Monitoring of Deforestation and Land Use in Indonesia with Multitemporal ERS Data

Steffen Kuntz and Florian Siegert (*)

Remote Sensing Services GmbH, Blumenstraße 48, 82515 Wolfratshausen, Germany

Phone: +49-8171-4897-29, Fax: (49)-8171-4897-30

e-mail: Steffen.Kuntz@T-online.de

*)Institute for Zoology, Ludwig-Maximilians-University, Luisenstr. 14, 8033 Munich, Germany; Phone: +49-89-5902-442, Fax: (49)-5902-450

e-mail: FSiegert@zi.biologie.uni-muenchen.de

ABSTRACTThe ESA ERS1-pilot project PP2-D11 was aimed at investigating the capabilities of the ERS-1/2 satellites for continuos land use and deforestation monitoring over a period of 5 years (1993-1997) in a tropical rain forest environment. Deploying texture analysis techniques several land use classes and different forest types could be identified in single ERS-1/2 SAR images. The texture features created allowed to discriminate 1.) undisturbed forest of at least two different types 2.) shifting cultivation and agriculture, 3.) secondary forest, 4.) selectively logged forest 5.) clearings by fire and clear-cutting and 6.) settlements and major roads. The results of the multitemporal data analysis showed that changes in area of classes # 2, # 4 and # 5, respectively, can be readily detected in a a multemporal sequence of ERS SAR images. KFA1000 photographs from the Russian MIR Space station, Landsat TM images and ground data acquired during two extensive field excursions served as a reference for the interpretation of the ERS images. All evaluation procedures were performed with standard computer equipment and commercial software packages to ensure operationality and technology transfer towards Indonesia. The results suggest that the European radar satellites ERS-1/2 can be used to monitor and analyse forest conversion and land use patterns in tropical rain forests at scales 1 : 100,000.

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1. IntroductionTropical rain forests are highly endangered ecosystems. Fast economy as well as population growth result in increasing rain forest conversion rates all over the world (Myers, 1989; FAO, 1997). In many countries of the wet tropics the importance of rain forests for the global ecosystem and as an valuable natural resource is still poorly understood. Tropical rain forests are the natural habitat of more than 80 % of all plant and animal species of the world, while covering only approx. 7 % of the land surface on Earth (Whitmore, 1984; Peters et al. 1989). Of great importance are tropical rain forests for both, the local climate and global climatic processes (Goldammer and Crutzen, 1993). Large scale deforestation in the tropics normally results in decreasing humidity and rainfall, which in turn decreases agricultural and forestry yields and increases the probability of large scale forest fires as observed during the 1997/98 El Nino event in SE-Asia and S-America. A reduced evaporation and the accelerated draining of precipitation frequently leads to soil erosion and water shortages not only in regions immediately affected by deforestation. Furthermore, rain forests often grow on very poor soils allowing only subsistence farming (shifting cultivation) and requiring relatively long time intervals for soil and vegetation recovering (15 - 20 years). In many areas where the exploitation and conversion of tropical rain forests proceed uncontrollably and with increasing rate unrecoverable degradation of the tropical ecosystems have to be expected. It has been recognised only recently, that sustainable development and careful utilisation of natural resources is mandatory to assure future prosperity.

Until now, mainly optical satellite images and aerial photos have been deployed to analyse changing land use patterns in vast, inaccessible rain forests (e.g. Kalensky et al., 1991). The major disadvantage of optical systems for operational monitoring is the frequent cloud coverage in tropical regions. Problematic projections from small, cloud free spots, to forest conversion statistics for whole nations is the major reason for the extreme discrepancy between published conversion rates from several authors. The European radar satellite ERS-1 and its successor ERS-2 have provided a new powerful tool for deforestation monitoring from Space. Consequently, RADARSAT will be a very useful tool, but on a commercial basis (the authors have investigated that topic in Indonesia as Principle Investigators for RADARSAT International, in preparation). For the first time the cloud penetrating capability of spaceborne SAR allows the monitoring of large tropical regions on a monthly basis (Malingreau et al., 1995; Malingreau and Duchossois, 1995). Thus, the principal goal of the German ESA pilot project "Tropical Rain Forest and Use of Land Investigation" (TRULI, PP2-D11) was to investigate the capabilities of the ERS-1/2 SAR data for land use and deforestation monitoring in a tropical rain forest environment. TRULI started already in July 1993. It was funded by the German Space Agency (DARA) and finished 1997.

The project focused on the following issues:

Analysis of radar backscatter in tropical environment in single and multitemporal data sets

Development of an operational monitoring system

Assessment of the usefulness of SAR images for land use planning

Technology transfer to Indonesia.

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The project area covers approximately 5.000 km2 in the province Kalimantan Timur on the island of Borneo, Indonesia. It is located at the middle reaches of the Mahakam river (geographical centre at 0°35´ South, 115° 10´ East). The area was chosen in close co-operation with the Indonesian land use planning board (Badan Pertanahan Nasional - BPN) and is part of the bilateral German-Indonesian "Land use Planning and Mapping - LUPAM" project.

