ANALYSIS OF PHYSICAL FACTORS AFFECTING ILLEGAL LOGGING OF TROPICAL FOREST OF BERAU, EAST KALIMANTAN, INDONESIA USING REMOTE SENSING AND GIS

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1 ANALYSIS OF PHYSICAL FACTORS AFFECTING ILLEGAL LOGGING OF TROPICAL FOREST OF BERAU, EAST KALIMANTAN, INDONESIA USING REMOTE SENSING AND GIS Anita Zaitunah Yousif Ali Hussin Ali Sharifi Department of Natural Resources, The International Institute for Geoinformation Science and Earth Observation (ITC), Hengelosstraat 99, 7500 AA, Enschede, Netherlands, Fax: (31) , Abstract There is a decreasing of forest area and the quality itself worldwide especially in developing and tropical countries. Illegal logging is one of the factors considered in causing this condition. Illegal logging in this research is in a form of single tree felling. This research tried to investigate the physical factors affecting single tree felling of illegal logging using optical remotely sensed data and GIS. 1. Introduction During the 1990s, the loss of natural forests was 16.1 million hectares per year, of which 15.2 million occurred in the tropics. The major causes of loss in a form of deforestation and forest degradation are: conversion to other land uses (mainly agriculture), pests and diseases, fire, overexploitation of forest products (industrial wood, fuel wood), poor harvesting practices, overgrazing, air pollution and storms. The annual deforestation rate in Indonesia was 1.8 million ha between 1985 and 1997, with which the ratio of forest area to total land area fell from 62.7 percent to 50.6 percent. Recently, deforestation and forest degradation are mostly caused by illegal activities. These include overcutting of forests and cutting in unauthorised areas. In Indonesia, there is a smuggling of illegal logs to neighbouring countries. Widespread of illegal logging networks operate at the district level. About 40-60% of the total industrial round wood supply has been estimated come from illegal logging. In mid-1997, monetary crisis and political situation has affected many sectors in Indonesia, including natural resources management. The change of government structure was followed by new policy and legislation. The Indonesian Government have passed new legislation call for decentralization process in natural resources management. To manage natural forest, the legislation transfers the authority from the central government to the provincial and district authorities. This process of regional autonomy in Indonesia has created conditions that are conducive to the continuation and/or further differentiation of illegal logging activities. In this research, illegal logging is defined as illegal felling of a single tree. It is not in a form of clear cutting or cutting many trees or cluster of trees. The loggers would go inside the forest looking for trees to be cut. They select a big enough and commercial tree and then cut it. The selected tree would then sawn in the spot and transport outside. Knowing the potential area of illegal logging gives valuable information, a serious attention should be given to combat illegal logging. The current status of illegally logged area is needed

2 to determine the potential area of illegal logging. The spatial information of illegal logging could raise people awareness to protect the forest. Establishing a practical and effective information system is an important step to monitor and support certification process of Production Forest in Indonesia. Remote Sensing and Geoinformation is needed to achieve that step. The study on possibility of detecting, monitoring and determining potential area of illegal logging would give a valuable contribution to stop illegal logging and improve the forest concession management and forest certification process. Analyzing physical factors affecting illegal logging can be a starting point to build up the monitoring system. Those factors would be able to help us in determining the potential area of illegal logging. The objective of this research is to investigate the biophysical factors affecting illegal logging using optical remotely sensed data and GIS. 2. Method Two sets of Landsat-7 ETM+ data acquired on 16 August 2002 and 31 May 2003 were used in this research. Sub-pixel Classifier was applied to obtain the classified map showing newly logged points, which is illegal logging spots. Forest Canopy Density Mapper was applied to both images. Six physical factors were analyzed to find out their relationship with illegal logging. Those factors are distance from roads, slope, elevation, distance from entrance to the forest, distance from river and aspect. Each factor was identified in 2 sources of information: illegal logging map of 2002 and illegal logging map of Both maps are the result of Subpixel classifier (Figure 1). Each input map was crossed with each illegal logging map. This step gave the evidence which factors have relationship with illegal logging. Overlay was also conducted between factors to have further analysis on their relationship with illegal logging (Figure 2). Maps were built representing classes on those factors. Those maps were individually crossed with illegal logging maps of year 2002 and The relationship between each factor and percentage of illegal logging was analyzed using Spearman s correlation. Illegal logging intensity (number of spots/ha) of each factor was analyzed. Each illegal logging map was crossed with Forest Canopy Density maps of the same year to see the canopy density within illegal logging spots. The fieldwork had given the knowledge on what is going on in the spot related to illegal logging. Illegal logging in this research is in a form of single tree felling. Tree was sawn in the spot and transported outside. Spots were found along the main and secondary roads. The test site of this research is the area of RKL 1. RKL 1 is one of the five year working plan area inside the concession. This area has been logged in period of It will be logged again in the next 35 years felling cycle start in Therefore, any logging occurred in RKL 1 considered to be illegal logging. Large amount of recent illegal logging is going on in RKL1, which is the first block where logging will be repeated after the end of the first rotation cycle.

