Blue colour-coated steel roofs(BCCSRs)offer a lightweight and economical option to concrete and other cladding in buildings,but they are also controversial for altering the surface energy budget and water cycle.Obtain...Blue colour-coated steel roofs(BCCSRs)offer a lightweight and economical option to concrete and other cladding in buildings,but they are also controversial for altering the surface energy budget and water cycle.Obtaining spatial information about BCCSRs is crucial for exploring the environmental impacts of man-made landscapes.However,existing methods are not always effective due to the variety of BCCSR types and background conditions.To overcome these limitations,we proposed a new index(called BCCSI)based on Sentinel-2 multispectral images to map the commonly used BCCSRs.Five typical study areas were chosen worldwide to develop and validate the BCcSl.Based on spectral analysis,we constructed the BCCSl using the blue,red,green,and shortwave infrared 2(SWIR2)bands to highlight the BCCSR while suppressing the background condition.Compared with five existing indices,the BCCSl was effective in the visual evaluation,separability analysis and BCCSR mapping.Moreover,the BCCSI achieved similar accuracy to the supervised classifier while avoiding the time-consuming and laborious effort of sample collection.Furthermore,the BCCSl showed its applicability in medium-resolution satellite data,such as Landsat-8 imagery.Thus,the proposed BCCSI provides a viable scheme for global BCCSR mapping and analysis.展开更多
Various indicators derived from thematic maps have been widely used to determine the strata needed to perform stratified sampling.However,these indicators typically do not quantify the spatial errors in the crop thema...Various indicators derived from thematic maps have been widely used to determine the strata needed to perform stratified sampling.However,these indicators typically do not quantify the spatial errors in the crop thematic maps that are needed to reduce the uncertainty.To address this lack of error information,this paper introduces a hybrid entropy indicator(HEI).Two conventional indicators,the acreage indicator(AI)and the fragmentation indicator(FI),were also evaluated to compare the results of the three indicators in a homogeneous agricultural area(Pinghu,PH)and a heterogeneous agricultural area(Zhuji,ZJ).The results show that HEI performs the best in heterogeneous areas with the lowest coefficient of variation(CV)(as low as 1.59%)and also has the highest estimation accuracy with the lowest standard deviation of estimation.For both areas,the performances of HEI and AI are very similar,and better than FI.These results highlight that the HEI should be considered as an effective indicator and used in place of AI and FI to help improve sampling efficiency of crop acreage estimation,while FI is not recommended.Furthermore,the positive performance achieved using HEI indicates the potential for incorporating thematic map uncertainty information to improve sampling efficiency.展开更多
To analyze the efficiency of area estimations(i.e.estimation accuracy and variation of estimation)impacted by crop mapping error,we simulated error at eight levels for thematic maps using a stratified sampling estimat...To analyze the efficiency of area estimations(i.e.estimation accuracy and variation of estimation)impacted by crop mapping error,we simulated error at eight levels for thematic maps using a stratified sampling estimation methodology.The results show that the estimation efficiency is influenced by the combination of the sample size and the error level.Evaluating the trade-offs between sample size and error level showed that reducing the crop mapping error level provides the most benefit(i.e.higher estimation efficiency).Further,sampling performance differed based on the heterogeneity of the crop area.The results demonstrated that the influence of increasing the error level on estimation efficiency is more detrimental in heterogeneous areas than in homogeneous ones.Therefore,to obtain higher estimation efficiency,a larger sample size and lower error level or both are needed,especially in heterogeneous areas.We suggest that existing land-cover maps should first be used to determine the heterogeneity of the area.The appropriate sample size for these areas then can be determined according to all three factors:heterogeneity,expected estimation efficiency,and sampling budget.Overall,extending our understanding of the impacts of crop mapping error is necessary for decision making to improve our ability to effectively estimate crop area.展开更多
基金funded by the National Natural Science Foundation of China(grant number 42192581)Open Fund of State Key Laboratory of Remote Sensing Science and Beijing Engineering Research Center for Global Land Remote Sensing Products(grant number 12800-310430005).