All major land use types typical for Kalimantan are found in this area. The vegetation is dominated by lowland Dipterocarp forest. Normally this vegetation type has a heterogeneous canopy structure. An other natural forest type, Heath forest, is found especially on nutrient-poor soil. This vegetation type is characterised by a homogenous canopy structure with little vertical variance. Most of the Dipterocarp forests in the test area had already been subjected to selective logging, that led to an extremely heterogeneous canopy structure. Most clearings are found close to the rivers, were local farmers grow hill rice by slash and burn agriculture.

Russian KFA1000 images from the manned Space station MIR with approx. 7 meters ground resolution and field data including GPS measurements acquired during 2 extensive ground truth campaigns (October/November 1993 and September 1995) served as a reference for SAR image interpretation and for the verification of results (Kuntz and Siegert, 1994; Kuntz and Siegert, 1996). A first field excursion was aimed at creating an reliable interpretation key for the KFA-1000 and the ERS images. A second ground truth excursion was undertaken to verify the ERS interpretation including an extensive helicopter survey, which lasts about 5 hours.

2. ERS-Data Processing 8 ERS-1 SAR GEC Images and 5 ERS-2 SAR Precision Images (PRI) acquired on nine dates were evaluated (Orbits / date of acquisitition: 10397 / 12-07-93; 11900 / 25-10-93; 12401 / 29-11-93; 16560 / 15-09-94; 21763 / 13-09-95; 7601 / 3-10-96; 12110 / 14-08-97; 12611 / 18-09-97; 13112 / 23-10-97). For the years 1993 and 1994 two adjacent frames (3609 and 3591, respectively, had to be mosaicked in order to cover the project area. Since 1995 it was possible to order frames shifted 50% to the south. Thus the project area could be covered by one ERS scene.

For better comparison we focused the evaluation on descending orbits acquired during the end of the dry season (September - October). Rainfall data were provided by the IFFM/GTZ project (Integrated Forest Fire Management Project based in Samarinda, Kalimatan Timur). The knowledge of the day to day rainfalls during the orbits made it possible to take into account the effects of moisture on backscatter, because the dielectric constant and, accordingly, the moisture content of the vegetation or the soils have a significant influence on the backscatter intensity (Ulaby and Dobson, 1989).

Because in ERS data relevant thematic information is normally found only in a dynamic range of about 1200 grey values all SAR scenes have been resampled from 16 bit to 8 bit. This lead to a significant reduction of the CPU time and disk storage capacities required for the image processing. By comparing the backscatter signatures of 8 bit and 16 bit data, respectively, as well as by visual interpretation of the different texture features created we found that this reduction does not affect the mapping accuracy. All ERS images were georeferenced using a common set of ground control points derived

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from GPS measurements and topographic maps. The applied techniques and ERS image interpretation has been already described in Kuntz and Siegert, 1994; Kuntz et al., 1996 and Siegert et al. 1995). Here, we will focus on the results of the 5 years investigation period.

Visual inspection of raw, unprocessed ERS images (PRI or GEC images without further image processing) allowed only to discriminate geomorphologic structures and large water bodies like rivers. Even a distinction between forest/non-forest was impossible. To be able to discriminate different vegetation and land use classes monotemporal ERS images were subjected to three separate speckle and texture filtering operations as shown in the flow chart in Figure 1. The filter products (7 x 7 gamma map filter for speckle reduction, 15 x 15 variance filter, and 31 x 31 variance filter; for details on the variance filter refer to the ERDAS Field Guide, pp. 203 ff.) were then combined into artificial ERS RGB composite images by assigning each filter product to a different colour channel (Figure 2). A comparison of Figure 2A+B and 2E shows that much more features can be discriminated visually in the RGB composites than in the unprocessed or simply speckle filtered ERS image. Since the information on land use and vegetation type is not only based on backscatter intensity of the reflectors but also on their spatial pattern (visualised as texture) it was not possible to apply standard supervised classification procedures to the RGB composites. Therefore we resorted to visual interpretation of the RGB images to create vegetation and land use maps (e.g. Figure 6).

The size of the texture filter boxes used here might seem very large, however, it should be considered that texture features are created by computing grey value distributions. In order to describe to a certain extent the effects of extreme values (i.e. very bright and dark pixels = speckle) to the grey value distribution representing a specific class, the number of pixels involved should be large. Thus, small kernels will not be able to highlight spatial differences in SAR data very well (Bloom and Daily, 1982). Nevertheless, it should be clear that the resolution of the images will be degraded by using large filter boxes. This means that only classes which cover larger areas (in case of a 31 x 31 kernel > 5 ha) will be highlighted. In our test site only shifting cultivation parcels are frequently smaller than 5 ha. But the class shifting cultivation – as defined in this study - consists of a mosaic of many small field parcels intermingled with secondary vegetation of different age. Such a mosaic of many heterogeneous parcels can be highlighted with the texture operators quite well. However, applying large filter boxes it is often difficult to delineate the boundaries between two classes exactly. This problem contributes significantly to the interpretation error.