3 Figure 1. Procedure of image classification Figure 2. Assessment of logging intensity 3. Results and Discussions In order to see the relationship between physical factors and illegal logging, overlay operation was conducted between each factor map and illegal logging map of 2002 and It was decided to consider the percentage of illegal logging by dividing illegal logging area found in each class with the total area of illegal logging of whole study area. It is clear that illegal logging in this area found mostly in close distance from roads. The trend is the same in 2002 and 2003 (Figure 3). In year 2002, 61% of illegal logging area are within 200

4 m from roads while in year % are within 1 km distance. The illegal logging found in 2003 are reaching beyond the range of distance in within distance from road classes (2002,2003) Illegal logging area (%) Year Year Distance from road classes Figure 3. Illegal logging area within distance from road classes (Class 1:1-200, Class 2: , Class 3: , Class 4: , Class 5: , Class 6: , Class 7: , Class 8: , Class 9: , Class 10: ) There was an increasing area being logged in all classes in ha. In term of percentage, there was decreasing number of illegal logging in lower classes ( m) and increasing number of illegal logging percentage in classes 3-10 ( m). It means more logging in the closer and further distance from the roads. In both years, the correlation between percentage of illegal logging area and the distance from road is found very high (r s =-1 (year 2002), r s = (year 2003), 0.01 level, 2-tailed). The closer to the road, the more Illegal logging found. It is in accordance with what was found in the field. Good accessibility is the most important factor affecting illegal logging in term of transporting the illegal sawn timber of single-tree felling. In slope degrees classes within the area (Figure 4), it shows that most of illegal logging area are in lower slopes. In term of area being illegally logged, there are increasing an increasing number in all slope classes. In the year 2002, 71% of illegal logging found within class 0-10 slope degrees. From the figure above, we can see illegal logging occurred also in upper slope classes in the year The percentage of illegal logging was found decreasing in going to upper classes of slope. No logging was found in slope range between o. In year 2002, less than 1 % of illegal logging found in slope more than 30 o. While in year 2003, less than 2% of illegal logging found in slope classes of o. In both cases, the correlation between percentage of illegal logging area and the slope degrees was high (r s = (year 2002), r s = (year 2003), 0.01 level; 2-tailed). The lower the slope classes, the more Illegal logging found. It means slope also play an important role to accessibility in single tree felling. It is in accordance with what was found in the field.

5 Illegal logging area (%) within slope classes (2002,2003) Year Year slope classes Figure 4. within slope classes (Class 1:0-5, Class 2:6-10, Class 3:11-15, Class 4:16-20, Class 5:21-25, Class 6:26-30, Class 7: 31-35, Class 8:36-40 Class 9:41-45) Illegal logging are mostly found within the lower elevation ( m) (Figure 5). In term of area being illegally logged (in ha), there was an increasing number in all elevation classes. No logging found in elevation between m (class 6 and 7). Illegal logging area (%) within elevation classes (2002,2003) Year Year elevation classes Figure 5. within elevation classes (Class 1:44-75, Class 2:76-100, Class 3: , Class 4: , Class 5: The roads passing through the study area are provincial roads. It is a connection of Berau District and its major city Tanjung Redeb and the capital of the province of East Kalimantan, the city of Samarinda. All main transportation to Samarinda such as buses, trucks and public car pass through the road. People can pass through the area easily. The entrance to the study area is considered to be important as the entrance for many people, including transmigrant and people from outside of the area. This entrance also leads to the market outside the area. Using distance calculation from entrance, the distance map of entrance was obtained. Figure 6, shows that in year 2002 most of illegal logging area was concentrated in closest distance to the entrance. While in year 2003, people looked after new spots and therefore go further from the entrance. Figure 6 shows the trend of illegal logging percentage related to the distance from entrance. The most illegal logging area was found in the close distance with the entrance. The further the

6 distance, the lower the percentage of illegal logging. This trend is also in accordance with the correlation analysis. It shows a high correlation (year 2002: r s =-0.859, 0.01 level, 2-tailed; year 2003: r s =-0.788, 0.01 level, 2-tailed). within distance from entrance classes (2002,2003) Illegal logging area (%) Year Year Distance from entrance classes Figure 6. within distance from entrance 4. Conclusions The result of overlay of each factors and illegal logging maps shows their relationship in both years. More illegal logging spots found in the closer distance to roads, lower slope, lower elevation, and closer distance to the entrance of the forest area. More illegal logging was found in the further distance from the river. The result also shows that illegal logging mostly found in flat area (no aspect), North North East ( ) and South West West ( o ). Logging intensity in each factor has also shown similar trend with the percentage of illegal logging. In the year 2002, the area was mostly covered with 51-90% of forest canopy cover, while in year 2003 there was an increasing of lower classes. In both years, most illegal logging spots have 51-60% of canopy density. The research concluded that there is a relationship between illegal logging and distance to roads, slope, elevation, and distance to entrance of the forest area.