Application of Envisat/ASAR for Monitoring of Tropical Forest Plantation Biomass in Indonesia

Transcription

1 Application of Envisat/ASAR for Monitoring of Tropical Forest Plantation Biomass in Indonesia M. A. Raimadoya (1), B.H. Trisasongko (1), D.R. Panuju (1), D. Shiddiq (1), and R. Maulida (1) (1) Radar Analysis Working Group (RAWG), Department of Soil Sciences, Bogor Agricultural University, P.O. Box 2049, Bogor Timur 16020, Indonesia ABSTRACT A study for SAR biomass mapping in tropical plantation forest in Indonesia is now undergoing by using Envisat ASAR images. This study is expected will fill the gap, since most of the existing SAR biomass studies in Amazon (global) and Indonesia Radar Experiment INDREX (local) are mostly concentrated in tropical natural forest. In the first step, the study will explore the link between ASAR precision image (IMP) parameters with ground measurement parameters, namely : Mid Rotation Inventory (MRI) and Pre-Harvest Inventory (PRI). In the second step, the InSAR method will be applied. This will follows with the PolinSAR method in the last step. The paper presents the research framework and the preliminary result by using ASA_IMP_1P 4 February 2003 for application of radar biomass analysis in the precision image during the first cycle of the spatial information cycle. Further improvement will be made in the second cycle to be completed in Keywords: SAR tropical plantation forest biomass, CDM/LULUCF, Kyoto Protocol INTRODUCTION Indonesia ratified the UN Framework Convention on Climate Change (UNFCCC) under Act no. 6/1996 and signed the Kyoto Protocol (KP 1997) at COP3 in Kyoto, Japan, 1997 (ratification in progress). As the consequence, it is obligated to submit National Communication to the Conference of the Parties (COP) of UNFCCC, and has the opportunity to participate in the Clean Development Mechanism (CDM) investment activity. First National Communication was submitted October 1999 [8] where an inventory of the most significant greenhouse gasses for 1994 was developed. The 1996 IPCC Methodology was used in the inventory, and most sectors considered by IPCC were covered. However, the accuracy of estimating the emission and removal of the greenhouse gas (GHGs) from the atmosphere largely depends on the availability and accuracy of the activity data and emissions factor. The Communication noted that among the main three sectors (energy, agriculture, and forestry), forestry is the sector with the highest uncertainty compared to the energy sector. In 1990 inventory, it was reported that Indonesian forest was a net sink. However, with improvement of activity data and emission factors, Indonesian forest is becoming net emitter. The magnitude of the net emission depends on assumptions used in defining area of logged-over forest under growing stage. Since the forestry sector is a significant contributor to the emissions and removal of carbon dioxide, the reliability of activity data and emissions factors of this sector need to be verified and improved by means of more measurement. Despite of the above situation, the National Strategy Study (NSS) was completed in 2001 [7] and submitted to COP6 Plus in Bonn, Germany, with support of the World Bank and the German Agency for Technical Cooperation (GTZ). The forestry issue was not addressed intentionally, due to the limitation in the availability of land use change inventory. Nevertheless the Study confirmed an amount of 156 million tons of net CO 2 emissions caused by changes in land use, primarily deforestation. This paper presents the approach for operational application of Envisat/ASAR image for CDM (Article 12 KP 1997), due to the country interest and as a starting point to fill the data gap on Land Use, Land Use Change, and Forestry (LULUCF) sector described above. European Space Agency (ESA) provides the imageries under the Operational Application of Envisat-1 Advanced Synthetic-Aperture Radar (ASAR) for Production Forest Management in Indonesia (AOE-869). It is expected that upon completion of this exercise, the tenth session of the Conference of the Parties Proc. of FRINGE 2003 Workshop, Frascati, Italy, 1 5 December 2003 (ESA SP-550, June 2004) 110_raima

2 (COP10) of UNFCCC will approve LULUCF sector as part of CDM. The resulted system, therefore, could be applied to improve the rural livelihood, by using it as a monitoring system for carbon trade of community plantation forest, within the first commitment period ( ) of KP TEST SITE 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 E). Figure 1(a) provides details of the plantation forest selected as the test sites and its appearance in the quick look ASAR Wide Swath (WS) strip lines image dated 31 December (a) 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. City of Singapore is located in the top right of the image. The test sites are distributed within the area to represent different field conditions. TS-C represents flat wetland area with peat soil and Acasia casiacarpa tree plantation. TS-A is in the mineral soil with undulating topography and Acasia mangium tree plantation. TS-B is also in the mineral soil with undulating to hilly topography and Acasia mangium tree plantation. All the test sites are in the large timber plantations area, to allow a clear first attempt detection process in ASAR image. In forest plantation management, a test site is equal to a Sector, which then be subdivided into Estates, Compartments and Stands. Stand is the lowest management unit with minimum size of 5 Ha, and a uniform species and age (planting date differences less than 6 months). The study is implemented in the selected compartments of each test site. Figure 2(a) presents close look of TS-A and TS-B, while TS-C is given in. (a) Fig. 2. Example of the test sites in : (a) TS-A and TS-B, ASAR IMP VV, ascending, 4 Feb. 03; and TS-C, ASAR APP VV & HH, ascending, 10 Jul. 03.

