Spatial Prioritization for Wildfire Mitigation by Integrating Heterogeneous Spatial Data: A New Multi-Dimensional Approach for Tropical Rainforests

Sakti, AD; Fauzi, AI; Takeuchi, W; Pradhan, B; Yarime, M; Vega-Garcia, C; Agustina, E; Wibisono, D; Anggraini, TS; Theodora, MO; Ramadhanti, D; Muhammad, MF; Aufaristama, M; Perdana, AMP; Wikantika, K

Spatial Prioritization for Wildfire Mitigation by Integrating Heterogeneous Spatial Data: A New Multi-Dimensional Approach for Tropical Rainforests

Sakti, AD Fauzi, AI Takeuchi, W Pradhan, B

Yarime, M Vega-Garcia, C Agustina, E Wibisono, D Anggraini, TS Theodora, MO Ramadhanti, D Muhammad, MF Aufaristama, M Perdana, AMP Wikantika, K

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Remote Sensing, 2022, 14, (3)
Issue Date:: 2022-02-01

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Field	Value	Language
dc.contributor.author	Sakti, AD
dc.contributor.author	Fauzi, AI
dc.contributor.author	Takeuchi, W
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Yarime, M
dc.contributor.author	Vega-Garcia, C
dc.contributor.author	Agustina, E
dc.contributor.author	Wibisono, D
dc.contributor.author	Anggraini, TS
dc.contributor.author	Theodora, MO
dc.contributor.author	Ramadhanti, D
dc.contributor.author	Muhammad, MF
dc.contributor.author	Aufaristama, M
dc.contributor.author	Perdana, AMP
dc.contributor.author	Wikantika, K
dc.date.accessioned	2023-03-21T02:23:10Z
dc.date.available	2023-03-21T02:23:10Z
dc.date.issued	2022-02-01
dc.identifier.citation	Remote Sensing, 2022, 14, (3)
dc.identifier.issn	2072-4292
dc.identifier.issn	2072-4292
dc.identifier.uri	http://hdl.handle.net/10453/167904
dc.description.abstract	Wildfires drive deforestation that causes various losses. Although many studies have used spatial approaches, a multi-dimensional analysis is required to determine priority areas for mitigation. This study identified priority areas for wildfire mitigation in Indonesia using a multi-dimensional approach including disaster, environmental, historical, and administrative parameters by integrating 20 types of multi-source spatial data. Spatial data were combined to produce susceptibility, carbon stock, and carbon emission models that form the basis for prioritization modelling. The developed priority model was compared with historical deforestation data. Legal aspects were evaluated for oil-palm plantations and mining with respect to their impact on wildfire mitigation. Results showed that 379,516 km2 of forests in Indonesia belong to the high-priority category and most of these are located in Sumatra, Kalimantan, and North Maluku. Historical data suggest that 19.50% of priority areas for wildfire mitigation have experienced deforestation caused by wildfires over the last ten years. Based on legal aspects of land use, 5.2% and 3.9% of high-priority areas for wildfire mitigation are in oil palm and mining areas, respectively. These results can be used to support the determination of high-priority areas for the REDD+ program and the evaluation of land use policies.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Remote Sensing
dc.relation.isbasedon	10.3390/rs14030543
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0203 Classical Physics, 0406 Physical Geography and Environmental Geoscience, 0909 Geomatic Engineering
dc.title	Spatial Prioritization for Wildfire Mitigation by Integrating Heterogeneous Spatial Data: A New Multi-Dimensional Approach for Tropical Rainforests
dc.type	Journal Article
utslib.citation.volume	14
utslib.for	0203 Classical Physics
utslib.for	0406 Physical Geography and Environmental Geoscience
utslib.for	0909 Geomatic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-21T02:23:08Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	14
utslib.citation.issue	3

Abstract:

Wildfires drive deforestation that causes various losses. Although many studies have used spatial approaches, a multi-dimensional analysis is required to determine priority areas for mitigation. This study identified priority areas for wildfire mitigation in Indonesia using a multi-dimensional approach including disaster, environmental, historical, and administrative parameters by integrating 20 types of multi-source spatial data. Spatial data were combined to produce susceptibility, carbon stock, and carbon emission models that form the basis for prioritization modelling. The developed priority model was compared with historical deforestation data. Legal aspects were evaluated for oil-palm plantations and mining with respect to their impact on wildfire mitigation. Results showed that 379,516 km2 of forests in Indonesia belong to the high-priority category and most of these are located in Sumatra, Kalimantan, and North Maluku. Historical data suggest that 19.50% of priority areas for wildfire mitigation have experienced deforestation caused by wildfires over the last ten years. Based on legal aspects of land use, 5.2% and 3.9% of high-priority areas for wildfire mitigation are in oil palm and mining areas, respectively. These results can be used to support the determination of high-priority areas for the REDD+ program and the evaluation of land use policies.

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http://hdl.handle.net/10453/167904