# Analysis and Applications of GlobeLand30: A Review
## 1 Introduction  
Land cover refers to the biophysical material over the surface of the Earth and immediate sub-surfaces and man-made structures [1]. In the last three centuries, and particularly the past few decades, humans have extensively modified the Earth's land cover because of continuing population and economic growth. Although these changes enabled the provision of critical material goods or resources (i.e., food, fiber, shelter, and freshwater) for immediate human needs, they have also undermined environmental conditions, ecosystem services, and human welfare in the long run [2; 3; 4]. For example, the expansion of cropland or urbanization at the cost of forests results in an increase of atmospheric carbon dioxide or urban heat islands [5]. Moreover, land cover changes have significant impacts on ecosystem structures and functioning, greenhouse gases (GHG) emissions, continental and global atmospheric circulation, nutrient and hydrological cycles, biogeo-chemical cycles, as well as biodiversity [4; 5]. Reducing negative impacts of land cover change on our planet while sustaining the production of essential resources has become a major concern and challenge for policy-makers and the scientific community around the world [2; 6; 7]. Land cover and change information is fundamental for understanding the state, trends, drivers, and impacts of different land activities on social and natural processes, as well as designingtransformations towards sustainable development [8; 9; 10]. A number of global land cover (GLC) datasets had been developed and used in environmental change studies, Earth system simulation, sustainable development, and many other areas [11; 12; 13]. These applications can be classified into three groups. The first is generic statistical analysis, which derives spatially-referenced and quantitative information from the land cover datasets, including acreage statistics and geographic distribution (i.e., the extent and patterns), magnitude and type of change (i.e., expansion, shrinkage or intensification, actual areas altered) [4; 14]. Such studies provide insight into the state, patterns, and changes of the main land cover classes. The second group addresses how the land cover pattern forms, that is, global divergence of artificial surface [15] and local urban expansion patterns [16], as well as how the land cover change affects the environment, such as the evaluation of ecosystem services [17; 18], estimates of carbon dioxide evasion [19], impact on the terrestrial water cycle [20], and so on. The third incorporates land cover information into earth system models to simulate the climate, biological, geochemical processes, as well as to forecast future environmental conditions and their consequences [4; 6]. Reliable GLC data at higher spatial, temporal, and thematic resolutions is the key to success for many applications. Previous coarse spatial resolution (300 m-1 km) GLC datasets did not provide sufficient spatial and thematic details of land activities, and has limited their usability in both scientific analysis and forecasting, policy debate, and political decisions [21; 22]. Given the free availability of Landsat and similar resolution satellite data, a few 30 m GLC datasets had been developed and released in the past few years, including a decadal-scale global forest cover change data [23], and a wall-to-wall GLC data product (GlobeLand30) that shows change in ten land cover types and ten years [24]. These 30 m GLC datasets provide more details of land cover patterns, permit the detection of land cover change at the scale of most human land activities, and enable a better understanding of landscape heterogeneity, as well as increase the performance of modeling and simulations [14; 21]. They have stimulated the analysis and application of land cover and change in the past few years. This paper takes GlobeLand30 as an example to review the state-of-the-art of the analysis and applications of the 30 m GLC datasets and to discuss the future directions.