3 IMAGERY AND DATA SETS Establishment of the test sites were then followed with the selection of a test site as a priority for further study, to anticipate the result of COP 9 (2003) for approval of aforestation and reforestation in CDM for first commitment period. TS- A is selected as the priority test site, and the first ASAR IMP image of this site was recorded in 4 February 2003 and its repeat pass of 11 March 2003 in IMS image mode. As seen in Fig. 2(a) the image is also covered test site TS-B. It were followed with several image acquisition during Although it is not a priority site, TS-C was recorded first time in ASAR APP VV & HH images on 10 July Several estates within TS-A were selected for detail study at higher tier (Tier 3) as described in the Good Practice Guidance for Land Use, Land Use Change and Forestry [3]. The information requirement for CDM (Article 12) is more detail compared to National Communication purpose (Articles 3.3 and 3.4). The ground data collection is done in accordance with the plantation inventory cycle, namely Mid Rotation Inventory (MRI) at three years old and Pre- Harvest Inventory (PHI) at harvesting year minus one. Since the planting date of the selected estates in TS-A is mostly in 1997, no Envisat/ASAR image could be used for MRI purpose. As the consequences the image of ERS-1/2 Tandem SAR SLC of 20 and 21 February 2000 respectively, were used for MRI study. Since the forest plantation cycle in Indonesia is seven years, the monitoring of TS-A will involve different SAR system i.e. ERS-1/2 SAR and Envisat/ASAR for first rotation and later on possibly between Envisat/ASAR and ALOS/PalSAR or other SAR sensor in orbit for the second rotation. Given the relatively short lifetime of most spaceborne SAR, and the requirement of CDM/LULUCF long-time series measurement, the traceability is an important issue for SAR measurements flown on different satellites in the foreseeable future. Each SAR needs to be calibrated traceably against a standard which is expected to be stable over a long term horizon [ ]. METHODOLOGY COP 9 of UNFCCC in 2003 made important decision in relation the CDM/LULUCF, among them are the acceptance of modalities and procedures for aforestation and reforestation of LULUCF sector in CDM, approval of LULUCF Good Practice Guidance (GPG), and preparation for small scale LULUCF (net anthropogenic GHG less than 8 kilotonnes of CO2 per year) to be approved in COP [2]. Application of Envisat/ASAR for CDM/LULUCF requirements is an applied research, where the result is expected to be used immediately for operational solution in CDM/LULUCF monitoring. The spatial information cycle [5] is applied in this exercise, to allow a flexible and clear path of approach to reach the expected target solution. This cycle involves tasking, processing, exploitation, and dissemination (TPED) activities. Data are tasked and acquired from data measurements system such as ESA facilities, and processed to develop a set of data products based on standards for format and interoperability. The data products are then exploited transforming remote sensing data and information into knowledge. Data handling systems will help in distribution effectively data products and knowledge to decision-makers for accurate decision and action. During the first year of this exercise, three elements of the cycle (tasking, processing and exploitation) were implemented. The tasking element included : the preparation and selection of the representative test sites; long term EO scenario (continuity, traceability and reliability) and EO SAR data sets (imaging mode and pass); planning, acquisition and production of Envisat/ASAR image from ESA; selection of CDM/LULUCF tier level to be applied; and map based DEM generation. Processing stage involved the preparation of SAR related software, ASAR PDS format export and import, and ASAR IMP radar brightness biomass processing. The exploitation step implemented the following activities : test site ground data sets collection; benchmarking of ground data set; determination of activity data and emission factors for forest plantation in CDM/LULUCF; and radar brightness biomass mapping. The result of these elements will be discussed during the dissemination stage, and the feedback will be used to improve the result in the next cycle (2004). It is expected that the result of second cycle will be ready when COP 10 make final decision for the small scale CDM/LULUCF implementation in the same year. The result presents in this paper highlight radar brightness biomass mapping by using single ASAR IMP VV image of the TS-A, as a first check of ASAR application for tropical plantation forest. Many attempts has been made to estimate biomass by using different data sets i.e. SAR [1], and Landsat TM [2]. The analysis of this exercise is based on simple correlation and linear regression of Pearson-based Correlation analysis. The coefficient is formulated as follows:

4 where and ρ cov( X, Y ), = (1) ρ ρ X Y X Y ρ X = ( X i µ X ) n (2) ρ Y = ( Y i µ Y ) n (3) RESULTS The biomass analysis for this approach included the characterization of image parameters, statistical description to relate image parameters with ground observation data (PHI) by using correlation and regression analysis, transformation to transform analysis result to segmentation process, and the last step is segmentation for biomass map. In this analysis Landsat 7 ETM+ of April 2000 was used for comparison. The result of first analysis is presented in Table 1 and Figure 3 below. The TVOL data was well correlated with MVOL data. Since ASAR provides good correlation with TVOL (0.51), it can be used to estimate MVOL and later on the biomass by using aloemetric formula. Table 1. Correlation between Image Parameters and Total Volume (TVOL) and Merchantable Volume (MVOL) based on GPS reading COMPARTMENT AVERAGE INTENSITY AVERAGE TM3 AVERAGE TM4 MVOL TVOL COR TO TVOL COR TO MVOL Based on this positive indication, the analysis was repeated in the second analysis by using plot data directly and application of different radar filter (Lee, Frost and Gamma) with different kernel size, to improve the result. Table 2 presents the correlation result where Lee filter was the best approach. The filtered image was then used to generate the regression and segmentation biomass map as given in Figure 4.

5 (a) Fig. 3. Regression result of first analysis (a) and the segmentation result of biomass in TS-A Table 2. Correlation of TVOL with different radar filter images parameter. PLOT INT LEE FRO GMA TVOL CORRELATION CONCLUSIONS This paper presents in general the research framework to be used in Envisat/ASAR application for CDM/LULUCF of the Kyoto Protocol. The preliminary result of this exercise gave a positive indication for ASAR application

6 Fig. 4. (a) Improved regression result (a), and the improved segmentation result in TS-A to relate image parameters with the ground measurements. This result will give a good opportunity for further improvement in the second cycle, for fast above ground carbon stock and carbon stock change of CDM/LULUCF of the Kyoto Protocol. Although Landsat TM Band 4 gave better correlation to TVOL compared to Band 3, this passive sensor suffer from cloud cover and atmospheric attenuation in the tropical region (no data could be acquired for TS-A in 2003 due to cloud problem, and the exercise had to use year 2000 archive data). Therefore, the applicability of the data for long term observation is limited. With recent development of radar remote sensing such as Envisat, this obstacle can be reduced. Envisat ASAR gave a good correlation value of 0.51 which is good indication for Envisat ASAR application of biomass extraction in the tropical condition. ACKNOWLEDGEMENTS The authors would like to thanks ESA for Envisat/ASAR data support in this exercise through AOE-869. The field work funding and data were fully supported by PT Riau Andalan Pulp and Paper Tbk. The Ministry of Research and Technology, Government of Indonesia, for research funding to this exercise. Without this strong and continuous support of these institutions, this research could not be implemented. REFERENCES Bergen, K.M., Classification, Biomass Estimation and Carbon Dynamics of a Northern Forest Using SIR-C/X-SAR Imagery, Dissertation, The University of Michigan, USA, 1997 IISD, Earth Negotiations Bulletin, A Reporting Service for Environment and Development negotiations, COP 9 Final, Vol. 12 No. 231, 2003 IPCC, Good Practice Guidance for Land Use, Land Use Change and Forestry, IPCC National Greenhouse Gas Inventory Programme, Institute for Global Environmental Strategies (IGES), Lee, N.J. and K. Nakane, Forest Vegetation Classification and Biomass Estimation Based on Landsat TM Data in a Mountainous Region of West Japan. In H.L. Gholz, K. Nakane and H. Shimoda. The Use of Remote Sensing in the Modeling of Forest Productivity, Kluwer, Dordrecht, The Netherlands, 1997 NASA, Earth Science Enterprise Application Strategy for , National Aeronautics Administration, USA, Nigel Fox, Traceability to SI for EO Measurements, CEOS CAL/VAL Newsletter, Issue 9, pp 1-9, State Ministry for Environment, National Strategy Study on the Clean Development Mechanism in Indonesia, Government of Indonesia, State Ministry for Environment, Indonesia : The First National Communication on Climate Change Convention, Government of Indonesia, 1999.