From many studies on various targets it was demonstrated that multitemporal ERS data offer more information than single scenes (e.g. Kattenborn et al., 1993; Keil et al., 1994, Leysen et al., 1993; Malingreau et al., 1994; Nezry et al., 1993). However, the "traditional" approach, combining three speckle reduced images from different orbits in a RGB-coded image, we achieved only inferior results. We found, that various texture features, their combinations in principal component transformations or in combination with other artificial feature channels increase the thematic information of SAR images significantly. To highlight changes in ERS time series we investigated several band combinations with and without the texture features. Table 1 shows a comparison of the information content of four different change detection images. The table was created by analysing the interpretation results of three different and independent interpreters.

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Table 1: Comparison of the information content of different Change-detection-procedures. Symbols: (+, ++, +++) = visible to excellent visible; (-) not visible; (o) only visible with additional ground information. Method 1: Speckle-reduced multitemporal RGB image composed from three dates, e.g. 1995-1997 or 8/97, 9/97, 10/97. Method 2: RGB composite: 15 x 15 variance filter composed from three dates, e.g. 1995-1997 or 8/97, 9/97, 10/97. Method 3: RGB image composed from principal components 1/4/6 from all texture features from three dates, e.g. 1995-1997 or 8/97, 9/97, 10/97. Method 4: RGB image composed from the mean-value feature of three speckle reduced images and 15 x 15 texture feature of the first date and 31 x 31 texture feature of the second date.

Class #1 #2 #3 #4

Undisturbed Dipterocarp forest - o - +

Heath forest + ++ ++ +++

Secondary forest - o - ++

Shifting-Cultivation - - ++ ++

Clearcuts o - ++ ++

Selective logging - o ++ ++

Infrastructure + + - +

Change detection - o +++ ++

According to Table 1 method # 1, a simple RGB combination of different orbits, does not provide substantial thematic information (Figure 3A). It was even not possible to identify one certain land use class in this multitemporal RGB-image. Nevertheless, this method is frequently applied when multitemporal SAR images have to be analysed. We believe that the poor performance of this simple approach has led to the prejudice of many users that SAR imagery contains only little thematic information. Image products derived from methods # 2, # 3 and # 4, respectively, have been found much more suitable for interpretation of changes. Here, the appearance of certain image pattern could be assigned to specific land use classes.

In Figure 3B three 15 x 15 texture filtered images from the years 1995-1997 were combined into a multitemporal RGB image (Table 1, Method #2). This image displays change information much better. The colours indicate changes which have occurred at the successive orbits. The changes mostly relate to deforestation based on variations in the moisture content of the soil and vegetation. The deforestation appears in red, green, blue and mixed colours depending on the season when the clear-cuts occurred.

Furthermore we found that using the Principal Component Transform (PCT) of multitemporal ERS images and their texture features delivered significantly better results, than simple multitemporal composites (Table 1, Method #3). The PCT of multitemporal ERS series of gamma map filtered images acquired in August, September and October 1997 showed that most of the unchanged image information is found in PCT 1 while PCT 2 and 3 predominantly contain change information.

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Figure 3C shows a RGB composite of PCT 1-3, Figure 3D shows only PCT 2. The images were acquired during the El Nino drought 1997. Only 2 weeks before the October orbit some rainfall occurred in the project area whereas July to September were absolutely dry. The purple areas in Figure 3C and the dark areas in Figure 3D highlight different backscatter values in the time series. In the August and September images they appear predominantly dark, whereas in October they become bright. A comparison of all processed ERS scenes shows that such strong differences appear only in areas with no or very sparse vegetation. The radar backscatter is low on dry soils and high on moist soils (French et al., 1996). Therefore, the detected changes correlate to deforestation due to land clearing operation or due to forest fires. In Figure 3D newly deforested areas are quite well visible and can be quantified easily. In order to find out if deforestation occurred due to forest fires or by clear-cuts we superimpossed georeferenced NOAA hot spot data (provided by IFFM) with our results. We found a good agreement of NOAA hot spots detected during August and September and the dark areas in Figure 3D in the centre of the image (data not shown, paper in preparation). Thus, it is relativley sure that these areas (approx. 2,100 ha) were devastated by uncontrolled forest fires.

Visual interpretation was performed using the GIS-software ArcView 3.0 by manually delineating areas in the processed ERS-1/2 images belonging to each of the vegetation and land use classes. The resulting GIS of the first image acquired in 1993 was placed over the ERS RGB-image taken in 1994. By comparing the 1994 image and the 1993 GIS and subsequently changing the size and shape of the polygons a new GIS layer was created. The 1994 GIS interpretation was used for the 1995 image interpretation and so on. All changes were recorded through adding, deleting, enlarging or shrinking of polygons resulting in a continuous record of the vegetation and current land use for the years 1993-97.

3. Signature analysis of texture features

In order to investigate the separability of different classes in RGB composites and simply speckle filtered ERS data we performed a signature analysis. Usually 16-bit ERS raw images are transformed to σ°-values (dB). Since we used only 8-bit image data and artificial texture channels the following analysis was carried out using grey values of calibrated, processed ERS images. The ability to separate different vegetation and land use classes was investigated by performing a signature analysis of the three artificial image channels (red: 15 x 15 texture filter, green: gamma map 7 x 7, blue: 31 x 31 texture filter). For each class and year several test areas were chosen interactively for which the average grey value and the standard deviation was calculated. The test areas included about 10.000 – 250.000 pixel.

