs

Team Leaders (2 #) person-month 13 32,000 416,000

Assistants (2 #) person-month 13 28,000 364,000

Helpers (8 #) person-day 792 250 198,000

Operational Costs

Consumables (stationaries, batteries, paint, steel rods, etc.)

86,000

Transportation 148,000

Total 1,212,000

Future inventories should consider higher personnel costs, and foresee a food allowance for the helpers.



5.7 Data Processing and Analysis

To reduce licensing fees and to promote open standards, the FRA data were encoded and processed using the leading open source cross platform Object Relational Database Management System (ORDBMS) database engine MySQL (cf. http://www.mysql.com/) using the popular Structured Query Language (SQL). The database architecture is described in detail in a separate document (BARROIS V., 2015: Forest Resources Assessment Database Architecture). A user-friendly FRA Database System Application has been developed using the equally cross platform Java Development Kit (JDK) (cf. http://www.oracle.com/technetwork/java/ javase/overview/index.html). Its installation, including the installation of the required free software (MySQL Community Server 5.6.27 and Java Runtime Environment [JRE] 8 Update 66) under MICROSOFT Windows operating system environments, is described in a separate installation guide (BARROIS V., 2015: Forest Resources Assessment Database System Application installation guide). Version 3.1 of the FRA Database System Application features:

all essential data management operations: add, delete, edit, print to PDF, backup, restore data;

a series of data integrity checks, attracting the data encoder's attention with the help of "traffic lights" (green = integrity check passed; orange = warning; red = integrity check failed) to missing, out-of-range, incompatible and unusual values;

populated reference tables, notably:

o all Philippine LGUs (currently 18 Regions, 81 Provinces, 144 Cities, 1,504 Municipalities and 42,036 Barangays), following the Philippine Standard Geographic Code (PSGC), published by the National Statistical Coordination Board (NSCB, cf. http://www. nscb.gov.ph/activestats/psgc/), in its version of 31 December 2015;

o a growing number of Philippine tree / bamboo / palm / rattan / tree fern species (currently 531 species), whose scientific names have been validated / updated consulting The Plant List Version 1.1 (cf. http://www.theplantlist.org/), a joint effort of Kew and Missouri Botanical Gardens, completed with the wood specific gravity (see Chapter 2.3);

o a global gridded layer of E at 2.5 arc-minute resolution for the use of the latest allometric equations developed 2014 by CHAVE J. et al. (see Chapter 3.7.2, equation {6});

a comprehensive and versatile data analysis framework:

o computing relative frequency, relative density, relative dominance, importance, N/ha, G/ha, V/ha, AGB/ha, BGB/ha, LB/ha by tree / bamboo / palm / rattan / tree fern species and diameter class,

o computing SDW/ha, LDW/ha, LI/ha, DOM/ha, SOM/ha and Total C/ha,

o providing tabular results including statistical precision estimates that can be exported to MS Excel, printed to PDF or depicted as pie or bar charts, and

o displaying species occurrence by geographic coordinates and/or in Google Earth).



6. QUALITY ASSURANCE / QUALITY CONTROL

6.1 Quality Assurance

Apart from the selection of experienced inventory team mates (see Chapter 5.2) and their training (see Chapter 5.4), the key elements of the Quality Assurance (QA) were the following:

A specific and detailed FRA field manual (LENNERTZ R., FIEL R. and MEGRASO C.P., 2014: Field Manual for the Forest Resources Assessments in Eastern Samar and Davao Oriental), with instructions to be complied with to ensure that the field work followed SOPs, minimizing operating errors and maximizing the homogeneity of the data acquisition. The manual was designed to be incremental, i.e. to be enhanced and/or amended based on the feedback from the teams for situations initially not covered or procedures not clearly described. Actually, no technical concerns have been raised, apart from the (granted) request to replace the steel diameter tapes with fiberglass tapes. The team's grievances essentially concerned (i) organizational matters, notably the need for sufficient prior coordination with the local officials and the Armed Forces of the Philippines (AFP), (ii) security issues considering the presence of insurgents, and (iii) long hiking distances to the Sample Points, which could only be reduced by locating inventory camps in inhabited places, a solution that was rejected for security reasons.

Regular accompaniment of the inventory teams by the Junior Advisor assigned in Davao Oriental, to observe whether the inventory procedures, assessments and measurements are carried out correctly. Actually, the accompaniment was limited, due to the busy schedule of the Advisor.

Data encoding by a person with technical background (as a matter of fact by the Junior Advisor) closely following the data acquisition in the field, so that eventual gaps and errors observed could be ironed out with minimal effort, and the inventory teams be cautioned on typical and critical issues.

Thorough verification of the encoded inventory data by the Chief Advisor, paying attention to unusual values and the coherence of the data.

6.2 Quality Control

Due to budgetary constraints, only five percent (5%) instead of the initially intended ten percent (10%) of the SUs chosen at random and without prior knowledge of the Inventory Teams were subject to an independent re-measurement, conducted under the lead of the Junior Advisor, in cooperation with representatives from DENR and the partner-LGUs. The re-measurement concerned SUs No. DAOM0014, DAOM0073, DAOM0097 and DAOM0137. Table 5 summarizes the differences between the initial measurements and the (presumably correct) re-measurements (serving as reference) can be assessed through the mean absolute deviation (MAD) and the root mean square deviation (RMSD). Such deviations must be interpreted cautiously as long as the number of re-measured SUs remains low (say less than about 16 SUs). The following differences between the initial and re-measurements have been observed:

very frequently diverging merchantable height measurements, because of the reduced visibility in the stands; under such conditions, height measurements tend to be made from positions too close to the trees1, leading to steep sighting angles, resulting in inaccurate estimates; this source of error was anticipated, hence the preference for allometric equations relying on DBH / DAB measurements only;

diverging DBH / DAB measurements, at times observed during the re-measurements to be due to the non-removal of vines during the initial measurements; other sources of



these differences are non-standard measurement points above ground, and diameter tapes either not tightened or not held horizontally;

diverging assessments of "borderline" trees1 (at the fringe of the 5 m and 10 m radii plots), falsely considered either to be part or not to be part of the sample; hence the importance of a through checking of such trees1;

diverging species identifications.

Table 5: Deviation of initial from control measurements

Variable of Interest MAD RMSD

[%]* [%]*

Density (N/ha) 19.2 31.7

Basal Area (G/ha) 23.8 29.1

Merchantable Volume (V/ha) 27.3 33.0

Above-Ground Biomass (AGB/ha) 19.9 21.5

Standing Dead Wood (SDW/ha) 146.5 293.0

Lying Dead Wood (LDW) 100.0 137.2

Litter (LI/ha) 39.5 41.2

Number of plant species 20.4 25.9

* with reference to the control measurement

Overall, the mean deviations are reasonable, as expected higher when height measurements are involved (i.e. for the estimation of V/ha, and of SDW/ha). Another ten percent (10% of the encoded SUs chosen at random were printed and subject to an independent comparison with the original field data forms. The comparison was done for SUs No. DAOM0008, DAOM0028, DAOM0051, DAOM0070, DAOM0071, DAOM0099, DAOM0127 and DAOM0147. For 25% of the SUs, no discrepancies were found between the original field data and the encoded data. For the remaining SUs, the following differences between the original field data and the encoded data have been observed:

typing errors with minimal impact on the variables of interest;

omission of data with little impact on the variables of interest

Table 6: Deviation of encoded from field data

Variable of Interest MAD RMSD

[%]* [%]*

Density (N/ha) 0.0 0.0

Basal Area (G/ha) 2.0 5.4

Merchantable Volume (V/ha) 2.5 7.2

Above-Ground Biomass (AGB/ha) 2.5 6.8

Standing Dead Wood (SDW/ha) 0.0 0.0

Lying Dead Wood (LDW) 0.0 0.0

Litter (LI/ha) 0.0 0.0

Number of plant species 0.0 0.0

* with reference to the field data forms



7. DETAILED RESULTS OF THE FOREST RESOURCES ASSESSMENT

The detailed results of the FRA are provided in Appendix 4 (Closed Forests, based on 18 SUs) and Appendix 5 (Open Forests, based on 102 SUs), as computed and printed to PDF by the FRA Database System Application (see Chapter 5.7. A summary analysis is presented hereafter, focusing successively on the following:

species diversity, see Chapter 7.1;

stand composition, see Chapter 7.2;

stand structure, see Chapter 7.3;

timber stocks, see Chapter 0;

carbon stocks, see Chapter 7.5.

The results pertain to trees1 with DBH / DAB ≥ 5.0 cm. The merchantable volume in cubic meter (m³) inside bark has been estimated using the Philippine regional volume equations for dipterocarps and non-dipterocarps, see Chapter 3.7.1. The AGB of live trees has been estimated using the allometric equation developed by CHAVE J. et al., 2014, see Chapter 3.7.2, equation {3} (the FRA Database System Application offers the option to alternatively estimate the AGB of live trees using the allometric equation developed by S. BROWN [FAO, 1997], see Chapter 3.7.2, equation {5}).

7.1 Species Diversity

In ecological studies, the terms "relative frequency", "relative density", "relative dominance" and "importance", used to analyze and particularly to compare species diversity are defined as follows:

The relative frequency of a particular species is defined as the proportion in percent (%) of the SUs where that species has been sampled.

The relative density of a particular species is defined as its proportion in percent (%) of the total stand density (N/ha), all species combined.

The relative dominance of a particular species is defined as its proportion in percent (%) of the total basal area (G/ha), all species combined.

The importance of a particular species, typically used to determine the rank of species, is defined as the sum of its relative frequency, density and dominance.

The following brief analysis of the species diversity refers to these definitions.

