荒漠生态林区大沙鼠鼠洞密度的无人机遥感监测技术初探  

Monitoring Mouse-Hole Density by Rhombomys opimus in Desert Forests with UAV Remote Sensing Technology

作　　者：温阿敏 郑江华[1,2] 陈梦 穆晨 马涛 Wen Amin1,2, Zheng Jianghua1,2, Chen Meng3, Mu Chen4, Ma Tao1,2(1. College of Resources ＆ Environment Science, Xinjiang University Urumqi 830046; 2. Key Laboratory of Xinjiang Smart City and Environment Modelling, Xinjiang University Urumqi 830046; 3. Master Station of Prevention and Quarantine of Forestry Plant Diseases and Insect Pests in Xinjiang Urumqi 830000; 4. Xinjiang Grassland Central Station Urumqi 83004)

机构地区：[1]新疆大学资源与环境学院,乌鲁木齐830046 [2]新疆大学智慧城市与环境建模普通高校重点实验室,乌鲁木齐830046 [3]新疆维吾尔自治区林业有害生物防治检疫局,乌鲁木齐830000 [4]新疆维吾尔自治区草原总站,乌鲁木齐830049

出　　处：《林业科学》2018年第4期186-192,共7页Scientia Silvae Sinicae

基　　金：新疆准噶尔盆地周缘森林鼠(兔)灾害评估信息管理系统研建;新疆林业有害生物防治检疫局委托项目(2015-2016);新疆维吾尔自治区青年科技创新人才培养工程项目(2017)

摘　　要：【目的】采用无人机遥感技术对古尔班通古特沙漠南缘荒漠林的大沙鼠洞密度进行监测,探究大沙鼠鼠洞航空影像最佳解译方法,为快速解译低空遥感航拍鼠害数据提供解决途径。【方法】采用固定翼无人机搭载高清数码相机,获取0.024 m空间分辨率的航空影像,预处理得到TDOM影像;分别采用人工目视解译、最大似然分类方法及面向对象分类方法,对TDOM影像进行解译,进而提取鼠洞空间分布信息,然后分析评价各方法在荒漠林鼠洞提取中的适用性和分类精度。【结果】对比3种解译方法发现,人工目视解译准确度高,但工作量太大;最大似然分类方法的分类精度较低,不适用;而面向对象分类方法不仅分类精度与人工目视解译结果基本吻合,而且很大程度上提高了大数据的处理速度,提高了大面积鼠害遥感监测的效率。【结论】无人机遥感技术在荒漠林大沙鼠灾害监测中有很好的适用性和应用潜力,为今后采用低空遥感技术进行鼠害监测提供了参考。【Objective 】In this paper,the unmanned aerial vehicle（ UAV） remote sensing technique was used to monitor mousehole density by great gerbil（ Rhombomys opimus） in the southern margin of the Gurbantunggut desert forest.The purpose was to explore the best interpretation method of aerial imagery of the rat-holes and to provide a solution for the rapid interpretation of the data of the low altitude remote sensing aerial rodent damage. 【Method 】Fixed-wing UAV equipped with a digital cameras sensor was used to obtain 2. 4 cm resolution aerial images,and TDOM imagery was acquired after pre-processing. The TDOM images were interpreted by artificial visual interpretation, supervised classification and object-oriented classification method, respectively, by which we extracted the spatial distribution information of the rodent cave,and then analyzed and evaluated the applicability and classification accuracy of each method in the rodent extraction in desert forest. 【Result】By comparing the three methods of interpretation,it was found that the accuracy of artificial visual interpretation was high,but workload was too heavy for a large scale image; the classification accuracy of the supervised classification method was low and it was not applicable; and the object-oriented classification method not only had high precision,the classification results were consistent with visual interpretation method,but also improved the processing speed of big data. 【Conclusion】This paper validated the availability of the UAV technique for dynamically monitoring rodent pests in desert forest,and laid a critical foundation for further study.

关 键 词：无人机 荒漠林 大沙鼠 面向对象分类 

分 类 号：S764.5[农业科学—森林保护学]