检索结果-南通市图书馆

Evaluation of global land cover maps for cropland area estimation in the conterminous United States

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

International Journal of Digital Earth 2015年第2期8卷 102-117页

作者： Lu Liang Peng Gong Department of Environmental Science Policy and ManagementUniversity of CaliforniaBerkeleyCAUSA Ministry of Education Key Laboratory for Earth System Modeling Center for Earth System ScienceTsinghua UniversityBeijingChina

global land cover data could provide continuously updated cropland acreage and distribution information,which is essential to a wide range of applications over large geographical *** area estimates were evaluated in the conterminous USA from four recent global land cover products:MODIS land cover(MODISLC)at 500-m resolution in 2010,Globcover at 300-m resolution in 2009,FROM-GLC and FROM-GLC-agg at 30-m resolution based on landsat imagery circa 2010 against the US Department of Agriculture survey *** estimators derived from the 30-m resolution Cropland Data Layer were applied to MODIS and Globcover land cover products,which greatly improved the estimation accuracy of MODISLC by enhancing the correlation and decreasing mean deviation(MDev)and RMSE,but were less effective on Globcover *** found that,in the USA,the CDL adjusted MODISLC was more suitable for applications that concern about the aggregated county cropland acreage,while FROM-GLC-agg gave the least deviation from the survey at the state *** between land cover map estimates and survey estimates is significant,but stronger at the state level than at the county *** regions where most mismatches happen at the county level,MODIS tends to underestimate,whereas MERIS and landsat images incline to *** uncertainties should be taken into consideration in relevant *** interannual and seasonal effects,R 2 of the FROM-GLC regression model increased from 0.1 to 0.4,and the slope is much closer to *** analysis shows that images acquired in growing season are most suitable for landsat-based cropland mapping in the conterminous USA.

关键词： cropland area global land cover FROM-GLC area estimation NASS survey

A global land cover map produced through integrating multi-source datasets

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Big Earth Data 2019年第3期3卷 191-219页

作者： Min Feng Yan Bai Center of Three Poles Observation and Big Data CAS Center for Excellence in Tibetan Plateau Earth Sciences and Institute of Tibetan Plateau ResearchChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application NanjingChina

In the past decades,global land cover datasets have been produced but also been criticized for their low accuracies,which have been affecting the applications of these *** a new global dataset requires a tremendous amount of efforts;however,it is also possible to improve the accuracy of global land cover mapping by fusing the existing datasets.A decision-fuse method was developed based on fuzzy logic to quantify the consistencies and uncertainties of the existing datasets and then aggregated to provide the most certain *** method was applied to produce a 1-km global land cover map(SYNLcover)by integrating five global land cover datasets and three global datasets of tree cover and *** were carried out to assess the quality:1)inter-comparison of the datasets revealed that the SYNLcover dataset had higher consistency than these input global land cover datasets,suggesting that the data fusion method reduced the disagreement among the input datasets;2)quality assessment using the human-interpreted reference dataset reported the highest accuracy in the fused SYNLcover dataset,which had an overall accuracy of 71.1%,in contrast to the overall accuracy between 48.6%and 68.9%for the other global land cover datasets.

关键词： global land cover data integration accuracy evaluation

High-resolution remote sensing mapping of global land water

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Science China Earth Sciences 2014年第8期57卷 2305-2316页

作者： LIAO AnPing CHEN LiJun CHEN Jun HE ChaoYing CAO Xin CHEN Jin PENG Shu SUN FangDi GONG Peng National Geomatics Center of China State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University International Institute of Earth System Science Nanjing University Center for Earth System Science Tsinghua University

land water, one of the important components of land cover, is the indispensable and important basic information for climate change studies, ecological environment assessment, macro-control analysis, etc. This article describes the overall study on land water in the program of global land cover remote sensing mapping. Through collection and processing of landsat TM/ETM+, China's HJ-1 satellite image, etc., the program achieves an effective overlay of global multi-spectral image of 30 m resolution for two base years, namely, 2000 and 2010, with the image rectification accuracy meeting the requirements of 1:200000 mapping and the error in registration of images for the two periods being controlled within 1 pixel. The indexes were designed and selected reasonably based on spectral features and geometric shapes of water on the scale of 30 m resolution, the water information was extracted in an elaborate way by combining a simple and easy operation through pixel-based classification method with a comprehensive utilization of various rules and knowledge through the object-oriented classification method, and finally the classification results were further optimized and improved by the human-computer interaction, thus realizing high-resolution remote sensing mapping of global water. The completed global land water data results, including global land 30-water 2000 and global land 30-water 2010, are the classification results featuring the highest resolution on a global scale, and the overall accuracy of self-assessment is 96%. These data are the important basic data for developing relevant studies, such as analyzing spatial distribution pattern of global land water, revealing regional difference, studying space-time fluctuation law, and diagnosing health of ecological environment.

关键词： global land cover land surface water 30 m resolution classification method remote sensing mapping

High-resolution land cover classification:cost-effective approach for extraction of reliable training data from existing land cover datasets

维普期刊数据库

同方期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

International Journal of Digital Earth 2023年第1期16卷 3618-3636页

作者： Gorica Bratic Vasil Yordanov Maria Antonia Brovelli Politecnico di Milano MilanItaly

There has been a significant increase in the availability of global high-resolution land cover(HRLC)datasets due to growing demand and favorable technological ***,this has brought forth the challenge of collecting reference data with a high level of detail for global *** photo-interpretation is considered optimal for collecting quality training data for global HRLC mapping,some producers of existing HRLCs use less trustworthy sources,such as existing land cover at a lower resolution,to reduce *** work proposes a methodology to extract the most accurate parts of existing HRLCs in response to the challenge of providing reliable reference data at a low *** methodology combines existing HRLCs by intersection,and the output represents a Map Of land cover Agreement(MOLCA)that can be utilized for selecting training ***’s effectiveness was demonstrated through HRLC map production in Africa,in which it generated 48,000 *** best classification test had an overall accuracy of 78%.This level of accuracy is comparable to or better than the accuracy of existing HRLCs obtained from more expensive sources of training data,such as photo-interpretation,highlighting the cost-effectiveness and reliability potential of the developed methodology in supporting global HRLC production.

关键词： High-resolution land cover global land cover training data reference data data quality