7.1.1 Species diversity of Closed Forests

A total of 156 different species have been found and identified in the 37 SUs in the Closed Forests. From 14 to 36, on average 24 different higher plant species have been observed per SU. Table 7 lists the 20 most "important" species (in the sense of the definition given in Chapter 7.1), led by Tangile. As expected, 6 Dipterocarps (Tangile, Narig, Guijo, Yakal, White Lauan, and Almon) are among the most "important" species, but also two Fagaceae (Ulayan and Ulaian), Nato, Salingkugi, Kalingag and Bitanghol.



Table 7: Relative frequency, density and dominance, importance and rank of the 20 most "important" species in Closed Forests

Species Relative Frequency

Relative Density

Relative Dominance

Importance

[%] Rank [%] Rank [%] Rank [-] Rank

Tangile 62,16 1 4,05 3 10,38 1 76,60 1

Ulayan 54,05 2 6,57 1 4,97 3 65,60 2

Nato 54,05 2 1,65 15 3,56 7 59,27 3

Narig 45,95 3 2,35 9 3,82 6 52,12 4

Guijo 37,84 4 2,66 6 5,05 2 45,55 5

Yakal 37,84 4 1,62 16 3,23 9 42,69 6

White Lauan 32,43 5 1,19 20 2,83 10 36,45 7

Salingkugi 32,43 5 2,06 10 1,06 26 35,55 8

Kalingag 29,73 6 3,53 4 2,17 12 35,43 9

Bitanghol 29,73 6 2,58 7 1,15 24 33,46 10

Hindang 29,73 6 1,16 21 1,97 14 32,86 11

Almon 27,03 7 1,71 14 3,42 8 32,15 12

Ulaian 21,62 9 5,21 2 4,75 4 31,59 13

Lapo-lapo 27,03 7 2,75 5 1,04 27 30,82 14

Saguimsim 27,03 7 1,30 18 1,85 15 30,18 15

Badling 27,03 7 2,06 10 0,66 37 29,74 16

Putian 27,03 7 0,67 33 1,25 20 28,94 17

Lanipau 27,03 7 1,10 23 0,81 30 28,94 18

Malaruhat 24,32 8 1,51 17 1,20 22 27,03 19

Pagsahingin-bulog 24,32 8 2,00 11 0,53 40 26,85 20

Figure 11 shows that it takes a relatively important number of species, ranked in decreasing order of their contribution to N/ha, G/ha, V/ha and AGB/ha, to constitute 50% of the totals:

nine species, namely Tangile, Guijo, Almon, Narig, White Lauan, Yakal, Nato, Ulaian and Ulayan (without considering Balete) together represent almost 52% of the merchantable volume;

nine species, namely Tangile, Guijo, Balete, Ulaian, Narig, Yakal, Ulayan, Dacrydium beccarii (Podocarpaceae) and Nato represent together just over 50% of the AGB;

twelve species, namely Tangile, Guijo, Ulayan, Ulaian, Balete, Narig, Nato, Almon, Yakal, White Lauan, Dacrydium beccarii (Podocarpaceae) and Kalingag represent together almost 51% of the basal area;

nineteen species represent together some 50% of the density.

It takes as many as 114, 95, 62 and 82 species to "explain" 95% of the total N/ha, G/ha, V/ha and AGB/ha, respectively. 97% of the SUs in Closed Forests are located at elevations of 800 m a.s.l. and above, 50% at elevations of 1,200 m a.s.l. and above. Consequently, sub-montane and montane species such as Almaciga, Badling, Dacrydium elatum and Kalingag are relatively frequent and abundant.



Figure 11: N/ha, G/ha, V/ha and AGB/ha by number of species in Closed Forests

Table 8 lists the "threatened" species (according to the International Union for Conservation of Nature and Natural Resources [IUCN] red list of threatened species, see http://www.iucnredlist.org/) sampled in the Closed Forests. Practically all Dipterocarps (12 species) are considered "critically endangered" by IUCN.

Table 8: Threatened species in Closed Forests

Vulnerable (VU) Endangered (EN) Critically Endangered (CR)

Almaciga, Balobo, Dalinsi, Hamindang, Is-is, Kalingag, Laneteng gubat, Magabuyo, Malak-malak, Malasantol, Malatapai, Mankono, Nato, Pili, Piling-liitan, Puso-puso, Takip-asin, Tanglin, Tindalo

Narig Almon, Apitong, Guijo, Malapanau, Manggachapui, Mayapis, Panau, Red Lauan, Tangile, White Lauan, Yakal, Yakal-Kaliot

7.1.2 Species diversity of Open Forests

A total of 162 different species have been found and identified in the 44 SUs in the Open Forests. From 5 to 37, on average 22 different plant species have been observed per SU. Table 9 lists the 20 most "important" species (in the sense of the definition given in Chapter 7.1), led by Ulayan. 6 Dipterocarps (Tangile, Bagtikan, Guijo, White Lauan, Narig and Almon) are among the most "important" species, but also Nato, Bitanghol, several



pioneer species (Badling, Hagimit, Hindang, Igyo, Lipang-kalabaw, Putian, Talisay-gubat, Tibig) and a palm (Sagisi).

Table 9: Relative frequency, density and dominance, importance and rank of the 20 most "important" species in Open Forests

Species Relative Frequency

Relative Density

Relative Dominance

Importance

[%] Rank [%] Rank [%] Rank [-] Rank

Ulayan 54,55 1 8,99 1 7,44 2 70,97 1

Tangile 40,91 2 2,67 3 11,81 1 55,39 2

Nato 34,09 3 1,53 11 5,09 3 40,71 3

Bagtikan 27,27 5 2,33 4 2,70 8 32,30 4

Bitanghol 29,55 4 1,32 15 0,87 27 31,73 5

Guijo 25,00 6 1,63 10 2,97 6 29,60 6

White Lauan 25,00 6 0,83 25 1,13 25 26,97 7

Lipang-kalabaw 22,73 7 2,19 5 0,76 33 25,68 8

Narig 20,45 8 1,39 13 3,57 5 25,41 9

Sagisi 22,73 7 2,08 6 0,33 57 25,14 10

Badling 20,45 8 1,80 7 0,84 30 23,10 11

Tibig 20,45 8 1,53 11 0,38 55 22,36 12

Hagimit 20,45 8 1,32 15 0,43 50 22,20 13

Putian 20,45 8 0,94 23 0,77 32 22,16 14

Almon 18,18 9 1,70 9 2,16 10 22,05 15

Malatambis 18,18 9 1,21 17 1,29 22 20,69 16

Igyo 18,18 9 0,80 26 1,54 18 20,52 17

Balete 15,91 10 0,42 36 4,06 4 20,39 18

Talisay-gubat 18,18 9 0,38 37 1,77 15 20,33 19

Hindang 18,18 9 0,66 30 0,85 28 19,69 20

Figure 12 shows that like in the Open Forests, it takes a relatively important number of species, ranked in decreasing order of their contribution to N/ha, G/ha, V/ha and AGB/ha, to constitute 50% of the totals:

eight species, namely Tangile, Nato, Ulayan, Narig, Balukanag, Bagtikan, Guijo and Almon (without considering Balete) together represent some 52% of the merchantable volume;

nine species, namely Tangile, Balete, Ulayan, Nato, Narig, Guijo, Balukanag, Yakal and Bagtikan represent together some 52% of the AGB;

thirteen species, namely Tangile, Ulayan, Nato, Balete, Narig, Guijo, Balukanag, Bagtikan, Yakal, Almon, Balobo, Moluccan Sau and Mankono represent together almost 51% of the basal area;

twenty five species represent together some 50% of the density.

It takes as many as 118, 99, 51 and 79 species to "explain" 95% of the total N/ha, G/ha, V/ha and AGB/ha, respectively. 89% of the SUs in Open Forests are located at elevations below 1,200 m a.s.l., 43% at elevations below 800 m a.s.l. Hence, less sub-montane and montane species (e.g. Badling and Kalingag; no Almaciga) have been sampled, and these are less frequent and less abundant than in the Closed Forests.



Figure 12: N/ha, G/ha, V/ha and AGB/ha by number of species in Open Forests

Table 10 lists the "threatened" species (according to the IUCN red list of threatened species) sampled in the Open Forests. Practically all Dipterocarps (12 species) sampled in the Open Forests are considered "critically endangered" by IUCN.

Table 10: Threatened species in Open Forests

Vulnerable (VU) Endangered (EN) Critically Endangered (CR)

Antipolo, Balobo, Hamindang, Kalingag, Katmon, Laneteng gubat, Lanutan, Magabuyo, Malakape, Malak-malak, Malasantol, Malatapai, Mankono, Nato, Pili, Piling-liitan, Puso-puso, Takip-asin

Narig Almon, Gisok-gisok, Guijo, Malapanau, Manggachapui, Mayapis, Panau, Red Lauan, Tangile, White Lauan, Yakal, Yakal-Kaliot

The relatively limited number of SUs (37 in the Closed Forests, 44 in Open Forests) precludes a thorough comparison of the species diversity. Closed Forests seem to be slightly more diverse than Open Forests. Yakal and Ulaian occupy significantly higher "importance" ranks in Closed than in Open Forests. Pioneer species, on the other hand, occupy higher "importance" ranks in Open than in Closed Forests, notably Lipang-kalabaw, Tibig, Hagimit and Igyo.



7.2 Stand Composition

7.2.1 Stand composition of Closed Forests

Table 11 summarizes, and Figure 13, illustrates the stand composition of the Closed Forests in terms of N/ha, G/ha, V/ha and AGB/ha. In terms of basal area and AGB, Dipterocarps account for slightly more than 1/3 of the total stock. Their share is even higher (almost 50%) in terms of merchantable volume, but lesser (1/5) in terms of density. This stems from the fact that the average size of Dipterocarps, as revealed through the quadratic mean diameter (Dg), is considerably larger (32.9 cm) than the Dg of Non-Dipterocarps (22.5 cm).

