Land Cover Change and Fire Damage Monitoring using ERS-1/2 SAR multi-temporal data sets in East-Kalimantan, Indonesia 1

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1 Land Cover Change and Fire Damage Monitoring using ERS-1/2 SAR multi-temporal data sets in East-Kalimantan, Indonesia 1 Ruandha Agung Sugardiman Department of Environmental Sciences Sub-department of Water Resources Wageningen University Tel: , ; Fax: ruandha.sugardiman@users.whh.wau.nl Keyword: Fire damage monitoring, Radar, ERS-1/2, NOAA-AVHRR, segmentation, maximum likelihood classification, image ratioing Abstract A large number of ERS-1/2 SAR scenes of the East-Kalimantan test site of the October November 1996 period have been studied in support of the INDonesian Radar Experiment (INDREX-96), with the objective to study its potential for land cover change monitoring. Subsequently, an additional series of ERS-2 SAR scenes was acquired in support of studies to assess fire damage caused by a severe El Niño event, occurring at the same test site in the period June 1997-April The result shows that fire affected areas can be delineated well, but that it is sometimes hard to estimate the intensity of the fire damage accurately. Combining ERS-2 SAR observations during the fire period with land cover class information obtained by ERS-1/2 SAR in the pre-fire period and observations of hotspots by NOAA-AVHRR, together with knowledge on the types of fire occurring in this area, can be shown to yield very reliable results. Introduction There is a strong need for fast detection of fires and assessment of the extent of burned areas. This information is very valuable to enable proper action to be taken. This study relates to land cover change and fire damage monitoring with main emphasis on early detection. The terrain is very hilly and typical for the rugged topography encountered in most Indonesian forest areas. The modulating effects of slope angle and slope aspect on the backscatter intensity complicate processing of data of hilly terrain. New multi-temporal segmentation techniques (Oliver and Quegan, 1998) in combination with backscatter change classification techniques have been applied to deal with this problem. Multi-temporal Maximum Likelihood classification with ML_class algorithm by Dirk H. Hoekman and Martin Vissers with two independent databases for training and evaluation have been executed to obtain the best combination in order to get a good result. Test site The test site is located in East Kalimantan Indonesia in the neighbourhood of the Tropenbos research station Wanariset- Samboja, approximately between Longitude 116 o 40 to 117 o 00 East, Latitude 0 o 50 to 1 o 15 South, and lays just under the equator in the southern hemisphere, see figure 1. The test site covers about 18 by 20 km. The central part of the test area is covered with secondary forest, rubber plantation and forest with enrichment planting. Within these, a small part of secondary forest still under good protection, which is the Bukit Bangkirai preservation area, part of it has been saved from the fires. In the north transmigration area Semoi and Sepaku are located, surrounded with agriculture (wet and dry rice fields, also mixed farming occur). In the western part of the area the Bay of Balikpapan can be found, with is accompanying mangrove and nipah (palm mangrove) forest. The southern part is covered with secondary and swamp forest, which is part of Sungai 1 Proc. INDREX Final results workshop, ESTEC 9 Nov & Jakarta 30 Nov. 1999: ESA SP-489, Nov. 2000, p (revised)

2 Wain protected area. In the south-east corner close to the road Balikpapan-Samarinda, surrounded by small villages which has mixed farming gardens ( kebun/ladang ). Terrain of the test area is undulated to very hilly and typical for the rugged topography encountered in most Indonesian forest areas. There are almost no flat areas except for the mangrove part in the west. This condition makes classification with ERS-SAR radar images quite difficult because of influence of relief on radar backscatter. Table 1. ERS-SAR PRI image data sets No Satellite Date Remarks 1 ERS-1 October 22, 1993 Pre-fire 2 ERS-1 November 26, ERS-1 September 10, ERS-2 April 07, ERS-2 May 13, ERS-2 July 22, ERS-2 September 30, ERS-2 November 04, ERS-2 February 17, ERS-2 June 02, 1997 Severe fire 11 ERS-2 April 13, 1998 Severe fire 12 ERS-2 May 18, 1998 Post fire ERS coverage Figure 1. The test area with the main landcover types (In RGB multitemporal color composite: ) Data The spaceborne SAR (Synthetic Aperture Radar) C-band VV data, acquired from ESA s ERS-1 and ERS-2 satellite were used for this study. Twelve data ERS-SAR PRI (Precision Image) were calibrated and cropped to the size of the test area and segmentated, see table 1. During the process, the image of April 7, 1996 has been rejected, due to calibration problem. Hence, remaining 11 ERS-SAR segmentation images were used for this study. The image no. 1 to 9 was happen and grouped in the before/pre-fire period. Multi-temporal composite of those images expose in some intensity of grey, the amount of backscatter did not change much (that means there are no intense land cover or soil moisture changes). The image of April 13, 1998 is dated just before the end of extensive fire period (rainfall is under the average = 160 mm), this is showing the most damage to vegetation, and therefore radar backscatter in this image shows the lowest mean. The last image, May 18, 1998 has been taken after the rains have fallen

