The Global 2000-2020 Land Cover and Land Use Change Dataset Derived From the Landsat Archive: First Results

Abstract

Recent advances in Landsat archive data processing and characterization enhanced our capacity to map land cover and land use globally with higher precision, temporal frequency, and thematic detail. Here, we present the first results from a project aimed at annual multidecadal land monitoring providing critical information for tracking global progress towards sustainable development. The global 30-m spatial resolution dataset quantifies changes in forest extent and height, cropland, built-up lands, surface water, and perennial snow and ice extent from the year 2000 to 2020. Landsat Analysis Ready Data served as an input for land cover and use mapping. Each thematic product was independently derived using locally and regionally calibrated machine learning tools. Thematic maps validation using a statistical sample of reference data confirmed their high accuracy (user’s and producer’s accuracies above 85% for all land cover and land use themes, except for built-up lands). Our results revealed dramatic changes in global land cover and land use over the past 20 years. The bitemporal dataset is publicly available and serves as a first input for the global land monitoring system.

Spatiotemporal land use and cover changes across agroecologies and slope gradients using geospatial technologies in Zoa watershed, Southwest Ethiopia

“…For instance, agriculture expanded due to increasing demand for food products in Brazil, Africa, Central Asia, Eastern China and Southeast Asia ( Hu et al., 2021 ). Likewise, Potapov et al. (2022) found that the largest net cropland expansion was in Africa, followed by Asia and South America.…”

“…While some of the land cover themes can be directly mapped using a single-day satellite images, others may not be directly retrieved from the optical medium resolution data. The incompleteness of the Landsat observation time series decreases map accuracy in a given area with persistent cloud cover ( Potapov et al., 2022 ). Moreover, pixels are acquired at different times and different viewing geometries, under different atmospheric conditions ( Shen et al., 2022a ).…”

“…In contrast, the livelihoods of poor people are adversely disrupted since they rely on land and natural resources due to expansions of cultivated and grazing land turned into forests and grasslands in the world ( Lambin et al., 2003 ; Nkonya et al., 2012 , 2016 ). In addition, global cropland area increased by 11.5% between the 2000–2003 and 2016–2019 interval, and its largest net was increased in Africa, followed by Asia and South America ( Potapov et al., 2022 ). Similarly, agriculture has increased by 57%, while the country has lost 5% of its woodland and pasture land, and 16% of its forest acreage between 1975 and 2000 in Africa and more than 50,000 km 2 of natural vegetation has been lost per year ( Eva et al., 2006 ).…”

Assessment of Land Degradation Factors

“…It appears that these uncertainties will continue with normalized difference vegetation index (NDVI) imagery using satellite technologies that are not supported by ground reality on a global scale. LU/LC algorithms that classify global land cover into different types may not be suitable for producing scientific Assessment of Land Degradation Factors DOI: http://dx.doi.org/10.5772/intechopen.107524 approaches and realistic values [32]. Global and national LU/LC data produced using different approaches are inadequate or inappropriate for land use management and reporting [33,34].…”

On the use of Earth Observation to support estimates of national greenhouse gas emissions and sinks for the Global stocktake process: lessons learned from ESA-CCI RECCAP2

“…The long-term high resolution (10–30 m) records of Landsat and the recent Sentinel-2 data are now used to derive tree cover loss [ 42 ] and land-cover changes [ 1 , 75 , 103 ], and to improve the mapping of small fires which can result in a doubling of burned area [ 12 ]. These data can be combined with satellite-based biomass maps to estimate biomass carbon changes [ 45 ] as discussed above.…”