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Using a simulation analysis to evaluate the impact of crop mapping error on crop area estimation from stratified sampling

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International Journal of Digital Earth 2019年第9期12卷 1046-1066页

作者： Peijun Sun russell g.congalton Yaozhong Pan State Key Laboratory of Remote Sensing Science Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of SciencesBeijingPeople’s Republic of China Institute of Remote Sensing and Engineering Faculty of Geographical ScienceBeijing Normal UniversityBeijingPeople’s Republic of China Department of Natural Resources&the Environment University of New HampshireDurhamNHUSA

To analyze the efficiency of area estimations(*** accuracy and variation of estimation)impacted by crop mapping error,we simulated error at eight levels for thematic maps using a stratified sampling estimation *** results show that the estimation efficiency is influenced by the combination of the sample size and the error *** the trade-offs between sample size and error level showed that reducing the crop mapping error level provides the most benefit(*** estimation efficiency).Further,sampling performance differed based on the heterogeneity of the crop *** results demonstrated that the influence of increasing the error level on estimation efficiency is more detrimental in heterogeneous areas than in homogeneous ***,to obtain higher estimation efficiency,a larger sample size and lower error level or both are needed,especially in heterogeneous *** suggest that existing land-cover maps should first be used to determine the heterogeneity of the *** appropriate sample size for these areas then can be determined according to all three factors:heterogeneity,expected estimation efficiency,and sampling ***,extending our understanding of the impacts of crop mapping error is necessary for decision making to improve our ability to effectively estimate crop area.

关键词： Crop mapping error area estimation estimation efficiency landscape heterogeneity stratified sampling

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A quantitative performance comparison of paddy rice acreage estimation using stratified sampling strategies with different stratification indicators

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International Journal of Digital Earth 2018年第10期11卷 1001-1019页

作者： Peijun Sun Jinshui Zhang russell g.congalton Yaozhong Pan Xiufang Zhu State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal UniversityBeijingPeople’s Republic of China Institute of Remote Sensing and Engineering Faculty of Geographical ScienceBeijing Normal UniversityBeijingPeople’s Republic of China Department of Natural Resources and the Environment University of New HampshireDurhamNHUSA

Various indicators derived from thematic maps have been widely used to determine the strata needed to perform stratified ***,these indicators typically do not quantify the spatial errors in the crop thematic maps that are needed to reduce the *** 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 *** both areas,the performances of HEI and AI are very similar,and better than *** 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 ***,the positive performance achieved using HEI indicates the potential for incorporating thematic map uncertainty information to improve sampling efficiency.

关键词： Acreage estimation stratification sampling hybrid entropy indicator acreage indicator fragmentation indicator

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Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data

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International Journal of Digital Earth 2017年第9期10卷 944-977页

作者： Pardhasaradhi Teluguntla Prasad S.Thenkabail Jun Xiong Murali Krishna gumma russell g.congalton Adam Oliphant Justin Poehnelt Kamini Yadav Mahesh Rao Richard Massey U.S.geological Survey(USgS) Western Geographic Science CenterFlagstaffAZUSA Bay Area Environmental Research Institute(BAERI) PetalumaCAUSA International Crops Research Institute for the Semi-Arid Tropics(ICRISAT) PatancheruHyderabadIndia Department of Natural Resources and the Environment University of New HampshireDurhamNHUSA Department of Forestry and Wildland Resources Humboldt State UniversityArcataCAUSA School of Earth Science and Environmental Sustainability Northern Arizona UniversityFlagstaffAZUSA

Mapping croplands,including fallow areas,are an important measure to determine the quantity of food that is produced,where they are produced,and when they are produced(***).Furthermore,croplands are known as water guzzlers by consuming anywhere between 70%and 90%of all human water use *** these facts and the increase in global population to nearly 10 billion by the year 2050,the need for routine,rapid,and automated cropland mapping year-after-year and/or season-after-season is of great *** overarching goal of this study was to generate standard and routine cropland products,year-after-year,over very large areas through the use of two novel methods:(a)quantitative spectral matching techniques(QSMTs)applied at continental level and(b)rule-based Automated Cropland Classification Algorithm(ACCA)with the ability to hind-cast,now-cast,and *** was chosen for the study given its extensive croplands,rich history of agriculture,and yet nonexistent routine yearly generated cropland products using multi-temporal remote *** research produced three distinct cropland products using Moderate Resolution Imaging Spectroradiometer(MODIS)250-m normalized difference vegetation index 16-day composite time-series data for 16 years:2000 through *** products consisted of:(1)cropland extent/areas versus cropland fallow areas,(2)irrigated versus rainfed croplands,and(3)cropping intensities:single,double,and continuous *** accurate reference cropland product(RCP)for the year 2014(RCP2014)produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000–2015.A comparison between the ACCA-derived cropland products(ACPs)for the year 2014(ACP2014)versus RCP2014 provided an overall agreement of 89.4%(kappa=0.814)with six classes:(a)producer’s accuracies varying between 72%and 90%and(b)user’s accuracies varying between 79%and 90%.ACPs

关键词： Croplands food security automated cropland classification algorithms machine learning algorithms quantitative spectral matching techniques Australia

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