ENVISAT-INDONESIA RADAR BIOMASS EXPERIMENT (EIRBEX)

M. A. Raimadoya(1), M.C. Dobson (2), R. van Rensburg(3), and B.H. Trisasongko(1)

(1) Radar Analysis Working Group (RAWG), Department of Soil Sciences, Bogor Agricultural University,

P.O. Box 2049, Bogor Timur 16020, Indonesia Email: mar@rawg.org ; bht@rawg.org

(2)Advanced Technical Planning and Development, Office of Earth Science, Code YS, NASA Headquarters, 300 E. Street, S.W., Washington, DC 20546, USA

Email : Craig.Dobson-1@nasa.gov

(3) PT Riaupulp, P.O. Box 1080, Pekanbaru, Riau 2800, Indonesia

Email : rudolf.rensburg@aprilasia.com

ABSTRACT

A study of the operational application of Envisat/ASAR in the sector of Land Use and Land Use Change and Forestry (LULUCF) of the Kyoto Protocol is conducted under Envisat AO (AOE-869). Of primary consideration is the implementation of Article 12 (Clean Development Mechanism – CDM) in the tropical environment, which requires higher tier input data compared to national greenhouse gases inventory exercise.

Keywords : forestry, above ground biomass, LULUCF, AR/CDM, Kyoto Protocol

1. INTRODUCTION

The ninth session (2003) of the Conference of the Parties (COP9) to the United Nations Framework Convention on Climate Change (UNFCCC) eventually approved the modality and procedure (M&P) for afforestation and reforestation (AR) project activities under the Land Use, Land Use Change and Forestry (LULUCF) of the Clean Development Mechanism (CDM) of the Kyoto Protocol [10]. At the same time the simplified M&P for small-scale (less than 8 kilotonnes of CO2 per year) AR project activities under CDM is being prepared for approval at COP10 (2004).

An early attempt to review for the application of remote sensing technology for the Kyoto Protocol is presented in [8]. Five areas to be applied in LULUCF are identified : (1) provision of systematic observations of relevant land cover, (2) support to the establishment of a 1990 carbon stock baseline, (3) detection and spatial quantification of change in land cover, (4) quantification of above-ground vegetation biomass stocks and associated changes therein and (5) mapping and monitoring of sources of anthropogenic CH4. Within the

Treaty Enforcement Service Using Earth Observation (TESEO) program, European Space Agency (ESA) initiated the real application of remote sensing in international environmental convention, including UNFCCC [2]. This was then followed up by a joint program between ESA and the European Commission (EC) named Global Monitoring for Environment and Security (GMES) [3]. The main target is to establish European capacity in global monitoring of environment and security by 2008. The Envisat-Indonesia Radar Biomass Experiment (EIRBEX) is in line with GMES Service Element - Forest Monitoring (GSE-FM) but concentrated in the tropical region. The focus is on the development of representative AR monitoring model for CDM (Article 12). Especially, on the above-ground biomass carbon pool of the large timber plantation, by exploring the application of Envisat’s Advanced Synthetic-Aperture Radar (ASAR) imageries. The result is expected to be adapted, later on, to the small community plantation forest, as an anticipation to the future simplified small scale AR of CDM. Since the first Envisat/ASAR image of the first test site was recorded on 4 February 2003 and delivered by ESA at the end of March 2003, this paper describes the progress of the research in the recent one and a half years. 2. DATA AND METHODS Due to AR/CDM complexity and the existing international political negotiation process within the UNFCCC, the following strategy was applied in this study : (1) use of commercial tropical timber plantation to get one large contiguous block of land as the test sites, (2) focus on the capability of remote sensing on the above ground biomass of the AR, (3) check the

_____________________________________________________Proc. of the 2004 Envisat & ERS Symposium, Salzburg, Austria 6-10 September 2004 (ESA SP-572, April 2005)

performance of the Envisat/ASAR biomass detection ability within the selected test site, and (4) upon the positive checking result, implement Envisat/ASAR application for the whole AR/CDM requirements in the tropical environment.

