文献类型：期刊文献

英文题名：Estimating Fractional Vegetation Cover Changes in Desert Regions Using RGB Data

作者：Xie, Lu[1] Meng, Xiang[2] Zhao, Xiaodi[3] Fu, Liyong[1,2] Sharma, Ram P.[4] Sun, Hua[1]

第一作者：Xie, Lu

通信作者：Sun, H[1]

机构：[1]Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Peoples R China;[2]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[3]Chinese Acad Forestry, Res Inst Forestry Policy & Informat, Beijing 100091, Peoples R China;[4]Tribhuwan Univ, Inst Forestry, Kritipur 44600, Nepal

年份：2022

卷号：14

期号：15

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20223612686756);Scopus(收录号：2-s2.0-85137096323);WOS:【SCI-EXPANDED(收录号:WOS:000839955700001)】；

基金：We thank the Central Public-Interest Scientific Institution Basal Research Fund (Grant No. CAFYBB2019QD003) and the National Natural Science Foundation of China (Grant Nos. 31470641, 31300534 and 31570628) for financial support.

语种：英文

外文关键词：fractional vegetation cover; pixel-based machine learning; vegetation classification; desert ecosystem

摘要：Fractional vegetation cover (FVC) is an important indicator of ecosystem changes. Both satellite remote sensing and ground measurements are common methods for estimating FVC. However, desert vegetation grows sparsely and scantly and spreads widely in desert regions, making it challenging to accurately estimate its vegetation cover using satellite data. In this study, we used RGB images from two periods: images from 2006 captured with a small, light manned aircraft with a resolution of 0.1 m and images from 2019 captured with an unmanned aerial vehicle (UAV) with a resolution of 0.02 m. Three pixel-based machine learning algorithms, namely gradient enhancement decision tree (GBDT), k-nearest neighbor (KNN) and random forest (RF), were used to classify the main vegetation (woody and grass species) and calculate the coverage. An independent data set was used to evaluate the accuracy of the algorithms. Overall accuracies of GBDT, KNN and RF for 2006 image classification were 0.9140, 0.9190 and 0.9478, respectively, with RF achieving the best classification results. Overall accuracies of GBDT, KNN and RF for 2019 images were 0.8466, 0.8627 and 0.8569, respectively, with the KNN algorithm achieving the best results for vegetation cover classification. The vegetation coverage in the study area changed significantly from 2006 to 2019, with an increase in grass coverage from 15.47 +/- 1.49% to 27.90 +/- 2.79%. The results show that RGB images are suitable for mapping FVC. Determining the best spatial resolution for different vegetation features may make estimation of desert vegetation coverage more accurate. Vegetation cover changes are also important in terms of understanding the evolution of desert ecosystems.