Because land cover plays an important role in global climate change studies, assessing the agreement among different land cover products is critical. Significant discrepancies have been reported among satellite-derive...Because land cover plays an important role in global climate change studies, assessing the agreement among different land cover products is critical. Significant discrepancies have been reported among satellite-derived land cover products, especially at the regional scale. Dif- ferent classification schemes are a key obstacle to the comparison of products and are considered the main fac- tor behind the disagreement among the different products. Using a feature-based overlap metric, we investigated the degree of spatial agreement and quantified the overall and class-specific agreement among the Moderate Resolution Imaging Spectoradiometer （MODIS）, Global Land Cover 2000 （GLC2000）, and the National Land Cover/Use Data- sets （NLCD） products, and the author assessed the prod- ucts by ground reference data at the regional scale over China. The areas with a low degree of agreement mostly occurred in heterogeneous terrain and transition zones, while the areas with a high degree of agreement occurred in major plains and areas with homogeneous vegetation. The overall agreement of the MODIS and GLC2000 products was 50.8% and 52.9%, and the overall accuracy was 50.3% and 41.9%, respectively. Class-specific agree- ment or accuracy varied significantly. The high-agreement classes are water, grassland, cropland, snow and ice, and bare areas, whereas classes with low agreement are shru- bland and wetland in both MODIS and GLC2000. These characteristics of spatial patterns and quantitative agree- ment could be partly explained by the complex landscapes, mixed vegetation, low separability of spectro-temporal- texture signals, and coarse pixels. The differences of class definition among different the classification schemes also affects the agreement. Each product had its advantages and limitations, but neither the overall accuracy nor the class-specific accuracy could meet the requirements of climate modeling.展开更多
As an important part of land use/cover change(LUCC), historical LUCC in long time series attracts much more attention from scholars. Currently, based on the view of combining the overall control of cropland area and ...As an important part of land use/cover change(LUCC), historical LUCC in long time series attracts much more attention from scholars. Currently, based on the view of combining the overall control of cropland area and ′top-down′ decision-making behaviors, here are two global historical land-use datasets, generally referred as the Sustainability and the Global Environment datasets(SAGE datasets) and History Database of the Global Environment datasets(HYDE datasets). However, at the regional level, these global datasets have coarse resolutions and inevitable errors. Considering various factors that influenced cropland distribution, including cropland connectivity and the limitation of natural and human factors, this study developed a reconstruction model of historical cropland based on constrained Cellular Automaton(CA) of ′bottom-up′. Then, an available labor force index is used as a proxy for the amount of cropland to inspect and calibrate these spatial patterns. Applied the reconstruction model to Shandong Province, we reconstructed its spatial distribution of cropland during 8 periods. The reconstructed results show that: 1) it is properly suitable for constrained CA to simulate and reconstruct the spatial distribution of cropland in traditional cultivated region of China; 2) compared with ′SAGE datasets′ and ′HYDE datasets′, this study have formed higher-resolution Boolean spatial distribution datasets of historical cropland with a more definitive concept of spatial pattern in terms of fractional format.展开更多
基金supported by the National Basic Research Program of China (Grant No. 2009CB723904)the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA05090201)the National Natural Science Foundation of China (Grant No. 40810059003)
文摘Because land cover plays an important role in global climate change studies, assessing the agreement among different land cover products is critical. Significant discrepancies have been reported among satellite-derived land cover products, especially at the regional scale. Dif- ferent classification schemes are a key obstacle to the comparison of products and are considered the main fac- tor behind the disagreement among the different products. Using a feature-based overlap metric, we investigated the degree of spatial agreement and quantified the overall and class-specific agreement among the Moderate Resolution Imaging Spectoradiometer （MODIS）, Global Land Cover 2000 （GLC2000）, and the National Land Cover/Use Data- sets （NLCD） products, and the author assessed the prod- ucts by ground reference data at the regional scale over China. The areas with a low degree of agreement mostly occurred in heterogeneous terrain and transition zones, while the areas with a high degree of agreement occurred in major plains and areas with homogeneous vegetation. The overall agreement of the MODIS and GLC2000 products was 50.8% and 52.9%, and the overall accuracy was 50.3% and 41.9%, respectively. Class-specific agree- ment or accuracy varied significantly. The high-agreement classes are water, grassland, cropland, snow and ice, and bare areas, whereas classes with low agreement are shru- bland and wetland in both MODIS and GLC2000. These characteristics of spatial patterns and quantitative agree- ment could be partly explained by the complex landscapes, mixed vegetation, low separability of spectro-temporal- texture signals, and coarse pixels. The differences of class definition among different the classification schemes also affects the agreement. Each product had its advantages and limitations, but neither the overall accuracy nor the class-specific accuracy could meet the requirements of climate modeling.
基金Under the auspices of National Basic Research Program of China(No.2011CB952001)National Natural Science Foundation of China(No.41340016,412013860)
文摘As an important part of land use/cover change(LUCC), historical LUCC in long time series attracts much more attention from scholars. Currently, based on the view of combining the overall control of cropland area and ′top-down′ decision-making behaviors, here are two global historical land-use datasets, generally referred as the Sustainability and the Global Environment datasets(SAGE datasets) and History Database of the Global Environment datasets(HYDE datasets). However, at the regional level, these global datasets have coarse resolutions and inevitable errors. Considering various factors that influenced cropland distribution, including cropland connectivity and the limitation of natural and human factors, this study developed a reconstruction model of historical cropland based on constrained Cellular Automaton(CA) of ′bottom-up′. Then, an available labor force index is used as a proxy for the amount of cropland to inspect and calibrate these spatial patterns. Applied the reconstruction model to Shandong Province, we reconstructed its spatial distribution of cropland during 8 periods. The reconstructed results show that: 1) it is properly suitable for constrained CA to simulate and reconstruct the spatial distribution of cropland in traditional cultivated region of China; 2) compared with ′SAGE datasets′ and ′HYDE datasets′, this study have formed higher-resolution Boolean spatial distribution datasets of historical cropland with a more definitive concept of spatial pattern in terms of fractional format.