Figure 4A shows the temporal stability of the backscatter signal for different vegetation and land use classes. Only the grey value intensity of the variance 31 x 31 texture band averaged over all test areas is shown. The graph demonstrates the stability of the texture (i.e. the sensor) over time. Each class shows less than 10-20% variance over a period of 5 years. Figure 4B gives a comparison of the average intensity values (and standard deviation) of the gamma map (green) and the 31 x 31 texture filter (blue). Using the gamma map channel alone for image interpretation it would be impossible to

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discriminate different classes. Even a clear separation between forest/non-forest is not possible. Only the 15 x 15 variance, and, more pronounced, the 31 x 31 variance channel allow a clear discrimination. From the statistics classes clear-cuts/shifting cultivation, selective logging, Dipterocarp and Heath forest can be discerned unequivocally by a T-test. The difference is significant for all class combinations, except clearings/shifting and Heath forest/secondary forest (at 5% probability p=0.001; clear-cuts/shifting cultivation: p=0.03; heath forest/secondary forest: p=0.17). The classes clear-cuts and shifting cultivation can not be discriminated since both classes are based on the total elimination of vegetation. However, both classes can be discerned by image interpreters using the spatial pattern and additional planning information. The same is true for the classes heath forest and secondary forest as well as for old selective logging and selective logging when multitemporal data sets are evaluated.

4. Results

4.1 Visual image interpretation and GIS-evaluation

Different image processing techniques have been investigated in order to obtain 1.) basic land use classification (thematic maps) from monotemporal ERS-1 images and 2.) change detection information from multitemporal ERS-1 image series. Compared to simply speckle filtered radar images (the "traditional" approach) the RGB colour composites based on texture features contained much more information for visual interpretation. In flat terrain, they allow a skilled interpreter to discriminate several land use classes which are important for monitoring of tropical environments. The classification key for the interpretation of the ERS RGB-images was derived from a Russian KFA-1000 photograph and field surveys. The thorough analysis of the ERS RGB images made it possible to discern five different vegetation and land use classes using both backscatter and texture characteristics:

1. Two natural forest formations: Dipterocarp forest and Heath forest show unequivocal differences in species composition, density and tree height visible in the backscattered radar signal. Heath forest is homogeneously dark green in the RGB-image. Dipterocarp forest has a far more heterogeneous canopy structure resulting in a higher variance of the backscatter values (see upper right corner in Figure 2E).

2. Selective logging (which here means over-exploitation) can be readily detected in RGB ERS images. Figure 5A+B shows a comparison of two processed ERS-1 images 13 months apart. The arrow indicates a more or less undisturbed forest canopy in 7/93, which is broken by many magenta-white spots in 9/94 (right arrow). The appearance of these spots is directly correlated to gaps in the forest canopy. Figure 5C+D shows the effects of selective logging on forest canopy structure viewed from a helicopter. The closed canopy of the undisturbed lowland Dipterocarp forest (C) is severely damaged after logging (D). Ground survey showed that selective logging as practised in this area often destroys more than 50% of the canopy (Kuntz and Siegert, 1996). We believe that remaining tree trunks act as corner reflectors since the magenta-white spots correspond to high backscatter values in all filtered images. Furthermore, the radar signal may be significantly influenced by the water content of the vegetation. Fast growing Macaranga tree

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species fill the gaps shortly after the logging operation. The leaves of these species are much larger and have a higher water content than the leaves of the surrounding climax tree species leading to higher backscatter signals. The field survey showed that selective logging activities can be detected even 5 years after logging. After 5-7 years the gaps are filled only halfway to the top and Macaranga species still dominate the regrowing vegetation. The multitemporal analysis showed, that these properties are not correlated to short term differences in water content, since the signatures remain clearly distinguishable over time.

3. Secondary forest has a smooth canopy and appears homogeneously green.

4. Clearings for new logging-plantations appear in bright colours, due to high backscatter by corner reflection on tree trunks and wet soil.

5. Cultivated areas are characterised by small rice fields (bare soil in September and October) and regenerating vegetation at different stages. In RGB-images these areas appear as an heterogeneous pattern of bright (white, new clearings) and green (new vegetation) colours.

6. Settlements always have maximal backscatter values in all channels (plain white) due to corner reflection on corrugated iron roofs. Dirt roads can only be detected if they are more than 20m wide and not covered by tree canopies.

Figure 6 shows the results of the land use mapping for the years 1993-97. The geo-referenced GIS interpretations were superimposed on the gamma-map filtered ERS image. Mountainous regions unsuitable for image interpretation were masked out. Light green and ochre represent selectively logged forest, dark green yet undisturbed Dipterocarp forests. Bright green mark secondary forests. Clearings for plantations appear in red, clearings for shifting cultivation in purple. The complex, dispersed pattern of rice-fields and regrowing forest vegetation is coloured light yellow. The implementation of this class was necessary because the spatial pattern was to small for the ERS-image resolution and the scale of mapping (1:100.000). A comparison of the GIS layers of 1993-1997 shows that the most dynamic changes in land use occurred next to the river. The large logging concessions (light green and ochre) to the left and right of the Mahakam remain unchanged. The development and expansion of the timber plantation (red) can be monitored in detail over the years. Table 2 shows the computed areas for each vegetation and land use class.

