Finer-Resolution Mapping of Global Land Cover: Recent Developments, Consistency Analysis, and Prospects

Abstract

Land-cover mapping is one of the foundations of Earth science. As a result of the combined efforts of many scientists, numerous global land-cover (GLC) products with a resolution of 30 m have so far been generated. However, the increasing number of fine-resolution GLC datasets is imposing additional workloads as it is necessary to confirm the quality of these datasets and check their suitability for user applications. To provide guidelines for users, in this study, the recent developments in currently available 30 m GLC products (including three GLC products and thematic products for four different land-cover types, i.e., impervious surface, forest, cropland, and inland water) were first reviewed. Despite the great efforts toward improving mapping accuracy that there have been in recent decades, the current 30 m GLC products still suffer from having relatively low accuracies of between 46.0% and 88.9% for GlobeLand30-2010, 57.71% and 80.36% for FROM_GLC-2015, and 65.59% and 84.33% for GLC_FCS30-2015. The reported accuracies for the global 30 m thematic maps vary from 67.86% to 95.1% for the eight impervious surface products that were reviewed, 56.72% to 97.36% for the seven forest products, 32.73% to 98.3% for the six cropland products, and 15.67% to 99.7% for the six inland water products. The consistency between the current GLC products was then examined. The GLC maps showed a good overall agreement in terms of spatial patterns but a limited agreement for some vegetation classes (such as shrub, tree, and grassland) in specific areas such as transition zones. Finally, the prospects for fine-resolution GLC mapping were also considered. With the rapid development of cloud computing platforms and big data, the Google Earth Engine (GEE) greatly facilitates the production of global fine-resolution land-cover maps by integrating multisource remote sensing datasets with advanced image processing and classification algorithms and powerful computing capability. The synergy between the spectral, spatial, and temporal features derived from multisource satellite datasets and stored in cloud computing platforms will definitely improve the classification accuracy and spatiotemporal resolution of fine-resolution GLC products. In general, up to now, most land-cover maps have not been able to achieve the maximum (per class or overall) error of 5%–15% required by many applications. Therefore, more efforts are needed toward improving the accuracy of these GLC products, especially for classes for which the accuracy has so far been low (such as shrub, wetland, tundra, and grassland) and in terms of the overall quality of the maps.

Spatiotemporal Analysis of Land Cover and the Effects on Ecosystem Service Values in Rupandehi, Nepal from 2005 to 2020

“…Land cover (LC) is an important parameter to study and track the local, regional, and global changes of the earth's surface. It has been a crucial variable for different studies such as climate change, food security, environmental studies, conservational strategies, hydrology, and landscape planning [1][2][3][4]. LC, being subjected to natural phenomena and anthropogenic causes of land use, is very dynamic [2,3].…”

“…Various global LC data have been produced over the years, such as GLC2000 [8], MCD12, and GLCNMO, GlobCover [9], IGBP DISCover [10], MODIS Collection 5 global land cover [11], Global Land Surface Satellite Global Land Cover (GLASS-GLC) [12], GLC30 [13], GLC10 [6], ESA CCI Land Cover time-series [8], ESRI 10 m [14], etc. While some researchers are concentrated on a local or regional level to map out specific phenomena, for example, meteorology [15], future prediction [16,17], urban heat islands [18], climate change [19], water [20], forest [21] and biodiversity [22], and agricultural monitoring [23], current studies are also focused on improvements such as finer resolution and classification procedures [1,[24][25][26].…”

Automatically Monitoring Impervious Surfaces Using Spectral Generalization and Time Series Landsat Imagery from 1985 to 2020 in the Yangtze River Delta

“…The "cross-comparison-based" method was implemented to intuitively compare our time series impervious surface results with the existing multitemporal products. Based on the review of [48], there were eight global 30 m impervious surface products for free access, but only 5 of them contained multitemporal characteristics, including the Global Human Settlement Layer (GHSL, four epochs of 1975, 1990, 2000, and 2014) [49], the GlobeLand30 impervious layer (three epochs of 2000, 2010, and 2020) [50], Global Artificial Impervious Area (GAIA, 34 epochs from 1985 to 2018) [8], the global multitemporal urban land (NUACI, seven epochs of 1980,1990,1995,2000,2005,2010, and 2015) [11], and the Global Urban Dynamic (GUD, 31 epochs from 1985 to 2015) [51]. It should be noted that the GAIA and GUD annual impervious surface products were grouped into eight epochs at an interval of five years for the sake of cross-comparisons.…”

A spatiotemporal ensemble machine learning framework for generating land use / land cover time-series maps for Europe (2000 – 2019) based on LUCAS, CORINE and GLAD Landsat

“…Key applications of land cover change maps are to inform policy (Duveiller et al, et al, 2021;Homer et al, 2007;Malinowski et al, 2020;Pflugmacher et al, 2019) land cover products based on using Machine Learning and offering predictions (or their refinements) at high spatial resolutions for the whole of continental Europe (Table 1). The increasing number of land cover applications and datasets in Europe can largely be attributed to (1) the extensive Land use and Coverage Area frame Survey (LUCAS) in-situ point data being publicly available for research, and (2) NASA's Landsat and ESA's Sentinel multispectral images being increasingly available for spatial analysis (L. Liu et al, 2021;Szantoi et al, 2020).…”