Table 11: Stand composition (N/ha, G/ha, V/ha and AGB/ha) of Closed Forests

Species / Dg N/ha G/ha V/ha AGB/ha

Species Group [cm] [/ha] [%] [m²/ha] [%] [m³/ha] [%] [t. d.m./ha] [%]

Dipterocarps

Tangile 39.3 30.1 4.1 3.66 10.4 33.56 15.2 44.79 11.3

Guijo 33.8 19.8 2.7 1.78 5.1 14.39 6.5 26.14 6.6

Narig 31.4 17.4 2.3 1.35 3.8 10.53 4.8 19.62 4.9

Almon 34.7 12.7 1.7 1.20 3.4 11.35 5.1 10.15 2.6

Yakal 34.8 12.0 1.6 1.14 3.2 9.61 4.4 18.04 4.5

Other Dipterocarps 27.4 49.5 6.7 2.91 8.3 25.36 11.5 29.19 7.3

Sub-Total Dipterocarps 32.9 141.5 19.1 12.04 34.2 104.80 47.5 147.92 37.2

Non-Dipterocarps

Ulayan (Oak) 21.4 48.8 6.6 1.75 5.0 7.23 3.3 16.48 4.1

Ulaian 23.5 38.7 5.2 1.68 4.8 9.01 4.1 20.15 5.1

Balete 78.2 3.0 0.4 1.44 4.1 10.11 4.6 26.01 6.5

Nato 36.1 12.3 1.7 1.26 3.6 9.04 4.1 13.37 3.4

Dacrydium beccarii Parl. 88.5 1.3 0.2 0.80 2.3 5.74 2.6 15.40 3.9

Kalingag 19.2 26.2 3.5 0.76 2.2 2.94 1.3 5.07 1.3

Lanipga 68.5 1.9 0.3 0.70 2.0 5.45 2.5 10.32 2.6

Hindang 32.2 8.6 1.2 0.70 2.0 4.38 2.0 9.20 2.3

Saguimsim 29.2 9.7 1.3 0.65 1.8 3.65 1.7 7.38 1.9

Talisay-gubat 42.4 3.9 0.5 0.55 1.6 4.62 2.1 7.70 1.9

Other Non-Dipterocarps 19.5 420.5 56.6 12.51 35.5 53.80 24.4 118.07 29.7

Sub-Total Non-Dipterocarps 22.5 574.9 77.4 22.80 64.7 115.97 52.5 249.15 62.6

Palms 25.4 3.4 0.39 1.1 0.90 0.2

Bamboos 0.9 0.1 0.01 0.0 0.02 0.0

Total 742.7 100.0 35.24 100.0 220.77 100.0 398.00 100.0

The five most dominant Dipterocarps in terms of basal area are Tangile, Guijo, Narig, Almon and Yakal. Together, they represent 76% of the total Dipterocarp merchantable volume, and around 36% of the total merchantable volume, all species combined.



N/ha [/ha]

G/ha [m²/ha]

V/ha [m³/ha]

AGB/ha [t d.m./ha]

Dipterocarps Non-Dipterocarps

Palms Bamboos Tangile Guijo Narig Almon Yakal Other Dipterocarps

Figure 13: Stand composition (N/ha, G/ha, V/ha and AGB/ha) of Closed Forests



The ten most dominant Non-Dipterocarps in terms of basal area, led by Ulayan and Ulaian, closely followed by Balete (being stranglers, their size is arguable) and Nato, represent together a substantial share of G/ha (45.1%), and the lion share of V/ha (53.6%) and AGB/ha (52.6%) of their group. The palms encountered are essentially Sagisi, and to a lesser extent Pugahan and Ulango. In the Closed Forests, not a single Coconut has been sampled!

7.2.2 Stand composition of Open Forests

Table 12 summarizes and Figure 14 illustrates the stand composition of the Open Forests in terms of N/ha, G/ha, V/ha and AGB/ha. Like in the Closed Forests, Dipterocarps account for about 1/3 of the basal area and AGB of the Open Forests. Their share is even higher (44%) in terms of merchantable volume, but lesser (13%) in terms of density. This is thanks to their average size, in terms of Dg, which is again considerably larger (36.1 cm) than the Dg of Non-Dipterocarps (21.1 cm).

Table 12: Stand composition (N/ha, G/ha, V/ha and AGB/ha) of Open Forests

Species / Dg N/ha G/ha V/ha AGB/ha

Species Group [cm] [/ha] [%] [m²/ha] [%] [m³/ha] [%] [t. d.m./ha] [%]

Dipterocarps

Tangile 49.1 13.9 2.7 2.63 11.8 29.24 20.4 33.52 13.5

Narig 37.4 7.2 1.4 0.79 3.5 6.69 4.7 14.10 5.7

Guijo 31.4 8.5 1.6 0.66 3.0 5.08 3.5 9.83 4.0

Bagtikan 25.1 12.1 2.3 0.60 2.7 5.42 3.8 6.68 2.7

Yakal 41.6 3.6 0.7 0.49 2.2 3.41 2.4 8.19 3.3

Other Dipterocarps 31.3 21.7 4.2 1.67 7.5 13.14 9.2 19.70 7.9

Sub-Total Dipterocarps 36.1 67.1 12.9 6.85 30.8 62.98 44.0 92.02 37.1

Non-Dipterocarps

Ulayan (Oak) 21.3 46.8 9.0 1.66 7.5 8.02 5.6 16.21 6.5

Nato 42.4 8.0 1.5 1.13 5.1 10.26 7.2 14.37 5.8

Balete 72.2 2.2 0.4 0.90 4.0 5.10 3.6 18.02 7.3

Balukanag 38.2 5.4 1.0 0.62 2.8 6.35 4.4 8.74 3.5

Balobo 29.2 6.7 1.3 0.45 2.0 1.72 1.2 5.53 2.2

Moluccan sau 49.3 2.2 0.4 0.42 1.9 2.85 2.0 3.80 1.5

Mankono 38.5 3.6 0.7 0.42 1.9 2.91 2.0 6.26 2.5

Saguimsim 26.8 7.1 1.4 0.40 1.8 1.75 1.2 4.58 1.8

Talisay-gubat 49.8 2.0 0.4 0.39 1.8 3.61 2.5 5.25 2.1

Binuang 98.4 0.5 0.1 0.38 1.7 3.38 2.4 3.31 1.3

Other Non-Dipterocarps 17.5 347.8 66.7 8.40 37.7 34.24 23.9 69.58 28.0

Sub-Total Non-Dipterocarps 21.1 432.3 83.0 15.17 68.1 80.21 56.0 155.66 62.7

Palms 14.5 2.8 0.18 0.8 0.48 0.2

Bamboos 7.2 1.4 0.05 0.2 0.16 0.1

Total 521.1 100.0 22.26 100.0 143.19 100.0 248.32 100.0



N/ha [/ha]

G/ha [m²/ha]

V/ha [m³/ha]

AGB/ha [t d.m./ha]

Dipterocarps Non-Dipterocarps

Palms Bamboos Tangile Guijo Narig Bagtikan Yakal Other Dipterocarps

Figure 14: Stand composition (N/ha, G/ha, V/ha and AGB/ha) of Open Forests



The five most dominant Dipterocarps in terms of basal area are Tangile, Narig, Guijo, Bagtikan and Yakal (the same species as in the Closed Forests, except Almon, which is superseded by Bagtikan, and following a slightly different ranking). Together, they represent 79% of the total Dipterocarp merchantable volume, and around 35% of the total merchantable volume, all species combined. The ten most dominant Non-Dipterocarps in terms of basal area, led by Ulayan followed by Nato, Balete (being stranglers, their size is arguable) and Balukanag, together represent 44.6% of G/ha, 57.3% of V/ha and 55.3% of AGB/ha of their group. The palms encountered are essentially Sagisi, some Pugahan, and very few Coconuts.

7.3 Stand Structure

7.3.1 Stand structure of Closed Forests

The stand structure of the Closed Forests is summarized hereafter in terms of the following:

density (N/ha) by diameter class, summarized in Table 13 and illustrated in Figure 15;

basal area (G/ha) by diameter class, summarized in Table 14 and illustrated in Figure 16; and

above-ground biomass (AGB/ha) by diameter class, summarized in Table 15 and illustrated in Figure 18.

Table 13: Stand structure in terms of N/ha of Closed Forests

Species / Density by Diameter Class

Species Group [5 cm - 20 cm[ [20 cm - 40 cm[ [40 cm - 60 cm[ [60 cm - 80 cm[ [80 cm - Total

[/ha] [/ha] [/ha] [/ha] [/ha] [/ha]

Dipterocarps

Almon 6.9 3.0 1.5 1.1 0.2 12.7

Guijo 10.3 5.2 2.6 1.3 0.4 19.8

Narig 8.6 5.2 3.4 - 0.2 17.4

Tangile 18.0 4.4 3.4 2.1 2.2 30.1

Yakal 7.8 1.5 1.5 0.8 0.4 12.0

Other Dipterocarps 35.2 7.6 2.9 3.1 0.7 49.5

Total Dipterocarps 86.8 26.9 15.3 8.4 4.1 141.5

Non-Dipterocarps

Bitanghol 17.2 1.8 0.2 - - 19.1

Hindang 6.0 1.0 1.0 0.2 0.2 8.6

Kalingag 20.6 4.8 0.8 - - 26.2

Nato 2.6 5.4 3.5 0.9 - 12.3

Salingkugi 12.9 1.9 0.2 0.2 - 15.3

Ulayan 37.0 10.3 1.0 0.2 0.2 48.8

Other Non-Dipterocarps 351.9 65.6 17.1 5.4 4.8 444.6

Total Non-Dipterocarps 448.2 90.8 23.8 6.9 5.2 574.9

Palms 23.3 1.9 0.2 - - 25.4

Bamboos 0.9 - - - - 0.9

Total 559.2 119.6 39.4 15.2 9.3 742.7

Standing Dead Wood 7.7 6.0 3.5 0.9 - 18.1



Figure 15: Stand structure in terms of N/ha of Closed Forests

On average, the Closed Forests count per hectare 142 Dipterocarp trees, 575 Non-Dipterocarp trees, 25 Palms, 1 Bamboo and 18 Standing Dead Wood.