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Table 2: Computed areas of different years for vegetation and land use classes

Class Description Area in ha

1993 1994 1995 1996 1997

Mountainous area (constant) 171,698

Dipterocarp forest 2,503 1,485 1,065 1,065 1,028

Heath forest 7,596 7,605 7,604 7,604 7,605

Secondary forest 11,077 9,157 7,646 6,722 6,579

Old secondary forest 21,324 21,324 21,293 21,263 21,321

Selectively logged forest 187,452 178,247 177,526 172,908 172,016

Secondary forest/Shifting cultivation 26,777 26,406 28,853 29,597 29,472

Shifting cultivation 2,282 5,703 5,409 6,062 4,675

Clearings 2,450 6,290 7,852 12,095 12,365

0.1 4.2 Multitemporal analysisFigure 7 shows the land use changes from one year to the next. The changes were calculated by combining the GIS-layers of successive years in order to create new GIS-layers highlighting the changes only. From many possible combinations 5 change classes were selected and assigned a specific colour: 1.) regrowth of secondary forest: light green; 2.) conversion of Dipterocarp forest into selectively logged forest: light blue; 3.) conversion of selectively logged forest into clearings for plantation: red; 4.) conversion of selectively logged forest into shifting cultivation: purple; 5.) conversion of shifting cultivation/secondary forest into new shifting cultivation: light yellow. The change maps in Figure 7 show that human activities are focused on two areas: most changes happened close to the Mahakam river and are related to agricultural activities of local Dayak people. Another area is the timber plantation to the right of the Mahakam river, that expands from year to year (red). Furthermore, it can be observed that local peasants slash and burn new rice fields within logging concessions. Table 3 presents the changes in area for each class from 1993-1997.

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Table 3: Annual changes in land use from 1993 to 1997.

Land use Change Sum Area ( in ha)

1993 - 1994 1994 - 1995 1995 - 1996 1996 - 1997

Selectively logged to clear-cut 3,840 1,563 4,243 370

Selectively logged to shifting cultivation

3,535 992 762 212

Regrowth of secondary forest 1,198 1,503 1,847 1,437

Conversion of shifting cultivation/secondary forest into shifting cultivation

5,563 4,311 2,703 2,721

Conversion of Dipterocarp into selectively logged forest

1,008 420

Selective logging of yet unexploited Dipterocarp forest was observed only until 1995. By then there was not much Dipterocarp forest left for logging. Of the total forest cover (229.952 ha) only 1,1% Dipterocarp forest (2503 ha) was left undisturbed, 98,9% were more or less degraded by selective logging or shifting cultivation. The most prominent changes had occurred within the plantation. The plantation had a size of 2450 ha in 1993. In the following years 3840 ha (1994), 1563 ha (1995) and 4243 ha (1996) of former selectively logged forest were cleared. In 1997 only minor clearing activity (370 ha) was observed. The size of formerly selectively logged forest was reduced by 15.468 ha from 187.484 ha. 10.016 ha of formerly selectively logged forest was cleared for the plantation, 5501 ha were cleared for agricultural use. This result is of considerable interest for concession holders, but also for the ministry of forestry. Local people which were formerly subsistence farmers had begun to cultivate significant areas for surplus rice production to sell the produce. This trend was confirmed by the observed reduction of secondary forest by almost 50% during the investigation period. The change detection maps show that the regrowth of forest can be observed in ERS images, however, the net loss was much higher (about 41%). The observation that the area of secondary forest decreased emphasizes the fact that more land was clear-cutted than needed for subsistence.

In 1993 11% of the whole test area had been used for agriculture. 13% were calculated in 1997, meaning that agriculture occurred mostly on formerly cultivated areas. Exceptional large clearings for agricultural use occurred next to the to TRANSMIGRASI-settlements SPI and SPII near the southern border of the plantation. The settlers who usually work in the plantation had to clear new fields every year, because an area of 1-3 ha allocated for them by the local government is not enough to sustain their needs. The average size of polygons classified as shifting cultivation was 30-60 ha. These areas were 10 times larger than usual field sizes of 1.5 ha in subsistence economies. Here lies the limits of the resolution of ERS-RGB-images.

