Measuring and Monitoring Carbon Stocks at The_split_1

98

Measuring and Monitoring Carbon Stocks at the Guaraqueçaba Climate Action Project, Paraná, Brazil

Gilberto Tiepolo1), Miguel Calmon2), André Rocha Feretti1)

Abstract

Any climate action project that seeks to obtain recognition, and potentially carbonbenefits, for the GHG emissions reductions it achieves must accurately calculate thosereductions over time using scientifically rigorous methods that will stand up to externalreview. Carbon inventory and monitoring plans are designed to quantify the changes inkey carbon (C) pools in and around the project area and to project local land-use changes by monitoring patterns of land-use in proxy regions, trend modeling, and analyzingsocio-economic and other data. Data from the inventory and monitoring activities areused to calculate the difference between the with- and without-project scenarios.

The Guaraqueçaba Climate Action Project is being implemented by SPVS (Sociedade de Pesquisa em Vida Selvagem e Educação Ambiental), in partnership with TNC (TheNature Conservancy) and AEP (American Electric Power). It has an area ofapproximately 7,000 hectares and is located in Paraná State, Brazil, within theEnvironmental Protection Area of Guaraqueçaba in the Atlantic Rain Forest. Thisecosystem is recognized by the United Nations Economic and Social Organization(UNESCO) as one of the planet’s highest priorities for conservation and has designated it World Biosphere Reserve.

The main goals of the project are biodiversity conservation, restoration of degradedpasture, sustainable development of local communities, and generation of carbon offsetsthat are real, measurable, and verifiable.

The requirements for a good monitoring program should include the following: (i)use the most appropriate and cost-effective methodology for the region; (ii) follow quality assurance/control plan (QA/QC) and standard operating procedures (SOPs); (iii) train local NGOs and field personnel on SOPs; (iv) elaboration of a vegetation map and stratification of the project area; (v) post-monitoring independent verification; (vi) record-keeping anddata entry, analysis, interpretation, maintenance, and archiving. The Winrock internationalmethodology (MacDicken, 1997), adapted to the local conditions, was selected to measure and monitor carbon at the project. A QA/QC and SOP documents were developed for the

1) Sociedade de Pesquisa em Vida Selvagem e Educação Ambiental – SPVSRua Gutemberg, 296, Batel. 80420-030 Curitiba - PR, Brazil. http://www.spvs.org.br, E-Mail:[email protected]

2) The Nature Conservancy – TNCAlameda Júlia da Costa, 1240, Bigorrilho. 80730-070 Curitiba – PR, Brazil. http://www.nature.org –www.tnc.org.br, E-Mail: [email protected]

International Symposium

on Forest Carbon Sequestration and Monitoring

99

project to guarantee that carbon measurements done during the lifetime of the project areconsistent and accurate. Researchers and technicians from SPVS were trained on theSOP before the beginning of the carbon inventory and monitoring field work. A total of 12 strata were identified, but only 6 forest strata were under threat of deforestation andtherefore used to estimate carbon stocks and offsets. A total of 188 permanent plots wereestablished with 68 in the submontane forest (1162.5 ha), 11 in the lowland forest (427 ha), 10 in the floodplain forest (173 ha), 63 in advanced/medium forest (1,783 ha), 24 inmedium secondary forest (545 ha), and 12 in young secondary forest (279 ha).Twenty-eight clip plots were established on the pasture (409 ha) and shrublands (279 ha).

The preliminary average carbon stock (aboveground woody biomass) estimated forthe 6 forest strata were the following: submontane forest: 135.9 t C ha-1; lowland forest: 106.8 t C ha-1; floodplain forest: 64.12 t C ha-1; advanced/medium forest: 106.1 t C ha-1;medium secondary forest: 101.96 t C ha-1; young secondary forest: 42.89 t C ha-1. Theabove ground carbon for the pasture strata was 2.4 t C ha-1 and for the shrublands 7.4 Cha-1. The general wet biomass equation that is currently being used to estimate the carbon stock is being verified and adjusted from the destructive sampling effort that is beingconducted by SPVS. A new biomass equation for tree fern was also developed, whichshowed a strong correlation between biomass and height.