2.1 Test Sites The location of the test sites is in the Province of Riau, Indonesia, in the eastern region of Central Sumatera, near Pekanbaru (0.52 N 101.47 E). All of these sites are timber plantation, and are selected under close cooperation with PT Riaupulp. Fig. 1(a) provides the details of the three test sites. The appearance of the test sites and its surrounding area as displayed in the quick look ASAR Wide Swath (WS) strip lines image of 31 December 2002, is given in Fig. 1(b).

(a)

(b)

Fig. 1. Location of the tropical plantation forest (blue, pink and purple) in the Province of Riau, Indonesia (a). The three selected test sites are marked with code TS-A, TS-B, and TS-C; The appearance of the area in the quick look ASAR WS image is provided (b). City of Singapore is located in the top right of the image. 2.2 AR/CDM Requirements

The steps required to estimate, measure, monitor and report changes in carbon stocks and emissions and removals of non-CO2 greenhouse gases for AR/CDM is consist of two components : (1) area identification, and (2) estimation of carbon stocks changes and non-CO2 greenhouse gases emissions. In relation to these components, a guidance for defining project boundaries; measuring, monitoring, and estimating changes in carbon stocks and non-CO2 greenhouse gases; implementing plans to measure and monitor, and; developing quality assurance (QA) and quality control (QC) plans, are completely provided in [5]. Following the strategy given above, in this exercise different mode of Envisat/ASAR imageries were explored for the whole AR/CDM applications, such as : representation of land area for timber plantation as a land use category; project boundaries; stratification of project area; detection of selected carbon pool; sampling design; estimating carbon stock, and; detection of leakage. Two other tasks (measuring and monitoring plan, and QA and QC plan) are not completed yet. 2.3 Imagery and Data Sets Remote sensing imageries directly collected for this study are Envisat/ASAR, ERS-1 and ERS-2 SAR, JERS-1 SAR, Landsat-TM, and small format aerial photography (SFA). In addition to that NOAA/ AVHRR, and MODIS images are used indirectly. Most of the analyses were operated in ASAR/SAR imageries, while Landsat-TM and SFA were used for supporting purpose. Table 1 lists existing Envisat/ASAR images received from ESA. All of field data sets for this study were provided by PT Riaupulp in the confidential basis. It consist of two types of forest plantation inventory data : Mid-Rotation Inventory (MRI) and Pre-Harvest Inventory (PHI). Actual production data per compartment will be available from destructive approach called Bench Mark Operation (BMO), during logging operation. GPS direct data collections were made as necessary during the study, in addition to archive data provided by PT Riaupulp. Timber plantation for pulp in Indonesia has seven years rotation cycle (Acacia mangium and Acacia crasicarpa). Field data is collected in different age of timber plantation : MRI is collected at three years old, PHI at six years old, and BMO at logging operation (seven years old). The plantation operation is managed in different level of land management unit : Sector, Estate, Compartment and Stand. Stand is the smallest management unit, with the same species and age.

