HOTSPOT VALIDATION OF THE HIMAWARI-8 SATELLITE BASED ON MULTISOURCE DATA FOR CENTRAL KALIMANTAN

The Advanced Himawari Imager (AHI) is the sensor aboard the remote-sensing satellite Himawari-8 which records the Earth’s weather and land conditions every 10 minutes from a geostationary orbit. The imagery produced known as Himawari-8 has 16 bands which cover visible, near infrared, middle infrared and thermal infrared wavelength potentials to monitor forestry phenomena. One of these is forest/land fires, which frequently occur in Indonesia in the dry season. Himawari-8 can detect hotspots in thermal bands 5 and band 7 using absolute fire pixel (AFP) and possible fire pixel (PFP) algorithms. However, validation has not yet been conducted to assess the accuracy of this information. This study aims to validate hotspots identified from Himawari images based on information from Landsat 8 images, field surveys and burnout data. The methodology used to validate hotspots comprises AFP and PFP extraction, determining firespots from Landsat 8, buffering at 2 km from firespots, field surveys, burnout data, and calculation of accuracy. AFP and PFP hotspot validation of firespots from Landsat-8 is found to have higher accuracy than the other options. In using Himawari-8 hotspots to detect land/forest fires in Central Kalimantan, the AFP algorithm with 2km radius has accuracy of 51.33% while the PFP algorithm has accuracy of 27.62%

DETECTION OF ACID SLUDGE CONTAMINATED AREA BASED ON NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI) VALUE

The solid form of oil heavy metal waste is  known as acid sludge. The aim of this research is to exercise the correlation between acid sludge concentration in soil and NDVI value, and further studying the Normalized Difference Vegetation Index (NDVI) anomaly by multi-temporal Landsat satellite images. The implemented method is NDVI.  In this research, NDVI is analyzed using the  remote sensing data  on dry season and wet season.  Between 1997 to 2012, NDVI value in dry season  is around – 0.007 (July 2001) to 0.386 (May 1997), meanwhile in wet season  NDVI value is around – 0.005 (November 2006) to 0.381 (December 1995).  The high NDVI value shows the leaf health or  thickness, where the low NDVI indicates the vegetation stress and rareness which can be concluded as the evidence of contamination. The rehabilitation has been executed in the acid sludge contaminated location, where the high value of NDVI indicates the successfull land rehabilitation effort

Coastal Physical Vulnerability of Surabaya and Its Surrounding Area to Sea Level Rise

The study for coastal vulnerability to sea level rise was carried out in Surabaya and its surrounding area, it has focused on calculations of the physical vulnerability index were  used coastal vulnerability index (CVI) methods. It was standardized by the multi criteria analysis (MCA) approach according to the study area.  The score of each physical variable derived from remote sensing satellite data and the results of studies that have been done, such as modeling results and thematic maps, and then integrated into geographic information systems (GIS). Result of this study shows that the coastal areas of Gresik, Surabaya, and Sidoarjo in the very low to very high vulnerability level. Physically, the low land areas with open and slightly open coastal have a high vulnerability category. The high level vulnerability was found located in the northern of Madura Strait (Gresik Regency) that overlooks to the Java Sea is about 28.81% from the entire of study areas. Meanwhile, the moderate, low and very low levels of vulnerability were located on Surabaya and Sidoarjo Regency that have more protected coastal area, relatively. According to the physical condition, the coastal elevation is the most variable that contributes to the high of vulnerability index in the coastal of Surabaya City and Sidoarjo Regency.&nbsp

