Vegetation Coverage in the Desert Area of the Junggar Basin of Xinjiang, China, Based on Unmanned Aerial Vehicle Technology and Multisource Data

Abstract

Vegetation coverage information is an important indicator of desert ecological environments. Accurately grasping vegetation coverage changes in desert areas can help in assessing the quality of ecosystems and maintaining their functions. Improving remote sensing methods to detect the vegetation coverage in areas of low vegetation coverage is an important challenge for the remote sensing of vegetation in deserts. In this study, based on the fusion of MOD09GA and MOD09GQ data, 2019–2021 low-altitude unmanned aerial vehicle (UAV) remote sensing data, and other factors (such as geographical, topographic, and meteorological factors), three types of inversion models for vegetation coverage were constructed: a multivariate parametric regression model, a support vector machine (SVM) regression model, and a back-propagation neural network (BPNN) regression model. The optimal model was then used to map the spatial distribution of vegetation coverage and its dynamic change in the Junggar Basin of Xinjiang, China, over 22 years (from 2000 to 2021). The results show that: (1) The correlation between enhanced vegetation index (EVI) obtained from image fusion and vegetation coverage in desert areas is the highest (r = 0.72). (2) Among the geographical and topographic factors, only longitude and latitude were significantly correlated with vegetation coverage (p < 0.05). The average monthly temperature and precipitation from the previous six months were correlated with the vegetation coverage (p < 0.05), but the vegetation coverage of the current month had the highest correlation with the average temperature (r = −0.27) and precipitation (r = 0.33) of the previous month. (3) Among the multivariate parametric models established by selecting the five aforementioned factors, the multiple linear regression model performed the best (R2 = 0.64). (4) The SVM regression model was superior to the other regression models (R2 = 0.80, mean squared error = 8.35%). (5) The average vegetation coverage in the desert area of the Junggar Basin was 7.36%, and from 2000–2021, the vegetation coverage in 54.59% of the desert area increased.

Review of Desert Mobility Assessment and Desertification Monitoring Based on Remote Sensing

“…Numerous scholars have employed various methods to assess vegetation coverage and predict desert mobility. Based on these predictions, interventions such as artificial sand fixation and other techniques can be employed to effectively mitigate desertification in a timely manner [6,51,52].…”

A High Spatiotemporal Enhancement Method of Forest Vegetation Leaf Area Index Based on Landsat8 OLI and GF-1 WFV Data

“…The structure of a back propagation neural network (BPNN) model is multi-layered, which involves highly nonlinear mapping from input to output and is learned and trained through a neural network with a certain capacity of samples [39][40][41]. During the training process, the network has feedback signals to modify the weights of the connected nodes in the neural network and further determine the parameters related to each structure of the network [42][43][44][45]. The calculation process between the input and output layers in the BPNN model, excluding the input layer, can be described in the following:…”

Mountain Vegetation Classification Method Based on Multi-Channel Semantic Segmentation Model

“…The types of data are also becoming more diverse, and the acquisition time is growing faster [2]. The use of high-resolution remote sensing imagery for intelligent interpretation has become an important means of studying vegetation cover [3,4], its structural composition [5] and its dynamic changes [6,7].…”