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1 STUDY ON VEGETATION COVER CHANGES IN THE PROVINCE OF SOUTH KALIMANTAN USING RGB-NDVI UNSUPERVISED CLASSIFICATION METHOD Dewi Kania Sari* Edhis Triyono Hermawan Gun Gun Gunawan Hudman Department of Geodetic Engineering Institute of Technology Nasional (Itenas) Bandung Indonesia * ABSTRACT Vegetation is one of the largest supporting structures in biosphere ecological unit. Satellite images are very useful in monitoring vegetation cover changes at regional and global scales. This study employed multitemporal SPOT Vegetation images acquired in 2000, 2002, and 2004 for analyzing recent vegetation cover changes over the Province of South Kalimantan using RGB-NDVI unsupervised classification method. This visual RGB-NDVI method involved creation of color composite images and utilization of additive color theory, where each NDVI from three dates is combined with the red, green, and blue color write functions of the computer monitor. The procedure classified events of change and no-change in the study area in all periods into nine classes of events, with 80% agreement with 810 reference sample points. Research results showed that locations of vegetation index change in South Kalimantan area in small part located around the boundary area of the province, i.e. in western and northern area, whereas the larger part scattered from southern to eastern areas and in almost all of its coastal zone, and spread around urban areas. In Laut Island and Sebuku Island, locations of vegetation index changes scattered over the island areas. The mean value of vegetation index change areas tend to decrease with reduction rate of 0.32% of its land region. Key words: Change Detection, Vegetation Cover, RGB-NDVI Method, SPOT Vegetation, South Kalimantan INTRODUCTION Vegetation cover is the condition of land which surfaces are covered by vegetation. The interaction between man and its environment can lead to an accelerating rate of land cover changes, which in the end, would jeopardize the balance of natural ecosystem. Tropical rainforest, including forests, wetlands, mangrove, and savanna are part of vegetation that hold the structure of earth s natural ecosystem. (Kehati, 2003). Most of Indonesian vegetation, especially forest, can be found at Kalimantan, whose land is 75% covered by it (GWF, 2003). The vegetation cover in the Province of South Kalimantan were not too impressive compared to other province in Kalimantan, but it still hold hectare of forest (Badan Planologi Kehutanan, 2003). Excessive exploitation had caused an uncontrollable deforestation, and resulting in an ever decreasing forest area (WALHI, 2004). This forest need to be monitored continuously, in order to detect the pattern of change and phenomena. At present, many techniques were employed to monitor vegetation cover change, and using remote sensing data images is one of it. Satellite images with multi-temporal and multi-spectral characteristics enabled us to monitor changes accurately. One method to detect the change in vegetation cover is RGB-NDVI, as research from Hayes and Sader (2001) concluded that this method yield a better accuracy compared to NDVI Image Differencing and Principal Components Analysis, and recommended it for its interpretation capability. RGB-NDVI was developed to detect changes based on visualization using three NDVI dates, combined in the color of red (R), green (G), and blue (B), and color additive theory (Sader & Winne, 1992 in Hayes & Sader, 2001), and then the three NDVI dates were classified using unsupervised classification analysis. Class of change and no-change events nomenclature were derived from analysing cluster s statistics, and by following the visual interpretation of RGB-NDVI color composite. Main changes between dates of NDVI will appear in combination of main color red, green, and blue (RGB), or Cyan, Magenta, Yellow (CMY) (Sader & Wilson, 2002). Map Asia Conference

2 This research analyzed the vegetation cover changes in the Province of South Kalimantan in the year of 2000, 2002, and 2004, using SPOT Vegetation image data. RGB-NDVI method was employed to detect the changes. Study Area & Data The study area covered all of the Province of South Kalimantan, which lies between longitudes 114 o 19'13" and 116 o 33'28" E, and latitudes 1 o 21'38" and 4 o 10'14" S, and has approximately 3.7 million hectare area. South Kalimantan is mainly consisted of tropical rainforests, with highest rainfall of mm in December. Maximum temperature varied between 33,1 o C and 35,0 o C, with minimum temperature between 22,6 o C and 23,8 o C. Maximum air pressure varied between 1.012,2 mm ,5 mm, and its minimum between 1.007,4 mm ,5 mm. ( heading.jsp). Data used in this research: - 10-day composite (S-10) NDVI SPOT Vegetation images for Australasia area, covering Australia and part of Southeast Asia, maximum NDVI composite dated May, 11-20, 2000, 2002, and 2004, from VITO, pixel brightness value between Map of administrative boundary of the Province of South Kalimantan, with the scale of 1 : , dated in the year of 1998, geodetic projection system and WGS 1984 datum issued by Bakosurtanal. METHODS Figure 1 Study Area Reference Point Registration Reference point registration was conducted following the steps of point registration in the software, which was placed at the upper left of each image. Point information was obtained from the file 0001_LOG.txt in NDVI SPOT Vegetation image package. This downloaded file has geodetic projection system and WGS 1984 datum with pixel size of degree. Then, image resulted from point registration was overlayed with digital map of administrative boundary of the Province of South Kalimantan. Coordinate transformation from geodetic to UTM Zone 50 S projection system was performed for final visualization. RGB-NDVI Image Composite In this process, RGB image composite from NDVI image was generated for the year of 2000, 2002, and 2004 in red(r), green(g), and blue(b), respectively. Figure 2 shows RGB-NDVI image composite. NDVI changes was visually detected by examining color combination in all three NDVI images, as appear in basic color red, green, and blue (RGB) and/or Cyan, Magenta, Yellow (CMY). Fifty multi-temporal clusters were created to categorize NDVI value distribution by process of classification. Mean value for each cluster for each year was then classified in 5 classes of Vegetation Greeness Index (VGI) according to LAPAN ( Usually, the NDVI values range between -1 and +1, but for SPOT vegetation image, this value was then stretched between 0 and 255 by using linear contrast enhancement equation (Jensen, 1986). Table 1 shows the classification of VGI. Map Asia Conference