COASTAL PHYSICAL VULNERABILITY OF SURABAYA AND ITS SURROUNDING AREA TO SEA LEVEL RISE

The study for coastal vulnerability to sea level rise was carried out in Surabaya and its surrounding area, it has focused on calculations of the physical vulnerability index were  used coastal vulnerability index (CVI) methods. It was standardized by the multi criteria analysis (MCA) approach according to the study area.  The score of each physical variable derived from remote sensing satellite data and the results of studies that have been done, such as modeling results and thematic maps, and then integrated into geographic information systems (GIS). Result of this study shows that the coastal areas of Gresik, Surabaya, and Sidoarjo in the very low to very high vulnerability level. Physically, the low land areas with open and slightly open coastal have a high vulnerability category. The high level vulnerability was found located in the northern of Madura Strait (Gresik Regency) that overlooks to the Java Sea is about 28.81% from the entire of study areas. Meanwhile, the moderate, low and very low levels of vulnerability were located on Surabaya and Sidoarjo Regency that have more protected coastal area, relatively. According to the physical condition, the coastal elevation is the most variable that contributes to the high of vulnerability index in the coastal of Surabaya City and Sidoarjo Regency. Keywords: coastal vulnerability, sea level rise, remote sensing, CVI, MC

Analysis of Agricultural Drought in East Java Using Vegetation Health Index

Drought is a natural hazard indicated by the decreasing of rainfall and water storage and impacting agricultural sector. Agricultural drought assessment has been used to monitor agricultural sustainability, particularly in East Java as national agricultural production center. Identification of drought characteristics –correlated with El Niño-Southern Oscillation, and agricultural impact on paddy fields and rice production using VHI (Vegetation Health Index) were conducted. VHI is produced by TCI (Temperature Condition Index) and VCI (Vegetation Condition Index) derived from MODIS satellite data, LST (Land Surface Temperature) and EVI (Enhanced Vegetation Index), respectively. The results showed agricultural drought usually started in June, maximum in October and ended in November. Onset and end time drought tends to follow monsoonal rainfall pattern. El Niño 2015 showed long duration of agricultural drought (i.e. ± 5 months), high severity (i.e. mild-extreme drought; VHI 0-40) and areal extent of drought approx. 197,343 km2, while during La Niña 2010 the areal extent was approx. 28,685 km2 with mild-severe drought (VHI 10-40). Impact of agricultural drought on paddy fields showed wider (smaller) areal extent in sub-round 3 (sub-round 1) from September-December (January-April). Areal extent of drought was negatively correlated with rice production (r=-0.79) which significant in 99 % confidence level

OBSERVING THE INUNDATED AREA USING LANDSAT-8 MULTITEMPORAL IMAGES AND DETERMINATION OF FLOOD-PRONE AREA IN BANDUNG BASIN

Flood is the most frequent hydro-meteorological disaster in Indonesia. Flood disasters in the Bandung basin result from increasing population density, especially in the Citarum riverbank area, accompanied by land use changes in upstream of the Citarum catchment area which has disrupted the river’s function. One of the basic issues that need to be investigated is which areas of the Bandung basin are prone to flooding. This study offers an effective and efficient method of mapping flood-prone areas based on flood events that have occurred in the past through the use of historical remote sensing image data. In this research, Landsat-8 imagery was used to observe the inundated area in the Bandung basin in the past (2014–2018) using an improved algorithm, the modified normalized water index (MNDWI). The results of the study show that MNDWI is the appropriate parameter to be used to detect flooded areas in the Bandung basin area that have heterogeneous land surface conditions. The flood-prone area was determined based on flood events for 2014 to 2018, identified as inundated areas in the images. The estimation of the flood-prone area in the Bandung basin is 11,886.87 ha. Most of the flood-prone areas are in the subdistricts of Rancaekek, Bojongsoang, Solokan Jeruk, Ciparay, Cileunyi, Bale Endah and Cikancung. This area geographically or naturally is a water habitat area. Therefore, if the area will be used for residential, this will have consequences that flood will always be a threat to the area.

