Evaluating forest policy implementation effectiveness with a cross-scale remote sensing analysis in a priority conservation area of Southwest China

China’s Natural Forest Protection Program (NFPP) and Sloping Land Conversion Program (SLCP), introduced in 1998 and 1999, respectively, are integral parts of the world’s largest reforestation effort. Statereported forest cover data indicate effective policy implementation through net forest cover expansion but overlook the scale-dependence of and spatial variation in forest cover change patterns and also lack reliable data on small-scale and illegal logging. As a result, there is considerable uncertainty over the spatial distribution of forest cover change and ultimately the policies’ effectiveness at increasing forest cover. This research uses Landsat Thematic Mapper imagery-derived multitemporal Tasseled Cap variables and a decision tree classifier to map shortand long-term forest cover change across three administrative levels in the priority conservation area of Diqing Tibetan Autonomous Prefecture in Yunnan Province. Results indicate a 73% reduction in the rate of forest cover loss and a more than doubled rate of forest cover gain from 1990e1999 to 1999e2009 across the prefecture, both of which support a positive assessment of policy implementation. However, prefectural results are countered by spatially disparate forest cover gain and loss trends at the countyand township-level in the decade following the policies’ introductions. Further, more than half of Diqing’s townships, mainly those in the prefecture’s south where tourism has been rapidly developing, saw continued net forest cover loss attributable to small-scale timber harvesting for tourism-driven construction. This research thus exposes cross-scale spatially disparate forest cover change indicative of highly differentiated policy implementation effectiveness, and shows the pattern by which regional development has redirected, rather than reduced, forest cover loss, contrary to the goals of the NFPP and SLCP. 2014 Elsevier Ltd. All rights reserved.