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SPECTRAL ANALYSIS OF THE HIMAWARI-8 DATA FOR HOTSPOT DETECTION FROM LAND/FOREST FIRES IN SUMATRA Hana Listi Fitriana; Sayidah Sulma; Any Zubaidah; Suwarsono; Indah Prasasti
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.a2836

Abstract

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.
TECHNIQUE FOR IDENTIFYING BURNED VEGETATION AREA USING LANDSAT 8 DATA Bambang Trisakti; Udhi Catur Nugroho; Any Zubaidah
International Journal of Remote Sensing and Earth Sciences Vol. 13 No. 2 (2016)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2016.v13.a2447

Abstract

During the last two decades, forest and land fire is a catastrophic event that happens almost every year in Indonesia. Therefore, it is necessary to develop a technic to monitor forest fires using satellite data to obtain the latest information of burned area in a large scale area. The objective of this research is to develop a method for burned area mapping that happened between two Landsat 8 data recording on August 13rd and September 14th 2015. Burned area was defined as a burned area of vegetation. The hotspot distribution during the period August - September 2015 was used to help visual identification of burned area on the Landsat image and to verify the burned area resulted from this research. Samples were taken at several land covers to determine the spectral pattern differences among burned area, bare area and other land covers, and then the analysis was performed to determine the suitable spectral bands or indices and threshold values that will be used in the model. Landsat recorded on August 13rd before the fire was extracted for soil, while Landsat recorded on September 14th after the fire was extracted for burned area. Multi-temporal analysis was done to get the burned area occurring during the certain period. The results showed that the clouds could be separated using combination of ocean blue and cirrus bands, the burned area was extracted using a combination of NIR and SWIR band, while soil was extracted using ratio SWIR / NIR. Burned area obtained in this study had high correlation with the hotspot density of MODIS with the accuracy was around 82,4 %.
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

Abstract

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%.