As expected, N/ha by diameter class shows a typical inverse "J"-shaped distribution, except for trees with a DBH / DAB < 10 cm, which appear to be lacking in numbers. The rise of N/ha for trees with DBH / DAB ≥ 90 cm is due to a few quite large Dipterocarps (essentially Tangile), Balete (being stranglers, their size is arguable), Lanigpa, Talisay-gubat and Hindang. The distribution of the shares of the predominant Dipterocarps and Non-Dipterocarps by diameter class does not reveal any peculiarity.

Table 14: Stand structure in terms of G/ha of Closed Forests

Species / Basal Area by Diameter Class


[m²/ha] [m²/ha] [m²/ha] [m²/ha] [m²/ha] [m²/ha]

Dipterocarps

Almon 0.11 0.18 0.29 0.44 0.19 1.20

Guijo 0.16 0.33 0.49 0.47 0.34 1.78

Narig 0.15 0.36 0.66 - 0.18 1.35

Tangile 0.20 0.30 0.72 0.74 1.70 3.66

Yakal 0.09 0.12 0.32 0.35 0.28 1.14



Non-Dipterocarps

Bitanghol 0.26 0.11 0.03 - - 0.41

Hindang 0.06 0.07 0.20 0.07 0.29 0.70

Kalingag 0.29 0.33 0.14 - - 0.76

Nato 0.03 0.36 0.61 0.26 - 1.26

Salingkugi 0.16 0.10 0.04 0.08 - 0.37

Ulayan 0.63 0.67 0.21 0.08 0.16 1.75



Palms 0.25 0.11 0.03 - - 0.39

Bamboos 0.01 - - - - 0.01

Total 7.15 7.81 7.31 5.33 7.61 35.24

Standing Dead Wood 0.11 0.46 0.54 0.24 0.00 1.35

On average, the basal area of the Closed Forests amounts to 35.2 m²/ha, a respectable level, actually more than the 30.5 m²/ha (for trees with DBH / DAB ≥ 15 cm) observed 1979 to 1983 by the FAO-assisted Northeastern Mindanao Pilot Project in 92 SUs in "Old Growth Forests" of Region XI. The distribution of G/ha by diameter class does not reveal any particularity.



Figure 16: Stand structure in terms of G/ha of Closed Forests



Table 15: Stand structure in terms of AGB/ha of Closed Forests

Species / Above-Ground Biomass by Diameter Class


[t d.m./ha] [t d.m./ha] [t d.m./ha] [t d.m./ha] [t d.m./ha] [t d.m./ha]

Dipterocarps

Almon 0.49 1.13 2.39 4.06 2.09 10.15

Guijo 1.24 3.85 7.07 7.65 6.33 26.14

Narig 1.32 4.47 10.14 - 3.68 19.62

Tangile 1.05 2.52 7.70 8.88 24.64 44.79

Yakal 0.74 1.42 4.73 6.00 5.16 18.04



Non-Dipterocarps

Bitanghol 1.43 0.83 0.30 - - 2.56

Hindang 0.41 0.65 2.35 0.92 4.86 9.20

Kalingag 1.44 2.37 1.25 - - 5.07

Nato 0.14 3.29 6.67 3.27 - 13.37

Salingkugi 1.00 0.87 0.44 1.03 - 3.34

Ulayan 4.33 6.19 2.34 1.14 2.49 16.48



Palms 0.72 0.16 0.02 - - 0.90

Bamboos 0.02 - - - - 0.02

Total 44.71 71.87 86.46 68.13 126.83 398.00

On average, the above-ground biomass of the Closed Forests amounts to 398 t d.m./ha, which corresponds to the median of the range from 280 t d.m./ha to 520 t d.m./ha referred to by IPCC as Tier 1 estimate for tropical rainforest of insular Asia. The distribution of AGB/ha by diameter class does not reveal any particularity. Figure 17 shows that 99% of AGB/ha is composed of trees1 with DBH / DAB ≥ 10 cm.

Figure 17: AGB/ha of Closed Forests by DBH / DAB threshold



Figure 18: Stand structure in terms of AGB/ha of Closed Forests



7.3.2 Stand structure of Open Forests

The stand structure of the Open Forests is summarized hereafter in terms of the following: density (N/ha) by diameter class,

summarized in Table 16 and illustrated in Figure 19;

basal area (G/ha) by diameter class, summarized in Table 17 and illustrated in Figure 20; and

above-ground biomass (AGB/ha) by diameter class, summarized in Table 18 and illustrated in Figure 22.

Table 16: Stand structure in terms of N/ha of Open Forests

Species / Density by Diameter Class


[/ha] [/ha] [/ha] [/ha] [/ha] [/ha]

Dipterocarps

Bagtikan 10.0 0.9 0.4 0.4 0.4 12.1

Guijo 5.0 1.5 1.5 0.4 0.1 8.5

Narig 5.0 1.0 0.2 0.2 0.8 7.2

Tangile 6.5 2.4 2.0 1.0 2.0 13.9

Yakal 2.1 - 0.7 0.4 0.4 3.6



Non-Dipterocarps

Badling 8.6 0.7 - - - 9.4

Bitanghol 5.0 1.6 0.2 - - 6.9

Lipang-kalabaw 10.8 0.5 - - - 11.4

Nato 4.3 1.0 0.6 1.3 0.8 8.0

Tibig 7.2 0.7 - - - 8.0

Ulayan 36.2 6.9 3.5 0.4 - 46.8



Palms 13.8 0.8 - - - 14.5

Bamboos 7.3 - - - - 7.2

Total 425.4 57.2 21.4 8.5 8.7 521.1

Standing Dead Wood 2.9 2.8 0.7 - - 6.5

On average, the Open Forests count per hectare 67 Dipterocarp trees, 432 Non-Dipterocarp trees, 15 Palms, 7 Bamboos and 7 Standing Dead Wood. A t-test confirms that at a confidence level of 99%, the density of live trees1 (521 /ha) is significantly lower than in the Closed Forests (743 /ha). The distribution of N/ha by diameter class follows a similar pattern as in the Closed Forests, though at a lower level.



Figure 19: Stand structure in terms of N/ha of Open Forests

The rise of N/ha for trees with larger diameters is observed again, here for trees with DBH / DAB ≥ 80 cm, due to a few large Dipterocarps (essentially Tangile), Nato, Balukanag and Talisay-gubat.



The relative density of Dipterocarps by diameter class reveals that Yakal has a reduced share in the lower diameter classes (hinting that it is not regeneration well). Among the Non-Dipterocarps, a similar, though less pronounced trend can be observed for Nato.

Table 17: Stand structure in terms of G/ha of Open Forests

Species / Basal Area by Diameter Class


[m²/ha] [m²/ha] [m²/ha] [m²/ha] [m²/ha] [m²/ha]

Dipterocarps

Bagtikan 0.11 0.06 0.06 0.15 0.22 0.60

Guijo 0.07 0.07 0.25 0.13 0.14 0.66

Narig 0.08 0.07 0.04 0.08 0.52 0.79

Tangile 0.08 0.17 0.37 0.42 1.59 2.63

Yakal 0.03 - 0.13 0.13 0.20 0.49



Non-Dipterocarps

Badling 0.12 0.07 - - - 0.19

Bitanghol 0.05 0.11 0.03 - - 0.19

Lipang-kalabaw 0.15 0.02 - - - 0.17

Nato 0.07 0.09 0.10 0.42 0.44 1.13

Tibig 0.04 0.04 - - - 0.08

Ulayan 0.42 0.50 0.59 0.14 - 1.66



Palms 0.14 0.05 - - - 0.18

Bamboos 0.05 - - - - 0.05

Total 4.96 3.78 3.84 2.98 6.70 22.26

Standing Dead Wood 0.03 0.20 0.09 - - 0.32

On average, the basal area of the Open Forests amounts to 22.3 m²/ha. This is significantly less than G/ha of the Closed Forests (35.2 m²/ha), as confirmed by a t-test at a confidence level of 99%. The order of magnitude is reasonable, and actually much higher than the 14.0 m²/ha (for trees with DBH / DAB ≥ 15 cm) observed 1979 to 1983 by the FAO-assisted Northeastern Mindanao Pilot Project in 169 SUs in "Residual Forests" of Region XI. The distribution of G/ha by diameter class does not reveal any particularity, but confirms the observation derived from the distribution of N/ha that Yakal and Nato are underrepresented in the lower diameter classes.



Figure 20: Stand structure in terms of G/ha of Open Forests



On average, the above-ground biomass of the Open Forests amounts to 248 t d.m./ha, which is significantly less than AGB/ha of the Closed Forests (398 t d.m./ha), as confirmed by a t-test at a confidence level of 99%.

