文献类型：期刊文献

英文题名：A Hybrid Approach of Combining Random Forest with Texture Analysis and VDVI for Desert Vegetation Mapping Based on UAV RGB Data

作者：Zhou, Huoyan[1,2] Fu, Liyong[2] Sharma, Ram P.[3] Lei, Yuancai[2] Guo, Jinping[1]

第一作者：Zhou, Huoyan

通信作者：Guo, JP[1]

机构：[1]Shanxi Agr Univ, Coll Forestry, Taigu 030801, Peoples R China;[2]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[3]Tribhuwan Univ, Inst Forestry, Kathmandu 44600, Nepal

年份：2021

卷号：13

期号：10

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20212210426695);Scopus(收录号：2-s2.0-85106504328);WOS:【SCI-EXPANDED(收录号:WOS:000662542000001)】；

基金：This research was funded by the Central Public-interest Scientific Institution Basal Research Fund (Grant No. CAFYBB2019QD003).

语种：英文

外文关键词：RF; Maximum likelihood classification; desertification

摘要：Desert vegetation is an important part of arid and semi-arid areas, which plays an important role in preventing wind and fixing sand, conserving water and soil, maintaining the balanced ecosystem. Therefore, mapping the vegetation accurately is necessary to conserve rare desert plants in the fragile ecosystems that are easily damaged and slow to recover. In mapping desert vegetation, there are some weaknesses by using traditional digital classification algorithms from high resolution data. The traditional approach is to use spectral features alone, without spatial information. With the rapid development of drones, cost-effective visible light data is easily available, and the data would be non-spectral but with spatial information. In this study, a method of mapping the desert rare vegetation was developed based on the pixel classifiers and use of Random Forest (RF) algorithm with the feature of VDVI and texture. The results indicated the accuracy of mapping the desert rare vegetation were different with different methods and the accuracy of the method proposed was higher than the traditional method. The most commonly used decision rule in the traditional method, named Maximum Likelihood classifier, produced overall accuracy (76.69%). The inclusion of texture and VDVI features with RGB (Red Green Blue) data could increase the separability, thus improved the precision. The overall accuracy could be up to 84.19%, and the Kappa index with 79.96%. From the perspective of features, VDVI is less important than texture features. The texture features appeared more important than spectral features in desert vegetation mapping. The RF method with the RGB+VDVI+TEXTURE would be better method for desert vegetation mapping compared with the common method. This study is the first attempt of classifying the desert vegetation based on the RGB data, which will help to inform management and conservation of Ulan Buh desert vegetation.