A Review of the Available Land Cover and Cropland Maps for South Asia

Abstract

A lack of accuracy, uniqueness and the absence of systematic classification of cropland categories, together with long-pending updates of cropland mapping, are the primary challenges that need to be addressed in developing high-resolution cropland maps for south Asia. In this review, we analyzed the details of the available land cover and cropland maps of south Asia on national and regional scales in south Asia and on a global scale. Here, we highlighted the methodology adopted for classification, datasets used, classification system used for classifying different land covers and croplands and the resolution of datasets available. This listed review of different available datasets can help the reader to know which datasets to be used in their study and to understand which methodology to be chosen to further developing the accurate high-resolution land cover and cropland maps for advanced studies and for better understanding of ground reality in a timely updated version. We tried to identify the major concerns, particularly the inadequacy of knowledge regarding the spatial distribution of major crop types within south Asia, which hinder policy and strategic investment and delay the efforts to improve food security for a rapidly growing human population at a time of constant market instability and changing global climate. The overall focus of this paper is on reviewing the need for timely updated high-resolution cropland maps of south Asia.

Evaluating Forest Cover and Fragmentation in Costa Rica with a Corrected Global Tree Cover Map

“…Despite the cost in time and effort taken in this study to produce a reference data set of tree cover and map agricultural cover, there are three approaches that could lower the burden of this investment. First, many countries already possess detailed information on agriculture and other land cover types (e.g., pastures, mangroves, and swamps) that can be used for improving global forest cover maps (e.g., [93]). Existing geospatial data could directly correct the GFC product-for example, consider the coffee vector data incorporated into our agricultural cover map.…”

Cropland expansion

“…Finally, highly divergent results have been reported in published landcover datasets due to differences in classification algorithms, different satellite sensors used to collect primary imagery, and different datasets used to train the landcover identification algorithms.5 Taking into account these challenges, remote sensing data provides the opportunity for an objective, frequent, and consistent measure of landcover over time; however, ground-truthing is also important to verify satellite interpretations and analysis. Patil and Gumma (2018) provide a comprehensive review of different landcover data products and their advantages and disadvantages. We take into account the inadequacies in remote sensing data in this analysis in several ways.…”

“…For example, GlobCover cropland distribution estimates are 20 percent higher than MODISderived global cropland area estimates(Patil and Gumma 2018).…”

Mapping Land Use Land Cover Change in the Lower Mekong Basin From 1997 to 2010

“…Unfortunately, it is difficult to effectively compare results from such change maps given that they can differ in terms of the kind of satellite data used, the observed time span, the methods for generating and validating such products, the LULC change classification scheme employed, spatial resolution of the map, the geographic domain covered by the map, the objectives of the mapping project, and the organizations responsible for making LULC maps. Some of these issues are discussed by Patil and Gumma (2018) with respect to updating south Asia cropland and other land cover types. The challenges arising from the differences in LULC mapping methods may be addressed in part by comparing provenance of geospatial workflows (Tullis et al, 2015).…”