Table 18: Stand structure in terms of AGB/ha of Open Forests

Species / Above-Ground Biomass by Diameter Class


[t d.m./ha] [t d.m./ha] [t d.m./ha] [t d.m./ha] [t d.m./ha] [t d.m./ha]

Dipterocarps

Bagtikan 0.63 0.48 0.67 2.01 2.88 6.68

Guijo 0.57 0.81 3.47 2.18 2.79 9.83

Narig 0.64 0.88 0.67 1.49 10.41 14.10

Tangile 0.48 1.49 3.86 5.09 22.60 33.52

Yakal 0.27 - 1.85 2.23 3.83 8.19



Non-Dipterocarps

Badling 0.79 0.63 - - - 1.42

Bitanghol 0.29 0.86 0.22 - - 1.36

Lipang-kalabaw 0.97 0.16 - - - 1.13

Nato 0.51 0.85 1.17 5.52 6.34 14.37

Tibig 0.22 0.39 - - - 0.61

Ulayan 2.63 4.84 6.86 1.89 - 16.21



Palms 0.42 0.07 - - - 0.48

Bamboos 0.16 - - - - 0.16

Total 29.07 33.50 43.38 38.37 103.99 248.32

The distribution of AGB/ha by diameter class does not reveal any particularity. Figure 21 shows that like in the Closed Forests, 99% of AGB/ha in the Open Forests is composed of trees1 with DBH / DAB ≥ 10 cm.

Figure 21: AGB/ha of Open Forests by DBH / DAB threshold



Figure 22: Stand structure in terms of AGB/ha of Open Forests



7.4 Timber Stocks

7.4.1 Timber stocks of Closed Forests

Table 19 summarizes and Figure 23 illustrates the distribution of the merchantable volume in the Closed Forests by diameter class and main species.

Table 19: Merchantable volume in Closed Forests

Species / Merchantable Volume by Diameter Class

Species Group [5 cm - 40 cm[ [40 cm - 60 cm[ [60 cm - 80 cm[ [80 cm - Total

[m³/ha] [m³/ha] [m³/ha] [m³/ha] [m³/ha]

Dipterocarps

Almon 1.54 2.92 4.56 2.32 11.35

Guijo 3.07 4.53 4.00 2.79 14.39

Narig 0.80 4.56 2.80 2.36 10.53

Tangile 2.14 6.48 7.97 16.98 33.56

Yakal 0.82 2.36 3.38 3.05 9.61

Other Dipterocarps 6.72 6.85 7.59 4.21 25.36

Total Dipterocarps 15.09 27.70 30.30 31.71 104.80

Non-Dipterocarps

Bitanghol 0.76 0.34 - - 1.10

Hindang 0.43 1.28 0.33 2.34 4.38

Kalingag 2.04 0.90 - - 2.94

Nato 2.37 4.62 2.05 - 9.04

Salingkugi 0.47 0.13 0.45 - 1.05

Ulayan 4.01 1.13 0.45 1.63 7.23

Other Non-Dipterocarps 25.31 22.49 13.04 29.40 90.23

Total Non-Dipterocarps 35.39 30.89 16.32 33.37 115.97

Total 50.47 58.59 46.62 65.09 220.77

On average, the merchantable volume in the Closed Forests amounts to 221 m³/ha. This comes close to the order of magnitude of 233 m³/ha (for trees with DBH / DAB ≥ 15 cm) observed 1979 to 1983 by the FAO-assisted Northeastern Mindanao Pilot Project in 92 SUs in "Old Growth Forests" of Region XI. Then and now, around 1/3 of V/ha is concentrated on trees with DBH / DAB ≥ 75 cm. However, the proportion of Dipterocarps has dramatically reduced over time, from then 65.6% to now 47.5%. Dipterocarp species whose share has strongly diminished are Apitong, Bagtikan, Red Lauan and Mayapis.



Figure 23: Merchantable volume in Closed Forests



7.4.2 Timber stocks of Open Forests

Table 20 summarizes and Figure 24 illustrates the distribution of the merchantable volume in the Open Forests by diameter class and main species.

Table 20: Merchantable volume in Open Forests

Species / Merchantable Volume by Diameter Class

Species Group [5 cm - 40 cm[ [40 cm - 60 cm[ [60 cm - 80 cm[ [80 cm - Total

[m³/ha] [m³/ha] [m³/ha] [m³/ha] [m³/ha]

Dipterocarps

Bagtikan 0.43 0.73 1.87 2.39 5.42

Guijo 0.54 2.11 1.39 1.03 5.08

Narig 0.59 0.23 0.94 4.93 6.69

Tangile 1.70 3.80 3.76 19.99 29.24

Yakal - 0.87 1.14 1.40 3.41

Other Dipterocarps 1.93 2.91 3.26 5.05 13.14

Total Dipterocarps 5.19 10.65 12.36 34.79 62.98

Non-Dipterocarps

Badling 0.33 - - - 0.33

Bitanghol 0.66 0.15 - - 0.81

Lipang-kalabaw 0.08 - - - 0.08

Nato 0.72 0.68 3.88 4.98 10.26

Tibig 0.17 - - - 0.17

Ulayan 3.14 4.00 0.88 - 8.02

Other Non-Dipterocarps 13.49 13.24 8.48 25.34 60.54

Total Non-Dipterocarps 18.59 18.07 13.24 30.32 80.21

Total 23.78 28.72 25.59 65.10 143.19

On average, the merchantable volume in the Open Forests amounts to 143 m³/ha. Based on t-tests, this is significantly less than the merchantable volume in the Closed Forests (221 t d.m./ha) at a confidence level of 95%, but not at a confidence level of 99%. The merchantable volume in the Open Forests is actually considerably higher than the 91 m³/ha (for trees with DBH / DAB ≥ 15 cm) observed 1979 to 1983 by the FAO-assisted Northeastern Mindanao Pilot Project in 169 SUs in "Residual Forests" of Region XI. At that time, only about 10% of V/ha was concentrated on trees with DBH / DAB ≥ 75 cm, compared to 48% now. Obviously, the "Residual Forests" inventoried from 1979 to 1983 were very recently logged-over, hence the very low (i) merchantable volume and (ii) proportion of trees with DBH / DAB ≥ 75 cm, while the Open Forests inventoried from 2015 to 2016 have to some extent recovered. The proportion of Dipterocarps is quite comparable, 48% by then compared to 44% now. Dipterocarp species whose share has strongly diminished are Apitong and Mayapis.



Figure 24: Merchantable volume in Open Forests



7.5 Carbon Stocks

7.5.1 Carbon stocks of Closed Forests

Table 21 summarizes, and Figure 25 illustrates the carbon stocks of the Closed Forests.

Table 21: Carbon stocks of Closed Forests

Carbon Pool Biomass/ha by Diameter Class Carbon/ha

[5 cm - 40 cm[ [40 cm - Total Total

[t d.m./ha] [t d.m./ha] [t d.m./ha] [t C/ha] [%]

Living Biomass (LB)

Above-Ground Biomass (AGB) 116.58 281.42 398.00 187.06 56.0

Below-Ground Biomass (BGB) 43.13 104.13 147.26 69.21 20.7

Total Living Biomass 159.71 385.54 545.26 256.27 76.8

Dead Organic Matter (DOM)

Standing Dead Wood (SDW) 7.92 2.93 0.9

Lying Dead Wood (LDW) 2.93 1.08 0.3

Litter (LI) 5.35 1.98 0.6

Total Dead Organic Matter (DOM) 16.20 5.99 1.8

Soil Organic Matter (SOM) 71.62 21.5

Total 333.89 100.0

C/ha [t C/ha] C/ha [t C/ha]

Figure 25: Carbon stocks of Closed Forests

On average, the Closed Forests feature a Living Biomass of 545 t d.m./ha, Dead Organic Matter of 5.99 t C/ha, composed of (i) 4.01 t C/ha of Dead Wood and (ii) 1.98 t C/ha of Litter, plus 71.62 t C/ha of Soil Organic Matter. The bulk of the carbon stock is in the Above-Ground Biomass (56.0%), and thereof in Non-Dipterocarps (62.6%). Extrapolated to the 21,750 ha of Closed Forests in Caraga, Manay and Tarragona, the forest carbon stock amounts to 7.262 million t C.



7.5.2 Carbon stocks of Open Forests

Table 22 summarizes and Figure 26 illustrates the carbon stocks of the Open Forests.

Table 22: Carbon stocks of Open Forests

Carbon Pool Biomass/ha by Diameter Class Carbon/ha

[5 cm - 40 cm[ [40 cm - Total Total

[t d.m./ha] [t d.m./ha] [t d.m./ha] [t C/ha] [%]

Living Biomass (LB)

Above-Ground Biomass (AGB) 62.57 185.74 248.32 116.71 52.4

Below-Ground Biomass (BGB) 23.15 68.72 91.88 43.18 19.4

Total Living Biomass 85.72 254.48 340.20 159.89 71.8

Dead Organic Matter (DOM)

Standing Dead Wood (SDW) 2.80 1.03 0.5

Lying Dead Wood (LDW) 3.36 1.24 0.6

Litter (LI) 5.18 1.92 0.9

Total Dead Organic Matter (DOM) 11.33 4.19 1.9

Soil Organic Matter (SOM) 58.61 26.3

Total 222.70 100.0

C/ha [t C/ha] C/ha [t C/ha]

Figure 26: Carbon stocks of Open Forests

On average, the Open Forests feature a Living Biomass of 340 t d.m./ha, Dead Organic Matter of 4.19 t C/ha, composed of (i) 2.27 t C/ha of Dead Wood and (ii) 1.92 t C/ha of Litter, plus 58.61 t C/ha of Soil Organic Matter. The bulk of the carbon stock is in the Above-Ground Biomass (52.4%), and thereof in Non-Dipterocarps (62.7%). Extrapolated to the 17,465 ha of Open Forests in Caraga, Manay and Tarragona, the forest carbon stock amounts to 3.889 million t C.



