Comparing three transition potential modeling for identifying suitable sites for REDD+ projects

• Published in: Spatial information research (Online) Vol. 28; no. 2; pp. 159 - 171
• Main Authors: Parsamehr, Koosha, Gholamalifard, Mehdi, Kooch, Yahya
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.04.2020; 대한공간정보학회
• Subjects: Database Management; Earth and Environmental Science; Geographical Information Systems/Cartography; Geography; Remote Sensing/Photogrammetry; 공학일반; Multi-criteria evaluation; Central Hyrcanian forests; REDD; Site selection; Empirical transition potential modeling
• ISSN: 2366-3286; 2366-3294
• DOI: 10.1007/s41324-019-00273-1

In recent decades, rapid population growth and human improper activities accelerated deforestation. Reducing Emissions from deforestation and forest degradation (REDD+) has been introduced as a strategy for reducing deforestation in developing countries. Thus, identifying areas with high-deforestation is important for site selection of the REDD+ projects. Transition potential modeling is applied as a tool for deforestation simulation. Drastic land use changes in the Central Hyrcanian forests caused a substantial reduction in forests cover. In this research, forest cover changes of the Central Hyrcanian forests were examined. Then, transition potential modeling using of three empirical procedures included: multi-layer perceptron (MLP) neural network, logistic regression, and similarity weighted instance-based learning were performed. Model validation was examined using relative operating characteristic and figure of merit. Using multi-criteria evaluation, suitable areas for REDD+ projects were identified and site selection using zonal land suitability method was performed. The results showed that the Central Hyrcanian forest decreased about 188,607 ha during 1984–2014 and the MLP model obtained the best accuracy. This study provides a general framework for site selection of REDD+ projects and also showed that sites with different suitability were found for REDD+ projects.