DETECTING TROPICAL DEFORESTATION USING SATELLITE RADAR DATA

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1 The Balance between Biodiversity Conservation and Sustainable Use oftropical Rain Forests DETECTING TROPICAL DEFORESTATION USING SATELLITE RADAR DATA Belinda Arunarwati and Yousif Ali Hussin Indonesia is the most richly tropical forested nation in Southeast Asia. Over most of the country, forest land use is regulated by a system of land assessment known as TGHK (Tata Guna Hutan Kesepakatan) or consensus of forest land use. The key to wisely managing forest land and its resources is information. In the case of Indonesian deforestation, information is required not only about the rate and the extent of deforestation, but also about certain related factors such as the presence of deforestation in relation to TGHK classes, its location within each class of TGHK and its type. Data derived from remote sensors are increasingly being utilised as a data source in GIS. Conventional methods of remote sensing using optical systems have failed in some parts of Indonesia because of cloud cover. Radar, which is free from time and weather restrictions, may be a useful alternative source of remote sensing data in Indonesia. Deforestation is expressed by the rate of change of forest cover area caused by the change of use of forest land from forest to non-forest. Several factors are directly responsible for the change in the forest, such as commercial logging, pasture, colonisation programmes or spontaneous migration, slash-and-burn agriculture, construction of highways, mining and hydro-electricity projects. Deforestation is mainly caused by expansion of agricultural activities and other land use practices, such as logging activity. It has been suggested that more than 80 percent of deforestation can be attributed to agricultural expansion. Logging, indirectly contributes to this major cause of deforestation by providing farmers with access through the construction of timber extraction roads. The main objectives of this research were to investigate the potential of satellite radar data for detecting, differentiating and classifying deforestation in the forest concession area which has been selectively logged by PT Sylva Gama, Jambi, Central Sumatra, Indonesia. The following satellite images were used for this research project: Landsat-5 TM data of September 15, 1993, Spot XS data of March 21, 1993, ERS-1 images of October 17, 1993, June 6, 1994, and July 7, 1994, and JERS-1 of August 16, The research methodology is illustrated in Figure 2. Figures 3 and 4 and Tables 1, 2 and 3 show a comparison between the ability of ERS and JERS satellite radar images to differentiate land and forest cover types, using both image classification and image visual interpretation. Table 1 Differences between JERS-1 and ERS-1 radar images visual interpretation JERS (L band) Recognises 11 classes Can separate old and young Can distinguish rubber from forest Not good for distinguishing settlement Can distinguish annual crops from agricultural land Can detect clear cut Plantation pattern of oil palm is not so clear ERS (C band) Recognises 8 classes Cannot separate old and young Cannot distinguish rubber from forest Good for distinguishing settlement Cannot distinguish the area of annual crops from agricultural land Can also detect clear cut Plantation pattern of oil palm is more clear 245

2 The Tropenbos Foundation, Wageningen, the Netherlands Table 2 Comparison between two data sets of ERS and JERS images First Data Sets (DS1) (ers1:red, ers2:green, ers3:blue) Recognises 6 classes Cannot separate the forest into logged-over forest and Cannot recognise oil palm, because mixed with the forest itself Can recognise the wet area Clear cut can be seen, but bit difficult to detect Rice is more easy to detect Agriculture mixed with the trees (rubber and forest itself) Cannot separate the forest stand from other perennial trees like rubber Second Data Sets (DS2) (jers:red, ers2:green, res3:blue) Recognises 7 classes Cannot separate the forest into logged-over forest and Can see oil palm separately from forest, but cannot distinguish it from rubber Can recognise the wet area Clear cut easy to detect Rice can be seen, but is not so clear because mixed with water and wet area Agriculture mixed with the trees (rubber) Can separate the forest stand from other perennial trees like rubber Table 3 The comparison of the result of both classification approaches Some remarks Visual Interpretation Digital Processing Single JERS Single ERS Data sets with JERS and ERS Data sets with ERS only Number of classes Separates logged-over yes yes no no and Separates old and yes no no no Separates rubber from forest yes no yes no Detects clear cut yes yes yes yes, but not so clear Separates oil palm from forest yes, but not so clear yes yes no 246

3 The Balance between Biodiversity Conservation and Sustainable Use oftropical Rain Forests Indonesian Forest Situation preparation Topomap (1982) Forest Land Use Map 1982 Forest Land Use Map 1996 ERS s JERS Digitizing Filtering Rasterization Digitizing Geometric correction Digital Topomap Segment and raster Hardcopy manipulation Training sample Knowledge of area Digital Forest Landuse map Visual Interpretation Signature area Deforestation In the field Detection and delineation classification Differentiation and classification Report Digitizing and rasterization Classified images Analysis and discussion Classified images Accuracy assessment Forest Landuse Change (Deforestation Type) Deforestation: In Integration with forest Landuse planning Deforestation Map Figure 2 Research methodology 247

4 The Tropenbos Foundation, Wageningen, the Netherlands Figure 3 Classification results of JERS-1 Radar showing the deforestation in the study area 248

5 The Balance between Biodiversity Conservation and Sustainable Use oftropical Rain Forests Figure 4 Classification Results of ERS-1 Radar showing the deforestation in the study area 249

6 The Tropenbos Foundation, Wageningen, the Netherlands 250

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