8. UNCERTAINTY OF THE ESTIMATES

The estimates of all variables of interest, such as N/ha, G/ha, V/ha, AGB/ha, DOM/ha, SOM/ha and C/ha, to cite the most important ones that are summarily presented in Chapter 7 are affected with uncertainties. An evaluation of these uncertainties is presented hereafter, analyzing successively the following five main sources:

statistical sampling error (see Chapter 8.1);

poor representativeness of the sampling network (see Chapter 8.2);

measurements errors (see Chapter 8.3);

data encoding errors (see Chapter 8.4);

estimation design uncertainties (see Chapter 8.5).

Chapter 8.6 combines the different sources of uncertainty for the estimates of V/ha and AGB/ha to summarize the overall error budget.

8.1 Statistical Sampling Error

The detailed statistical parameters of the FRA estimates (in terms of number of SUs, arithmetic mean, variance, standard deviation, coefficient of variation, standard error of the mean and margin of error at confidence levels of 90%, 95% and 99%, respectively) are provided in Appendix 6 (Closed Forests) and Appendix 7 (Open Forests), as computed and printed to PDF by the FRA Database System Application (see Chapter 5.7). Table 23 summarizes the statistical sampling error in terms of the margin of error (E%) at a confidence level of 95% for the main variables of interest.

Table 23: Statistical sampling errors of the main variables of interest in Closed and Open Forests

Variable Closed Forests Open Forests

Based on 37 Sampling Units Based on 44 Sampling Units

Mean Margin of Error* Mean Margin of Error*

N/ha [/ha] 742.65 ± 14.72% 521.05 ± 17.67%

G/ha [m²/ha] 35.24 ± 13.16% 22.26 ± 18.88%

V/ha [m³/ha] 220.77 ± 19.60% 143.19 ± 26.40%

AGB/ha [t. d.m./ha] 398.00 ± 17.01% 248.32 ± 25.63%

BGB/ha [t. d.m./ha] 147.26 ± 17.01% 91.88 ± 25.63%

LB/ha [t C/ha] 256.27 ± 17.01% 159.89 ± 25.63%

SDW/ha [t C/ha] 2.93 ± 104.04% 1.03 ± 116.64%

LDW/ha [t C/ha] 1.08 ± 89.71% 1.24 ± 78.43%

LI/ha [t C/ha] 1.98 ± 16.66% 1.92 ± 17.00%

DOM/ha [t C/ha] 5.99 ± 53.67% 4.19 ± 37.80%

SOM/ha [t C/ha] 71.62 ± 6.21% 58.61 ± 4.92%

Total C/ha [t C/ha] 333.89 ± 13.16% 222.70 ± 18.46%

* 95% confidence level

As expected, the coefficients of variation (s%) are higher in Open Forests than in Closed Forests (for AGB/ha for instance 84.3% compared to 51.0%). The margin of error of the SOM/ha estimates appears to be very low. This is due to the fact that following the Tier 1 estimate, only two (2) soil types and two (2) climate regions were



found in the Closed and Open Forests of Caraga, Manay and Tarragona: High Activity Clays (HAC) and Low Activity Clays (LAC) in tropical wet and in tropical montane climate, respectively, see Chapter 2.4, with four (4) corresponding SOM/ha stocks: 60 t C/ha (tropical wet LAC), 44 t C/ha (tropical wet HAC), 63 t C/ha (tropical montane LAC) and 88 t C/ha (tropical montane HAC. Hence, there is limited variation.

8.2 Poor Representativeness of the Sampling Network

The design of the sampling network (see Chapter 3.5) has been made in accordance with the statistical theory to prevent poor representativeness of the SUs. However, not all the 150 SUs initially distributed in the Closed and Open Forests of Caraga, Manay and Tarragona have ultimately been measured, partly because of operational difficulties (remoteness and very limited accessibility of the SUs, unfavorable weather conditions, etc.), but mainly because of the limited available resources and time, combined with a lower than expected output (see Chapter 5.6). It cannot be excluded that the failure to measure all allocated SUs slightly affects the representativeness of the sampling network. An uncertainty of an order of magnitude of 2.5% may conservatively be assumed.

8.3 Measurement Errors

The impact of the measurement errors has been evaluated through the re-measurement of 5% of the SUs (see Chapter 6.2). While the estimates of AGB/ha are affected by very limited uncertainties related to measurement errors (less than 1%), those of N/ha, G/ha and particularly V/ha are affected by 14% to as much as 24%, i.e. about as much as the statistical sampling error.

8.4 Data Encoding Errors

The effect of the data encoding errors has been evaluated through the comparison of the original field data and the encoded data of 10% of the SUs (see Chapter 6.2). The estimates of the variables of interest are affected by limited uncertainties not exceeding 2.5% related to data encoding errors.

8.5 Estimation Design Uncertainties

Except for N/ha and G/ha, where no allometric models, volume equations, wood specific gravities nor conversion and/or extrapolation factors are used, the estimates of all other variables of interest are affected by uncertainties due to the lack of fit of the estimation design (see Chapter 3.7) used. The uncertainty arising from the use of the regional volume equations for dipterocarps and non-dipterocarps (see Chapter 3.7.1) for the estimation of V/ha is not documented. It may conservatively be estimated to be of an order of magnitude of 15%. According to the authors, the uncertainty arising from the use of the allometric equation developed by CHAVE J. et al., 2014 (see Chapter 3.7.2, equation {5}) for the estimation of AGB/ha is of the order of magnitude of 10%. The uncertainties of the other metrics used to estimate BGB/ha (the root to shoot ratio), SDW/ha (the Biomass Conversion and Expansion Factor [BCEFs]), LDW/ha, LI/ha, and to convert the biomass to carbon equivalent (Carbon Fraction [CF] of dry matter) are difficult to evaluate.



8.6 Overall Error Budget

Table 24 and Table 25 show the overall error budget of the estimates of V/ha and AGB/ha, respectively.

Table 24: Overall error budget for V/ha

Source of Uncertainty Stratum Uncertainty

Statistical sampling error Closed Forests ± 19.60%

Open Forests ± 26.40%

Representativeness of the sampling network Closed & Open Forests ± 2.50%

Measurement errors Closed & Open Forests ± 24.00%

Data encoding errors Closed & Open Forests ± 2.50%

Estimation design uncertainties Closed & Open Forests ± 15.00%

Table 25: Overall error budget for AGB/ha

Source of Uncertainty Stratum Uncertainty

Statistical sampling error Closed Forests ± 17.01%

Open Forests ± 25.63%

Representativeness of the sampling network Closed & Open Forests ± 2.50%

Measurement errors Closed & Open Forests ± 14.00%

Data encoding errors Closed & Open Forests ± 2.50%

Estimation design uncertainties Closed & Open Forests ± 10.00%

The largest uncertainties pertain to the statistical sampling error, followed by measurement errors when height measurements are involved (for the estimation of V/ha) and estimation design uncertainties. The statistical sampling error can be reduced by augmenting the number of SUs. However, one has to keep in mind that to halve the statistical sampling error, four times more SUs must be measured, since the sampling error is inversely proportional to the square root of the number of SUs.



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Davao Oriental FRA Results Appendix 1

List of Recorded Species Page 1


Appendix 1:

List of Recorded Species

Common name Scientific name Family Gravity* [gr /cm³]

Agoho Casuarina equisetifolia L. Casuarinaceae 0.80

Alagasi Leucosyke capitellata Wedd. Urticaceae

Alagau Premna odorata Blanco Lamiaceae

Alangas Ficus heteropoda Miq. Moraceae 0.39

Alim Melanolepis multiglandulosa (Reinw. ex Blume) Rchb. & Zoll. Euphorbiaceae 0.34