Sensing (YYYYMMDD

HH:MM:SS) No Product Type Prod. Date

Start Stop

1 ASA_IMP_1P 02/10/2003 20030311 15:25:31

20030311 15:25:46

2 ASA_IMP_1P 14/03/2003 20030204 15:25:14

20030204 15:25:31

3 ASA_IMS_1P 19/03/2003 20030204 15:25:31

20030204 15:25:46

4 ASA_APS_1P 18/11/2003 20031111 15:25:22

20031111 15:25:38

5 ASA_APP_1P 13/06/2003 20030619 18:54:57

6 ASA_APP_1P 19/06/2003 20030619 18:55:01

20030619 18:55:17

7 ASA_APP_1P 17/09/2003 20030710 15:22:43

20030710 15:22:59

8 ASA_APS_1P 02/10/2003 20030729 15:25:40

20030729 15:25:56

9 ASA_APP_1P 13/10/2003 20031013 03:02:52

20031013 03:02:68

Tabel 1. List of Envisat/ASAR images received from ESA. 3. RESULTS The selected strategy as given in Section 2 above has been implemented in the study of Riau Province, Central Sumatera, Indonesia. The results in test site TS-A demonstrated the positive ability of Envisat/ASAR images for good practice CDM/LULUCF estimation and reporting requirements, such as : (1) representation of land area for timber plantation as a land use category, (2) stratification of the timber plantation area into two types of categories, (3) identification of relevant carbon pools and non-CO2 greenhouse gases, (4) the sampling framework design, and (5) identification of method (field and model) for monitoring the pools. The last task : (6) monitoring plan development, including quality assurance/quality control plan, are not completed yet. Furthermore, the plan to complete the whole AR/CDM requirement in the existing rotation could not be achieved. The selected estates of the timber plantation in the test site TS-A is in the first rotation. The planting date was in 1997, with MRI should have been recorded on the year of 2000, PHI in the year of 2003, and logging operation (BMO) in 2004. Tandem images of ERS-1 and ERS-2 SAR of 2000 were collected for MRI application, but no MRI data was available. Therefore, only Envisat/ASAR images of 2003 were used for PHI application, with the expectation that BMO data will be available in 2004. Tandem ERS-1 and ERS-2 SAR SLC were used to derive Digital Elevation Model (DEM) of TS-A, for calibration of Envisat/ASAR images. The result presented in this paper is mostly

concentrated in the PHI application, as BMO data is still in progress 3.1 Land Areas Representation Information about land area is required to estimate carbon stocks and emissions and removals of greenhouse gases associated with AR. Several methods could be used including annual census, periodic surveys and remote sensing to obtain area data. According to [5], there are three approaches for representing land area. Approach 1 identifies the total area for each individual land-use category, but does not provide detailed information on changes of areas between categories and is not spatially explicit other than at the national or regional level. Approach 2 provides tracking of land-use changes between categories. Approach 3, on the other hand, extends further Approach 2 by allowing land-use changes to be tracked on a spatial basis. In this study, two types of Envisat/ASAR image mode were used for Approach 3 : ASAR IMP (Image Mode Precision) V/V polarization of 4 February 2003, and ASAR APP (Alternating Polarization Precision) VV and VH polarization of 10 July 2003. The result showed that ASAR image provides a very clear detection of timber plantation compared to other land uses, due to its specular reflection (Fig.2). Such kind of detection was difficult to be seen on Landsat TM image of the same area. Further examination of ASAR APP image indicated a strong role of VV polarization to differentiate palm oil plantation from timber plantation. This ability could not be found in ASAR APP VH polarization image and in Landsat TM image. ASAR VV polarization image, therefore, could be used for Approach 3 purpose. All weather SAR capability is an advantage in acquisition planning, as the new collected SAR image could be acquired as planned, and in accordance to field operation. Such capability is not possible to optical satellite image due to cloud problem. 3.2 Project Boundaries AR/CDM projects require a clearly define spatial boundaries of project lands so as to facilitate accurate measuring, monitoring, accounting and verifying the project. According to [5], project boundary of AR/CDM is the geographically delineation of AR project activity under the control of the project participants. Fig. 2(a) demonstrate the ability of ASAR IMP VV polarization to be used in the project boundary application. The large timber plantation sites and several smaller timber plantation in its vicinity could be clearly seen visually. It only requires good GPS ground

control points for image registration process. As the result the GIS boundary polygon for each large timber plantation will match to its image boundary. In anticipating of small scale AR/CDM project, as proposed in COP9, ASAR VV polarization image is suitable for this kind of application.

(a)

(b)

Fig.2. (a) ASAR IMP VV polarization provides a clear differentiation between timber plantation and natural forest/oil palm; (b) Landsat TM image of the same area gives no clear differentiation. 3.3 Area Stratification Area stratification is required in AR/CDM to increase sampling efficiency in two main ways : (1) by improving the accuracy of the estimate for the entire population, and (2) by ensuring the adequate results are obtained for the certain sub-population [5]. On the first case, stratification increase sampling efficiency if a subdivision of the population is made so that the variability between units within a stratum is reduced as compared to the variability within the entire population. The second case is important for the purpose of providing results at a specific degree of accuracy for all units of interest.