3 (rainfall is above the average) and almost all fires were out. Unlike the image before, in this image the soil was wet again, it is increasing the soil moisture and probably regrowth had already started for some weeks. Rainfall (mm) Figure 2. Rainfall at the study area and the acquisition date of ERS-1/ Months 6 Approach The following procedure/approach has been performed in this study: 1. Multi-temporal segmentation x 20 km 2 intensive fieldwork area (mostly hilly) - Helicopter flight - GPS measurement (accuracy meters) for 50 sample plots - GPS tracking for the roads and river (mangrove areas) 3. Inspection using NOAA-AVHRR derived from NOAA-14 imagery from January to December Multi-temporal Maximum Likelihood classification with ML_class algorithm by Dirk H. Hoekman and Martin Vissers using two independent databases for training and evaluation. 5. Image ratioing of the ERS Segmentation data sets to avoid effect of relief and to define threshold for fire risk map. 6. Regrouping the training areas, which have high confusion of temporal signature to improve the accuracy. Results Input is database containing labelled multichannel data samples to produce a fire risk map with the procedure is as follows: Rainfall 1996 Rainfall 1998 ERS ERS ERS ERS ERS : image no. Table 2. Two independent databases for training and evaluation Database Ruandha Database Vincent Prefire Landcover Code Landcover Code Code Mangrove, including nipah 10 Mangrove 6 1 Nipah 7 2 Forest, unburnt 4 Forest, unburnt 4 3 Forest, burnt 4 Forest, burnt 5 3 Forest, severely burnt 5 3 Forest Enrich Forest Enrich plant, 2 plant, burnt burnt 4 4 Forest Enrich plant, severely burnt 4 4 Rubber Rubber plantation, 4 plantation burnt 5 5 Rubber plantation, severely burnt 4 5 Kebun 4 Kebun 4 6 Agriculture 4 7 Water Water 8 Figure 3. Averaged mean temporal signature of ERS-Segmentation backscatter (db) /10/22 93/10/22 93/11/26 Averaged mean class signatures 95/09/10 96/05/13 96/07/22 Averaged mean class signatures image date without water 93/11/26 95/09/10 96/05/13 96/07/22 96/09/30 96/09/30 image date 96/11/04 96/11/04 97/02/17 97/02/17 97/06/02 97/06/02 98/04/13 98/04/13 98/05/18 98/05/18 mangrove nipa forest f.e.p. rubber kebun water

4 Since radar signature mostly influenced by moisture or water and results showed that water signature was much different from other classes then water, mangrove and nipah could be masked (figure 2). As a note most mangrove and nipah forests influenced by tidal. Post-fire Figure 4. Creating the mask Water Nipah Mangrove Swamp forest To improve the accuracy of classification it could be sufficient by regrouping training area (region of interest), which have high confusion of temporal signature. Figure 5. Classification results after regrouping the classes Pre-fire Forest Burnt forest Burnt plantation Burnt kebun/ agriculture The accuracy can be summarised and displayed in the following boxes: Mask: Water 100% Mangrove 100% Nipah 100% Swamp unknown Pre-fire: Forest 89.4% Plantation 88.5% Kebun/Agriculture 34.5% Post-fire: Non-burnt Forest 98.8% Burnt Forest 85.2% Burnt Plantation 98.4% Burnt Kebun/Agriculture 28.5% Forest Plantation Kebun/ agriculture

