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INDONESIA
Jurnal Penginderaan Jauh Indonesia
Published by Masyarakat Ahli Penginderaan Jauh Indonesia
ISSN : -     EISSN : 26570378     DOI : -
Core Subject : Science, Agriculture, Social, Engineering,
Aerospace Engineering Agriculture, Biological Sciences & Forestry Earth & Planetary Sciences Engineering Environmental Science
Jurnal Penginderaan Jauh Indonesia (JPJI) adalah media komunikasi dan diseminasi hasil penelitian, kajian dan pemikiran terkait teori, sains, dan teknologi penginderaan jauh serta pemanfaatannya yang diterbitkan oleh Masyarakat Ahli Penginderaan Jauh Indonesia (MAPIN). Fokus jurnal mencakup penginderaan jauh untuk objek dipermukaan bumi, baik di darat, laut maupun atmosfer. JPJI terbit 2 kali setahun, pada bulan Februari dan Agustus.
Arjuna Subject : -
Articles 2 Documents
 1 
Search results for , issue "Vol 2 No 1 (2020)" : 2 Documents clear
Pemetaan Presentase Kepadatan Bangunan Menggunakan Model Regresi Berdasarkan Citra Landsat 8 Rosyadi, Amri; Azahra, Maulidini Fatimah
Jurnal Penginderaan Jauh Indonesia Vol 2 No 1 (2020)
Publisher : Masyarakat Ahli Penginderaan Jauh Indonesia (MAPIN) /Indonesian Society of Remote Sensing (ISRS)

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Abstract

The development of cities and the population causes an increasing number of buildings in an area. Building density identification can use relatively faster remote sensing imagery. This study aims to map the density of buildings in the city of Bandung based on the regression model. The method used to identify building density in this study is regression using the Normalized Difference Built-up Index (NDBI) index. The results of this study are regression models Y = 0.071 + 0.001X which can be used to map building densities with r square values at 0.383. This method is considered to be very fast and accurate compared to other methods for building density.
Pemanfaatan Data Sentinel-2 untuk Analisis Indeks Area Terbakar (Burned Area) Rahmi, Khalifah Insan Nur; Febrianti, Nur
Jurnal Penginderaan Jauh Indonesia Vol 2 No 1 (2020)
Publisher : Masyarakat Ahli Penginderaan Jauh Indonesia (MAPIN) /Indonesian Society of Remote Sensing (ISRS)

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Abstract

The Use of Sentinel-2 Image to Analysis Burned Area Index Burned area mapping can be extracted from remote sensing imagery using burned area index. Various indices have been developed to identify burned areas including NBR, NBR2, MIRBI, and BAIS2. This study aims to determine the index that best distinguishes burning and non-burning areas in the detailed scale of small fires. Burned areas were identified from the delta index before and after the fire. Date of Sentinel-2 image before fires on May 1, 2019, after fires on September 8, 2019. The NBR index uses the comparison of SWIR and NIR band, the NBR2 and MIRBI indexes use the comparison of SWIRL and SWIRS band, while the BAIS2 index plays the red-edge spectral range, NIR, and SWIR. The result of the separability index analysis shows that the MIRBI index is good for distinguishing burned areas from bare land. The NBR index is good at distinguishing burned areas from vegetation and built-up land while the NBR2 index is good at distinguishing smoked burned areas from vegetation and built-up land.

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