Envisat/ASAR IMP VV polarization image showed a very good information compared to Landsat-TM for stratification purpose of AR/CDM. The TS-A area could be clearly divided into two strata : timber plantation and riparian area. The sample plot for inventory purpose (PHI), therefore, could be placed purely within the plantation only. 3.4 Carbon Pool Selection Carbon pools for AR/CDM are consisted of four types : above-ground biomass, below-ground biomass, litter, dead wood and soil organic carbon [5]. In addition to that, the major non-CO2 greenhouse gases in AR projects are N2O and CH4 [10]. The selection of which pool to measure and monitor is depend to several factors, including expected rate of change, magnitude and direction of the change, availability and accuracy of methods to quantify change, and cost to measure. The CDM project should select which pool to be used for their purpose. Since the tropical environment produce higher above-ground biomass compared to temperate or boreal environment [4], the above-ground biomass is selected as a carbon pool for this study. This is also in accordance to the fact that, remote sensing technique is more appropriate for above-ground biomass application, compared to other carbon pools (see Fig. 2(a)). 3.5 Sampling Design Field data in this study were obtained from sample surveys and were used for estimating biomass. Sampling theory provides the means for scaling up the information from the sample plots to the selected geographical level. In general field sampling is required in developing biomass inventory, because even if remote sensing data gives a complete territorial coverage, there always a need for ground-based data from sample sites for interpretation and verification purpose [5]. Sampling design determines how the sampling units are selected from the population and what statistical estimation procedures will be applied to make inferences from the sample. In this study sampling design for inventory purpose (MRI and PHI) followed the standard procedure from PT Riaupulp as given in [1]. In addition to this standard procedure, Envisat/ASAR IMP VV polarization image was used for stratification purpose to increase efficiency and accuracy. 3.6 Estimating Carbon Stock

Estimation of carbon stock in this study was limited to the aboveground biomass of the trees. There are two approaches for estimating above-ground biomass in the trees [5] : direct approach using allometric equations in permanent sample plots, and indirect approach using biomass expansion factors (BEF) in temporary sample plots. These plot based measurement needs to be related to overall project site by using high-resolution satellite image (Envisat/ASAR). Government regulation for timber plantation in Indonesia requires inventory to be applied just at once using temporary sample plots, that is one year prior to harvesting, with minimum coverage of 10% of the planned logging operation area. Fortunately, the standard procedure of PT Riaupulp operates two inventories (MRI and PHI), which help to develop the model for carbon stock and carbon stock change for AR/CDM. However, as the actual start of the study was March 2003 (after the receipt of first ASAR IMP image of 4 February 2003), the attention was made among them in the following issues : selection of test sites, planning of ASAR image acquisition, study of remote sensing application for AR/CDM requirements, exploration of ASAR image analysis for PHI application (first rotation), and field data collection and planning for complete application (MRI and PHI) in the second rotation. In this section several backscatter-biomass relationship for PHI were explored, by using different type of ASAR images. The first exploration attempt used simple correlation and linear regression of Pearson-based correlation analysis between ASAR IMP backscatter and PHI plots parameter. Different radar filters were applied (Lee, Frost and Gamma) to improve the result. The Lee filter was found to be the best for this application and the result is presented in Fig. 3. Upon the successful of the above exploration, the same method were implemented for PHI application to different type of SAR images, to improve the result. The image used among them were : ASAR APP (VV, VH) image, ASAR IMS single polarization (VV) coherence image and calibrated ASAR image of the selected polarization. The calibrated ASAR image requires DEM information which was produced from ERS-1/ERS-2 tandem images by using SAR Interferometry (InSAR) method. The work of this exercise is in progress and not included in this paper. In the mean time, the biomass estimation in the temperate region by using advanced Polarimetric SAR Interferometry (POLinSAR) techniques was also noted, such as in the Fichtelgebirge (Germany) [6] [7], CORSAR (Carbon Observation and Retrieval from SAR) project [9] and the SHAC (SAR and

Hyperspectral Airborne Campaign) test site in Glen Affric [11], both located in United Kingdom. All of these method were applied in airborne SAR with promising result. The expectation is to implement the best result of these method to the tropical environment through a scientific cooperation. The plan of ESA to field E-SAR airborne mission to India and Indonesia at the end of 2004, will be a good opportunity for such kind of cooperation.

