Abstract
Abstract. The land cover detection on our planet at high spatial resolution has a key role in many scientific and operational applications, such as climate modeling, natural resources management, biodiversity studies, urbanization analyses and spatial demography. Thanks to the progresses in Remote Sensing, accurate and high-resolution land cover maps have been developed over the last years, aiming at detecting the spatial resolution of different types of surfaces. In this paper we propose a review of the high-resolution global land cover products developed through Earth Observation technologies. A series of general information regarding imagery and data used to produce the map, the procedures employed for the map development and for the map accuracy assessment have been provided for every dataset. The land cover maps described in this paper concern the global distribution of settlements (Global Urban Footprint, Global Human Settlement Built-Up, World Settlement Footprint), water (Global Surface Water), forests (Forest/Non-forest, Tree canopy cover), and a two land cover maps describing world in 10 generic classes (GlobeLand30 and Finer Resolution Observation and Monitoring of Global Land Cover). The advantages and shortcomings of these maps and of the methods employed to produce them are summarized and compared in the conclusions.
Highlights
The number of high-resolution (HR) global land cover (LC) maps has increased
HRLC is useful for numerous applications such as climate modeling, biodiversity studies, natural resource management, inter-comparison, etc
Global Surface Water (GSW) is a dataset representing the spatio-temporal variability of the global water surface and the changes occurred over the time span 1984-2016 (32 years), with a resolution of 30 m (Pekel et al, 2016)
Summary
The number of high-resolution (HR) global land cover (LC) maps has increased. This is not a surprise given the advances in Remote Sensing. We made a review of the relevant literature of the existing HRLC maps to make an overview of their main characteristics, and to make a comparison among them. The scope of the review is to make an outline of the available HRLC and their characteristics. The review will be based on the scientific literature on binary and multiclass global HRLCs. Binary datasets include Global Urban Footprint (GUF) (Esch et al, 2018), Global Human Settlement Built-Up Grid – Sentinel-1 (GHS BU S1NODSM) (Corbane et al, 2017), Global Human Settlement Built-Up Grid – Landsat (GHS BU LDSMT) (Corbane et al, 2017), Global Surface Water (GSW) (Pekel et al, 2016), Forest / Non-Forest (FNF) (Shimada et al, 2014), Tree canopy cover (Hansen et al, 2013), World Settlement Footprint (WSF) (Marconcini et al, 2020), GlobeLand (GL30) (Chen et al, 2015), Finer Resolution Observation and Monitoring of Global Land Cover (FROM-GLC).
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