Accurately estimating forest aboveground biomass (AGB) based on remote sensing (RS) images at the regional level is challenging due to the uncertainty of the modeling sample size. In this study, a new optimizing method for the samples was suggested by integrating variance function in Geostatistics and value coefficient (VC) in Value Engineering. In order to evaluate the influence of the sample size for RS models, the random forest regression (RFR), nearest neighbor (K-NN) method, and partial least squares regression (PLSR) were conducted by combining Landsat8/OLI imagery in 2016 and 91 Pinus densata sample plots in Shangri-La City of China. The mean of the root mean square error (RMSE) of 200 random sampling tests was adopted as the accuracy evaluation index of the RS models and VC as a relative cost index of the modeling samples. The research results showed that: (1) the statistical values (mean, standard deviation, and coefficient of variation) for each group of samples based on 200 experiments were not significantly different from the sampling population (91 samples) by t-test (p = 0.01), and the sampling results were reliable for establishing RS models; (2) The reliable analysis on the RFR, K-NN, and PLSR models with sample groups showed that the VC decreases with increasing samples, and the decreasing trend of VC is consistent. The number of optimal samples for RFR, K-NN, and PLSR was 55, 54, and 56 based on the spherical model of variance function, respectively, and the optimal results were consistent. (3) Among the established models based on the optimal samples, the RFR model with the determination coefficient R² = 0.8485, RMSE = 12.25 Mg/hm², and the estimation accuracy P = 81.125% was better than K-NN and PLSR. Therefore, they could be used as models for estimating the aboveground biomass of Pinus densata in the study area. For the optimal sample size and sampling population, the RFR model of Pinus densata AGB was established, combining 26 variable factors in the study area. The total AGB with the optimal samples was 1.22 × 10⁷ Mg, and the estimation result with the sampling population was 1.24 × 10⁷ Mg based on Landsat8/OLI images. Respectively, the average AGB was 66.42 Mg/hm² and 67.51 Mg/hm², with a relative precision of 98.39%. The estimation results of the two sample groups were consistent.

Compared with the previous full-waveform data, the new generation of ICESat-2/ATLAS (Advanced Terrain Laser Altimeter System) has a larger footprint overlap density and a smaller footprint area. This study used ATLAS data to estimate forest aboveground biomass (AGB) in a high-altitude, ecologically fragile area. The paper used ATLAS data as the main information source and a typical mountainous area in Shangri-La, northwestern Yunnan Province, China, as the study area. Then, we combined biomass data from 54 ground samples to obtain the estimated AGB of 74,873 footprints using a hyperparametric optimized random forest (RF) model. The total AGB was estimated by combining the best variance function model in geostatistics with the slope that is the covariates. The results showed that among the 50 index parameters and three topographic variables extracted based on ATLAS, six variables showed a significant correlation with AGB. They were, in order, number of canopy photons, Landsat percentage canopy, canopy photon rate, slope, number of photons, and apparent surface reflectance. The optimized random forest model was used to estimate the AGB within the footprints. The model accuracy was the coefficient of determination (R²) = 0.93, the root mean square error (RMSE) = 10.13 t/hm², and the population estimation accuracy was 83.3%. The optimized model has a good estimation effect and can be used for footprint AGB estimation. The spatial structure analysis of the variance function of footprint AGB showed that the spherical model had the largest fitting accuracy (R² = 0.65, the residual sum of squares (RSS) = 2.65 × 10⁻⁴), the nugget (C₀) was 0.21, and the spatial structure ratio was 94.0%. It showed that the AGB of footprints had strong spatial correlation and could be interpolated by kriging. Finally, the slope in the topographic variables was selected as the co-interpolation variable, and cokriging spatial interpolation was performed. Furthermore, a continuous map of AGB spatial distribution was obtained, and the total AGB was 6.07 × 10⁷ t. The spatial distribution of AGB showed the same trend as the distribution of forest stock. The absolute accuracy of the estimation was 82.6%, using the statistical value of the forest resource planning and design survey as a reference. The ATLAS data can improve the accuracy of AGB estimation in mountain forests.