The results of this effort will help to improve and develop models to measure andmonitor carbon stock in very complex and heterogeneous landscapes, such as the onesfound in the Atlantic Forest Biome, and to promote projects that are designed to generatemultiple benefits such as biodiversity, soil and water conservation, restoration of degraded lands, and sustainable development of local communities.

Introduction

The Guaraqueçaba Climate Action Project is an innovative effort to combinereforestation and forest stewardship strategies to help manage levels of carbon dioxide inthe atmosphere. Over a period of forty years (the project term), the project will restore and protect approximately 7,000 hectares (17,000 acres) of partially degraded and/ordeforested tropical forest within the Guaraqueçaba Environmental Protection Area (EPA)of Paraná State, in southern Brazil. The land is titled to SPVS, which assumedresponsibility for its long-term protection and stewardship, and will be registered as aprivate reserve (Serra do Itaqui Natural Reserve). By protecting and restoring threatenedtracts of Atlantic Forest, the project will conserve biodiversity while contributing to themitigation of global climate change.

The project – a collaborative effort between Central and South West Corporation, aTexas-based electric utility (now American Electric Power), The Nature Conservancy(TNC), a US-based conservation organization, and Sociedade de Pesquisa em VidaSelvagem e Educação Ambiental (SPVS), a Brazilian conservation organization. Theproject is promoting assisted natural forest regeneration and regrowth on pastures and

100

degraded forests on the acquired lands. It is also protecting standing forest that still exists within the project area, but It is under threat of deforestation.

The main goals of the project are to protect and restore the ecological health andbiodiversity of the area in the project site and establish models for the adequate use ofresources in the Guaraqueçaba EPA, and generate carbon offsets that are real, measurable, and verifiable. Through a rigorous monitoring and verification program, the carbonbenefits generated by the project will be quantified and validated in such a way as tomaximize the possibility that they will be accepted under any future international carbontrading regime(s) and to serve as a scientifically-based pilot project in ecosystem restoration.

In addition to these primary objectives, the project also seeks to improve local environmentalquality, support sustainable economic development by creating opportunities for localpeople, and promote environmental awareness of the Guaraqueçaba region.

While the project is designed as a stand-alone effort, it will benefit from a variety of existing programs and activities currently being undertaken by the project partners,including other two climate action projects (The Antonina Pilot Reforestation Project, acollaborative effort between Chevron-Texaco, TNC and SPVS; and The Atlantic Rainforest Restoration Project, a joint effort between General Motors, TNC and SPVS). This project leveraging will enable it to have a broad impact and to contribute to a regional strategy for protecting the Guaraqueçaba EPA.

Materials and methods

Project LocationThe present study is based on the carbon inventory and monitoring activities of the

Guaraqueçaba Climate Action Project, located in the Atlantic Forest biome in Paraná State between latitudes 25o 26’ and 25o 21’ South, on the coastal plain. Lying about 45kilometers from the seat of the municipality and 140 Kilometers from Curitiba, the capital of the state. Its geographic location is strategic for conservation purposes, as it connects500 meters mountains and 1,200 hectares of mangroves, borders Guaraqueçaba Bay to the South, PR-405 highway to the North, Itaqui hills to the West and Tagaçaba River to theEast. It is a region of great environmental fragility composed of freshwater and marineenvironments, in close proximity to Guaraqueçaba Bay (Figure 1).

The Guaraqueçaba EPA is in the middle of the largest continuous piece of Atlanticforest remaining today. Brazil’s Atlantic Forest is an internationally recognized worldbiosphere reserve and home to one of the planet’s most diverse and endangered ecosystems. Today, only seven percent of the original vegetation cover remains, making the AtlanticForest one of the most threatened tropical forest in the world.