文摘Blue colour-coated steel roofs(BCCSRs)offer a lightweight and economical option to concrete and other cladding in buildings,but they are also controversial for altering the surface energy budget and water cycle.Obtaining spatial information about BCCSRs is crucial for exploring the environmental impacts of man-made landscapes.However,existing methods are not always effective due to the variety of BCCSR types and background conditions.To overcome these limitations,we proposed a new index(called BCCSI)based on Sentinel-2 multispectral images to map the commonly used BCCSRs.Five typical study areas were chosen worldwide to develop and validate the BCcSl.Based on spectral analysis,we constructed the BCCSl using the blue,red,green,and shortwave infrared 2(SWIR2)bands to highlight the BCCSR while suppressing the background condition.Compared with five existing indices,the BCCSl was effective in the visual evaluation,separability analysis and BCCSR mapping.Moreover,the BCCSI achieved similar accuracy to the supervised classifier while avoiding the time-consuming and laborious effort of sample collection.Furthermore,the BCCSl showed its applicability in medium-resolution satellite data,such as Landsat-8 imagery.Thus,the proposed BCCSI provides a viable scheme for global BCCSR mapping and analysis.
基金the National Natural Science Foundation of China[grant number 41301444]China Scholarship Council Qinggu Program[grant number 201406045036]+1 种基金the Major Project of High-Resolution Earth Observation System,China[grant number 20-Y30B17-9001-14/16]the China Scholarship Council(CSC).
文摘Various indicators derived from thematic maps have been widely used to determine the strata needed to perform stratified sampling.However,these indicators typically do not quantify the spatial errors in the crop thematic maps that are needed to reduce the uncertainty.To address this lack of error information,this paper introduces a hybrid entropy indicator(HEI).Two conventional indicators,the acreage indicator(AI)and the fragmentation indicator(FI),were also evaluated to compare the results of the three indicators in a homogeneous agricultural area(Pinghu,PH)and a heterogeneous agricultural area(Zhuji,ZJ).The results show that HEI performs the best in heterogeneous areas with the lowest coefficient of variation(CV)(as low as 1.59%)and also has the highest estimation accuracy with the lowest standard deviation of estimation.For both areas,the performances of HEI and AI are very similar,and better than FI.These results highlight that the HEI should be considered as an effective indicator and used in place of AI and FI to help improve sampling efficiency of crop acreage estimation,while FI is not recommended.Furthermore,the positive performance achieved using HEI indicates the potential for incorporating thematic map uncertainty information to improve sampling efficiency.
基金the Major Project of High-Resolution Earth Observation System,China[grant number 09-20A05-9001-17/18]the New Hampshire Agricultural Experiment Station.This is Scientific Contribution Number 2728the USDA National Institute of Food and Agriculture McIntire Stennis Project#NH00077-M(Accession#1002519)。
文摘To analyze the efficiency of area estimations(i.e.estimation accuracy and variation of estimation)impacted by crop mapping error,we simulated error at eight levels for thematic maps using a stratified sampling estimation methodology.The results show that the estimation efficiency is influenced by the combination of the sample size and the error level.Evaluating the trade-offs between sample size and error level showed that reducing the crop mapping error level provides the most benefit(i.e.higher estimation efficiency).Further,sampling performance differed based on the heterogeneity of the crop area.The results demonstrated that the influence of increasing the error level on estimation efficiency is more detrimental in heterogeneous areas than in homogeneous ones.Therefore,to obtain higher estimation efficiency,a larger sample size and lower error level or both are needed,especially in heterogeneous areas.We suggest that existing land-cover maps should first be used to determine the heterogeneity of the area.The appropriate sample size for these areas then can be determined according to all three factors:heterogeneity,expected estimation efficiency,and sampling budget.Overall,extending our understanding of the impacts of crop mapping error is necessary for decision making to improve our ability to effectively estimate crop area.