The problem of desertification in the Tarim Basin, an area with a unique geography and climatic conditions, has received extensive research attention not only in China but also around the world. Between natural factors and human activities, the latter are considered the main cause of desertification, with the excessive use of land resources accelerating its risk. This study classified the degree of desertification into five types, no, light, moderate, severe, and extremely severe desertification, and focused on the spatio-temporal changes in LULC, desertification development, and their relationship in the Circum-Tarim Basin during the period of 1990–2020, and the results indicated the following. (1) Over the 30-year study period, farmland development was frequent in the basin. The total farmland area increased significantly by 1.40 × 10⁴ km², which resulted from the occupation of grassland (mainly low-covered and medium-covered grassland) and unused land (mainly saline–alkali land). (2) There was a general alleviation of the effects of desertification, but also local deterioration. The area of no-desertification land has significantly increased (an increase of 2.10 × 10⁴ km²), and the degree of desertification has shifted significantly to adjacent lighter degrees, but the area of extremely severe desertification in certain regions has increased (an increase of 7.89 × 10⁴ km²). (3) There was an inseparable relationship between LULC and desertification. Oasisization and desertification were two processes that interacted and were interrelated. There was an approximately 54.42% increase in no-desertification land area mainly occurring in the region where LULC types changed (Region II), although this area only accounted for 9.71% of the total area of the basin. There was an approximately 98.28% increase in the area of extremely severe desertification occurring where there were no changes in LULC types (Region I). Region II demonstrated the best effects of desertification prevention and control in the 30-year study period in the Circum-Tarim Basin. Land development and oasis expansion have led to concentrated water use, resulting in water scarcity in certain areas, which cannot support the needs of vegetation growth, thus aggravating the degradation. Hence, “adapting measures to local conditions, rational planning, zoning policies, precise prevention and control” will be the way forward for desertification control in the future in the Circum-Tarim Basin.

Accurately estimating forest canopy cover (FCC) is challenging by using traditional remote sensing images at the regional level due to the spectral saturation phenomenon. In this study, to improve the estimation accuracy, a new method of FCC wall-to-wall mapping was suggested based on ice, cloud, and land elevation satellite/advanced topographic laser altimeter system (ATLAS) data. Specifically, one dataset of FCC's observations was combined with preprocessed ATLAS data and topographic factors to build a random forest regression (RFR) model. Moreover, the Co-Kriging method was used to generate spatially explicit values that are required by the RFR from the point data of ATLAS parameters, and then the wall-to-wall mapping of the FCC was conducted. The results showed that the RFR model had an accuracy of relative root-mean-square error (rRMSE) = 0.09 with a coefficient of determination (R ² ) = 0.91. The best-fit semivariogram models between primary variables and covariates were asr and TR (Model: Gaussian model, R ² = 0.94, the residual sum of squares (RSS) = 1.73 × 10 ⁻⁶ ), landsat_perc and NDVI (Model: spherical model, R ² = 0.46, RSS = 1.58 × 10 ⁻⁴ ), and photon_rate_can and slope (Model: exponential model, R ² = 0.77, RSS = 6.45 × 10 ⁻⁴ ), respectively. FCC validation result showed that the FCC's wall-to-wall mapping was in great agreement with the dataset-2 (R ² = 0.79; rRMSE = 0.11).

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.

As an ecologically sensitive area with significant desertification problems, the Ring-Tarim Basin has a fragile ecological environment that is vulnerable to both natural and anthropogenic factors. Accurate long-term vegetation observations are ecologically, socially, and economically important for desertification control. In this study, based on the ground-measured data and the fractional vegetation cover (FVC) inversion dataset obtained by the image element dichotomy method, we used the methods of slope-trend analysis and multiple-regression residual analysis to analyze the spatial and temporal characteristics of the vegetation cover in the desertified area of the Ring-Tarim Basin. At the same time, we assessed the impacts of climate change and human activities on vegetation changes and the contribution of driving forces. The results showed that (1) The annual mean value of FVC in the growing season in the Ring-Tarim Basin generally showed a fluctuating and increasing trend during the period of 1993–2021; a decreasing trend during 1993–1999, with a change rate of −0.13 × 10⁻²a⁻¹; and the fastest increasing trend during 2010–2021, with a change rate of 0.23 × 10⁻²a⁻¹. (2) The effects of climate change and human activities on FVC changes in the growing season had great spatial heterogeneity. The areas where climate change and human activities had no significant effect on FVC changes in the growing season accounted for 86.25% and 77.91%, respectively, the areas where climate and human activities promoted FVC increase in the growing season accounted for 10.53% and 16.37%, respectively, and the areas where climate and human activities inhibited FVC increase in the growing season accounted for 3.22% and 5.72%, respectively. (3) About 76.9% of the FVC changes in the area around the Ring-Tarim Basin were caused by climate change and human activities. In addition to the eastern part of the study area, the vegetation cover of the oases in the west, north, and south generally showed an increasing trend, and the increasing area was proportional to the distribution density of the oasis cities. The trend of vegetation change in the area of the oasis and the fringes of the oasis was drastic. The contribution and inhibition of human activities to FVC, and the driving force of FVC change were greater than that of climate change. More than half of the area had an anthropogenic contribution of more than 60%, indicating that China’s ecological projects have had a significant effect on vegetation change in the extreme arid regions.