1. Introduction
Since the 20th century, the global average temperature has significantly increased. Global warming has brought about serious threats to human society, the economy, and the ecosystem [
1]. Natural lakes in desert areas often provide important ecosystem functions such as conservation of biological diversity and carbon storage [
2]. The ecosystems surrounding lakes in arid regions are important because they produce numerous beneficial services [
3,
4]. Vegetation is the main support system (terrestrial ecosystem) on which human beings depend for survival and sustainable development. The change of vegetation cover can reflect the situation of a regional ecological environment [
5]. The changes in vegetation coverage and land cover type surrounding lakes in arid regions affect whole wetland ecosystems, which may be serious impacted by surrounding human life, biodiversity, and ecological environment [
6,
7,
8]. Seasonality in arid Africa requires the assessment and prediction of vegetation response due to climate change. Scholars have used remote sensing to indirectly measure vegetation growth by calculating the vegetation index [
9]. Research in the Loess Plateau demonstrated that, in addition to improving vegetation coverage, vegetation restoration projects may also bring negative impacts on local water resources [
10]. In desert or arid regions, the greening situation followed by vegetation restoration projects and their human and climatic factors are still unclear. The integrated study for detection and prediction of the greening situation around important lakes in typical desert areas and its human and climatic factors analysis is essential for local ecological protection.
Hongjian Nur (HJN) Lake, which is known as the ‘Pearl of the Desert’, is the largest and youngest natural inland freshwater desert lake in China [
11]. It is located at the border of Shenmu County in Shanxi Province and Ejin Horo Banner in the Inner Mongolia Autonomous Region. Further, it is adjacent to the Mu Us Desert. The unique geographical environment and climatic conditions of HJN Lake form an important habitat for aquatic birds and fishes. At present, HJN Lake has the largest breeding population of relict gulls in the world. HJN Lake is an oasis in the desert and has characteristic topography. It is located in desert foothills and plays an important role in water conservation, windbreak functions, sand fixation, and biodiversity maintenance. The ecological status of HJN Lake is quite important because it is a good indicator of environmental changes and climate conditions in local and riparian areas [
12,
13]. HJN Lake provides safe agricultural production and domestic water for residents around the basin. In 2017, the Ministry of Environmental Protection of China approved the HJN Nature Reserve for promotion to a national nature reserve [
14].
HJN is on the southern margin of the Mu Us Desert, where serious desertification and low vegetation coverage (VC) have resulted in ecological fragility in the HJN watershed. Human influence is obvious in this area. Under the influence of natural factors and human activities, the water area of HJN Lake is gradually shrinking and the ecological function of the water is being degraded [
15]. The upstream dam interception leads to downstream dryness and the river connectivity is further deteriorated [
16]. The ecological environment in HJN Lake is deteriorating and the ecological security of the basin is threatened. VC and land use change are direct indicators of changes in the ecological environment and largely represent the overall state of the ecological environment [
17,
18]. The study of the change in VC and land use types in the HJN watershed is helpful for understanding and managing the overall ecological environment. The results of investigations of VC and land use in HJN Lake can also help simulate the dynamic change characteristics of terrestrial ecosystems [
11]. They can reveal the possible factors influencing ecological environmental changes in HJN Lake, which provide reference for the protection of relict gull habitat. Furthermore, understanding the vegetation and land use conditions in HJN Lake will provide useful information and theoretical support for the HJN National Nature Reserve that is under construction.
