冬小麦面积遥感识别精度与空间分辨率的关系
发布时间：2018-08-30 19:50
【摘要】：不同空间分辨率农作物面积识别精度是农情遥感监测数据源选择的依据。该文采用WFV(wide field view)、MODIS(moderate-resolution imaging spectroradiometer)、OLI(operational land imager)、Google Earth影像,在天津市武清区选择了12 km×14 km的冬小麦种植区作为研究区域,采用目视识别的方法,分析了2、5、10、15、30、100、250 m共7个空间分辨率尺度下冬小麦面积识别精度与遥感数据分辨率、农田景观破碎度之间的关系。结果表明,随着空间分辨率由2 m变化到250 m,冬小麦面积识别的总体精度逐步由98.6%降低到70.1%,精度降低28.5%;面积数量比例由5.5%扩大到110.6%,误差增加105.1个百分点;面积精度呈明显下降趋势,数量误差呈明显增加趋势,数量误差的增加速度高于精度下降的趋势。高、中、低3个景观破碎度条件下,随着分辨率由2 m降低到250 m,作物识别精度分别降低了72.8、63.2和47.0个百分点,破碎度的增加导致面积识别精度下降速度更快;同等分辨率下,破碎度越高的地区面积识别精度越低。像元内冬小麦占比与可识别能力密切相关,像元占比达到45.0%以上时才能够被正确识别为冬小麦类型,像元尺度降低导致细小斑块丢失是造成面积识别与数量精度降低的主要原因。像元空间分辨率越高,冬小麦像元的光谱一致性越强,越有利于冬小麦分类精度的提高。针对农情遥感监测业务运行的需要,上述研究结果可以作为区域范围不同用户精度要求前提下遥感数据源选择的依据。
[Abstract]:The precision of crop area recognition with different spatial resolution is the basis for the selection of data sources for remote sensing monitoring of agricultural conditions. In this paper, WFV (wide field view) MODIS (moderate-resolution imaging spectroradiometer) Oli (operational land imager) Earth) image was used to select winter wheat growing area of 12 km 脳 14 km in Wuqing District of Tianjin as research area, and visual recognition method was used. The relationship between the recognition accuracy of winter wheat area and the resolution of remote sensing data and the degree of farmland landscape fragmentation in 7 spatial resolution scales were analyzed. The results showed that with the change of spatial resolution from 2 m to 250 m, the overall precision of winter wheat area recognition was gradually reduced from 98.6% to 70.1%, and the precision was reduced by 28.55.The area ratio was increased from 5.5% to 110.6%, and the error increased by 105.1%. The area accuracy is obviously decreasing, the quantity error is obviously increasing, and the increasing speed of the quantitative error is higher than that of the precision decreasing. Under the condition of high, medium and low landscape fragmentation, with the resolution decreasing from 2 m to 250 m, the precision of crop identification decreased by 72.8% 63.2% and 47.0%, respectively. The higher the degree of fragmentation, the lower the accuracy of area recognition. The proportion of winter wheat in the pixel is closely related to the recognizable ability. When the proportion of the pixel is more than 45.0%, it can be correctly recognized as the winter wheat type. The loss of small patches caused by the reduction of pixel size is the main reason for the reduction of area recognition and quantitative accuracy. The higher the spatial resolution of the pixel, the stronger the spectral consistency of the pixel of winter wheat, which is beneficial to the improvement of the classification accuracy of winter wheat. In order to meet the needs of the operation of remote sensing monitoring, the above research results can be used as the basis for the selection of remote sensing data sources under the premise of different user precision requirements in the region.
【作者单位】： 中国农业科学院农业资源与农业区划研究所;
【基金】：农业部引进国际先进农业科学技术项目:农业遥感监测系统关键技术引进(2016-X38)
【分类号】：S512.11;S127
本文编号：2214110
[Abstract]:The precision of crop area recognition with different spatial resolution is the basis for the selection of data sources for remote sensing monitoring of agricultural conditions. In this paper, WFV (wide field view) MODIS (moderate-resolution imaging spectroradiometer) Oli (operational land imager) Earth) image was used to select winter wheat growing area of 12 km 脳 14 km in Wuqing District of Tianjin as research area, and visual recognition method was used. The relationship between the recognition accuracy of winter wheat area and the resolution of remote sensing data and the degree of farmland landscape fragmentation in 7 spatial resolution scales were analyzed. The results showed that with the change of spatial resolution from 2 m to 250 m, the overall precision of winter wheat area recognition was gradually reduced from 98.6% to 70.1%, and the precision was reduced by 28.55.The area ratio was increased from 5.5% to 110.6%, and the error increased by 105.1%. The area accuracy is obviously decreasing, the quantity error is obviously increasing, and the increasing speed of the quantitative error is higher than that of the precision decreasing. Under the condition of high, medium and low landscape fragmentation, with the resolution decreasing from 2 m to 250 m, the precision of crop identification decreased by 72.8% 63.2% and 47.0%, respectively. The higher the degree of fragmentation, the lower the accuracy of area recognition. The proportion of winter wheat in the pixel is closely related to the recognizable ability. When the proportion of the pixel is more than 45.0%, it can be correctly recognized as the winter wheat type. The loss of small patches caused by the reduction of pixel size is the main reason for the reduction of area recognition and quantitative accuracy. The higher the spatial resolution of the pixel, the stronger the spectral consistency of the pixel of winter wheat, which is beneficial to the improvement of the classification accuracy of winter wheat. In order to meet the needs of the operation of remote sensing monitoring, the above research results can be used as the basis for the selection of remote sensing data sources under the premise of different user precision requirements in the region.
【作者单位】： 中国农业科学院农业资源与农业区划研究所;
【基金】：农业部引进国际先进农业科学技术项目:农业遥感监测系统关键技术引进(2016-X38)
【分类号】：S512.11;S127
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