Forest cover maps of China in 2010 from multiple approaches and data sources: PALSAR, Landsat, MODIS, FRA, and NFI

摘要

Forests and their changes are important to the regional and global carbon cycle, biodiversity and ecosystem services. Some uncertainty about forest cover area in China calls for an accurate and updated forest cover map. In this study, we combined ALOS PALSAR orthorectified 50-m mosaic images (FED mode with HH and HV polarization) and MODIS time series data in 2010 to map forests in China. We used MODIS-based NDVI dataset (MOD13Q1, 250-m spatial resolution) to generate a map of annual maximum NDVI and used it to mask out built-up lands, barren lands, and sparsely vegetated lands. We developed a decision tree classification algorithm to identify forest and non-forest land cover, based on the signature analysis of PALSAR backscatter coefficient data. The PALSAR-based algorithm was then applied to produce a forest cover map in China in 2010. The resulting forest/non-forest classification map has an overall accuracy of 96.2% and a Kappa Coefficient of 0.91. The resultant 50-m PALSAR-based forest cover map was compared to five forest cover databases. The total forest area (2.02 x 10(6) km(2)) in China from the PALSAR-based forest map is close to the forest area estimates from China National Forestry Inventory (1.95 x 10(6) km(2)), JAXA (2.00 x 10(6) km(2)), and FAO FRA (2.07 x 10(6) km(2)). There are good linear relationships between the PALSAR-based forest map and the forest maps from the JAXA, MCD12Q1, and NLCD-China datasets at the province and county scales. All the forest maps have similar spatial distributions of forest/non-forest at pixel scale. Our PALSAR-based forest map recognizes well the agro-forests in China. The results of this study demonstrate the potential of integrating PALSAR and MODIS images to map forests in large areas. The resultant map of forest cover in China in 2010 can be used for many studies such as forest carbon cycle and ecological restoration. (C) 2015 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.