Almaciga Agathis philippinensis Warb. Araucariaceae 0.45

Almon Shorea almon Foxw. Dipterocarpaceae 0.39

Amamali Leea aculeata Blume ex Spreng Vitaceae

Anabiong Trema orientalis (L.) Blume Cannabaceae 0.33

Anii Erythrina fusca Lour Leguminosae 0.25

Anilao Colona serratifolia Cav. Malvaceae 0.38

Anislag Flueggea flexuosa Muell. Arg. Euphorbiaceae 0.69

Anonang Cordia dichotoma G. Forst. Boraginaceae 0.38

Antipolo Artocarpus blancoi (Elmer) Merr. Moraceae 0.43

Anubing Artocarpus ovatus Blanco Moraceae 0.61

Apanang Mallotus cumingii Muell. Arg. Euphorbiaceae 0.49

Apitong Dipterocarpus grandiflorus (Blanco) Blanco Dipterocarpaceae 0.67

Aplas Ficus ampelas Burm.f. Moraceae 0.38

Ata-ata Diospyros mindanaensis Merr. Ebenaceae 0.65

Aunasin Ardisia paniculata Roxb. Primulaceae

Badling Astronia cumingiana S. Vidal Melastomataceae

Bagarilao Cryptocarya ampla Merr. Lauraceae

Bagtikan Shorea malaanonan Blume Dipterocarpaceae 0.51

Bahai Ormosia calavensis Blanco Leguminosae 0.43

Bakan Litsea philippinensis Merr. Lauraceae

Bakauan-gubat Carallia brachiata (Lour.) Merr. Rizophoraceae 0.66

Balanti Homalanthus populneus (Geiseler) Pax Euphorbiaceae 0.29

Balat-buaya Fagraea racemosa Jack Gentianaceae 0.64

Balete Ficus balete Merr. Moraceae 0.65

Balobo Diplodiscus paniculatus Turcz. Malvaceae 0.63

Balukanag Chisocheton cumingianus (C.DC.) Harms Meliaceae 0.55

Banato Mallotus philippensis (Lam.) Muell. Arg. Euphorbiaceae 0.60

Bangkal Nauclea orientalis (L.) L. Rubiaceae 0.47

Bangkal, Kaatoan Breonia chinensis (Lam.) Capuron Rubiaceae 0.34

Bangkal, Southern / Hambabalud

Neonauclea formicaria (Elmer) Merr. Rubiaceae

Banitlong Cleistanthus pilosus C.B. Rob. Phyllanthaceae

Banuyo Wallaceodendron celebicum Koord. Leguminosae 0.56

Basikong Ficus botryocarpa Miq. Moraceae 0.43

Batete Kingiodendron alternifolium (Elmer) Merr. & Rolfe Leguminosae 0.49

Batino Alstonia macrophylla Wall. ex G.Don Apocynaceae 0.64

Bayanti Aglaia rimosa (Blanco) Merr. Meliaceae 0.69

Bayog Dendrocalamus merrillianus (Elmer) Elmer Poaceae

Bayok Pterospermum diversifolium Blume Sterculiaceae 0.57

Binggas Terminalia citrina Roxb. ex Fleming Combretaceae 0.71

Binuang Octomeles sumatrana Miq. Datiscaceae 0.30

Binucao Garcinia binucao (Blanco) Choisy Clusiaceae 0.75

Binunga Macaranga tanarius (L.) Muell. Arg. Euphorbiaceae 0.43

Bitanghol Calophyllum blancoi Planch. & Triana Clusiaceae 0.46

Bitanghol-sibat Calophyllum lancifolium Elmer Clusiaceae 0.53





Bolon Platymitra arborea (Blanco) P.J.A. Kessler Annonaceae 0.74

Bolong-eta Diospyros pilosanthera Blanco Ebenaceae 0.65

Bonot-bonot Glochidion camiguinense Merr. Phyllanthaceae

Bulala (Wild Rambutan)

Dimocarpus fumatus (Blume) Leenh. Sapindaceae

Buntan Engelhardtia rigida Blume Juglandaceae 0.42

Bunud Knema mindanaensis Merr. Myristicaceae 0.53

Coconut Cocos nucifera L. Arecaceae

Dacrydium beccarii Dacrydium beccarii Parl. Podocarpaceae 0.61

Dalinsi Terminalia pellucida C. Presl Combretaceae

Dalunot Pipturus arborescens (Link) C.B. Rob. Urticaceae

Danglin Grewia multiflora Juss. Malvaceae 0.48

Dita Alstonia scholaris (L.).R. Br. var. scholaris Apocynaceae 0.39

Duguan Myristica philippinensis Gand. Myristicaceae 0.36

Duklitan Planchonella duclitan (Blanco) Bakh.f. Sapotaceae 0.51

Dulit Canarium hirsutum Willd. Burseraceae 0.49

Dungon Heritiera sylvatica S.Vidal Malvaceae 0.70

Ebony Diospyros vera (Lour.) A.Chev. Ebenaceae 0.85

Firetree Delonix regia (Hook.) Raf. Leguminosae

Gisok-Gisok Hopea philippinensis Dyer Dipterocarpaceae 0.67

Gubas Endospermum peltatum Merr. Euphorbiaceae 0.30

Guijo Shorea guiso Blume Dipterocarpaceae 0.71

Hagimit Ficus minahassae (Teijsm. & Vriese) Miq. Moraceae 0.32

Hamindang Macaranga bicolor Muell. Arg. Euphorbiaceae 0.30

Hantatamsi Cyrtandra villosissima Merr. Gesneriaceae

Himbabao Broussonetia luzonica (Blanco) Bureau Moraceae 0.50

Hindang Myrica javanica Blume Myricaceae

Hindang-Laparan Myrica javanica Blume Myricaceae

Hinlaumo Mallotus mollissimus (Geiseler) Airy Shaw Euphorbiaceae 0.35

Igyo Dysoxylum gaudichaudianum (A. Juss.) Miq. Meliaceae 0.45

Is-is Ficus ulmifolia Lam. Moraceae 0.38

Kalantas Toona calantas Merr. & Rolfe Meliaceae 0.29

Kalingag / Cinamomon

Cinnamomum mercadoi S. Vidal Lauraceae 0.43

Kalomala Elaeocarpus calomala (Blanco) Merr. Elaeocarpaceae

Kalubkub Syzygium calubcob (C.B.Rob.) Merr. Myrtaceae 0.73

Kalumpit Terminalia microcarpa Decne. Combretaceae 0.53

Kamagong Diospyros discolor Willd. Ebenaceae 0.88

Kanapai Ficus magnoliifolia Blume Moraceae 0.28

Kape Coffea arabica L. Rubiaceae

Katmon Dillenia philippinensis Rolfe Dilleniaceae 0.63

Kubi Artocarpus nitidus Trécul Moraceae 0.48

Kulatingan Pterospermum obliquum Blanco Sterculiaceae

Kupang Parkia timoriana (DC.) Merr. Leguminosae 0.34

Lago Prunus grisea (Blume ex Muell .Berol.) Kalkman Rosaceae 0.55

Laloi Turpinia sphaerocarpa Hassk. Staphyleaceae

Lamog Planchonia spectabilis Merr. Lecythidaceae 0.58

Lanete Wrightia pubescens subsp. laniti (Blanco) Ngan Apocynaceae

Laneteng gubat Kibatalia gitingensis (Elmer) Woodson Apocynaceae

Lanipau Terminalia copelandi Elmer Combretaceae 0.46

Lanipga Toona philippinensis Elmer Meliaceae

Lanutan Mitrephora lanotan (Blanco) Merr. Annonaceae

Lanutan-dilau Polyalthia flava Merr. Annonaceae 0.51

Lanzones Lansium parasiticum (Osbeck) K.C. Sahni & Bennet Meliaceae 0.71

Lapnisan Polyalthia oblongifolia Burck Annonaceae





Lapo-lapo Gyrocarpus americanus Jacq. Hernandiaceae

Libas Spondias pinnata (L. f.) Kurz Anacardiaceae 0.34

Ligas Semecarpus cuneiformis Blanco Anacardiaceae

Lingaton Dendrocnide stimulans (L.f.) Chew Urticaceae

Lingatong Laportea brunnea Merr. Urticaceae

Lingo-lingo Vitex turczaninowii Merr. Lamiaceae 0.49

Lipang-kalabaw Dendrocnide meyeniana (Walp.) Chew Urticaceae

Lisak Neonauclea bartlingii (DC.) Merr. Rubiaceae

Loktob Duabanga moluccana Blume Lythraceae 0.34

Magabuyo Celtis luzonica Warb. Cannabaceae 0.55

Maguilik Premna cumingiana Schauer Lamiaceae

Makaasim Syzygium nitidum Benth. Myrtaceae 0.74

Malabagang Phyllanthus albus (Blanco) Muell. Arg. Phyllanthaceae

Malabayabas Tristaniopsis decorticata (Merr.) Peter G. Wilson & J.T. Waterh. Myrtaceae 0.91

Malabitaog Calophyllum pentapetalum var. cumingii (Planch. & Triana) P.F. Stevens

Clusiaceae

Malaikmo Celtis philippensis Blanco Cannabaceae 0.69

Malak-malak Palaquium philippense (Perr.) C.B. Rob. Sapotaceae 0.46

Malakadios Dehaasia cairocan (Vidal) C.K. Allen Lauraceae

Malakalumpit Terminalia calamansanay Rolfe Combretaceae 0.50

Malakape Psydrax dicoccos Gaertn. Rubiaceae

Malapanau Dipterocarpus kerrii King Dipterocarpaceae 0.61

Malapapaya Polyscias nodosa (Blume) Seem. Araliaceae 0.32

Malaputat Terminalia darlingii Merr. Combretaceae

Malasantol Sandoricum vidalii Merr. Meliaceae 0.45

Malasapsap Ailanthus integrifolia Lam. Simaroubaceae 0.31

Malatambis Syzygium hutchinsonii (C.B. Robinson) Merr. Myrtaceae 0.73

Malatapai Alangium longiflorum Merr. Cornaceae 0.68

Malatibig Ficus congesta Roxb. Moraceae

Malugai Allophylus cobbe (L.) Raeusch. Sapindaceae 0.58

Manggachapui Hopea acuminata Merr. Dipterocarpaceae 0.54

Manggasinoro Shorea assamica var. philippinensis (Brandis ex Koord.) Y.K. Yang & J.K. Wu

