PEATLAND FIRE DETECTION USING SPATIO-TEMPORAL DATA MINING ANALYSIS IN KALIMANTAN, INDONESIA

Peatland fire has been an important environmental issue in Indonesia as well as in ASEAN region as it is strongly related with transboundary haze pollution. Hotspot has been used as an indicator of forest and land fires detection. Source of hotspot in Indonesia varies from a low to a relatively high...

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Bibliographische Detailangaben
Veröffentlicht in:	Journal of Tropical Forest Science. - Forest Research Institute Malaysia. - 30(2018), 2, Seite 154-162
1. Verfasser:	Syaufina, L (VerfasserIn)
Weitere Verfasser:	Sitanggang, IS
Format:	Online-Aufsatz
Sprache:	English
Veröffentlicht:	2018
Zugriff auf das übergeordnete Werk:	Journal of Tropical Forest Science
Schlagworte:	Hotspot sequential pattern mining SPADE fire characteristics enviroment Physical sciences Health sciences Applied sciences Biological sciences Information science


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100	1		\|a Syaufina, L \|e verfasserin \|4 aut
245	1	0	\|a PEATLAND FIRE DETECTION USING SPATIO-TEMPORAL DATA MINING ANALYSIS IN KALIMANTAN, INDONESIA
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520			\|a Peatland fire has been an important environmental issue in Indonesia as well as in ASEAN region as it is strongly related with transboundary haze pollution. Hotspot has been used as an indicator of forest and land fires detection. Source of hotspot in Indonesia varies from a low to a relatively high degree of accuracy. Not all hotspots are strong indicators of forest and land fires. The incidence of hotspots in a sequence of at least three days in an adjacent location can be a strong indicator of land and forest fire occurrences. The use of spatio-temporal data mining approach to obtain sequence patterns of hotspots is still in question. This study aims to obtain sequence patterns of hotspot occurrence in Kalimantan peatland in 2015 by applying a spatio-temporal data mining approach. The algorithm used is Sequential Pattern Discovery Equivalent Classes (SPADE) that enables to discover all possible sequence patterns on hotspot datasets. Verification of hotspot sequences was conducted through ground truth and fire supression data. The study proved that real fire detection is indicated by three consecutive days of hotspot occurrences. The fire detection in peatland area is strongly affected by fire characteristics dominated by ground fire.
540			\|a © Forest Research Institute Malaysia
650		4	\|a Hotspot
650		4	\|a sequential pattern mining
650		4	\|a SPADE
650		4	\|a fire characteristics
650		4	\|a enviroment
650		4	\|a Physical sciences \|x Earth sciences \|x Geography \|x Land \|x Rangelands \|x Wetlands \|x Peatlands
650		4	\|a Health sciences \|x Health and wellness \|x Public health \|x Disasters \|x Fires \|x Wildfires \|x Forest fires
650		4	\|a Applied sciences \|x Materials science \|x Materials \|x Carbonaceous materials \|x Peat
650		4	\|a Applied sciences \|x Research methods \|x Data collection
650		4	\|a Physical sciences \|x Earth sciences \|x Geography \|x Land \|x Rangelands \|x Wetlands \|x Swamps
650		4	\|a Biological sciences \|x Ecology \|x Population ecology \|x Synecology \|x Biocenosis \|x Plant communities \|x Forests \|x Tropical forests
650		4	\|a Biological sciences \|x Biology \|x Botany \|x Forestry
650		4	\|a Information science \|x Information management \|x Data management \|x Data processing \|x Data mining
650		4	\|a Information science \|x Data products \|x Datasets
650		4	\|a Applied sciences \|x Engineering \|x Fire engineering \|x Fire detection
655		4	\|a research-article
700	1		\|a Sitanggang, IS \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Journal of Tropical Forest Science \|d Forest Research Institute Malaysia \|g 30(2018), 2, Seite 154-162 \|w (DE-627)385613326 \|w (DE-600)2142662-4 \|x 25219847 \|7 nnns
773	1	8	\|g volume:30 \|g year:2018 \|g number:2 \|g pages:154-162
856	4	0	\|u https://www.jstor.org/stable/26409964 \|3 Volltext
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