In the project site (Serra do Itaqui Natural Reserve), as well as in most of the region, the original vegetation has been submitted to intense exploitation that has stripped it of its

101

original characteristics, particularly in the plains. Easy access encouraged timberexploitation of mature forests, which implied either complete removal or drastic changes to their structure and composition. Such conditions resulted in a mosaic of secondary forests with varied floristic, structural and physiognomic characteristics, where initial formations are more prevalent in flat areas and intermediate stages on the hillsides.

The changes imposed on the vegetation, particularly in areas that underwent totalremoval of the original cover, have altered original drainage processes and caused severesoil degradation. Such conditions will require considerable efforts to fully restore thebiogeochemical processes needed to support the original ecological systems.

Vegetation MapThe vegetation map of the project area was based on color aerial photography (scale

1:30,000) and Ikonos satellite imagery. An orthophoto was also used as the base map.After photointerpretation and field checking several type of forests and other land useswere identified and classified. The level of human intervention and successional stages for the different forest types were also classified during this effort. A total of 12 strata,based on the classification scheme developed by IBGE (1992), were identified within theproject area (Figure 2).

StratificationFor the carbon inventory it was used a stratified sampling, which helped to make the

estimates more precise and cost-effective. From the 12 vegetation classes, 6 forestclasses (Submontane forest, Lowland forest, Floodplain forest, advanced / medium forest, medium secondary forest, and Young secondary forest) were assumed to be under threatand therefore were used during the carbon inventory work to estimate the carbon stock and benefits to be generated at the project area.

In addition to those forest strata, other non-forest classes such as pasture, herbaceous vegetation and shrus were also included as part of the carbon inventory effort, buttemporary plots were used on those strata.

Carbon InventoryThe methodology used for the carbon inventory was the one developed by Winrock

International (MacDicken, 1997) and adapted to the project conditions. A StandardOperating Procedure (SOP) was developed for the project and SPVS personnel was trained on every step (Brown and Delaney, 2000 A). Some of the main activities described on the SOP are listed below. Before de installation of the permanent plots, 6 preliminary plotswere installed on each strata to determine the number of plots necessary to represent thecarbon stock within each stratum. The number of plots was calculated by using asoftware (plot calculator) developed by Winrock International, which takes into accountthe maximum allowable error, desired level of precision, total area of the project and strata, variance, and approximate cost per plot. After that a Carbon Inventory and MonitoringPlan was developed for the project (Brown et al., 1999) in order to start the plot installation in 2000.

102

Establishment of TransectsTransects are lines cut through the forest or pastures to allow access to permanent or

clip plot locations. The selection of the places where the transects were opened wasbased on the vegetation map in areas that most represented each stratum composition and on the availability of existing trails to facilitate the access and reduce suppression ofvegetation within the project area. In general the length of the 35 transects vary from 200 to 700 meters.

Establishment of Plots A total of 188 nested circular plots were installed on the different forest strata (Table

1). Four nested plots were used to measure aboveground biomass. The 1-m radius plot was used to measure saplings with dbh <5 cm; the 4-m radius (0.005 ha) was used for trees between 5-19.9 cm dbh; the 14-m radius (0.06 ha) was used for trees between 20-69.9 cm dbh; and the 20-m radius was used for trees with ≥ 70 cm dbh (Figure 3). The actual size of each plots was adjusted depending on the percentage slope of the plot. At the center ofthe plot a PVC pipe was installed and painted with bright color and plastic tapes. A GPS was used to collect the coordinates of the center.

All trees were measured at dbh (1.3 m above ground) unless buttressed or withdefects at that height. Aluminum numbered tags were placed and nailed on each treemeasured within the four plots. More details can be found on Brown and Delaney (2000A).

