Using spatial statistics to identify emerging hot spots of forest loss. (7th February 2017)

• Record Type: Journal Article
• Authors: Harris, Nancy L; Goldman, Elizabeth; Gabris, Christopher; Nordling, Jon; Minnemeyer, Susan; Ansari, Stephen; Lippmann, Michael; Bennett, Lauren; Raad, Mansour; Hansen, Matthew; Potapov, Peter
• Is Part Of: Environmental research letters. Volume 12:Number 2(2017:Feb.)
• Publication Date: 2017-02-07
• Subjects: deforestation -- spatial statistics -- Brazil -- Democratic Republic of Congo -- Indonesia; Environmental sciences -- Periodicals; Human ecology -- Research -- Periodicals; Environmental health -- Periodicals; 333.7
• DOI: 10.1088/1748-9326/aa5a2f
• Languages: English
• ISSNs: 1748-9326

Abstract

Abstract: As sources of data for global forest monitoring grow larger, more complex and numerous, data analysis and interpretation become critical bottlenecks for effectively using them to inform land use policy discussions. Here in this paper, we present a method that combines big data analytical tools with Emerging Hot Spot Analysis (ArcGIS) to identify statistically significant spatiotemporal trends of forest loss in Brazil, Indonesia and the Democratic Republic of Congo (DRC) between 2000 and 2014. Results indicate that while the overall rate of forest loss in Brazil declined over the 14-year time period, spatiotemporal patterns of loss shifted, with forest loss significantly diminishing within the Amazonian states of Mato Grosso and Rondônia and intensifying within the cerrado biome. In Indonesia, forest loss intensified in Riau province in Sumatra and in Sukamara and West Kotawaringin regencies in Central Kalimantan. Substantial portions of West Kalimantan became new and statistically significant hot spots of forest loss in the years 2013 and 2014. Similarly, vast areas of DRC emerged as significant new hot spots of forest loss, with intensified loss radiating out from city centers such as Beni and Kisangani. While our results focus on identifying significant trends at the national scale, we also demonstrate the scalability of our approach to smaller or larger regions depending on the area of interest and specific research question involved. When combined with other contextual information, these statistical data models can help isolate the most significant clusters of loss occurring over dynamic forest landscapes and provide more coherent guidance for the allocation of resources for forest monitoring and enforcement efforts.