Dipterocarpaceae 0.46

Mankono Xanthostemon verdugonianus Náves ex Fern. - Vill. Myrtaceae

Mapilig Xanthostemon bracteatus Merr. Myrtaceae

Marang Litsea perrottetii (Blume) Fern.-Vill. Lauraceae 0.45

Marang-banguhan Artocarpus odoratissimus Blanco Moraceae 0.55

Matang-araw Melicope triphylla (Lam.) Merr. Rutaceae 0.39

Matang-hipon Breynia vitis-idaea (Burm.f.) C.E.C. Fisch. Euphorbiaceae

Mayapis Shorea palosapis Merr. Dipterocarpaceae 0.42

Milipili Canarium hirsutum Willd. Burseraceae 0.49

Moluccan sau Falcataria moluccana (Miq.) Barneby & J.W.Grimes Leguminosae 0.37

Nangka Artocarpus heterophyllus Lam. Moraceae 0.49

Narig Vatica mangachapoi Blanco Dipterocarpaceae 0.75

Nato Palaquium luzoniense (Fern.-Vill.) Vidal Sapotaceae 0.55

Pagsahingin-bulog Canarium asperum Benth. Burseraceae 0.47

Paguringon Cratoxylum sumatranum (Jack) Blume Hypericaceae 0.59

Pakiling Ficus odorata (Blanco) Merr. Moraceae 0.32

Palo-santo Triplaris cumingiana Fisch. & C.A. Mey. Polygonaceae

Palosapis Anisoptera thurifera (Blanco) Blume Dipterocarpaceae 0.59

Panau Dipterocarpus gracilis Blume Dipterocarpaceae 0.60

Pandakaking-gubat Tabernaemontana pandacaqui Lam. Apocynaceae

Pangi Pangium edule Reinw. Achariaceae 0.50

Pili Canarium ovatum Engl. Burseraceae

Piling-liitan Canarium luzonicum (Blume) A. Gray Burseraceae 0.31





Pugahan Caryota cumingii Lodd. ex Mart. Arecaceae

Puso-puso Neolitsea vidalii Merr. Lauraceae

Putian Alangium javanicum (Blume) Wang. var. jaheri Bloem. Cornaceae 0.73

Red Lauan Shorea negrosensis Foxw. Dipterocarpaceae 0.51

Sagisi Heterospathe elata Scheff. Arecaceae

Saguimsim Syzygium brevistylum (C.B. Rob.) Merr Myrtaceae

Salinggogon Cratoxylum formosum (Jacq.) Benth. & Hook.f. ex Dyer Hypericaceae 0.72

Salingkugi Albizia saponaria (Lour.) Miq. Leguminosae 0.57

Sangilo Pistacia chinensis Bunge Anacardiaceae

Sinaligan Sterculia rubiginosa Vent. Sterculiaceae

Spike pepper Piper aduncum L. Piperaceae

Subiang Bridelia insulana Hance. Phyllanthaceae

Tabian Elaeocarpus monocera Cav. Elaeocarpaceae

Tagatoi Palaquium foxworthyi Merr. Sapotaceae

Takip-asin Macaranga grandifolia (Blanco) Merr. Euphorbiaceae

Talisay-gubat Terminalia foetidissima Griff. Combretaceae 0.60

Taluto Pterocymbium tinctorium Merr. Sterculiaceae 0.25

Tamayuan Strombosia philippinensis S. Vidal Olacaceae 0.70

Tan-ag Kleinhovia hospita L. Malvaceae 0.39

Tangile Shorea polysperma Merr. Dipterocarpaceae 0.51

Tangisang-bayawak Ficus variegata Blume Moraceae 0.31

Tangisang-layugan Ficus aurita Blume Moraceae 0.31

Tanglin Adenanthera intermedia Merr. Leguminosae 0.78

Tara-tara Dysoxylum cumingianum C.DC. Meliaceae 0.72

Tibig Ficus nota (Blanco) Merr. Moraceae

Tiga Tristaniopsis micrantha (Merr.) Peter G. Wilson & J.T. Waterh. Myrtaceae 0.89

Tindalo Afzelia rhomboidea (Blanco) S.Vidal Leguminosae 0.59

Toog Petersianthus quadrialatus (Merr.) Merr. Lecythidaceae 0.54

Tuai Bischofia javanica Blume Euphorbiaceae 0.61

Tubal Syzygium trianthum (Merr.) Merr. Myrtaceae 0.73

Tungkao Glebionis coronaria (L.) Cass. ex Spach Compositae

Ulaian Lithocarpus celebicus (Miq.) Rehder Fagaceae 0.70

Ulango Pandanus acladus Merr Pandanaceae

Ulayan (Oak) Lithocarpus caudatifolius (Merr.) Rehder Fagaceae

Upling buntotan Ficus heteropleura Blume Moraceae 0.32

Upling gubat Ficus ampelas Burm.f. Moraceae 0.38

White Lauan Shorea contorta S. Vidal Dipterocarpaceae 0.43

White Nato Pouteria macrantha (Merr.) Baehni Sapotaceae 0.52

Yabnob Horsfieldia costulata Warb. Myristicaceae

Yakal Shorea astylosa Foxw. Dipterocarpaceae 0.73

Yakal-Kaliot Hopea malibato Foxw. Dipterocarpaceae 0.89

Yemane Gmelina arborea Roxb. Lamiaceae 0.43

* for tree species without specific wood gravity, the average wood specific gravity for tropical tree species in Asia of 0.57 g/cm³ published by Brown (1997) has been used


List of Inventoried Sampling Units in Davao Oriental Page 1


Appendix 2:

List of Inventoried Sampling Units in Davao Oriental

Sampling UTM Coordinates WGS 84 Geographic Coordinates Unit No. Zone East North Longitude Latitude

[m] [m] [°] [°]

0001 51N 869000 788000 126.339838 7.1168515

0003 51N 874000 812000 126.386655 7.3332424

0004 51N 875000 813000 126.395768 7.342204

0005 51N 869000 791000 126.340035 7.1439428

0006 51N 867000 790000 126.321887 7.1350429

0008 51N 866000 804000 126.313764 7.2615373

0010 51N 875000 811000 126.395631 7.3241443

0014 51N 870000 790000 126.349010 7.1348468

0017 51N 871000 792000 126.358183 7.1528416

0020 51N 866000 794000 126.313107 7.1712307

0022 51N 871000 800000 126.358714 7.2250837

0023 51N 868000 796000 126.331322 7.1891608

0024 51N 879000 807000 126.431531 7.2877515

0025 51N 867000 799000 126.322477 7.2163183

0028 51N 869000 790000 126.339969 7.1349124

0029 51N 868000 795000 126.331256 7.1801303

0031 51N 868000 802000 126.331719 7.2433438

0032 51N 866000 793000 126.313041 7.1622000

0033 51N 865000 798000 126.304326 7.2074189

0035 51N 876000 808000 126.404469 7.2969866

0038 51N 871000 811000 126.359453 7.3244160

0040 51N 872000 786000 126.366828 7.0985944

0042 51N 871000 808000 126.359251 7.2973254

0043 51N 876000 806000 126.404333 7.2789270

0045 51N 872000 792000 126.367224 7.1527756

0047 51N 870000 801000 126.349738 7.2341806

0048 51N 878000 814000 126.422970 7.3510273

0049 51N 868000 789000 126.330863 7.1259472

0051 51N 865000 792000 126.303935 7.1532344

0053 51N 880000 815000 126.441128 7.3599181

0055 51N 872000 793000 126.367290 7.1618058

0056 51N 875000 814000 126.395836 7.3512338

0057 51N 872000 811000 126.368498 7.3243484

0060 51N 869000 795000 126.340298 7.1800646

0062 51N 865000 790000 126.303805 7.1351728

0063 51N 874000 815000 126.386860 7.3603322

0064 51N 864000 805000 126.295743 7.2706999

0065 51N 870000 803000 126.349871 7.2522412

0069 51N 873000 813000 126.377678 7.3423405

0070 51N 864000 803000 126.295611 7.2526383

0071 51N 868000 799000 126.331520 7.2162523

0072 51N 875000 812000 126.395699 7.3331741

0073 51N 869000 798000 126.340496 7.2071558

0078 51N 874000 811000 126.386587 7.3242125

0079 51N 872000 809000 126.368362 7.3062881

0080 51N 871000 802000 126.358848 7.2431441

0081 51N 865000 803000 126.304655 7.2525726

0082 51N 867000 783000 126.321433 7.0718285

0086 51N 870000 787000 126.348813 7.1077557


List of Inventoried Sampling Units in Davao Oriental Page 2


Sampling UTM Coordinates WGS 84 Geographic Coordinates Unit No. Zone East North Longitude Latitude

[m] [m] [°] [°]

0087 51N 867000 802000 126.322675 7.2434100

0089 51N 866000 798000 126.313369 7.2073534

0091 51N 874000 809000 126.386450 7.3061526

0093 51N 869000 786000 126.339708 7.0987906

0095 51N 876000 807000 126.404401 7.2879568

0096 51N 871000 787000 126.357854 7.1076902

0097 51N 872000 808000 126.368295 7.2972580

0099 51N 866000 799000 126.313434 7.2163841

0102 51N 869000 803000 126.340828 7.2523078

0103 51N 869000 796000 126.340364 7.1890950

0104 51N 869000 782000 126.339448 7.0626687

0105 51N 879000 806000 126.431462 7.2787220

0107 51N 867000 798000 126.322411 7.2072877

0108 51N 867000 796000 126.322280 7.1892265

0112 51N 866000 801000 126.313566 7.2344454

0114 51N 873000 793000 126.376331 7.1617394

0118 51N 868000 793000 126.331125 7.1620693

0119 51N 871000 789000 126.357985 7.1257508

0120 51N 864000 806000 126.295809 7.2797307

0121 51N 871000 794000 126.358315 7.1709022

0124 51N 868000 782000 126.330408 7.0627334

0125 51N 875000 807000 126.395358 7.2880248

0126 51N 870000 809000 126.350274 7.3064230

0127 51N 865000 794000 126.304065 7.1712959

0128 51N 869000 783000 126.339512 7.0716991

0129 51N 877000 815000 126.413995 7.3601260

0131 51N 868000 801000 126.331652 7.2343133

0134 51N 872000 810000 126.368430 7.3153183

0137 51N 868000 794000 126.331190 7.1710998

0138 51N 869000 793000 126.340166 7.1620037

0139 51N 874000 813000 126.386723 7.3422723

0147 51N 865000 806000 126.304853 7.2796647


Field Data Forms Page 1


Appendix 3:

Field Data Forms


Detailed Results - Closed Forests


Appendix 4:





Appendix 5:

Detailed Results - Open Forests


Statistical Parameters - Closed Forests


Appendix 6:

Statistical Parameters - Closed Forests


Statistical Parameters - Open Forests


Appendix 7:

Statistical Parameters - Open Forests

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