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DETECTING THE AFFECTED AREAS OF MOUNT SINABUNG ERUPTION USING LANDSAT 8 IMAGERIES BASED ON REFLECTANCE CHANGE . Suwarsono; . Hidayat; Jalu Tejo Nugroho; . Wiweka; . Parwati; M. Rokhis Khomarudin
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 12, No 1 (2015)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

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.

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) Sayidah Sulma; Jalu Tejo Nugroho; Yenni Vetrita; Sri Harini
Majalah Ilmiah Globe Vol. 24 No. 2 (2022): GLOBE VoL 24 No 2 TAHUN 2022
Publisher : Badan Informasi Geospasial

Show Abstract | Download Original | Original Source | Check in Google Scholar

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 SURFACE WATER AREAS AS ALTERNATIVE WATER RESOURCE LOCATIONS DURING THE DRY SEASON USING SENTINEL-2 IMAGERY (CASE STUDY: LOWLAND REGION OF BEKASI-KARAWANG, WEST JAVA PROVINCE) Jalu Tejo Nugroho; Suwarsono; Galdita Aruba Chulafak; Atriyon Julzarika; R Johannes Manalu; Sri Harini; Argo Suhadha; Sayidah Sulma
International Journal of Remote Sensing and Earth Sciences Vol. 18 No. 2 (2021)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2021.v18.a3626

In Indonesia, drought is a type of disaster that often occurs, especially during the dry season. What is most needed at such times is the availability of sufficient water sources to meet shortages. Therefore, water source locations are vital during the dry season in order to meet needs. To meet this information need, remote sensing data offer a precise solution.Â This research proposes a rapid method of detecting surface water areas based on remote sensing image data. It focuses on the use of remote sensing satellite imagery to detect objects and the location of surface water sources. The purpose of the study is to rapidly identify objects and locate surface water sources using Sentinel-2 MSI (MultiSpectral Instrument), one of the latest types of remote sensing satellite data. Several water index (WI) methods were applied before deciding which was most suitable for detecting surface water objects. The lowland region of Bekasi-Karawang, a drought prone area, was designated as the research location. The results of the research show that by using Sentinel-2 MSI imagery, MNDWI (Modified Normalized Water Index) is the appropriate parameter to detect surface water areas in the lowland region of Bekasi-Karawang, West Java Province, Indonesia, during times of drought. The method can be employed as an alternative approach based on remote sensing data for the rapid detection of surface water areas as alternative sources of water during the dry season. The existence of natural water sources (swamps, marshes, ponds) that remain during this time can be used as alternative water resources. Further research is still needed which focuses on different geographical conditions and other regions in Indonesia.

DETECTING DEFORMATION DUE TO THE 2018 MERAPI VOLCANO ERUPTION USING INTERFEROMETRIC SYNTHETIC APERTURE RADAR (INSAR) FROM SENTINEL-1 TOPS Suwarsono; Indah Prasasti; Jalu Tejo Nugroho; Jansen Sitorus; Rahmat Arief; Khalifah Insan Nur Rahmi; Djoko Triyono
International Journal of Remote Sensing and Earth Sciences Vol. 16 No. 1 (2019)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2019.v16.a3145

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 Mayâ€“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 Mayâ€“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.

LAPAN-A3 SATELLITE DATA ANALYSIS FOR LAND COVER CLASSIFICATION (CASE STUDY: TOBA LAKE AREA, NORTH SUMATRA) Jalu Tejo Nugroho; Dony Kushardono; Zylshal
International Journal of Remote Sensing and Earth Sciences Vol. 15 No. 1 (2018)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2018.v15.a2782

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.

DETECTING THE LAVA FLOW DEPOSITS FROM 2018 ANAK KRAKATAU ERUPTION USING DATA FUSION LANDSAT-8 OPTIC AND SENTINEL-1 SAR Suwarsono; Indah Prasasti; Jalu Tejo Nugroho; Jansen Sitorus; Djoko Triyono
International Journal of Remote Sensing and Earth Sciences Vol. 15 No. 2 (2018)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2018.v15.a3078

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. Â 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. Â 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.

THE UTILIZATION OF REMOTE SENSING DATA TO SUPPORT GREEN OPEN SPACE MAPPING IN JAKARTA, INDONESIA Hana Listi Fitriana; Sayidah Sulma; Nur Febrianti; Jalu Tejo Nugroho; Nanik Suryo Haryani
International Journal of Remote Sensing and Earth Sciences Vol. 15 No. 2 (2018)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2018.v15.a2890

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 6â€™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.

DETECTION OF GREEN OPEN SPACE USING COMBINATION INDEX OF LANDSAT 8 DATA (CASE STUDY: DKI JAKARTA) Sayidah Sulma; Jalu Tejo Nugroho; Any Zubaidah; Hana Listi Fitriana; Nanik Suryo Haryani
International Journal of Remote Sensing and Earth Sciences Vol. 13 No. 1 (2016)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2016.v13.a2712

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%.

DETECTING THE AFFECTED AREAS OF MOUNT SINABUNG ERUPTION USING LANDSAT 8 IMAGERIES BASED ON REFLECTANCE CHANGE Suwarsono; Hidayat; Jalu Tejo Nugroho; Wiweka; Parwati; M. Rokhis Khomarudin
International Journal of Remote Sensing and Earth Sciences Vol. 12 No. 1 (2015)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2015.v12.a2672

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 showedÂ 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 ofÂ volcanic eruption.

A COMPARISON OF OBJECT-BASED AND PIXEL-BASED APPROACHES FOR LAND USE/LAND COVER CLASSIFICATION USING LAPAN-A2 MICROSATELLITE DATA Jalu Tejo Nugroho; Zylshal; Nurwita Mustika Sari; Dony Kushardono
International Journal of Remote Sensing and Earth Sciences Vol. 14 No. 1 (2017)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2017.v14.a2680

In recent years, small satellite industry has been a rapid trend and become important especially when associated with operational cost, technology adaptation and the missions. One mission of LAPAN-A2, the 2nd generation of microsatellite that developed by Indonesian National Institute of Aeronautics and Space (LAPAN), is Earth observation using digital camera that provides imagery with 3.5 m spatial resolution. The aim of this research is to compare between object-based and pixel-based classification of land use/land cover (LU/LC) in order to determine the appropriate classification method in LAPAN-A2 dataprocessing (case study Semarang, Central Java).The LU/LC were classified into eleven classes, as follows: sea, river, fish pond, tree, grass, road, building 1, building 2, building 3, building 4 and rice field. The accuracy of classification outputs were assessed using confusion matrix. The object-based and pixel-based classification methods result for overall accuracy are 31.63% and 61.61%, respectively. According to accuracy result, it was thought that blurring effect on LAPAN-A2 data may be the main cause ofaccuracy decrease. Furthermore, the result is suggested to use pixel-based classification to be applied inLAPAN-A2 data processing.

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