Mapping forest disturbance across the China-Laos border using annual Landsat time series

The China-Laos border area is one of the world's biodiversity hotspots and has undergone unprecedented social and economic shifts related to extensive land conversion to cash plantations in recent decades. However, spatially and temporally detailed information on land conversion and forest disturbance does not exist in this area. The aim of this study is to map and analyse spatiotemporal changes in forest disturbance from 1991 to 2016 along the China-Laos border using annual Landsat time series images. Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr), based on a temporal segmentation algorithm, was used with the Atmospherically Resistant Vegetation Index (ARVI) as a disturbance index in this study. The results show that the overall accuracy of forest disturbance is 89.72% +/- 0.67% and that the estimated forest disturbance area between 1991 and 2016 reaches 4366.14 km(2) +/- 887.17 km(2) (at the 95% confidence interval). This accounts for 16.73% of the total area of forest cover in 1991, which is based on the error-adjusted estimator of area. The trend in the forest disturbance area increased from 1991 to 1995 and then continued downward. The forest disturbance area across the China-Laos border is closely related to global rubber prices as well as the policies and economies of the two countries and cooperation between China and Laos. Compared to Laos, the percentage of disturbed forest area is higher within China, except for some individual years (e.g., 1998-1999, 2004-2005, 2009 and 2016). The average annual disturbed forest area is 98.44 km(2) (0.76%) within China and 69.49 km(2) (0.53%) within Laos. Large disturbed patches are much more common within China than within Laos. This study highlights the merit of using dense Landsat time series for mapping the human-induced processes of forest disturbance in tropical areas, and the role of economic globalization and regional geopolitics in cross-border forest management.