3 Figure 2 Composite Image of RGB-NDVI Unsupervised Classification Table 1 Vegetation Greeness Index (VGI) Classification No. Class Type NDVI value Streched NDVI Value 1 Non-Vegetation NDVI 0.11 NDVI Very Low VGI 0.11<NDVI <NDVI Low VGI 0.25<NDVI <NDVI Medium VGI 0.33<NDVI <NDVI High VGI NDVI>0.45 NDVI > Each cluster was marked and assigned name for class change (reduction or addition) and nochange by analyzing each cluster s mean which was categorized according to range of VGI in Table 1, and supported by color additive visual interpretation. Initial class definition was created on basic color combination, which produces 13 characteristics or events, consists of eight classes of change and five no-change events. Five classes of no-change events were then merged into one class, thus producing final classification of only nine classes of event as shown in Table 2. High (H), medium (M), and low (L) value referred to VGI classes which count the vegetation and green biomass density. Figure 3 shows RGB-NDVI image as the result of classification. Class Image Color Table 2 Final Classification of Vegetation Index Changes Class of Vegetation Index Changes Nomenclature Cluster Grouping 1 Reduction from 2000 to ,7,8,23,26 2 Reduction from 2000 to ,32,34 3 Addition from 2000 to 2002, Reduction from 2002 to ,14,16,17,24 4 Addition from 2002 to Addition from 2000 to ,25,44,45,46,47,48,49 6 Addition from 2000 to ,40 7 Reduction from 2000 to 2002, Addition from 2002 to ,35,36,37,38,41 8 Reduction from 2002 to ,15,22,27,28,29 9 No change 1,2,3,4,6,18,19,20,30,31,39,42,43,50 Map Asia Conference

4 Figure 3 Final Image of RGB-NDVI Classification Accuracy Assessment Error matrix was employed to asses the accuracy of change detection for each class, by calculating unclassified pixel error and classification error for each class (Congalton & Green, 1999). Error matrix calculation required reference data which portray real condition, derived from field check or from other reference data such as aerial photographs. In case of no field or other reference data, Cohen et al (1998, in Sader & Hayes, 2001) suggested visual interpretation method for developing reference data for error matrices. Since this research employed one-band NDVI image, the reference data were created using density slicing method to classify each NDVI image according to VGI. This method is common for oneband images, and can accurately show discrepancy between adjacent objects. This method changes grayscale image into color image according to data interval, and its accuracy was depended on that interval. This research used NDVI value range from LAPAN (Table 1). Ten sample points for each class of event were randomly selected, with window size of 3x3 pixel, where all majority of the neighbor pixel has the same class, thus generates 10x9x9 = 810 total sample points. These sample points were vectorized and overlayed with each image from VGI density slicing, then visually assigned for each class of event based on Table 2. Table 3 shows the error matrix, and Table 4 shows the calculation result. Table 3 Error Matrix Reference Data No. of Row No. of Column RGB-NDVI Classification Map Asia Conference