5 Image ratioing of the ERS Segmentation data sets to avoid effect of relief and to define threshold for fire risk map. Using band 3 (table 3) of the ratio images (d4-d3) with a cut-off set on and the pre-fire classification result is <-1.62: Low-risk dark green, >-1.62: High-risk brown. Figure 7. Fire risk map and fire damage evaluation Forest fire risk 1995 Table 3. Image ratio data sets No. Image Ratio Image date 1 d2-d d3-d d4-d d5-d d6-d d7-d d8-d d9-d d10-d d11-d Figure 6. Image ratio signature for forest Forest ratio signatures High risk Low risk backscatter ratio (db) Fire damage evaluation d2-d1 d3-d2 d4-d3 d5-d4 d6-d5 d7-d6 image date d8-d7 d9-d8 d10-d9 d11-d10 Forest ratio signatures (zooming on d4-d3) 2.00 forest_burnt-1 forest_burnt forest_burnt forest_burnt-4 forest_burnt forest_sev_burnt forest_sev_burnt-2 forest_sev_burnt forest_sev_burnt-4 forest_sev_burnt forest_unburnt forest_unburnt-2 forest_unburnt forest_unburnt-4 mean_forest d3-d2 d4-d3 d5-d4 High risk - Burnt Low risk - Burnt High risk - Not Burnt Low risk - Not Burnt

6 Conclusion Multi-temporal segmentation to support automating classification & detection is the key for a higher accuracy of forest fire hazard assessment. In this case the databases for training and validation should be verified or taken from a field-check activity. Temporal signatures and ML_class results showed the best combination. Pre-fire assessment found to be difficult as indicated by low accuracy. Masks of water, mangrove and nipah are important to avoid any confusion of classification due to radar sensitivity of water and moisture. However, other land classes forest, forest enrichment planting, and rubber plantation, dry-land agriculture (kebun), agriculture could to be differentiated. Surface fires were occurred but they were not easily detected because the ERS C-band was not able to penetrate deeper into the understory. Combination of ERS interferometric-coherence, backscatter intensity and NOAA-AVHRR data could be used to support the analysis. It seems well possible to: Assess fire damage, Verify extent plantations, Detect illegal logging. Still unclear are: Accuracy of land covers classes mapping before fire, Consequently, the risk of fire could be not predicted in an accurate way. C-band could not be the most suitable (see case Guaviare), therefore time series and welltrained personnel needed to implement this analysis. their assistance, i.e. materials and financial supports. References Congalton, R. G. and K. Green, 1999, Assessing the accuracy of remote sensed data: principles and practices. Boca Raton [etc.]: Lewis Henderson, F.M. and A.J. Lewis (eds.), 1998, Principles & Applications of Imaging Radar, Manual of Remote Sensing, 3-rd Ed. Vol.2, John Wiley & Sons. Hoekman, D.H. and M. Vissers, 1998, Manual Likelihood classification Manual, Wageningen University, Dept. of Water Resources Oliver, C and S. Quegan, 1998, Understanding Synthetic Aperture Radar Images, Artech House. Sugardiman, R.A., 2000, Fire damage assessment using remote sensing (A case study using Radar and NOAA~AVHRR data in East Kalimantan). MSc Thesis. Wageningen University, Wageningen, The Netherlands, 86 p. Van der Sanden, J.J. and D.H. Hoekman, 1999, Potential of airborne radar to support the assessment of land cover in a tropical rain forest environment, Remote Sensing of Environment, Vol.68, pp Wooding, M.G, A.D. Zmuda, D.H. Hoekman, J.J. de Jong and E.P.W Attema, 1999, The Indonesian Radar Experiment (INDREX- 96). ESA Earth Observation Quarterly (EOQ) No. 61, February ESA- ESRIN ras. Acknowledgements Special thanks are given to colleagues, i.e. Dirk Hoekman, Martin Vissers, Vincent Schut, Chris Varekamp, Chris Oliver, Muljanto Nugroho, Kemal Unggul Prakoso, and Bambang Suryokusumo for their valuable comments and materials. We would like to extent our gratitude to the Ministry of Forestry and Estate Crops, Tropenbos, and NUFFIC for