Improving the accuracy and reliability of land use/land cover simulation by the integration of Markov cellular automata and landform-based models __ a case study in the upstream Citarum watershed, West Java, Indonesia

Land use/land cover (LULC) is one of the important variables affecting human life and the physical environment. Modelling of change in LULC is an important tool for environmental management and for supporting spatial planning in environmentally important areas. In this study, a new approach was proposed to improve the accuracy and reliability of LULC simulation by integrating Markov cellular automata (Markov-CA) and landform-based models. Landform characteristics, positions and patterns influence LULC changes that are important in understanding the effects of environmental change and other physical factors. The results of this study showed that integration of Markov-CA and landform-based models increased correct rejection as a component of agreement and reduced incorrect hits and false alarms as components of disagreement for the percentage of the study area in each resolution (multiple of native pixel size). Correctly simulated hits as a component of agreement change also increased, even though nine of the 18 pairs of three-map comparisons showed a decline in this aspect. Meanwhile, misses as a component of disagreement change simulated as persistence also increased, although six of the 18 pairs of data showed a decline. Based on the overall three-map comparison analysis, there was an increase in the figure of merit (FOM) values after the Markov-CA and landform-based models were integrated, although six of the 18 pairs of data indicated a decrease in FOM values. This indicates improved results after integration of Markov-CA and landform-based models

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

DETEKSI DAERAH TERCEMAR LUMPUR ASAM MENGGUNAKAN DATA LANDSAT 7 ETM BERDASARKAN SUHU PERMUKAAN TANAH (DETECTING CONTAMINATED AREA BY ACID SLUDGE USING LANDSAT 7 ETM DATA BASED ON LAND SURFACE TEMPERATURE)

potensi pencemaran limbah bahan berbahaya dan beracun (B3). Salah satu bentuk limbah B3 adalah lumpur asam (acid sludge) yang merupakan campuran hidrokarbon dan asam sulfat yang berasal dari proses pembuangan pabrik lilin. Penelitian ini bertujuan untuk mendeteksi daerah tercemar lumpur asam berdasarkan suhu permukaan tanah (Land Surface Temperature/LST) dari data Landsat 7 ETM multi temporal. Tahapan penelitian meliputi pengumpulan data, penyusunan algoritma LST dari data Landsat 7 ETM berdasarkan hasil regresi dengan LST Terra-MODIS, perhitungan LST Landsat 7 ETM multitemporal dan pemantauan LST pada daerah tercemar.  Sebaran nilai LST MODIS dan Brightness Temperature(Tb) Landsat memiliki kemiripan pola sehingga MODIS dapat dijadikan acuan dalam penentuan LST dari Landsat. Untuk penentuan LST dari Landsat telah dibuat model pendugaan dari regresi linier antara LST MODIS dan Tb Landsat dengan koefisien determinasi sebesar 0.84. Berdasarkan analisis LST deret waktu pada daerah tercemar lumpur asam diketahui bahwa daerah tercemar memiliki suhu yang lebih tinggi dibandingkan dengan daerah tidak tercemar.  Tidak terlihat adanya hubungan yang signifikan antara pola LST dengan proses pemulihan lahan yang dilakukan. Hal ini menunjukkan bahwa proses pemulihan lahan tercemar tidak terlalu berpengaruh terhadap suhu lumpur asam di wilayah tersebut. Kata Kunci: Limbah B3, Lumpur asam, Suhu permukaan tanah, Landsat-7 ET

Pemanfaatan Citra VIIRS untuk Deteksi Asap Kebakaran Hutan dan Lahan di Indonesia

The observation of smoke because of land and forest fires in some regions in Indonesia mostly use the composite image visually. This study aims to develop the detection model of forest and land fire smoke using a digital analysis, which will be faster in supporting spatial information on emergency response in monitoring forest and land fire smoke. The method used is multi-threshold method and compare it with the existing model that is by modification of method Li et al. (2015). The data used is Suomi NPP-VIIRS satellite imagery. The results concluded that the VIIRS image can be used to detect the smoke and smoke distribution of forest fire and digital smoke. The multi-threshold model uses reflectance data obtained from the M4 visible channel, and the brightness temperature data obtained from the LWIR VIIRS M14 channel, with an average accuracy of 82.2% with a Commision error of 9.8% and an Ommision error of 10%. While the model of modification Li is based only on reflectance of visible-channel data i.e. channel M1, M2, M3, and SWIR VIIRS M11 channel, which has an average accuracy of 72.3% with a Commision error of 0.3% and an Ommision error of 27.4%. The multi-threshold model is a model that has the potential to be applied to detect forest and land fire smoke