MAPPING BURNT AREAS USING THE SEMI-AUTOMATIC OBJECT-BASED IMAGE ANALYSIS METHOD

Forest and land fires in Indonesia take place almost every year, particularly in the dry season and in Sumatra and Kalimantan. Such fires damage the ecosystem, and lower the quality of life of the community, especially in health, social and economic terms. To establish the location of forest and land fires, it is necessary to identify and analyse burnt areas. Information on these is necessary to determine the environmental damage caused, the impact on the environment, the carbon emissions produced, and the rehabilitation process needed. Identification methods of burnt land was made both visually and digitally by utilising satellite remote sensing data technology. Such data were chosen because they can identify objects quickly and precisely. Landsat 8 image data have many advantages: they can be easily obtained, the archives are long and they are visible to thermal wavelengths. By using a combination of visible, infrared and thermal channels through the semi-automatic object-based image analysis (OBIA) approach, the study aims to identify burnt areas in the geographical area of Indonesia. The research concludes that the semi-automatic OBIA approach based on the red, infrared and thermal spectral bands is a reliable and fast method for identifying burnt areas in regions of Sumatra and Kalimantan

SPECTRAL ANALYSIS OF THE HIMAWARI-8 DATA FOR HOTSPOT DETECTION FROM LAND/FOREST FIRES IN SUMATRA

Himawari-8 is the last generation of the low spatial resolution satellite imagery that has capability to detect the thermal variation on the earth of every 10 minute. This must be very potential to be used for detecting land/forest fire. This paper has explored the spectral prospective of the Himawari-8 for detecting land/forest fire hotspot. The main objective for this study is to identify the potential use of Himawari-8 for detecting of land forest fire hotspot. The study area was performed in Ogan Komering Ilir, South of Sumatra, which on 2015 occur great forest/land fire event. The main process included in this study are image projection, training sample collection and spectral statistical analysis measured by calculate statistic, they are average values, standard deviation values from reflectance visible band value and brightness temperature value, beside that validation of data obtained from medium resolution data of Landsat 8 with the similar acquisition time. The study found that the Himawari-8 has good capacity to identify land/forest fire hotspot as expressed for high accuracy assessment using band 3 and band 7

DETECTION OF GREEN OPEN SPACE USING COMBINATION INDEX OF LANDSAT 8 DATA (CASE STUDY: DKI JAKARTA)

Spatial information about the availability and presence of green open space in urban areas to be up to date and transparent was a necessity. This study explained the technique to get the green open spaces of spatial information quickly using an index approach of Landsat 8. The purpose of this study was to evaluate the ability of the method to detect the green open spaces, especially using Landsat 8 with a combination of several indices, namely Normalized Difference Build-up Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Build-up Index (NDBI) and Normalized Difference Bareness Index (NDBaI) with a study area of Jakarta. This study found that the detection and identification of green open space classes used a combination of index and band gave good results with an accuracy of 81%

THE UTILIZATION OF REMOTE SENSING DATA TO SUPPORT GREEN OPEN SPACE MAPPING IN JAKARTA, INDONESIA

Green open space becomes critical in maintaining the balance of the environment and improving the quality of urban living for a healthy life. The use of remote sensing data for calculation of green open space has been done notably using NDVI (Normalized Difference Vegetation Index) method from Landsat 8 and SPOT data. This research aims to calculate the accuracy of the green open space classification from multispectral data of Landsat 8 and SPOT 6 using the NDVI methods. Green open space could be assessed from the value NDVI. The value of NDVI generated from Landsat 8 and SPOT 6’s Red and NIR channels. The accuracy of NDVI values is then examined by comparing with Pleiades data. Pleiades data which has 50 cm panchromatic resolution and 2 m multispectral with 4 bands (B, G, R, NIR) can precisely visualize objects. So, it can be used as the reference in the calculation of the green open space based on NDVI. The results of the accuracy testing of Landsat 8 and SPOT 6 image could be used to identify the green open space by using NDVI SPOT of 6 can increase the accuracy of 5.36% from Landsat 8

DETECTING THE AREA DAMAGE DUE TO COAL MINING ACTIVITIES USING LANDSAT MULTITEMPORAL (Case Study: Kutai Kartanegara, East Kalimantan)

Coal is one of the most mining commodities to date, especially to supply both national and international energy needs. Coal mining activities that are not well managed will have an impact on the occurrence of environmental damage. This research tried to utilize the multitemporal Landsat data to analyze the land damage caused by coal mining activities. The research took place at several coal mine sites in East Kalimantan Province. The method developed in this research is the method of change detection. The study tried to know the land damage caused by mining activities using NDVI (Normalized Difference Vegetation Index), NDSI (Normalized Difference Soil Index), NDWI (Normalized Difference Water Index) and GEMI (Global Environment Monitoring Index) parameter based change detection method. The results showed that coal mine area along with the damage that occurred in it can be detected from multitemporal Landsat data using NDSI value-based change detection method. The area damage due to coal mining activities  can be classified into high, moderate, and low classes based on the mean and standard deviation of NDSI changes (ΔNDSI). The results of this study are expected to be used to support government efforts and mining managers in post-mining land reclamation activities

PERBANDINGAN HASIL KLASIFIKASI LIMBAH LUMPUR ASAM DENGAN METODE SPECTRAL ANGLE MAPPER DAN SPECTRAL MIXTURE ANALYSIS BERDASARKAN CITRA LANDSAT - 8