Measurement of BiomassTwo main carbon pools were measured during the carbon inventory: a) live biomass

which included live trees, understory, and roots and b) aboveground dead wood whichincluded litter and standing and lying dead trees;

Live treesThe criteria for including trees within each plot was based on the diametric density of

the different forest strata. A diameter class range was used for each different plot size(see above), with the exception of trees with less than 5 cm at dbh, lianas, heart-of-palm,and tree fern. In the last two cases the height and dbh were measured.

For trees > 1.3 m high and less than 5 cm dbh, the number of trees within the 1-m plot was counted and then multiplied by the average biomass that was estimated by destructive sampling (10 plots per strata) on the opposite side of the permanent plot. The average ofsampling biomass for each strata are:

submontane forest – 0.2 kg

lowland forest – 0.2 kg

floodplain forest – 0.2 kg

advanced/medium forest – 0.3 kg

medium secondary forest – 0.6 kg

young secondary forest – 1.6 kg

103

The young secondary forest got the highest coefficient because in this formation theunderstory is less shaded than formations more developed, allowing good condition to the establishment of natural regeneration.

The general wet biomass equation was used to estimate aboveground biomass carbon for trees because it matches most closely to the climatic conditions (rainfall amount anddistribution; and near the southern extreme of the tropical belt) in the project area (Brown, 1997, 2001). However, the suitability of this equation is being verified and adjusted bythe destructive sampling program that was initiated in 2001. The goal is to cut 15-20large trees (dbh > 50 cm) and adjust the equation according to the results. At the sametime other smaller trees will be cut and included in the adjustment. Other equations were also used to estimate the biomass of mature Cecropia, palms, and lianas (Table 2), which were developed from work in Noel Kempff Climate Action Project in Bolivia (Brown et al., 2000).

A new biomass equation for tree fern (Cyathea spp.) was also developed for theproject by destructively sampling 22 trees and developing a regression equation betweentree biomass and height and tree biomass and dbh.

To characterize the understory, within each plot were established 4 Clip plots(aluminum sample frames – 60 cm in diameter). Species with dbh < 5 cm and less than 1.3 height , were cut, weighed and collected sub-sample for determination of humidity percentage.

RootsA recent review of the literature on root biomass for the world’s forests including 39

studies for the tropics suggests that root:shoot ratios vary from 0.1 to 0.38 with rootbiomass ranging from 1 to over 130 t ha-1 (Cairns et al. 1997). We assumed a root:shoot ratio of 0.20 that represents the lower 95% CI for tropical forests and it is moreconservative (Brown and Delaney, 2000 B)

PastureFor sampling pasture biomass, it was used clip plots that are placed on the ground at

regular intervals along transects. The procedures for this were the same way used forunderstory estimation.

Dead aboveground biomassFor litter it was used the same methodology that understory. Standing dead trees were

measured according to the same criteria as live trees; i.e. classified either in the small,medium, or large nested plot. If the standing dead tree contained branches and twigs and resembles a live tree (except for leaves) the dbh was measured and its biomass wasestimated using the appropriate biomass regression equation as for live trees. If therewere branches, but no twigs remaining on the standing dead tree, the proportion of thebiomass was subtracted from the total for the tree. If the top of the standing dead tree was missing, the height of the remaining stem was measured with a clinometer and the topdiameter was estimated – this can be done by estimating the ratio of the top diameter to the

104

basal diameter.

Lying dead wood were measured using the line intersect method outlined in Harmon and Sexton (1986). Fallen coarse dead wood were defined as all woody material on theground with a diameter >10 cm. Several discs of dead wood in each of the three densityclasses were collected and their volume and dry mass were determined. The estimateddensities are given in Table 3.

Results and discussion

Biomass Regressions Tree FernsFor the Cyathea spp., height is more strongly correlated to biomass (R2 = 0.88) than is

DBH (R2 = 0.1). The equation for height provides an acceptably robust model forestimating carbon storage without destructively harvesting more individuals (Figure 4, 5,6). The biomass result can be converted to carbon by multiplying the biomass by thecarbon concentration found in the species (about 0.5).