In recent years, there has been an increasing number of studies on the ecological environment in the HJN Lake water body [
19,
20]. However, research on the ecological environment of the whole river basin is still insufficient. Related studies have shown that since the 1990s, the water surface area of HJN Lake has dramatically shrunk, the water level has rapidly declined, and the water quality has deteriorated [
20,
21,
22]. The shrinkage of the water surface is the result of a combination of climate change and human impacts [
23]. Under the combined effect of these two factors, the vegetation cover and land surface types around HJN Lake have changed. The changes in water area, water quality index, and surrounding vegetation of HJN Lake in the past 40 years were analyzed based on eight days of Landsat data and the normalized difference vegetation index (NDVI) from 1973 to 2013 [
24]. The increase in NDVI fluctuation around the lake area indicates a trend of water retreat [
24]. Li et al. studied the effects of human activities and climate change on the vegetation cover change in the HJN region from 1982 to 2007 [
19]. The results showed that the gradual increase in temperature might be the main influencing factor on the increasing trend in vegetation cover in the HJN area. Other related studies also revealed that the overall land-use change in the HJN watershed from 1989 to 2007 was characterized by a decrease in the area of lakes, other waterbodies, and sandy land, yet an increase in the area of farmland, woodland, and grassland [
25]. Grey forecasting models, especially the first-order and single-variable grey dynamic model (GM (1,1)), are robust tools for forecasting, especially when the original sequence data are limited [
26]. GM (1,1) has been successfully used for agricultural, environmental and resource predictions, proving its accuracy for modeling [
27,
28]. However, the GM (1, 1) model used for VC simulation and prediction is still insufficient.
The studies above have partially revealed the characteristics and trends of VC and land use change in the HJN region. The possible factors driving vegetation change in the HJN region were also discussed by previous studies [
11]. However, the changes of the overall ecological environment of HJN is inadequate. This may be because the relevant research data sources are varied and the time series is short. Most of the existing studies separately analyzed VC and land use in the HJN region. Remote sensing data sources for vegetation cover and land use change studies include hyperspectral data, multispectral data, microwave data, and LiDAR data [
29]. Moderate-resolution imaging spectroradiometer (MODIS) products have been widely used in regional VC and land use change investigations due to their wide coverage and high temporal resolution. Many models have been developed to detect VC and land use based on MODIS data [
30]. The commonly used models include regression models, mixed pixel decomposition methods, and machine learning methods [
18,
31,
32]. The application of the regression method as a regional empirical model to estimate VC at a large scale may cause problems because it is unable to accurately describe the complex land surface conditions [
33,
34,
35]. However, machine learning methods are somewhat limited in practical applications due to their computational complexity [
36]. The mixed pixel decomposition model is a commonly used method for calculating regional VC based on hyperspectral data. The binary pixel model is the most common model used in the linear mixed pixel decomposition method [
37]. It is widely used to estimate VC due to its simplicity of application and the way it represents physical mechanisms [
29]. Zribi et al. used the binary pixel model to decompose the radar ERS2/SAR (European Sensing Satellite—2/Synthetic Aperture Radar) signal and retrieved the vegetation coverage in the semiarid area [
38]. Qi j et al. combined NDVI with a binary pixel model and analyzed the spatiotemporal dynamic change characteristics of vegetation coverage in the San Pedro basin in the southwestern United States [
39]. Many other authors have used the binary pixel model to retrieve vegetation coverage and to explore its change trends over the years as well as the driving forces that affect these changes [
40]. In addition, we aimed to describe the rapid reduction in lake area in the HJN region in recent years and identify any relevant relationships. We used GIS (geographic information science) technology to analyze the land use map overlay to obtain the land use transfer matrix and studied the mutual conversion relationships of land use. This method has been applied to the study of land use change in various regions [
41,
42].
In summary, it is still unclear why there is a long-time trend of comprehensive vegetation environment around the desert wetland. Thus, the main objectives of this study are as follows. (1) Herein, we try to reveal the dynamic change characteristics of the greening situation followed by vegetation restoration projects and their human and climatic factors based on both VC and land use monitoring. (2) We assume that the influence factors remain unchanged, simulating the long-time trend of the VC that will help provide reference for future desert wetland ecological environment protection. (3) We investigate the influencing factors for the greening situation around the typical desert wetland from the perspective of human and climate change. This study will help provide a scientific basis for the restoration of the ecological and economic functions of desert wetland area under vegetation restoration projects and climate change backgrounds.