5. Discussion

Having analysed all together 13 ERS-1/2 scenes acquired over a period of 5 years we are convinced that even from a single ERS-1/2 scene important information about land use and deforestation can be derived. A drawback is that only more or less flat areas with relief differences of less then 50 meters can be used for image interpretation. Of special interest was if different vegetation and land use classes can be discriminated unambiguously due to their backscatter characteristics after the implementation of texture filters. The SAR backscatter is influenced to a large extent by the structure of the earth surface, the 3-dimensional structure of the vegetation and the moisture content of the vegetation and the soil. Most of the test area is covered by forest vegetation. Bare soil is only found temporarily after clear-cut activities for plantations and agriculture. We assume that the vertical stratification and the varying surface roughness of the forest canopies are the most significant factors determining average backscatter intensities and thus textures. Undisturbed Dipterocarp-forest has a very heterogeneous canopy with single trees towering as high as 70m. Heath forest grows much lower (20-30m) and has a homogeneous canopy. This results in much higher backscatter values for Dipterocarp forest than for Heath forest. The signature analysis confirms this hypothesis: Dipterocarp forest had average backscatter grey values of 44.1±7.1 while Heath forest had only 19.4±7.5. Furthermore the moisture of soil and vegetation has an important influence on the backscatter intensity. In both, Dipterocarp and

Heath forest the C-Band radar signal cannot reach the ground. Heath forest grows specifically on very infertile, siliceous, highly acidic soils that dry out quickly after a rainfall. The tree species in heath forests are especially adapted to these conditions and form small, xeromorphic leaves whose water content is much lower than that of tree species in Dipterocarp forests (Withmore, 1984). Therefore, mainly the water content of the canopies influences the backscatter.

Secondary forest describes a vegetation type that grows after rice cultivation. Beside young trees of similar age it includes different vegetation forms from grass to shrub land. The surface relevant to backscatter is usually homogeneous and, in case of grass and reeds, it is also influenced by backscatter from the soil. This results in low backscatter values (24.4± 10.1) not much different from heath forest. Selectively logged forest is characterised by a very rough, inhomogeneous opened up canopy with corner-reflection occurring on tree stems and thick branches. Here, the radar signal can reach the ground quite often. The backscatter within this class is highly variable having an average intensity of 68.1± 10.4. On clearings the backscatter is influenced mainly by the soil and dead vegetation. After clear-cutting huge amounts of dead biomass remain in place to dry out during the dry season, to be burnt afterwards. Clear-cuts detected in September and October every year had always very high backscatter values (135.7±28.9) distinguishing this class from all others except shifting cultivation. These high values can be attributed to high backscatter from wet clay soil and corner reflection on cutted trunks.

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5.1 Multitemporal stability of the signaturesMost of the ERS scenes for the multitemporal time series were acquired at the end of the dry season (September and October) to guarantee that the signatures were comparable and not influenced by varying weather conditions. As was shown in Figure 3 the variation of the backscatter over time was smaller than the absolute backscatter values of the different vegetation and land use classes. Additionally, the backscatter values changed in a similar manner, i.e. if backscatter values were low in one year they were low in all classes. In conclusion, we can state that four vegetation and land use classes can be discriminated unequivocally due to there backscatter values and their spatial texture pattern (Dipterocarp forest, Heath forest, clear-cuts and selective logging). Two further classes could be identified with the help of additional filed information and/or with a multitemporal approach. For example, clear-cuts for plantations were much larger and often rectangular shaped than clearings for agriculture (with small, irregular shapes). Selective logging could be detected by comparison with official planning information available from forest authorities. Heath forest could be discriminated from secondary forest in a multitemporal data set since no agriculture activities (changes) would take place on the infertile soils of Heath forests.5.2 Changes in land use from 1993-1997

The multitemporal analysis of land use changes showed that economic activity affects significant areas. The following losses in rain forest were calculated: 10.016 ha were cleared for the timber plantation, about 5501 ha were cleared for agriculture in formerly selectively logged areas, about 4450 ha in secondary forest and about 2100 ha were destroyed through forest fires. That amounts to about 10% of the total mapped forest area or 2% deforestation annually. This rate is almost twice as high as the data published by the FAO for all of Indonesia (1.1%). It has to be considered that the test area is far away from the more densely populated coastal areas. Close to the coast deforestation is proceeding much faster. There is no virgin upland Dipterocarp forest left in the region. 13% of the mapped area is being used agriculturally. Each year about 1000 ha of formerly selectively logged forest was converted into new rice fields.

5.3 Evaluation of the visual interpretation and estimation of the error rate A disadvantage of this study relates to the fact that it relies on visual interpretation of digitally enhanced ERS images. This required intensive experience on SAR data properties and their backscatter characteristics as well as a profound knowledge about the ecology and the socio-economic situation in the test area. Misinterpretations that are likely using single ERS images could be reduced significantly by multitemporal analysis. By comparing different acquisition dates it was possible to eliminate interpretation errors of relief structures as clearings. For example a confusion of clearings and relief structures can occur if geological structures, like undulating hills, are oriented towards the sensor. If the same area is surveyed a year later the relief structure will remain the same but the clearings will be covered by new vegetation and will change their signature. The multitemporal analysis will also minimise mis-interpretations due to changing water contents of vegetation and soil. Here, for this purpose it is useful to compare images from the dry and the wet season (Convay, 1996; Schmidt et al., 1997, French et al., 1996).

