文献类型：期刊文献

中文题名：基于无人机高光谱影像的薇甘菊分布提取研究——以云南德宏州为例

英文题名：Distribution Extraction of Mikania micrantha Based on UAV Hyperspectral Image:A Case Study in Dehong,Yunnan Province,China

作者：刘雪莲[1,2] 石雷[1] 李宇宸[3] 刘梦盈[1] 姚俊[1] 马云强[4] 杨绪兵[2]

第一作者：刘雪莲

机构：[1]中国林业科学研究院资源昆虫研究所,昆明650223;[2]南京林业大学信息科学技术学院,南京210037;[3]华南师范大学地理科学学院,广州510631;[4]西南林业大学生物多样性保护学院,昆明650223

年份：2021

卷号：29

期号：6

起止页码：579-588

中文期刊名：热带亚热带植物学报

外文期刊名：Journal of Tropical and Subtropical Botany

收录：CSTPCD;;北大核心:【北大核心2020】；CSCD:【CSCD_E2021_2022】；

基金：云南省产业技术领军人才计划项目;林业公益性行业科研专项经费(201504305)资助。

语种：中文

中文关键词：薇甘菊;无人机遥感;深度学习;支持向量机;随机森林

外文关键词：Mikania micrantha;UAV remote sensing;Deep learning;Support vector machine;Random forest

分类号：S451

摘要：为有效控制薇甘菊入侵,及时掌握其空间分布和动态变化,基于无人机高光谱数据,通过深度学习(DL)、支持向量机(SVM)、随机森林(RF)等方法提取云南省德宏州微甘菊分布情况。结果表明,DL、SVM和RF等3种方法均有效实现了薇甘菊的分布提取,以DL方法的提取效果最佳,制图精度和用户精度分别为96.61%和95.00%;其次为RF方法,制图精度和用户精度分别为94.83%和91.67%;SVM方法的制图精度和用户精度分别为92.45%和81.67%。这3种方法均能很好提取薇甘菊集中分布区域,且DL和RF方法对零散分布薇甘菊的识别效果优于SVM。因此,无人机高光谱影像为薇甘菊的监测、预警和精准防治提供了支撑和依据,对保护当地生态系统安全具有重要意义。
As a highly dangerous alien species, Mikania micrantha has become a serious threat to the ecosystem health and biodiversity of invasive sites. In order to effectively control its invasion, and grasp its spatial distribution and dynamic change, its distribution in Dehong Prefecture, Yunnan Province was extracted by deep learning(DL), support vector machine(SVM) and random forest(RF) methods based on UAV hyperspectral data.The results showed that three methods could effectively extract the distribution of M. micrantha, in which DL method had the best extraction effect with mapping accuracy and user accuracy of 96.61% and 95.00%,respectively, followed by the RF method with those of 94.83% and 91.67%, and the SVM method with those of 92.45% and 81.67%. All three methods could well extract the concentrated distribution areas of M. micrantha, the methods of DL and RF were better than SVM in identification of fragmented distribution of M. micrantha.Therefore, UAV hyperspectral images would provide supports and basis for the monitoring, early warning and precise control of M. micrantha invasion, which was of great significance to protect the security of local ecosystems.