Carbon in ForestThe total carbon in the forest strata (excluding soil) was 471547.89 t C with a 95%

confidence interval of ± 6.7% of the mean (Table 4). As expected, the highest amount of carbon was in the submontane forest stratum (135.89 t C/ha) that represents the oldestforest in the project site. It is an altered primary forest that is located in the slopes of hills in continental soils, usually more deep. The lowest amount occurs in the very youngsecondary forest stratum (42.89 t C/ha), that is characterized by small trees (5 m height)with sparse crown. Coefficients of variation for total carbon content by strata wererelatively low (29 -51 %), particularly for the advanced /medium stratum.

Although the total carbon had relatively low variation, individual components weremore variable (Table 5). The most variable component was standing dead biomass, with coefficients of variation of 128-287% (Appendix 1). Lying dead wood was also variable.On a plot by plot basis, there is generally some relationship between live and dead biomass, so that when combined the overall variation decreases.

The overall weighted mean of the total carbon content of forests is 108 t C/ha, 74% of which is in the live aboveground woody biomass (Table 5). Dead wood carbon represented about 5% and litter and understory combined represented about 3.6% of the total carbonstock. For the very young forests, the litter and understory represented 19% of theaboveground biomass, and thus is a more significant component.

Carbon in pasturesThe mean aboveground carbon content of pastures ranged from 0.7 to 3.5 t C/ha, with

the highest carbon content found in pastures dominated by shrub vegetation and the lowest in pure pastures (Table 6). The results were variable with 95% confidence interval of

105

25.6% of the mean. Pasture grasses generally have as much biomass below ground asabove, thus the total range of biomass carbon for pure pastures (7P) and pastures withshrubs (7PS) ranges from 1.4 to 2.4 t C/ha. For the shrub formation (7S), belowground biomass is about 30% of aboveground. Thus the shrub formation has a range of totalbiomass carbon of 3.0 to 7.4 t C/ha. The maximum values were used in the carbonbenefit calculations as described above.

Conclusions

With the Carbon inventory conducted in the Guaraqueçaba Climate Action Project it was possible to quantify the amount of carbon stored with a reasonable level of precision. This inventory was used to estimate the differences between the with- and without-projectcarbon pools and is the primary basis for determination of project GHG benefits.Through ongoing carbon inventory work, several aspects of the carbon inventories thatcould be improved or significantly strengthened were identified.

In the Allometric regression equations for fern trees it was observed that therelationship between height and biomass showed a strong correlation, where the dbh xbiomass showed a poor correlation. Therefore, we recommend the use of the relationship height x biomass when estimating aboveground biomass of ferns trees in the AtlanticForest Biome.

The results of this effort will help to improve and develop models to measure andmonitor carbon stock in very complex and heterogeneous landscapes, such as the onesfound in the Atlantic Forest Biome, and to promote projects that are designed to generatemultiple benefits such as biodiversity, soil and water conservation, restoration of degraded lands, and sustainable development of local communities.

Also brought a lot of challenges during the implementation of the carbon inventory.Many lessons were learned during this phase, but much more are still to be learned in order to improve and adjust the methodology for future measurements.

Acknowledgments

We thank Dr. Sandra Brown and Matt Delaney for making Winrock International’smethodology available to the Guaraqueçaba Climate Action Project and making it a veryimportant component of the project. Their participation during the planning andimplementation phases of the carbon inventory and their willingness to train and transferthe methodology to SPVS’s team were very important for the success of the carboninventory program. We also would like to thank Bill Stanley for their valuable work and input as a Forester during all phases of the carbon inventory work. This work could not be done without the help and assistance of Flavio , Guilherme, Elielson, Arildo, Luis, Nelson, Jair, Walkiria, that spent several weeks in the field, and also to Alia Ghandour Warwick

106

Manfrinato, Patricia Garffer, Joe Keenan , Alexandra Andrade, Clóvis Borges, FrancoAmato, Ricardo, Laércio, and many others that could share with us the beauties of Atlantic forest.

