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 use… Show more
“…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.…”
Global tree cover products face challenges in accurately predicting tree cover across biophysical gradients, such as precipitation or agricultural cover. To generate a natural forest cover map for Costa Rica, biases in tree cover estimation in the most widely used tree cover product (the Global Forest Change product (GFC) were quantified and corrected, and the impact of map biases on estimates of forest cover and fragmentation was examined. First, a forest reference dataset was developed to examine how the difference between reference and GFC-predicted tree cover estimates varied along gradients of precipitation and elevation, and nonlinear statistical models were fit to predict the bias. Next, an agricultural land cover map was generated by classifying Landsat and ALOS PalSAR imagery (overall accuracy of 97%) to allow removing six common agricultural crops from estimates of tree cover. Finally, the GFC product was corrected through an integrated process using the nonlinear predictions of precipitation and elevation biases and the agricultural crop map as inputs. The accuracy of tree cover prediction increased by ≈29% over the original global forest change product (the R2 rose from 0.416 to 0.538). Using an optimized 89% tree cover threshold to create a forest/nonforest map, we found that fragmentation declined and core forest area and connectivity increased in the corrected forest cover map, especially in dry tropical forests, protected areas, and designated habitat corridors. By contrast, the core forest area decreased locally where agricultural fields were removed from estimates of natural tree cover. This research demonstrates a simple, transferable methodology to correct for observed biases in the Global Forest Change product. The use of uncorrected tree cover products may markedly over- or underestimate forest cover and fragmentation, especially in tropical regions with low precipitation, significant topography, and/or perennial agricultural production.
“…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.…”
Global tree cover products face challenges in accurately predicting tree cover across biophysical gradients, such as precipitation or agricultural cover. To generate a natural forest cover map for Costa Rica, biases in tree cover estimation in the most widely used tree cover product (the Global Forest Change product (GFC) were quantified and corrected, and the impact of map biases on estimates of forest cover and fragmentation was examined. First, a forest reference dataset was developed to examine how the difference between reference and GFC-predicted tree cover estimates varied along gradients of precipitation and elevation, and nonlinear statistical models were fit to predict the bias. Next, an agricultural land cover map was generated by classifying Landsat and ALOS PalSAR imagery (overall accuracy of 97%) to allow removing six common agricultural crops from estimates of tree cover. Finally, the GFC product was corrected through an integrated process using the nonlinear predictions of precipitation and elevation biases and the agricultural crop map as inputs. The accuracy of tree cover prediction increased by ≈29% over the original global forest change product (the R2 rose from 0.416 to 0.538). Using an optimized 89% tree cover threshold to create a forest/nonforest map, we found that fragmentation declined and core forest area and connectivity increased in the corrected forest cover map, especially in dry tropical forests, protected areas, and designated habitat corridors. By contrast, the core forest area decreased locally where agricultural fields were removed from estimates of natural tree cover. This research demonstrates a simple, transferable methodology to correct for observed biases in the Global Forest Change product. The use of uncorrected tree cover products may markedly over- or underestimate forest cover and fragmentation, especially in tropical regions with low precipitation, significant topography, and/or perennial agricultural production.
“…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.…”
Section: Agricultural Area Expansionmentioning
confidence: 99%
“…For example, GlobCover cropland distribution estimates are 20 percent higher than MODISderived global cropland area estimates(Patil and Gumma 2018).…”
“…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).…”
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.