Mapping of native plant species and noxious weeds to investigate grassland degradation in the Three-River Headwaters region using HJ-1A/HSI imagery. Issue 5 (3rd March 2020)

• Record Type: Journal Article
• Authors: Ai, Zetian; An, Ru; Lu, Caihong; Chen, Yuehong
• Is Part Of: International journal of remote sensing. Volume 41:Issue 5(2020)
• Page Start: 1813
• Page End: 1838
• Publication Date: 2020-03-03
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2019.1675324
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Characterized by an alpine meadow, the ecological environment system in the 'Three-River Headwaters' region (TRHR) is considered to be a typical fragile ecological system. Numerous observations and research results have indicated that grassland degradation has occurred in the TRHR in recent years. However, research related to utilize the species information of grass communities to monitor grassland degradation remains rare. Therefore, the aim of this study is to produce the distribution maps of native plant species and noxious weeds to investigate grassland degradation for livestock farming perspective. In this study, the fused HJ-1A/HSI data was combined with field investigation samples to define the coverage of native plant species and noxious weeds at different coverage levels. Then, coverage distribution maps of native plant species and noxious weeds were produced by using support vector machine (SVM) classification and random forests (RF) regression methods. Meanwhile, the overall accuracy (OA) and root-mean-square error (RMSE) of each coverage map were assessed. Finally, a grassland degradation map was derived according to the native plant species and noxious weeds cover information. The experimental results show that (1) the spectral feature of native plant species and noxious weeds can be distinguished based on field measurement spectra in the TRHR; (2) the fractional coverage of native plant species and noxious weeds can be relatively accurately estimated when coverage is divided into nine levels; (3) the grass coverage estimation accuracies of SVM classification are similar with these of RF regression method. The OAs of SVM classification are 69.7% at nine grassland coverage levels for native plant species and noxious weeds, and corresponding RMSEs are 8.2% and 8.0%, respectively; and (4) the coverage of native plant species is generally higher than that of the noxious weeds in the study area.