References

Harmon, M. E. and J. Sexton. 1996. Guidelines for Measurements of Woody Detritus in Forest Ecosystems. US LTER Publication No. 20. US LTER Network Office,University of Washington, Seattle, WA, USA.

MacDicken, K. 1997. A Guide to Monitoring Carbon Storage in Forestry and Agroforestry Projects. Winrock International, 1611 N. Kent St., Suite 600, Arlington, VA 22209,USA.

Brown, S., M. Calmon, and M. Delaney. 1999. Carbon Inventory and Monitoring Planfor the Guaraqueçaba Climate Action Project, Brazil. Winrock International,Arlington, VA.

Brown, S. and M. Delaney. 2000 A. Standard Operating Procedures for the GuaraqueçabaClimate Action Project, GCAP-SOP, Version: 2.00. Winrock International, Arlington, VA.

Brown, S. and M. Delaney. 2000 B. Preliminary Carbon-Offset Report for theGuaraqueçaba Climate Action Project. Winrock International, Arlington, VA.

Brown, S., Burnham, M., Delaney, M., Vaca, R., Powell, M. and Moreno, A. 2000.Issues and challenges for forest-based carbon-offset projects: a case study of the Noel Kempff Climate Action Project in Bolivia. Mitigat. Adapt. Strategies Global Change 5, 99-121.

Brown, S. and M. Delaney. 2001. Preliminary carbon-offset report for the GuaraqueçabaClimate Action Project.

Brown, P. 1998, Climate, Biodiversity, and Forests: Issues and Opportunities Emergingfrom the Kyoto Protocol, Washington, D. C., World Resources Institute, 35p.

Brown, S. 1997. Estimating Biomass and Biomass Change of Tropical Forests: aPrimer. FAO Forestry Paper 134, Rome, Italy.

Cairns, M. A., S. Brown, E. H. Helmer, and G. A. Baumgardner. 1997. Rootbiomass allocation in the world’s upland forests. Oecologia 111:1-11.

107

Table 1 Description of the each stratum, area, and number of plots established.

Strata code Vegetation type Area (ha)Number of sample plots established

DM submontane forest 1162.55 68

LL lowland forest 427.3 11

FP floodplain forest 172.9 10

M advanced/medium forest 1782.9 63

Y medium secondary forest 544.92 24

VY Young secondary forest 278.58 12

P Pure pasture 386 12

PS Pasture/shrub 30 10

S Shrubs 297 6

TOTALS 4,694 168

Table 2 Regression equations used for estimating biomass carbon (Y) in the 2000 analysis of plots in the GCAP area.

Equation Species R2dbh and heightrange

Y=21.297-6.953(dbh)+0.74(dbh^2) General 0.91 4-116 cm

Y=0.3999+7.907*height Palms 0.75 1-33 m

Y =(-.48367+1.13488*(Sqr(dbh))*Log(dbh))^2 Cecropia 0.62 1-11 m

Y=563.56*(dbh)^2.6277 Lianas 0.89 0.3-2.5 cm

Y=-4266348/1-2792284e-0.313677 Fern tree 0.88 1 – 8 m

108

Table 3 Density of wood disc samples used in dead wood calculations.

Type/Decomposition class of wood sample Density (t/m3)

General/Sound 0.47

General/Intermediate 0.34

General/Rotten 0.17

Palm/Intermediate 0.14

Palm/Rotten 0.09(Source: Brown and Delaney, 2000 B)

Table 4 Total, mean, and statistical measures for the carbon content (excluding soil) of the four forest strata of the GCAP. See Table 6 for carbon content in different components of the forests.

