基于多时相遥感影像的东北三省作物分布信息提取
Crop area and its spatial distribution are generally considered to be essential data inputs for crop yield estimation, assessment of water productivity and adjustment of cropping structure to support science and policy applications focused on understanding the role and response of the agricultural s...
| Autores principales: | , , , , |
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| Formato: | Journal Article |
| Lenguaje: | chino |
| Publicado: |
2011
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/40429 |
| _version_ | 1855529575701807104 |
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| author | Hao, W. Mei, X. Xueliang Cai Du, J. Liu, Q. |
| author_browse | Du, J. Hao, W. Liu, Q. Mei, X. Xueliang Cai |
| author_facet | Hao, W. Mei, X. Xueliang Cai Du, J. Liu, Q. |
| author_sort | Hao, W. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Crop area and its spatial distribution are generally considered to be essential data inputs for crop yield estimation, assessment of water productivity and adjustment of cropping structure to support science and policy applications focused on understanding the role and response of the agricultural sector to environmental change issues. The objective of this research was to evaluate the applicability of time-series MODIS 250m normalized difference vegetation index (NDVI) data for large-area crop mapping over Northeast China. Spatial pattern of crop planting was obtained based on 16-day time-series MODIS 250m NDVI data from 2007 to 2008, Landsat enhanced thematic mapper plus (ETM+) images, and ground truth data using Optimal Iteration Unsupervised Classification, spectral matching technique (SMT) and Google Earth. Sub-pixel area fraction estimate was applied to estimate cropland area, rice area, spring maize area and soybean area. We found that the position precision was 85.7%, their correlation coefficient compared with statistic was 0.916, 0.685, 0.746 and 0.681 respectively, and that there was significant difference between these groups by using paired samples test. Results indicated that the method can accurately reflect various crop distributions in Northeast China and be applied for large-area crops classification and crop planting extraction. |
| format | Journal Article |
| id | CGSpace40429 |
| institution | CGIAR Consortium |
| language | Chinese |
| publishDate | 2011 |
| publishDateRange | 2011 |
| publishDateSort | 2011 |
| record_format | dspace |
| spelling | CGSpace404292025-09-08T09:09:13Z 基于多时相遥感影像的东北三省作物分布信息提取 Crop planting extraction based on multi-temporal remote sensing data in Northeast China Hao, W. Mei, X. Xueliang Cai Du, J. Liu, Q. crop yield water productivity remote sensing time series analysis Crop area and its spatial distribution are generally considered to be essential data inputs for crop yield estimation, assessment of water productivity and adjustment of cropping structure to support science and policy applications focused on understanding the role and response of the agricultural sector to environmental change issues. The objective of this research was to evaluate the applicability of time-series MODIS 250m normalized difference vegetation index (NDVI) data for large-area crop mapping over Northeast China. Spatial pattern of crop planting was obtained based on 16-day time-series MODIS 250m NDVI data from 2007 to 2008, Landsat enhanced thematic mapper plus (ETM+) images, and ground truth data using Optimal Iteration Unsupervised Classification, spectral matching technique (SMT) and Google Earth. Sub-pixel area fraction estimate was applied to estimate cropland area, rice area, spring maize area and soybean area. We found that the position precision was 85.7%, their correlation coefficient compared with statistic was 0.916, 0.685, 0.746 and 0.681 respectively, and that there was significant difference between these groups by using paired samples test. Results indicated that the method can accurately reflect various crop distributions in Northeast China and be applied for large-area crops classification and crop planting extraction. 该文选用覆盖东北三省2007年主要作物发育期内14个时相MODIS NDVI 250 m 16 d影像、2005年LandsatETM+30 m影像和大量地面调查数据,借助ISODATA非监督分类算法、作物植被系数变化曲线光谱耦合技术和GoogleEarth工具,提取了主要作物分布的空间信息.基于Landsat ETM+30 m影像采用亚像素估算方法对耕地面积系数进行了计算,分别估算了东北三省的耕地、水稻、玉米和大豆的面积,并用各个县级地图做掩模提取了耕地、水稻、玉米和大豆的面积,与同期的统计数据组成4组配对样本进行对比验证.结果表明,4组数据经配对样本的T检验都呈显著性差异,耕地相关系数最高为0.92,依次为玉米、水稻和大豆,都在0.68以上,与统计数据吻合较好,而分析结果位置精度为85.7%,提取结果能较好的反映东北三省主要农作物的空间分布状况,可为其他区域尺度主要作物空间分布信息的提取提供借鉴. 2011 2014-06-13T14:47:39Z 2014-06-13T14:47:39Z Journal Article https://hdl.handle.net/10568/40429 zh Limited Access Hao, W.; Mei, X.; Cai, Xueliang; Du, J.; Liu, Q. 2011. Crop planting extraction based on multi-temporal remote sensing data in Northeast China. In Chinese. Transactions of the Chinese Society of Agricultural Engineering, 27(1):201-207. doi: https://doi.org/10.3969/j.issn.1002-6819.2011.01.033 |
| spellingShingle | crop yield water productivity remote sensing time series analysis Hao, W. Mei, X. Xueliang Cai Du, J. Liu, Q. 基于多时相遥感影像的东北三省作物分布信息提取 |
| title | 基于多时相遥感影像的东北三省作物分布信息提取 |
| title_full | 基于多时相遥感影像的东北三省作物分布信息提取 |
| title_fullStr | 基于多时相遥感影像的东北三省作物分布信息提取 |
| title_full_unstemmed | 基于多时相遥感影像的东北三省作物分布信息提取 |
| title_short | 基于多时相遥感影像的东北三省作物分布信息提取 |
| title_sort | 基于多时相遥感影像的东北三省作物分布信息提取 |
| topic | crop yield water productivity remote sensing time series analysis |
| url | https://hdl.handle.net/10568/40429 |
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