Publications

Zhai, JH; Xiao, CW; Feng, ZM; Liu, Y (2023). Are there suitable global datasets for monitoring of land use and land cover in the tropics? Evidences from mainland Southeast Asia. GLOBAL AND PLANETARY CHANGE, 229, 104233.

Abstract
The freely available Land Use and Land Cover (LULC) datasets are effective tool for tracking land surface changes, ecosystem dynamics, and carbon cycle. However, the issue of the uncertainly, applicability, and limitations of LULC data products is always a challenge and varies distinctively worldwide, especially in the tropics where forest loss is rapid. Eight of the most widely used LULC datasets, here, including 500-m MCD12Q1, 300-m ESA CCI-LC, 30-m GlobelLand30, 30-m GLC_FCS30, 30-m FROM-GLC, 10-m World Cover, 10-m Esri Land Cover, and 10-m FROM-GLC10, were statistically compared and evaluated the consistency and reliability in Mainland Southeast Asia (MSEA). The results revealed that the FROM-GLC10, World Cover, and GLC_FCS30 have higher accuracy than the other five datasets, with overall accuracy ranging from 90.8 to 92.2%, while FROM-GLC had the lowest accuracy of 77.7%. GlobeLand30, FROM-GLC, and FROM-GLC10 were the most similar with a correlation coefficient of 0.999 compared to only 0.882 for ESA CCI-LC and World Cover. Eight LULC-based forests (52.2%-70.4%) and cropland (21.6%-43.9%) are the predominant LULCs in MSEA, with the ESA CCI-LC having the largest estimated area (844,800 km2, 43.9%) of cropland, which also results in it having the smallest area (52.2%) of forest. Compared to other datasets, Esri Land Cover had the most extensive distribution of built-up land (4.9%), while World Cover had larger areas of grassland cover (9.8%). In particular, the spatial patterns of cropland and forest are highly consistent, but there are significant local differences, e.g., in western Myanmar and southern Vietnam. The MCD12Q1, ESA CCI-LC, and GLC_FCS30 showed consistent overall trends in forest change across the five countries of MSEA over the past two decades, while GlobelLand30 produced different results. Our cross-comparison and evaluation based on eight global LULC datasets in MSEA highlighted the consistency and differences, which will help to select the suitable dataset for specific needs (e.g., regional ecosystem response to forest loss) in the future.

DOI:
10.1016/j.gloplacha.2023.104233

ISSN:
1872-6364