Application of CMORPH Data For Forest/Land Fire Risk Prediction Model in Central Kalimantan

Indah Prasasti and Rizaldi Boer and Lailan Syaufina (2014) Application of CMORPH Data For Forest/Land Fire Risk Prediction Model in Central Kalimantan. International Journal of Remote Sensing and Earth Sciences, 11 (1). pp. 41-54. ISSN 0216-6739

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Abstract

Central Kalimantan Province is a region with high level of forest/land fire, especially during dry season. Forest/land fire is a dangerous ecosystem destroyer factor, so it needs to be anticipated and prevented as early as possible. CMORPH rainfall data have good potential to overcome the limitations of rainfall data observation. This research is aimed to obtain relationship model between burned acreage and several variables of rainfall condition, as well as to develop risk prediction model of fire occurrence and burned acreage by using rainfall data. This research utilizes information on burned acreage (Ha) and CMORPH rainfall data. The method applied in this research is statistical analysis (finding correlation and regression of two phases), while risk prediction model is generated from the resulting empirical model from relationship of rainfall variables using Monte Carlo simulation based on stochastic spreadsheet. The result of this study shows that precipitation accumulation for two months prior to fire occurrence (CH2Bl) has correlation with burned acreage, and can be estimated by using following formula (if rainfall ≤ 93 mm): Burnt Acreage (Ha) = 5.13 – 21.7 (CH2bl – 93) (R2 = 67.2%). Forest fire forecasts can be determined by using a precipitation accumulation for two months prior to fire occurrence and Monte Carlo simulation. Efforts to anticipate and address fire risk should be carried out as early as possible, i.e. two months in advance if the probability of fire risk had exceeded the value of 40%.

Item Type: Article
Uncontrolled Keywords: Forest/Land Fire Risk, CMORPH, Monte Carlo Simulation, Central Kalimantan
Subjects: Teknologi Penginderaan Jauh > Penelitian, Pengkajian, dan Pengembangan > Pemanfaatan Penginderaan Jauh > Pengolahan Data > Klasifikasi
Divisions: Deputi Penginderaan Jauh > Pusat Pemanfaatan Penginderaan jauh
Depositing User: Dinar Indrasasi
Date Deposited: 04 Sep 2021 01:14
Last Modified: 04 Sep 2021 01:14
URI: http://repositori.lapan.go.id/id/eprint/1248

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