IDENTIFICATION OF INUNDATED AREA USING NORMALIZED DIFFERENCE WATER INDEX (NDWI) ON LOWLAND REGION OF JAVA ISLAND

Flood disaster is a major issues due to its frequently events on several areas in Indonesia. Delineation of inundated area caused by flood is needed to support disaster emergency response. The objective of this research was to identify inundated areas using NDWI methos from Landsat TM/ETM+ data on lowland regions of Java island. A pair of the data (before and during the flood) were in each observation areas. Observation areas were selected in several location of lowland regions of Java island where great event of flood occurred during the last decades. The thresholds values of NDWI change were used to separate the flood and non flood areas. The results showed that the extent of inundated area caused by flood on lowland regions can be identifyed and separated based on NDWI variables extracted from Landsat TM/ETM+

LAPAN-A3 SATELLITE DATA ANALYSIS FOR LAND COVER CLASSIFICATION (CASE STUDY: TOBA LAKE AREA, NORTH SUMATRA)

LAPAN-A3 is the 3rdgeneration satellite for remote sensing developed by National Institute of Aeronautics and Space (LAPAN). The camera provides imagery with 15 m spatial resolution and able to view a swath 120 km wide. This research analyzes the performance of LAPAN-A3 satellite data to classify land cover in Toba Lake area, North Sumatera. Data processing starts from the selection of region of interest up to the assessment of accuracy. Supervised classification with maximum likelihood approach and confusion matrix method was applied to classify and evaluate the assessment results. The land cover is classified into five classes; water, bare land, agriculture, forest and secondary forest. The result of accuracy test is 93.71%. It proves that LAPAN-A3 data could classify the land cover accurately. The data is expected to complement the need of the satellite data with medium spatial resolution

VARIABILITAS TINGKAT KEHIJAUAN VEGETASI BERDASARKAN ENHANCED VEGETATION INDEX SELAMA KEKERINGAN EKSTRIM TAHUN 2015 DI PULAU JAWA: (Variability of Vegetation Greenness Level based on Enhanced Vegetation Index during the 2015 Extreme Drought in Java Island)

Bencana kekeringan memiliki dampak yang sangat besar terhadap sektor pertanian dan perekonomian, sehingga pemantauan kekeringan perlu dilakukan secara berkala. Pemantauan kekeringan berbasis indeks vegetasi dari data satelit semakin berkembang dan perlu dikaji lebih lanjut khususnya untuk wilayah Indonesia. Pada tahun 2015 terjadi fenomena El Niño yang menyebabkan kondisi kekeringan ekstrim khususnya di wilayah Indonesia. Kondisi ini berpotensi untuk menjadi bahan kajian dalam pemantauan kekeringan menggunakan data penginderaan jauh. Tujuan penelitian ini adalah untuk mengetahui kemampuan pengkelasan Tingkat Kehijauan Vegetasi (TKV) dalam menggambarkan kondisi kekeringan, serta untuk menganalisis keterkaitan waktu terjadinya kekeringan meteorolgis dengan kekeringan pertanian. Pemantauan kondisi kekeringan dilakukan menggunakan indikator TKV. Variabilitas TKV diperoleh dari pengkelasan indeks vegetasi yaitu Enhanced Vegetation Index (EVI) dari data MODIS (Moderate Resolution Imaging Spectroradiometer), yang dianalisis mewakili kondisi kekeringan ekstrim yaitu pada saat El Niño tahun 2015 di Pulau Jawa dan dibandingkan dengan kondisi TKV 2019 yang mewakilli kondisi netral.  Hasil perbandingan menunjukkan bahwa TKV dapat digunakan untuk pemantauan kondisi kekeringan di suatu wilayah, dimana saat musim kemarau di kedua waktu tersebut sama-sama menunjukkan kondisi kering, namun pada tahun 2015 saat iklim ekstrim TKV menunjukkan tingkat kehijauan vegetasi yang rendah hingga sangat rendah di sebagian besar Pulau Jawa. Berdasarkan penelitian diketahui bahwa rendahnya tingkat kehijauan vegetasi dapat mengindikasikan terjadinya kekeringan pertanian, dimana terdapat jeda waktu sekitar 2 bulan, dampak dari kekeringan meteorologi terhadap menurunnya kondisi tutupan vegetasi secara alami

DETECTING THE AFFECTED AREAS OF MOUNT SINABUNG ERUPTION USING LANDSAT 8 IMAGERIES BASED ON REFLECTANCE CHANGE

The position of Indonesia as part of a "ring of fire" bringing the consequence that the life of the nation and the state will also be influenced by volcanism. Therefore, it is necessary to map rapidly the affected areas of a volcano eruption. Objective of the research is to detect the affected areas of Mount Sinabung eruption recently in North Sumatera by using optical images Landsat 8 Operational Land Imager (OLI). A pair of Landsat 8 images in 2013 and 2014, period before and after eruption, was used to analysis the reflectance change from that period. Affected areas of eruption was separated based on threshold value of reflectance change. The research showed that the affected areas of Mount Sinabung eruption can be detected and separated by using Landsat 8 OLI images based on the change of reflectance value band 4, 5 and NDVI. Band 5 showed  the highest values of decreasing and band 4 showed the highest values of increasing. Compared with another uses of single band, the combination of both bands (NDVI) give the best result for detecting the affected areas of  volcanic eruption

