Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm

• Published in: Science China. Earth sciences Vol. 63; no. 9; pp. 1390 - 1407
• Main Authors: Xu, Yidi, Yu, Le, Peng, Dailiang, Zhao, Jiyao, Cheng, Yuqi, Liu, Xiaoxuan, Li, Wei, Meng, Ran, Xu, Xinliang, Gong, Peng
• Format: Journal Article
• Language: English
• Published: Beijing Science China Press 01.09.2020; Springer Nature B.V
• Subjects: Additives; Afforestation; Agricultural land; Algorithms; Annual; Change detection; Computer applications; Datasets; Deforestation; Dynamics; Earth and Environmental Science; Earth Sciences; Land cover; Land management; Land use; Land use planning; Landsat; Landsat satellites; Mapping; Modelling; MODIS; Normalized difference vegetative index; Remote sensing; Research Paper; Resolution; Satellite imagery; Scientific research; Spaceborne remote sensing; Spatial discrimination; Spatial resolution; Spectroradiometers; Statistical analysis; Temporal resolution; Urbanization; Vegetation index; China; Land use land cover (LULC); Annual; Breaks for Additive Seasonal and Trend (BFAST); Change detection; China
• ISSN: 1674-7313; 1869-1897
• DOI: 10.1007/s11430-019-9606-4

Annual land use land cover (LULC) change information at medium spatial resolution (i.e. at 30 m) is required in numerous subjects, such as biophysical modelling, land management and global change studies. Annual LULC information, however, is usually not available at continental or national scale due to reasons such as insufficient remote sensing data coverage or lack of computational capabilities. Here we integrate high temporal resolution and coarse spatial resolution satellite images (i.e., Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Inventory Modelling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI)) with high spatial resolution datasets (China’s Land-Use/cover Datasets (CLUDs) derived from 30-meter Landsat TM/ETM+/OLI) to generate reliable annual nominal 30 m LULC maps for the whole of China between 1980 and 2015. We also test the performance of a statistical based change detection algorithm (Breaks for Additive Seasonal and Trend), originally designed for tracking forest change, in classifying all-type LULC change. As a result, a nominal 30 m annual land use/land cover datasets (CLUD-A) from 1980 to 2015 was developed for the whole China. The mapping results were assessed with a change sample dataset, a regional annual validation sample set and a three-year China sample set. Of the detected change years, 75.61% matched the exact time of conversion within ±1 year. Annual mapping results provided a detail process of urbanization, deforestation, afforestation, water and cropland dynamics over the past 36 years. The consistent characterization of land change dynamics for China can be further used in scientific research and to support land management for policy-makers.