5 No. Table 4 Classification Accuracy Vegetation Index Changes Class User Accuracy Producer Accuracy 1 Reduction from 2000 to % 74.2 % 2 Reduction from 2000 to % 76.5 % 3 Addtion from 2000 to 2002, Reduction from 2002 to % 63.6 % 4 Addition from 2002 to % 37.5 % 5 Addition from 2000 to % 82.0 % 6 Addition from 2000 to % 63.6 % 7 Reduction from 2000 to 2002, Addtion from 2002 to % 79.3 % 8 Reduction from 2002 to % 82.4 % 9 No changes 87.3 % 92.4 % Overall Accuracy 80 % RESULT AND ANALYSIS Vegetation Greeness Index Classification Figure 4 show the results of VGI classification from SPOT NDVI image based on NDVI value using density slicing method, for May 2000, 2002, and The color of dark green indicates very high vegetation density, which represents land vegetation in an optimal growth condition, thus classified as healthy land and unlikely to suffer dry condition. Blue and red interpreted as critical land condition and likely to suffer dry condition. Yellow and green indicate land with dense and fertile vegetation cover. From the result of the classification, the Province of South Kalimantan considered to acquire land with high vegetation density, since 75% of her land is covered with vegetation. According to BMG, South Kalimantan has maximum rainfall every May, and this condition leads to an optimal growth of vegetation. Land with low density vegetation cover were spotted around urban areas, southern and western areas, and scattered along the coastal zone. Vegetation Index Change Class Table 2 shows that change events that occured per two-year indicate a certain VGI class change in the detected location. Reduction indicates a decrease in vegetation density or vegetation greeness index, from higher to lower level. This change suggests that the area of land with vegetation was decreasing in the latest year, probably as a result of land damage or a decline in vegetation growth, thus indicating that the area was likely to suffer dry condition. Class that shows a continuous decrease in vegetation index from the year of 2000 to 2004 was detected by Class 2 with the color of orange. Areas appear in this color indicate a condition of land that need further attention to maintain its quality. Furthermore, class with increasing vegetation index shows an incline of vegetation densitiy from lower to higher level, indicating good growth of vegetation in the detected area. Optimum growth of vegetation between the year of 2000 to 2004 was detected in Class 6, which appear in the color of blue. Map Asia Conference

6 May 2000 May 2002 May 2004 Figure 4 Vegetation Greenes Index of May 2000, 2002, and 2004 Vegetation Index Changes Detection The results of land cover change detection are images that represent the distribution of the change events at the area. Table 5 shows the result of the detection using NDVI SPOT Vegetation image with RGB-NDVI method, classified into nine classes of events. Total area of South Kalimantan is 3,711,000 Ha. Class Table 5 Area of Vegetation index changes Class Image Color Class Nomenclature Area (Ha) Percentage (%) 1 Reduction from 2000 to Reduction from 2000 to Addition from 2000 to 2002, Reduction from 2002 to Addition from 2002 to Addition from 2000 to Addition from 2000 to Reduction from 2000 to 2002, Addition from 2002 to Reduction from 2002 to No Change Total 3,711, Table 6 shows the area of vegetation index changes for each Kabupaten/Kota in the Province of South Kalimantan and its rate of vegetation index that computed using the following formula: where: ( x y ) Rate of change = 100%... (1) L Map Asia Conference

7 x = mean value of increasing area y = mean value of decreasing area L = total area Negative (-) and positive (+) sign were called decline and incline, respectively. Computation using formula (1) indicates that the rate of vegetation index changes for the period tend to decline, with rate of 0.32% (=11,800 ha) from total land area of South Kalimantan. Table 6 shows that City of Banjarmasin, Kabupaten Banjar, Tanah Laut, Tapin, Hulu Sungai Selatan, and Hulu Sungai Tengah tend to experience a declining rate of vegetation index change, with Kabupaten Tanah Laut as the highest. On the other hand, Kabupaten Barito Kuala, Kota Baru, Hulu Sungai Utara, and Tabalong tend to experience an inclining rate of vegetation index change, whith Kabupaten Tabalong as the highest. The location of vegetation index changes between 2000 and 2004 occured mostly in the border of the province, i.e., the western and northern area, the larger part scattered from southern to eastern areas and in almost all of its coastal zone, and also spread around urban areas. In Laut Island and Sebuku Island, locations of vegetation index changes are randomly scattered over the island areas. Table 6 Area of Vegetation index changes for each Kabupaten/Kota No Kabupaten/ Time Vegetation Index Change No Change Area of Rate of Change Kota Period Addition Reduction Kab/Kota to the total area of Area (Ha) % Area (Ha) % Area (Ha) % (Ha) South Kalimantan (%) , , , Kota Banjarmasin , , , Mean 1, , , , , , Kab. Barito Kuala , , , Mean 4, , , , , , Kab. Banjar , , , , Mean 7, , , , , , Kab. Tanah Laut , , , , Mean 13, , , , , ,278, Kab. Kota Baru , , ,345, ,435, Mean 63, , ,312, , , , Kab. Tapin , , , Mean 1, , , , , , Kab. Hulu Sungai , , , Selatan Mean 4, , , , , , Kab. Hulu Sungai , , Tengah Mean 1, , , , , , Kab. Hulu Sungai , , , , Utara Mean 12, , , , , , Kab. Tabalong , , , , Mean 10, , , , , ,367, Total , , ,545, ,711, Mean 121, , ,456, One key factor in an increasing vegetation index was the continuous program of rehabilitation in the forest between 2000 and On the other hand, according to the Forestry Report from the Ministry of Forestry, the decreasing index was due to land use change, i.e., from forestry to agriculture and/or mining Map Asia Conference