Admissibility of Illegally Obtained Evidence

The increasing volcanic activity of Anak Krakatau volcano has raised concerns about a major disaster in the area around the Sunda Strait. The objective of the research is to fuse Landsat-8 OLI (Operational Land Imager) and Sentinel-1 TOPS (Terrain Observation with Progressive Scans), an integration of SAR and optic remote sensing data, in observing the lava flow deposits resulted from Anak Krakatau eruption during the middle 2018 eruption. RGBI and the Brovey transformation were conducted to merge (fuse) the optical and SAR data.  The results showed that optical and SAR data fusion sharpened the appearance of volcano morphology and lava flow deposits. The regions are often constrained by cloud cover and volcanic ash, which occurs at the time of the volcanic eruption.  The RGBI-VV and Brovey RGB-VV methods provide better display quality results in revealing the morphology of volcanic cone and lava deposits. The entire slopes of Anak Krakatau Volcano, with a radius of about 1 km from the crater is an area prone to incandescent lava and pyroclastic falls. The direction of the lava flow has the potential to spread in all directions. The fusion method of optical Landsat-8 and Sentinel-1 SAR data can be used continuously in monitoring the activity of Anak Krakatau volcano and other volcanoes in Indonesia both in cloudy and clear weather conditions

PENINGKATAN AKURASI PREDIKSI CURAH HUJAN BULANAN DIWILAYAH JAKARTA MENGGUNAKAN DATA TROPICAL RAINFALLMEASURING MISSION (TRMM) DAN DATA SINAR KOSMIKBERBASIS JARINGAN SYARAF TIRUAN[ACCURACY ENHANCEMENT FOR PREDICTING MONTHLYRAINFALL IN JAKARTA REGION USING TROPICAL RAINFALLMEASURING MISSION (TRMM) AND COSMIC RAYS DATA BASEDON ARTIFICIAL NEURAL NETWORK]

Telah diperoleh peningkatan akurasi prediksi curah hujan bulanan di wilayah Jakarta yang ditandai dengan peningkatan nilai koefisien korelasi data Tropical Rainfall Measuring Mission (TRMM) hasil simulasi jaringan syaraf tiruan. Dengan melibatkan faktor sinar kosmik sebagai masukan jaringan, diperoleh peningkatan akurasi sampai dengan 7,2% yang dicapai saat prediksi lima bulan ke depan. Untuk prediksi tiga dan empat bulan ke depan peningkatan akurasi yang diperoleh sebesar 4,8%. Hasil ini melengkapi bukti adanya kontribusi sinar kosmik dalam mempengaruhi curah hujan di wilayah Jakarta.Kata kunci: Tropical Rainfall Measuring Mission (TRMM), Jaringan syaraf tiruan, Sinar kosmi

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 DEFORMATION DUE TO THE 2018 MERAPI VOLCANO ERUPTION USING INTERFEROMETRIC SYNTHETIC APERTURE RADAR (INSAR) FROM SENTINEL-1 TOPS

This paper describes the application of Sentinel-1 TOPS (Terrain Observation with Progressive Scans), the latest generation of SAR satellite imagery, to detect displacement of the Merapi volcano due to the May–June 2018 eruption. Deformation was detected by measuring the vertical displacement of the surface topography around the eruption centre. The Interferometric Synthetic Aperture Radar (InSAR) technique was used to measure the vertical displacement. Furthermore, several Landsat-8 Thermal Infra Red Sensor (TIRS) imageries were used to confirm that the displacement was generated by the volcanic eruption. The increasing temperature of the crater was the main parameter derived using the Landsat-8 TIRS, in order to determine the increase in volcanic activity. To understand this phenomenon, we used Landsat-8 TIRS acquisition dates before, during and after the eruption. The results show that the eruption in the May–June 2018 period led to a small negative vertical displacement. This vertical displacement occurred in the peak of volcano range from -0.260 to -0.063 m. The crater, centre of eruption and upper slope of the volcano experienced negative vertical displacement. The results of the analysis from Landsat-8 TIRS in the form of an increase in temperature during the 2018 eruption confirmed that the displacement detected by Sentinel-1 TOPS SAR was due to the impact of volcanic activity. Based on the results of this analysis, it can be seen that the integration of SAR and thermal optical data can be very useful in understanding whether deformation is certain to have been caused by volcanic activity