Please login first
Utilizing GIS and remote sensing to inform spatial conservation planning: Assessing vulnerability to future tropical forest loss in southern Belize
* 1 , , ,
1  Ya'ache Conservation Trust

Published: 22 March 2018 by MDPI in 2nd International Electronic Conference on Remote Sensing session Applications

Throughout the world, deforestation, degradation, and fragmentation threaten the integrity of tropical forests and the biodiversity that they contain. Although southern Belize is generally recognized as a highly forested landscape, it is becoming increasingly threatened as unsustainable agricultural practices reduce its capacity to provide life-supporting ecosystem services. Deforestation data is necessary for forest managers to efficiently allocate resources and make decisions for proper conservation and resource management. This study utilized satellite imagery to map and analyze current forest cover and recent forest loss in southern Belize in order to identify the areas that are the most susceptible to future deforestation. A forest cover change analysis was conducted using a supervised classification of Landsat imagery and ground-truthed land cover points in Google Earth Engine. Then, a proximity-based model was used to predict where deforestation could occur in the future based on the drivers of deforestation. The assessment indicates that the agricultural frontier will continue to expand into recently untouched forests. The results of this study will be used in spatial conservation planning in order to strategically focus conservation efforts in the most threatened areas in southern Belize. The sites that were found to be most vulnerable to future deforestation will be locations for implementing law enforcement and compliance, sustainable agriculture, and community outreach. This method could be applied to conservation planning in other regions to prioritize the protection of threatened areas.

Keywords: land use, land cover, deforestation, spatial conservation planning, tropical forest, remote sensing, Landsat