Anjar Dimara Sakti, Adam Irwansyah Fauzi, Wataru Takeuchi, Biswajeet Pradhan, Masaru Yarime, Cristina Vega-Garcia, Elprida Agustina, Dionisius Wibisono, Tania Septi Anggraini, Megawati Oktaviani Theodora, Desi Ramadhanti, Miqdad Fadhil Muhammad, Muhammad Aufaristama, Agung Mahadi Putra Perdana, Ketut Wikantika
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. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Remote Sensing and Geographic Information Sciences Research Group, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung, 40132, Indonesia; Center for Remote Sensing, Institut Teknologi Bandung, Bandung, 40132, Indonesia; Geospatial Institute for Sustainability Action, Bandung, 40132, Indonesia; Department of Geomatics Engineering, Faculty of Regional and Infrastructure Technology, Institut Teknologi Sumatera, Lampung Selatan, 35365, Indonesia; Research and Innovation Center for Geospatial Information Science, Institut Teknologi Sumatera, Lampung Selatan, 35365, Indonesia; Institute for Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan; Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, Faculty of Engineering & IT, University of Technology Sydney, Sydney, 2007, NSW, Australia; Center of Excellence for Climate Change Research, King Abdulaziz University, Jeddah, 21589, Saudi Arabia; Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia (UKM), Selangor, Bangi, 43600, Malaysia; Division of Public Policy, The Hong Kong University of Science and Technology, Hong Kong; Graduate School of Public Policy, University of Tokyo, Tokyo, 113-0033, Japan; Department of Agricultural and Forest Engineering-JRU CTFC-AGROTECNIO, Universitat de Lleida, Lleida, 25003, Spain; Air and Waste Management Research Group, Faculty of Civil and Environmental Engineering, Institut Teknologi Bandung, Bandung, 40132, Indonesia; Department of Applied Earth Sciences, Faculty of Geo-Information Science and Earth Observation, University of Twente, Hengelosestraat 99, Enschede, 7514AE, Netherlands