引用本文:熊瑞东,程武学※,熊钰丹,狄 威,魏佳轩,王永祥,刘 轲,罗光荣.基于无人机影像的高寒草地鼠害信息提取研究[J].中国农业信息,2020,32(5):27-37
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基于无人机影像的高寒草地鼠害信息提取研究
熊瑞东1,程武学※1,熊钰丹1,狄 威1,魏佳轩1,王永祥1,刘 轲2,罗光荣3
1.四川师范大学地理与资源科学学院,成都610101;2.四川省农业科学院遥感应用研究所/农业农村部遥感应用中心成都分中心,成都610066;3.四川省龙日种畜场,阿坝州藏族羌族自治州624401
摘要:
【目的】草原鼠害是影响草原生态平衡的重要因素,基于低空遥感影像探索提取鼠害 信息的最佳方案和分辨率对解决草原鼠害意义重大。【方法】文章基于高分辨率无人机正射 影像,使用CART 决策树、支持向量机、最邻近、贝叶斯4 种监督分类方法对高原鼠兔和高原 鼢鼠两种鼠害进行分类并比较其精度,再使用不同飞行高度下获取的遥感影像提取鼠害信息。 【结果】在鼠兔鼠害信息提取中,基于决策树分类法的总体精度为89.00%,kappa 系数为0.79; 支持向量机分类方法的总体分类精度为92.00%,Kappa 系数为0.83;最邻近分类法的总体分类 精度为94.00%,Kappa 系数为0.87;基于贝叶斯分类法的混淆矩阵中得到的鼠洞的分类精度最 差,鼠洞的生产者精度与用户精度都在78.00% 以下。在鼢鼠鼠害信息提取中,基于决策树分 类结果的总精度为93%,Kappa 系数为0.86;支持向量机分类结果的总精度达到95%,Kappa 系数为0.90;最邻近法的分类结果的总精度达到97.00%,Kappa 系数为0.95;Bayes 分类法的总 体分类精度为98.00%,Kappa 系数达到了0.95。【结论】基于面向对象的最邻近分类法是高原鼠 兔鼠害信息提取的精度最优方法,基于面向对象的贝叶斯分类法是高原鼢鼠鼠害信息提取的最 佳方法。对于飞行相对高度分别为100 m、120 m 和200 m 的无人机遥感影像数据,随着飞行高 度的增大,影像的空间分辨率越低,其分类所需要的时间、分类精度和斑块数量均呈下降趋势。
关键词:  鼠害;低空遥感;遥感识别;监督分类;若尔盖草原
DOI:10.12105/j.issn.1672-0423.20200503
分类号:
基金项目:国家自然科学基金项目“藏东南冻融水力侵蚀交错带砾石空间分布格局及对土壤侵蚀影响机制” (32060370);四川省应用基础研究项目“星机地协同的若尔盖草地鼠害遥感监测研究”(2017JY0155); 四川省应用基础研究项目“基于互联网+ 多阶段遥感反演的区域水稻参数逐田块监测技术研究” (2017JY0284)
Research on extraction of rodent damage information in AlpineGrassland based on UAV images
Xiong Ruidong1, Cheng Wuxue※1, Xiong Yudan1, Di Wei1, Wei Jiaxuan1, Wang Yongxiang1, Liu Ke2, Luo Guangrong3
1.School of Geography and Resource Science,Sichuan Normal University,Chengdu 610101,China;2.Institute of Remote Sensing Application,Sichuan Academy of Agricultural Sciences/Chengdu Branch of Remote Sensing Application Center of Ministry of Agriculture and Rural Affairs,Chengdu 610066,China;3.Sclr Breeding Farm,Sichuan Tibetan and Qiang Autonomous Prefecture of Aba Prefecture 624401,China
Abstract:
[Purpose]Grassland rodent damage is an important factor affecting the ecological balance of grassland. This study is based on low-altitude remote sensing images to explore the best plan and resolution for extracting rodent damage information.[Method]Use CART decision tree,support vector machine,nearest neighbor,and Bayesian supervised classification methods to classify and compare the accuracy of the high-resolution UAV orthophotos of the plateau pika and plateau zokor. Use remote sensing images acquired at different flying altitudes to extract information on rodent damage.[Result]When extracting pika and rat damage information,the overall accuracy of the decision tree classification method is 89.00%,and the kappa coefficient is 0.79;the overall classification accuracy of the support vector machine classification method is 92.00%,and the Kappa coefficient is 0.83;the overall nearest neighbor classification method The classification accuracy is 94.00%,and the Kappa coefficient is 0.87;the classification accuracy of the mouse hole obtained from the confusion matrix based on Bayesian classification is the worst, and the accuracy of the producer and the user of the mouse hole are both below 78.00%. When extracting the information of zokor and rodent damage,the total accuracy of the classification results based on the decision tree is 93%,and the Kappa coefficient is 0.86;the total accuracy of the support vector machine classification results is 95%,and the Kappa coefficient is 0.90; the total classification results of the nearest neighbor method The accuracy is 97.00%,and the Kappa coefficient is 0.95;the overall classification accuracy of the Bayes classification method is 98.00%,and the Kappa coefficient is 0.95.[Conclusion]The object-oriented nearest neighbor classification method is the best method for the accuracy of the plateau pika and rodent damage information extraction,and the object-oriented Bayes classification method is the best method for the plateau zokor rodent damage information extraction. For three types of UAV remote sensing image data with relative flight altitudes of 100 m,120 m,and 200 m,as the flight altitude increases,the spatial resolution of the image decreases,and the time required for classification, classification accuracy,and number of patches are all Shows a downward trend.
Key words:  rodent damage;low altitude remote sensing;remote sensing recognition;supervised classification;Zoige grassland