Stratasubmontane

forestlowlandforest

floodplainforest

advanced/mediumforest

mediumsecondary

forest

youngsecondary

forest

n= 68 11 10 63 24 12

Area (ha) 1162.55 427.3 172.9 1782.9 544.92 278.58

Mean (t C/ha) 135.89 106.81 64.12 106.19 101.96 42.89

Min 61.1 27.5 18.5 44.8 38.3 9.8

Max 373.1 211.5 95.8 189 189.4 57

Variance 2314.1 2940.6 702.9 951 1565.7 267.6

StandardDeviation

48.1 54.2 26.5 30.8 39.6 16.4

Standard Error 5.8 16.4 8.4 3.9 8.1 4.7

C.V. (%) 35.4 50.8 41.3 29 38.8 38.1

Mean (t C/ha) 114.36 ± 7.7

Total (tons C) 471547.89±

31593.64CI % (+/-)(tons C)

6.7

CV= coefficient of variation; CI = 95% confidence interval 21,954.21

109

Table 5 Mean carbon content by forest component and by forest strata for the 2000 inventory in the GCAP (details of the carbon content of each component are given in Appendix 1 to 7.

AreaAbove-g

roundbiomass

Below-groundbiomass

Standindead

biomass

Lyingdead

biomass

Treesbiomass< 5 cm

dbh

Understoryvegetation Litter Total

Strata (ha) T C ha-1 T C ha-1 T C ha-1 T C ha-1 T C ha-1 T C ha-1 T C ha-1 T C ha-1

submontane 1162.55 109.30 21.86 2.86 1.43 0.44 nm nm 135.89

lowland 427.30 83.69 16.74 1.84 0.92 0.09 1.83 1.70 106.81

floodplain 172.94 43.96 8.79 5.48 2.74 0.32 0.52 2.32 64.12

Advancedmedium 1782.90 76.42 15.28 4.76 2.38 2.43 0.52 4.40 106.19

mediumsecondary 544.92 70.83 14.17 4.41 2.20 3.62 1.19 5.54 101.96

youngsecondary 278.58 21.24 4.25 1.25 0.62 5.94 0.91 8.67 42.89

Total 4369.20

DESPAD 40.1 8.0 5.0 2.5 3.4 8.0 2.7 46.9

Weightedmean 80.4 16.1 3.7 1.9 2 0.6 3.3 107.9

CI 5.7 1.1 0.7 0.4 0.5 1.4 0.5 8.4% 74.5 14.9 3.5 1.7 1.8 0.6 3.1

*CI is the 95% confidence interval expressed as a percent of the mean‡ nm= not measured

Table 6 Statistics for aboveground carbon in the pasture strata of the GCAP.

Strata Pasture(7P)

Pasture/Shrubs(7PS)

Shrubs(7S)

n= 12 10 6Area (ha) 386 30.4 296.7Mean (t C/ha) 0.7 0.8 3.5Min 0.2 0.4 2.3Max 1.1 1.2 5.7Variance 0.1 0.1 1.9Standard Deviation 0.3 0.3 1.4Standard Error 0.1 0.1 0.6C.V. (%) 50.8 37.5 39.9

Mean (t C/ha) 1.8 ± 0.5Total (tons C) 1305.7 ± 334.8CI % (+/-) (tons C) 25.6

CV= coefficient of variation; CI = 95% confidence intervalSource preliminary carbo-offset report 2000

