Inconsistency distribution patterns of different remote sensing land-cover data from the perspective of ecological zoning

Abstract

Analyzing consistency of different land-cover data is significant to reasonably select land-cover data for regional development and resource survey. Existing consistency analysis of different datasets mainly focused on the phenomena of spatial consistency regional distribution or accuracy comparison to provide guidelines for choosing the land-cover data. However, few studies focused on the hidden inconsistency distribution rules of different datasets, which can provide guidelines not only for users to properly choose them but also for producers to improve their mapping strategies. Here, we zoned the Sindh province of Pakistan by the Terrestrial Ecoregions of the World as a case to analyze the inconsistency patterns of the following three datasets: GlobeLand30, FROM-GLC, and regional land cover (RLC). We found that the inconsistency of the three datasets was relatively low in areas having a dominant type and also showing homogeneity characteristics in remote sensing images. For example, cropland of the three datasets in the ecological zoning of Northwestern thorn scrub forests showed high consistency. In contrast, the inconsistency was high in areas with strong heterogeneity. For example, in the southeast of the Thar desert ecological zone where cropland, grassland, shrubland, and bareland were interleaved and the surface cover complexity was relatively high, the inconsistency of the three datasets was relatively high. We also found that definitions of some types in different classification systems are different, which also increased the inconsistency. For example, the definitions of grassland and bareland in GlobeLand30 and RLC were different, which seriously affects the consistency of these datasets. Hence, producers can use the existing land-cover products as reference in ecological zones with dominant types and strong homogeneity. It is necessary to pay more attention on ecological zoning with complex land types and strong heterogeneity. An effective way is standardizing the definitions of complex land types, such as forest, shrubland, and grassland in these areas.

Consistency Analysis and Accuracy Assessment of Eight Global Forest Datasets over Myanmar

“…NDVI, EVI, and reflectance data are still the three main types of satellite data for forest mapping, making full use of the implied phenological information in NDVI and EVI is important [46]. In the development of remote sensing mapping, it has been shown that adding other data sources such as radar data, hyperspectral data, and so on, can provide new possibilities for surface coverage mapping [47][48][49][50].…”

Consistency Analysis and Accuracy Assessment of Three Global Ten-Meter Land Cover Products in Rocky Desertification Region—A Case Study of Southwest China

“…Currently, large regional land cover products are being developed by major agencies around the world, including the USGS IGBP DISCOVER product [6], 1km UMD land cover products at the University of Maryland, USA [7], GLC2000 products from the EU Joint Research Centre [8], MODIS land cover products from Boston University [9], GLOBCOVER products produced by the European Space Agency [10], China National Basic Geographic Information Centre global 30 m land cover product in 2014 [11], Copernicus Global Land Services Annual 100 m Global Land Cover Product [12], and the NLCD land cover products produced by the USGS [13]. While these coarser resolution land cover products have provided valuable information for many studies [14][15][16], some studies have shown that they have low classification accuracy in transition zones with heterogeneous landscapes, where finer resolution land cover products are needed [17,18].…”