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Adding all sources of error we estimate that the interpretation error is somewhere between 10-20% of the area. This was verified by field surveys. Despite this rather high value the visual interpretation of ERS RGB-images allows a continuous monitoring and an in depth analysis of the causes of deforestation in large areas of rain forests which is not possible with optical data. Using this technique Indonesian planning authorities can gather information about the extent and rate of economic development activities in inaccessible rainforest areas. Our study shows that the ERS system delivers data on a regular and reliable basis which allow for a good estimate of the relative percentages of deforestation through settlers, farmers and logging companies. If planning data (concession sites, development plans) are additionally available the ERS interpretations become even more meaningful: excessive exploitation of logging forests or logging activities in plantations can be monitored with high spatial and temporal resolution.

So far we have not been able to automate the classification process for ERS images. No standard classification algorithm is currently capable to produce reliable results which are, to some extent, comparable to a visual interpretation. Thus, this problem was out of scheme of the current project. However, all investigated classes are not only characterised by their backscatter (gray values) but also by a specific spatial texture pattern. For an automatic classification scheme this texture information (which is obvious to the human eye) is equally important as the average grey value. We are currently investigating this problem testing new classification algorithms based on highly innovative image segmentation and fractal-hirarchical fuzzy rules developed by the German company delphi Systemsimulation. Concerning the analysis of multitemporal data the principal component transform of ERS texture features seems to be a first step towards a semi-automatic classification scheme to discriminate forest/non forest.

6. AcknowledgementsWe wish to thank the German Space Agency DARA (now DLR) for financial support of the ESA ERS-1 pilot project “Tropical Rain Forest and Use of Land Investigation“ (TRULI, PP2-D11; DARA Project 50 EE 93125) and the GTZ, Germany together with BPN/LUPAM, Indonesia for their assistance during the field verification.

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7. ReferencesBloom, R.G. and Daily, M. (1982): Radar image processing for rock-type discrimination. IEEE

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Convay, J. (1997): Evaluating ERS-1 SAR data for the discrimination of tropical forest from other vegetation types in Papua New Guinea. Int. Journ. Rem. Sens., 1997, Vol. 18, No. 14, S. 2967-2984

ERDAS Field Guide (1997): Fourth Edition, ERDAS Inc., Atlanta, CA.

FAO, (1997). State of the worlds forests. Words and Publications, Oxford, UK

French N. H., Kasischke E.S., Bourgeau-Chavez L.L. and P.A. Harrel (1996). Sensitivity of ERS-1 SAR to variation in soil water in fire disturbed boreal forest ecosystems. Int. J. Remote Sensing Vol. 17, No.16, 3037-3053

Goldammer J.G. and Crutzen P.J. (1993). The ecological, atmospheric and climatic importance of vegetation fires. Dahlem Conference ES 13, john Wiley and Sons, Chichester.

Kattenborn, G., Nezry, E., De Grandi, G. and Sieber, A.J. (1993): High resolution Detection and Monitoring of changes using ERS-1 time series, Proc., 2nd ERS-1 Symp., Ham-burg, Germany, Oct., 11-14 1993, , pp. 635-642.

Keil, M., Scales, D. and Winter, R. (1994): Tropical Rain forest Investigation in Brazil Using Multitemporal ERS-1 SAR Data, Paper pres. at the First ERS1-Pilot Project Workshop 22.-24. June 1994, Toledo, Spain.

Kalensky, Z.D., Reichert, P.G. and Singh, K.D. (1991): Forest mapping and monitoring in developing countries based on remote sensing. In: Oesten, G., Kuntz, S. and Gross, C.P. (Hrsg.; 1991): Fernerkundung in der Forstwirtschaft - Stand und Entwicklungen. Wichmann Verlag, Karlsruhe; pp 230 - 251.

Kuntz, S. and Siegert, F. (1994): Evaluation of ERS-1 SAR data for Tropical rain forest Monitoring, Earth Observation Quarterly, No. 45, Sept. 1994, ESA.

Kuntz, S. and Siegert, F. (1996): Dipterocarp Forest Mapping and Monitoring by Satellite Data: A case study from East Kalimantan; invited contribution to: Dipterocarp Forest Ecosystems - Towards Sustainable Management. World Scientific Publishing Co., New York, London, Singapore, Hong Kong, Taipei, pp. 206 - 227.

Kuntz, S., C. Streck and Siegert, F. (1996). Multitemporal evaluation of ERS-SAR data for monitoring deforestation in tropical rain forests. 2. ERS-1 User Workshop, London, Dec. 6 – 8. ESA SP-383.

Leysen, M., Conway. J.A. and Sieber, A. (1993): Evaluating Multi-temporal ERS-1 data for Tropical Forest Mapping: Regional Mapping and Change Detection Applications. In: Proc., 2nd ERS-1 Symp., 11-14 Oct. 1993, Hamburg.

Malingreau, J.-P., Achard, F., Aschbacher, J., Conway, J., De Grandi, F. and Leysen, M. (1994): TREES ERS1 Study 1994: Assessment of the Usefulness and Relevance of ERS1 for TREES. Paper pres. at the First ERS1-Pilot Project Workshop, Toledo, Spain, June 22 - 24, 1994.

Malngreau J.P. and G. Duchossois (1995). The TREES/ERS1 SAR project. Earth Observation Quaterly No. 48, June 1995

Myers N. (1989). Deforestation rates in tropical forests and their climatic implications. Friends of the Earth, London.