110

Appendix 01: Stats for aboveground biomass

Strata Y M M/A SM LL FP

N 12 24 63 68 11 10Mean 21.24167 70.83333 76.42111 109.2975 83.68818 43.95556

Variance 92.47667 995.0112 638.4199 1512.872 1905.853 357.3587

Standard Desviation 9.616479 31.5438 25.26697 38.89566 43.65608 18.90393

Standard Error 2.776038 6.438851 3.183339 4.716791 13.1628 5.977948

CV % 45.27177 44.53242 33.06282 35.58696 52.16516 43.00692

Mean 85.85332 4.893639

Total 351193.4 20018.02

CI % 5.7

Appendix 02: Stats for belowground biomass


N 12 24 63 68 11 10Mean 4.248333 14.16667 15.28422 21.8595 16.73764 8.791111

Variance 3.699067 39.80045 25.5368 60.51489 76.23412 14.29435


Standard Error 0.555208 1.28777 0.636668 0.943358 2.632561 1.19559

CV % 45.27177 44.53242 33.06282 35.58696 52.16516 43.00692

Mean 17.17066 0.197004

Total 70238.68 805.8677

CI % 1.147328

Appendix 03: Stats for Standing dead


N 12 24 63 68 11 10Mean 1.249167 4.4075 4.76 2.859853 1.841818 5.475556

Variance 12.86866 31.7084 32.24936 13.67173 7.873636 59.649


Standard Error 1.035562 1.149427 0.715468 0.448391 0.846041 2.442315

CV % 287.1748 127.76 119.3036 129.2909 152.3495 141.0501

Mean 3.983516 0.028352

Total 16295.05 115.9778

CI % 0.711736

111

Appendix 04: Stats for Lying dead


N 12 24 63 68 11 10

Mean 0.624583 2.20375 2.38 1.429926 0.920909 2.737778

Variance 3.217166 7.927101 8.062341 3.417932 1.968409 14.91225


Standard Error 0.517781 0.574714 0.357734 0.224196 0.42302 1.221157

CV % 287.1748 127.76 119.3036 129.2909 152.3495 141.0501

Mean 1.991758 0.007088

Total 8147.527 28.99445

CI % 0.355868

Appendix 05: Stats for one meter


n 12 24 63 68 11 10

Mean 5.94179 3.620778 2.42522 0.440017 0.086812 0.31831

Variance 98.25101 9.11727 5.083811 0.81066 0.042371 0.177312


Standard Error 2.861396 0.616349 0.284069 0.109185 0.062064 0.133159

CV % 166.8212 83.39317 92.97014 204.6208 237.1123 132.2876

Mean 2.091599 0.010163

Total 8555.939 41.57481

CI % 0.485918

Appendix 06: Stats for understory


N 12 24 63 0 11 10Mean 0.910739 1.189191 0.521873 1.834093 0.523628

Variance 0.438179 5.049935 0.103712 7.172959 0.188951

Standard Desviation 0.661951 2.247206 0.322043 2.678238 0.434686

Standard Error 0.191089 0.458709 0.040574 0.807519 0.13746

CV % 72.68284 188.9693 61.70911 146.0252 83.0141

Mean 0.661624 0.009501

Total 2706.454 38.86659

CI % 1.43607

112

Appendix 07: Stats for litter


n 12 24 63 0 11 10Mean 8.674554 5.541915 4.40001 1.703824 2.321543

Variance 16.26518 4.651904 3.263792 1.612673 3.274465

Standard Desviation 4.033011 2.156827 1.806597 1.269911 1.809548

Standard Error 1.16423 0.440261 0.22761 0.382892 0.572229

CV % 46.49243 38.91845 41.05893 74.53296 77.94594

Mean 3.522895 0.017332

Total 14410.83 70.90053

CI % 0.491995

Figure 1 Location of the Project (1- The Antonina Pilot Reforestation Project; 2 – The Atlantic Rainforest Restoration Project; 3 – Guaraqueçaba Climate Action Project.

113

F igure 2 Vegetation Map of Guaraqueçaba Climate Action Project with the localization of permanent plots.

114

Figure 3 Nested plot layout. Small plot radius is 4m; medium plot radius is 14 m and large plot radius is 20 m; the 1.0-m radius plot for saplings is not shown.

F igure 4 Relationship between height and biomass for fern trees.

Sm al l P lot-SCode al l trees S, M,or L

Medium P lot-MCode all trees Mor L

La rge P lot-LCode al l trees L

4 m

14 m

20 m

115

F igure 5 Relationship between dbh and biomass for fern trees.

Figure 6 Allometric regression equations for fern trees.

Measuring and Monitoring Carbon Stocks at The_split_1

Documents

Transcript of Measuring and Monitoring Carbon Stocks at The_split_1