Mahmud A. Raimadoya
Tim Pengembangan LOSAT/LISAT, Institut Pertanian Bogor, Bogor 16680

Published : 2 Documents
Articles

Found 2 Documents
Search

Historical Fire Detection of Tropical Forest from NDVI Time-series Data: Case Study on Jambi, Indonesia Panuju, Dyah R.; Trisasongko, Bambang H.; Susetyo, Budi; Raimadoya, Mahmud A.; Lees, Brian G.
Journal of Mathematical and Fundamental Sciences Vol 42, No 1 (2010)
Publisher : ITB Journal Publisher, LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (469.515 KB) | DOI: 10.5614/itbj.sci.2010.42.1.5

Abstract

In addition to forest encroachment, forest fire is a serious problem in Indonesia. Attempts at managing its widespread and frequent occurrence has led to intensive use of remote sensing data. Coarse resolution images have been employed to derive hot spots as an indicator of forest fire. However, most efforts to verify the hot spot data and to verify fire accidents have been restricted to the use of medium or high resolution data. At present, it is difficult to verify solely upon those data due to severe cloud cover and low revisit time. In this paper, we present a method to validate forest fire using NDVI time series data. With the freely available NDVI data from SPOT VEGETATION, we successfully detected changes in time series data which were associated with fire accidents.
Simulation on the Use of LOSAT Data for Rice Field Mapping Trisasongko, Bambang H.; Panuju, Dyah R.; Tjahjono, Boedi; Barus, Baba; Wijayanto, Hari; Raimadoya, Mahmud A.; Irzaman, Irzaman
Makara Journal of Technology Vol 14, No 2 (2010)
Publisher : Directorate of Research and Community Services, Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.7454/mst.v14i2.187

Abstract

Since the launch of LAPAN-TUBSAT satellite in 2007, Indonesia has been developing mission on earth observation missions for various applications. The next generation mission, called LAPAN-ORARI Satellite (LOSAT), is currently under development and expected to be launched in 2011. In order to facilitate the applications, a thorough assessment of the sensor should be made. This paper presents an examination of simulated LOSAT data for rice monitoring and mapping purposes coupled with QUEST statistical tree. We found that three-band simulated LOSAT data were suitable for the task with reasonably high accuracy.