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Nezry, E, Mougin, E., Lopes, A., Gastellu-Etchegorry, J.P. and Laumonier, Y. (1993): Tropical vegetation mapping with combined visible and SAR spaceborne data. Int. J. Remote Sensing, Vol. 14, No. 11, 2165 - 2184.

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Schmidt, M., Keil, M, Scales, D.R., Dos Santos, J.R. and Kux, H. (1997): Investigation of deforestation dynamics and land use changes by ERS-1 SAR data in Rondonia, Brazil. Proc. Int. Seminar on „The use and applications of ERS in Latin America. Vina del Mar, Chile, 25-29 Nov., 1996; ESA SP-405, March 1997, S. 45-52,

Siegert F., Kuntz, S. Claudia Streck, and Wolfgang Bergbauer (1995). Land Use Planning and Monitoring in Indonesia using ERS-1 RADAR (SAR) Data. In: Proc. Int. Conf. on Remote Sensing and GIS for Environmental Resources Management - The Indonesian- European Experience, Jakarta, Indonesia, June 6-8, 1995.

Ulaby, F.T. and Dobson, M.C. (1989): Handbook of radar scattering statistics for terrain, Artech House, Inc. Norwood, MA.

Whitmore T.C., (1984). Tropical rain forests of the far east. Clarendon Press, Oxford

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Figures

T R U L I I m a g e P r o c e s s i n g F l o w c h a r t

R a d i o m e t r i c C o r r e c t i o nC h a p t . 3 . 1 . 1

G e o m e t r i c C o r r e c t i o nC h a p t . 3 . 1 . 2

S p e c k l e F i l t e r i n gC h a p t . 3 . 2 . 1

T e x t u r e F i l t e r i n gC h a p t . 3 . 2 . 2

S i g n a t u r e A n a l y s i sC h a p t . 3 . 4

C o m b . o f M u l t i t e m p . S p e c k l eF i l t e r e d I m a g e . C h a p t . 3 . 5 . 1

P r i n c i p a l C o m p o n e n tC h a p t . 3 . 5 . 2

C h a n g e M a t r i x A n a l y s i sC h a p t . 3 . 5 . 3

M a p P r o d u c t i o nC h a p t . 3 . 6

M u l t i t e m p o r a l A n a l y s i sC h a p t . 3 . 5

M u l t i t e m p o r a l A n a l y s i sC h a p t . 3 . 5

D e l i n e a t i o n P r o c e s sC h a p t . 3 . 3

F i l t e r i n gC h a p t . 3 . 1

P r e p r o c e s s i n gC h a p t . 3 . 1

E R S 1 - S A R . G E C - I m a g e s

Figure 1: Flow chart of image processing procedures.

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Figure 2: Comparison of the unprocessed ERS image (A), the speckle reduced image (B), the 15 x 15 texture filter (C), the 31 x 31 texture filter (D), and the RGB composite of C - Red, B - Green, D - Blue (E). The size of the area displayed is approx. 60 km x 80 km.

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Figure 3: Examples for multitemporal images products from ERS-2, acquired in 1995, 1996 and 1997 A. Multitemporal combination of 3 gamma map filtered images. B. Multitemporal combination of three 15 x 15 variance texture filtered ERS-2 images. Multitemporal analysis. C. RGB image of the principal component transformations (PCT) 1,2,3 from three ERS-2 images acquired in August, September and October 1997. D. PCT 2 from image C. The size of the area displayed is approx. 60 km x 80 km.

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Figure 4. A. Multitemporal stability of the signatures of different vegetation and land use classes. B. Comparison the average grey values and standard deviations for different land use and vegetation classes of the gamma map (green columns) and 31 x 31 texture filters (blue columns).

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Time series of texture filtered blue channel

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Figure 5: Processed ERS images to detect logging activities: A. and B. show two ERS-1 images one year apart. The arrow indicates newly selectively logged Dipterocarp forest. The small circle shows a clear-cut due to surplus rice production. The large circle shows a large clear cut for forest plantations. C. and D. present aerial views from a helicopter of the forest canopy of undisturbed virgin Dipterocarp forest C. in comparison with selectively logged forest D. The visible area in images A and B comprises approx. 20 km x 35 km

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Figure 6: Results of the ERS RGB-image interpretation for the years 1993-1997. Each vegetation and land use class is coloured differently. The GIS interpretations are placed on top of the gamma-map filtered ERS image. The size of the area displayed is approx. 60 km x 80 km.

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Figure 7: Change detection maps of the years 1993-1994, 1994-1995, 1995-1996 and 1996-1997. The colours show specific changes in land use and vegetation. Regrowth of secondary forest: light green. New selectively logged former Dipterocarp forest: light blue. Selectively logged forest clearings for plantations: red. Transformation of selectively logged forest to shifting cultivation: purple. Change from rice fields and regrowing vegetation to new shifting cultivation: light yellow. Dark purple indicates areas which burnt during the 1997 El Nino drought. The size of the area displayed is approx. 60 km x 80 km.

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