The unique characteristics of desert vegetation, such as different leaf morphology, discrete canopy structures, sparse and uneven distribution, etc., pose significant challenges for remote sensing-based estimation of fractional vegetation cover (FVC). The Unmanned Aerial Vehicle (UAV) system can accurately distinguish vegetation patches, extract weak vegetation signals, and navigate through complex terrain, making it suitable for applications in small-scale FVC extraction. In this study, we selected the floodplain fan with Caragana korshinskii Kom as the constructive species in Hatengtaohai National Nature Reserve, Bayannur, Inner Mongolia, China, as our study area. We investigated the remote sensing extraction method of desert sparse vegetation cover by placing samples across three gradients: the top, middle, and edge of the fan. We then acquired UAV multispectral images; evaluated the applicability of various vegetation indices (VIs) using methods such as supervised classification, linear regression models, and machine learning; and explored the feasibility and stability of multiple machine learning models in this region. Our results indicate the following: (1) We discovered that the multispectral vegetation index is superior to the visible vegetation index and more suitable for FVC extraction in vegetation-sparse desert regions. (2) By comparing five machine learning regression models, it was found that the XGBoost and KNN models exhibited relatively lower estimation performance in the study area. The spatial distribution of plots appeared to influence the stability of the SVM model when estimating fractional vegetation cover (FVC). In contrast, the RF and LASSO models demonstrated robust stability across both training and testing datasets. Notably, the RF model achieved the best inversion performance (R² = 0.876, RMSE = 0.020, MAE = 0.016), indicating that RF is one of the most suitable models for retrieving FVC in naturally sparse desert vegetation. This study provides a valuable contribution to the limited existing research on remote sensing-based estimation of FVC and characterization of spatial heterogeneity in small-scale desert sparse vegetation ecosystems dominated by a single species.

Desertification is one of the most severe environmental issues facing the world today, and effective desertification monitoring is critical for understanding its dynamics and developing prevention and control strategies. Although numerous studies on desertification monitoring using remote sensing have been conducted, there remain differences in indicator selection, and a unified monitoring system has yet to be established. In this study, we constructed the Desertification Composite Index (DCI) using Landsat satellite images, integrating six remote sensing indicators reflecting the natural and ecological characteristics of desertified areas. We also incorporated 383 UAV imagery datasets to accurately identify and analyze the spatial and temporal distributions of desertification in the Ring-Tarim Basin from 1990 to 2020 and subsequently assess its spatiotemporal trends. The results show the following: (1) The constructed DCI was used to identify desertification in 2020, achieving an overall accuracy of 0.86 and a Kappa coefficient of 0.8, indicating that the DCI is suitable for extracting regional desertification information. (2) From 1990 to 2020, the area of desertification decreased significantly, with an average annual reduction rate of −0.0022 ha/a, indicating continuous ecological improvement. Despite localized deterioration, the overall trend was one of “general improvement and local containment.” (3) GeoDetector-based analysis showed that cultivated land area and land use type were the primary single-factor drivers of desertification. The interaction between cultivated land and vegetation type exhibited a synergistic effect as a two-factor driver. (4) Desertification in the Ring-Tarim Basin is primarily influenced by human activities. Appropriate management and intervention measures, efficient and intensive cropland management, and rational land use planning can help develop effective strategies to combat desertification.

The terrestrial spatial patterns were affected by human activities, primarily on regional land use (LU) changes, with habitat quality (HQ) serving as a prerequisite for achieving regional sustainable development. Assessing and predicting the spatiotemporal evolution characteristics of regional LU changes and HQ is critical for formulating regional LU strategies and enhancing ecosystem service functions. Using the Poyang Lake Region as our research object, this research employs LU data and utilizes the ‘InVEST’ model and hot-spot analysis to quantitatively evaluate the spatiotemporal changes in HQ during 2000–2020. The PLUS model is then applied to predict LU and HQ trends from 2020 to 2050. The findings are as follows: (1). From 2000 to 2020, the areas of forestland, shrubland, sparse woodland, paddy fields, and dryland in the Poyang Lake Region showed a decreasing trend, with reductions mainly occurring in urban expansion zones such as Nanchang City and largely converted into urban construction land. (2). Since 2000, HQ in the Poyang Lake Region has shown a slight retrogressive evolution, with significant spatial heterogeneity. HQ spatially exhibits a pattern of improvement radiating outward from major cities. (3). Predictions for 2030 to 2050 indicate that HQ in the Poyang Lake Region will continue to decline, with the most significant downward trends occurring in urban built-up areas and their peripheries. The spatiotemporal characteristics reveal an expansion ring around Poyang Lake and an east–west urban expansion corridor linking Pingxiang, Yichun, Xinyu, Nanchang, Fuzhou, Yingtan, and Shangrao. This study provided a research basis for LU direction and urban planning policies in the Poyang Lake Region and its surrounding areas, while also contributing to the construction of agrarian security patterns and the enhancement of ecosystem service levels in the region.

The Ebinur Lake Basin, a key ecological security barrier for windbreak and sand control in northern Xinjiang, is crucial to the ecological safety of western China and the northern sand-prevention belt. Combining the basin’s geographical characteristics, this study comprehensively evaluated ecosystem service functions from four dimensions: water conservation, soil and water conservation, windbreak and sand-fixation, and biodiversity maintenance. Simultaneously, it conducted an ecological sensitivity assessment from four aspects: soil erosion, desertification, land use, and salinization sensitivity. The assessments of the importance of ecosystem service function and ecological sensitivity results were combined to create a tiered zoning plan for the basin. The basin was divided into four first-level zones: the Ebinur Lake Water Area and Wetland Biodiversity Protection Zone, the Desert Vegetation Windbreak and Sand Fixation Ecological Restoration Zone, the Oasis Agricultural Ecological Function Protection Zone, and the Mountain Water Conservation Zone. Six second-level zones were also delineated: the Ebinur Lake Wetland National Nature Reserve, Gobi Vegetation Distribution and Soil Erosion Sensitive Zone, Desert Vegetation Restoration Zone, Jinghe-Bortala Valley Oasis Agricultural Ecological Function Zone, Mountain Water Conservation and Forest-Grass Protection Zone, and Sayram Lake Water Body. This assessment and zoning plan provide support and scientific basis for the basin’s comprehensive ecological management, integrated protection and governance of mountains, rivers, forests, farmlands, lakes, grasslands and deserts, as well as regional ecological development.