The utilization of remote sensing data is an alternative way that could be used for rapid detection of large coverage hazardous waste area. This study aims to classify the acid sludge contaminated area using Landsat 8 by applying Spectral Angle Mapper (SAM) classification method with two spectral reference sources, namely field spectral measurement using a spectrometer and endmember spectral from the image, and then compare the classification results. The accuracy level of SAM classification result showed that classification using endmember spectral from the image as the reference spectral reached 66,7%, whereas classification using field spectral measurement as spectral reference only reached 33,3%. The accuracy level of Spectral Mixture Analysis (SMA) classification result showed that classification using endmember spectral from the image as the reference spectral reached 62,5%. The affecting factors for the low accuracy is the significant differences of the spectral profiles obtained from spectrometer with spectral Landsat-8 due to differences of spatial and altitude Keywords: Acid sludge Waste, Spectral Angle Mapper, Spectral Mixture Analysis, Landsat-8 ABSTRAKPemanfaatan data penginderaan jauh merupakan salah satu alternatif yang dapat digunakan untuk deteksi daerah tercemar limbah B3 secara cepat dengan wilayah yang luas. Penelitian ini bertujuan untuk mengklasifikasi daerah tercemar lumpur asam menggunakan data Landsat 8 dengan metode Spectral Angle Mapper (SAM), kemudian membandingkan hasil klasifikasi SAM menggunakan spektral referensi berdasarkan pengukuran spektrometer dengan spektral yang diperoleh dari endmember citra. Tingkat akurasi klasifikasi SAM dengan spektral referensi berdasarkan endmember citra adalah sebesar 66,7 %, sedangkan dengan menggunakan referensi spektrometer hanya mencapai 33,3 %. Tingkat akurasi klasifikasi Spectral Mixture Analysis (SMA) dengan spektral referensi berdasarkan endmember citra adalah sebesar 62,5 %. Faktor yang mempengaruhi rendahnya akurasi adalah perbedaan yang signifikan antara profil spektral yang diperoleh dari spektrometer dengan spektral Landsat-8 akibat perbedaan spasial dan ketinggian

PERBANDINGAN HASIL KLASIFIKASI LIMBAH LUMPUR ASAM DENGAN METODE SPECTRAL ANGLE MAPPER DAN SPECTRAL MIXTURE ANALYSIS BERDASARKAN CITRA LANDSAT - 8 (THE COMPARISON OF CLASSIFICATION OF ACID SLUDGE WITH SPECTRAL ANGLE MAPPER AND SPECTRAL MIXTURE ANALYSIS METHOD BASED ON LANDSAT-8)

Pemanfaatan data penginderaan jauh merupakan salah satu alternatif yang dapat digunakan untuk deteksi daerah tercemar limbah B3 secara cepat dengan wilayah yang luas. Penelitian ini bertujuan untuk mengklasifikasi daerah tercemar lumpur asam menggunakan data Landsat 8 dengan metode Spectral Angle Mapper (SAM), kemudian membandingkan hasil klasifikasi SAM menggunakan spektral referensi berdasarkan pengukuran spektrometer dengan spektral yang diperoleh dari endmember citra. Tingkat akurasi klasifikasi SAM dengan spektral referensi berdasarkan endmember citra adalah sebesar 66,7 %, sedangkan dengan menggunakan referensi spektrometer hanya mencapai 33,3 %. Tingkat akurasi klasifikasi Spectral Mixture Analysis (SMA) dengan spektral referensi berdasarkan endmember citra adalah sebesar 62,5 %. Faktor yang mempengaruhi rendahnya akurasi adalah perbedaan yang signifikan antara profil spektral yang diperoleh dari spektrometer dengan spektral Landsat-8 akibat perbedaan spasial dan ketinggian.Kata Kunci: Limbah lumpur asam, Spectral Angle Mapper, Spectral Mixture Analysis, Landsat-

DETECTING THE SURFACE WATER AREA IN CIRATA DAM UPSTREAM CITARUM USING A WATER INDEX FROM SENTINEL-2

This paper describes the detection of the surface water area in Cirata dam,  upstream Citarum, using a water index derived from Sentinel-2. MSI Level 1C (MSIL1C) data from 16 November 2018 were extracted into a water index such as the NDWI (Normalized Difference Water Index) model of Gao (1996), McFeeters (1996), Roger and Kearney (2004), and Xu (2006). Water index were analyzed based on the presence of several objects (water, vegetation, soil, and built-up). The research resulted in the ability of each water index to separate water and non-water objects. The results conclude that the NDWI of McFeeters (1996) derived from Sentinel-2 MSI showed the best results in detecting the surface water area of the reservoir

Mapping burned areas from landsat-8 imageries on mountainous region using reflectance changes

This research tried to detect a burned area that occurred in the mountainous region of Java Island. During this time, forest and land fires mostly occur in lowland areas in Sumatra and Kalimantan. However, it is possible that this phenomenon also occurs in mountainous regions, especially the mountainous regions of Java Island. The data used were Landsat-8, the latest generation of the Landsat series. The research location was on the Northeast slope of Mt. Ijen in East Java. The research methods include radiometric correction, data fusion, sample training retrieval, reflectance pattern analysis, Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR) extraction, separability analysis, parameter selection for burned area detection, parameter test, and evaluation. The results show that ρ5 and NBRL parameter shows the highest values of D-values (most sensitive), to detect the burned area. Then, compared to ρ5, NDVI and NBRS, Normalized Burn Ratio long (NBRL) provide better results in detecting burned areas