8 activity. For built-up area in the urban area, the pattern of decreasing vegetation index change was concentric, indicating an extension of housing area. A scattered vegetation index changes from southern to eastern area, i.e. in the Kabupaten Tanah Laut and Kota Baru, was a result of the closing of HTI (Hutan Tanaman Industri) and HPH (Hak Penguasaan Hutan), based on the map of land cover of South Kalimantan, issued by Badan Planologi Kehutanan Departemen Kehutanan, as interpretation from Landsat image mozaic ( /peta20%tematik/). The map shows the action of active timber industry and rehabilitation, but the rate shows a decreasing vegetation index in May 2000, 2002, and This condition indicates that the activity of timber industry was higher than forest rehabilitation. In a 2003 WALHI report, excessive illegal logging and overexploitation by mining industry were to be blame for damaged land, as shown by a decreasing vegetation index. Different planting- and harvestingseason for paddy field that scattered from northwestern to northern part of South Kalimantan could also be taken into consideration in vegetation index change. Change Detection Accuracy Table 4 shows that the overall accuracy of change detection is 80%, which means that 80% of vegetatation index change can be detected and classified accurately. The smallest user accuracy or the largest commision value is in Class 1 with the value of 47.9%, whereas the largest is in Class 7 with the value of 91.4%. This happens because there might be a large error in class defining process. The smallest producer accuracy or largest omission value is in Class 4 with the value of 37.5%, which means low accuracy in the detection and classification of change in Class 4 which interpreted by classification image when compared with image from VGI classification. The largest user accuracy is in Class 9 with the value of 92.4% which means that changes in this class can be well detected. CONCLUSION The result of TKV classification from NDVI SPOT Vegetation image of May 2000, 2002, and 2004 in the Province of South Kalimantan showed that high-density vegetation were dispersed in the whole region, and most located in the center. Low-density vegetation index mostly can be found in around urban areas, southern and western part, and scattered along the coastal zone of South Kalimantan. Research results showed that locations of vegetation index change in South Kalimantan area in small part located around the boundary area of the province, i.e. in western and northern area, whereas the larger part scattered from southern to eastern areas and in almost all of its coastal zone, and spread around urban areas. In Laut Island and Sebuku Island, locations of vegetation index changes scattered over the island areas. The mean value of vegetation index change areas tend to decrease with reduction rate of 0.32% of its land region. The change detection method of RGB-NDVI that applied to this research can well visualize the location of change using three NDVI dates simultaneously, and give the rate of change in the location from the change of NDVI value class for each date. The procedure classified nine classes of change and no-change events in the study area in all periods with 80% agreement with 810 reference sample points. REFERENCE Badan Planologi Kehutanan (2003) Luas Penutupan Lahan Dalam Kawasan Hutan dan Luar Kawasan Hutan Berdasarkan Penafsiran Citra Satelit Landsat 7 TM+ s/d Tahun Statistik Kehutanan Indonesia. Departemen Kehutanan. Online document: < accessed November 7, Congalton, R.G. & Green, K. (1999) Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Boca Raton-Danvers: CRC Press, Inc. GFW (2003) Sekilas Hutan Indonesia. Departemen Kehutanan dan Perkebunan. Online document: < accessed November 7, Jensen, J.R. (1986) Introductory Digital Image Proccesing A Remote Sensing Perspective. Englewood Cliffs, New Jersey: A Division of Simon & Schusster Inc. Kehati (2003) Siapa Tahu Berapa Luas Hutan Kita. Panduan Konservasi Alam. Hayes, D.J. and Sader, S.A. (2001) Change Detection Techniques for Monitoring Forest Clearing and Regrowth in a Tropical Moist Forest, Photogrammetric Engineering and Remote Sensing 67(9): Map Asia Conference

9 Sader, S.A., Hayes, D.J., Coan, M., Hepinstall, J.A., Soza, C. (2001) Forest Change Monitoring of a Remote Biosphere Reserve. International Journal of Remote Sensing 22(10): Sader, S.A. and Wilson, E.H. (2002) Detection Of Forest Harvest Type Using Multiple Dates of Landsat TM Imagery. Remote Sensing of Environment 80: Walhi (2004) Hutan Indonesia Menjelang Kepunahan. Online document: < Indonesia Menjelang Kepunahan> accessed November 7, Online documents: < accessed on November 7, < accessed on October 30, < accessed on November 5, < accessed on October 30, < accessed on November 7, Map Asia Conference