(a)

(b)

Fig. 3. Mapping of total volume estimation in test site (a) by using the regression model in (b) 3.7 Detection of Leakage Leakage is the increase in greenhouse gas emissions by sources which occurs outside the boundary of an afforestation or reforestation project activity under CDM which is measurable and attributable to the afforestation and reforestation project activity [5]. Two types of leakage was studied in this exercise : fire and flood. Real time monitoring of flood events with space-borne SAR was a first time experience for country like Indonesia. For this purpose the flood monitoring used both Envisat/ASAR IMP and Wide Swath Mode

(WSM) images collected during the rainy session of 2003. The WSM image was recorded in 16 December 2003, coincided with the peak of the flood, and the other image was recorded in 1 January 2004 The ASAR IMP image of 4 February 2003, demonstrated the ability of ASAR image to record the flood right away within the timber plantation. Fig. 4 shows the flood in WSM image of 16 December 2003. Based on this exercise, the location of flooded areas were found, and future monitoring will be concentrated in these locations.

Fig. 4. Flood area around the test sites recorded in ASAR WSM of 16 December 2003.

Fig.5. Fire detection (in red) with local station read out of NOAA/AVHRR direct broadcast dated 21 August 2004 (source : Ministry of Forestry, GOI/JICA) Fire monitoring required a continuous observation during the whole year. Fire data were received from two site : (1) local station in Jakarta, in the Department of Forestry, which recorded direct broadcast of NOAA/AVHRR data and processed it to fire data, and (2) Web Fire Mapper website of the University of Maryland USA, which routinely record and process MODIS data to detect fire spot at global and regional level, i.e. Souteast Asia, and freely distributed (http://maps.geog.umd.edu). Fig. 5 shows an example of fire spot detected by local receiver of

NOAA/AVHRR image. The fire spot coordinates derived from these two source could be overlaid to the test site area for more detailed analysis. Validation of these suspected fire spots are not yet done, and needs to be considered as important aspect for AR/CDM in the near future. DISCUSSION A preliminary effort to apply the remote sensing techniques in AR/CDM application in the tropical environment was implemented in this study. The intention was to cover as much as possible the AR/CDM requirement as guided in [5], and later on improve the method, especially SAR techniques to get a better result. Support of PT Riaupulp to provide field data (although in a confidential basis) and field operation requirement has provided an excellent condition to study the relation between SAR parameters and the forestry parameters. The BMO data which give the real and actual production volume after the logging operation will be available in late this year, which allow comparison of measurement model (PHI and SAR technique) with the actual data. Due to the seven-years of plant rotation cycle in timber plantation, not all of the inventory stages were completed in this study. It could only select and prepare the test sites, provide the scenario measurement plan, and run for a limited SAR techniques for exploration purpose. The plan to complete the approach in first rotation in a consistent test site by using ERS-1/ERS-2 (2000) for MRI and Envisat/ASAR (2003) for PHI could not be accomplished due to the lack of MRI data. The paper, therefore, could only report the work in PHI stage. As the consequences, the new experiment scenario was developed by using combination of MRI from Envisat/ASAR (2003) and future SAR satellite like Radarsat-2 (Canada) or ALOS/PALSAR (Japan), for PHI purpose in 2006, to affirm the observation continuity for the second rotation. ACKNOWLEDGEMENTS The authors would like to thanks ESA for Envisat/ASAR data support in this exercise through AOE-869. The field work research and data were fully funded and supported by PT Riaupulp. The Ministry of Research and Technology, Government of Indonesia, provides research funding for the CDM/Kyoto Protocol component under the Second Indonesian International Research Program (RUTI) . Without the strong and continuous support of these institutions, this research could not be implemented.

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