多时相GF-1卫星PMS影像提取农作物种植结构

发布时间：2018-04-12 21:01

本文选题：GF-种植结构 + 遥感　；参考：《中国农业资源与区划》2017年09期

【摘要】：[目的]及时准确获取农作物种植结构信息,对农作物种植结构调整具有重要意义。[方法]文章以多期GF-1卫星PMS影像为主要数据源,通过分层分类方法成功提取了朱仙镇一个种植年内秋播蔬菜、冬小麦、春花生、早熟西瓜、夏玉米、夏花生等农作物,并通过叠加分析得到朱仙镇的农作物种植模式。[结果]朱仙镇有10种种植模式,以一年两熟的冬小麦-夏玉米为主要倒茬模式,占比49.7%,其他9种种植模式所占比例之和接近50%;从整体景观的角度对朱仙镇的主要作物种植模式进行衡量,斑块丰富度PR达到9.0,香农多样性指数SHDI达到1.483 6,说明朱仙镇种植模式多样,有利于多元农业种植结构发展;分层分类的方法能够延续最优提取结果的精度,各类作物提取精度均达到了80%以上。[结论]PMS影像的高分辨率能够清晰的表达地块边界,一定程度上呈现了非大宗作物的空间分布,GF-1卫星PMS为多尺度的农业监测提供了更多的选择。
[Abstract]:Objective: to obtain the information of crop planting structure timely and accurately, which is of great significance to the adjustment of crop planting structure.[methods] based on multiphase GF-1 satellite PMS images, a crop such as autumn vegetables, winter wheat, spring peanut, early maturing watermelon, summer corn, summer peanut and so on were successfully extracted by hierarchical classification method.And through the superposition analysis, the crop planting pattern of Zhuxian Town is obtained.[results] there were 10 planting patterns in Zhu Xianzhen, with winter wheat and summer maize as the main cropping pattern.Accounting for 49.7%, the sum of the other nine planting patterns is close to 50. From the perspective of the overall landscape, the main crop planting patterns in Zhuxian Town are measured.Patch richness PR reached 9.0, Shannon diversity index (SHDI) reached 1.483, which indicated that the planting pattern of Zhuxian Town was various, which was beneficial to the development of multiple agricultural planting structure, and the hierarchical classification method could extend the precision of the optimal extraction results.The extraction accuracy of all kinds of crops has reached more than 80%.[conclusion] the high resolution of PMS images can clearly express the land boundary, which to some extent presents the spatial distribution of non-staple crops and PMS provides more choices for multi-scale agricultural monitoring.
【作者单位】：河南省农业科学院农业经济与信息研究所;郑州澍青医学高等专科学校;中国农业科学院农业资源与农业区划研究所;
【基金】：河南省科技攻关重点项目“基于‘互联网+’的农情遥感监测与服务研究”(172102110090) 国家自然科学基金项目“利用遥感与高光谱技术进行土壤有机碳的制图与不确定性研究”(41601213) 河南省农业科学院自主创新专项基金“基于多源遥感的河南省小麦面积提取研究”(2017ZC60)
【分类号】：S127

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