Abstract
Comprehensive forest cover mapping is essential for making policy and management decisions. However, creating a forest cover map from raw remote sensing data is a barrier for many users. Here, we investigated the effects of different tree cover thresholds on the accuracy of forest cover maps derived from the Global Forest Change Dataset (GFCD) across different ecological zones in a country-scale evaluation of Myanmar. To understand the effect of different thresholds on map accuracy, nine forest cover maps having thresholds ranging from 10% to 90% were created from the GFCD. The accuracy of the forest cover maps within each ecological zone and at the national scale was assessed. The overall accuracies of ecological zones other than tropical rainforest were highest when the threshold for tree cover was less than 50%. The appropriate threshold for tropical rainforests was 80%. Therefore, different optimal tree cover thresholds were required to achieve the highest overall accuracy depending on ecological zones. However, in the unique case of Myanmar, we were able to determine the threshold across the whole country. We concluded that the threshold for tree cover for creating a forest cover map should be determined according to the areal ratio of ecological zones determined from large-scale monitoring. Our results are applicable to tropical regions having similar ecological zones.
Highlights
Deforestation in tropical forests has been of concern for decades [1]
Because it is necessary to understand the accuracies of the Global Forest Change Dataset (GFCD) within different forest types and various canopy densities to be appropriate for specific local contexts [25], here, we investigated the effect of the tree cover thresholds on the accuracy of forest cover detection from the GFCD over different regions
We investigated the accuracy of forest cover maps created from the GFCD using different tree cover thresholds across the different ecological zones based on country-scale evaluation of Myanmar
Summary
Deforestation in tropical forests has been of concern for decades [1]. Because tropical deforestation negatively impacts the global carbon budget [2,3] and biodiversity [4,5,6], forest policy and management need to reverse forest loss. Forest cover maps, which identify forest and non-forest areas, are essential baseline information for tracking forest cover changes; comprehensive forest cover maps are necessary for policy and management decisions. Remote sensing is one of the tools used to provide complete forest cover maps over extensive land areas, such as entire countries. Satellite remote sensing is a commonly used to map land cover in a systematic and cost-effective fashion over a variety of spatial extents [7,8]. Creating a forest cover map from raw remote sensing data can be a barrier for users [9], because it requires expertise in remote sensing
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