Land cover data are important basic data for earth system science and other fields. Multi-source remote sensing images have become the main data source for land cover classification. There are still many uncertainties in the scale effect of image spatial resolution on land cover classification. Since it is difficult to obtain multiple spatial resolution remote sensing images of the same area at the same time, the main current method to study the scale effect of land cover classification is to use the same image resampled to different resolutions, however errors in the resampling process lead to uncertainty in the accuracy of land cover classification. To study the land cover classification scale effect of different spatial resolutions of multi-source remote sensing data, we selected 1 m and 4 m of GF-2, 6 m of SPOT-6, 10 m of Sentinel-2, and 30 m of Landsat-8 multi-sensor data, and explored the scale effect of image spatial resolution on land cover classification from two aspects of mixed image element decomposition and spatial heterogeneity. For the study area, we compared the classification obtained from GF-2, SPOT-6, Sentinel-2, and Landsat-8 images at different spatial resolutions based on GBDT and RF. The results show that (1) GF-2 and SPOT-6 had the best classification results, and the optimal scale based on this classification accuracy was 4–6 m; (2) the optimal scale based on linear decomposition depended on the study area; (3) the optimal scale of land cover was related to spatial heterogeneity, i.e., the more fragmented and complex was the space, the smaller the scale needed; and (4) the resampled images were not sensitive to scale and increased the uncertainty of the classification. These findings have implications for land cover classification and optimal scale selection, scale effects, and landscape ecology uncertainty studies.

Soil texture is one of the important physical and natural properties of soil. Much of the current research focuses on soil texture monitoring using non-imaging geophysical spectrometers. However there are fewer studies utilizing unmanned aerial vehicle (UAV) hyperspectral data for soil texture monitoring. UAV mounted hyperspectral cameras can be used for quickly and accurately obtaining high-resolution spatial information of soil texture. A foundation has been laid for the realization of rapid soil texture surveys using unmanned airborne hyperspectral data without field sampling. This study selected three typical farmland areas in Huangshui Basin of Qinghai as the study area, and a total of 296 soil samples were collected. Data calibration of UAV spectra using laboratory spectra and field in situ spectra to explore the feasibility of applying laboratory soil texture models directly to field conditions. This results show that UAV hyperspectral imagery combined with machine learning can obtain a set of ideal processing methods. The pre-processing of the spectral data can obtain high accuracy of soil texture estimation and good mapping effect. The results of this study can provide effective technical support and decision-making assistance for future agricultural land planning on the Tibetan Plateau. The main innovation of this study is to establish a set of processing procedures and methods applicable to UAV hyperspectral imagery to provide data reference for monitoring soil texture in agricultural fields on the Tibetan Plateau.

Urban expansion is often studied in large cities such as Beijing, Shanghai, and Guangzhou, while scant attention is paid to smaller cities such as Xining. However, Xining is the largest city on the Tibetan Plateau, and an important city in China’s “Belt and Road Initiative”. As its economy and society develops, Xining will play an increasingly important role in connecting the central and western regions. In order to quantify the impacts of rapid urbanization, it is extremely important to collect data on the time and space variations of impervious surfaces. As such, we collected Landsat long-term sequence data about Xining City from 1987-2019 using the random forest method, and then optimized the feature parameters to obtain the dataset. Our results demonstrated that the overall accuracy of land use classification in Xining city is 83.4% and that the urban impervious surface accuracy is 89.5%. Additionally, the overall accuracy improved by 2.4% after optimizing the characteristic parameters, while the urban impervious surface accuracy is 92.8%. In 27 of the 33 years we studied, the classification accuracy of impervious surfaces exceeded 90%. After correcting for the temporal consistency check, the accuracy of impervious surfaces improved by 2% compared to the original sequence. We analyzed the change of impervious surfaces in Xining based on the results of the final dataset and found that the impervious surface area of Xining increased from 55 km2 in 1987 to 334 km2 in 2019. Xining is a typical semi-open river valley city which shares spatial and temporal characteristics with other urban centers. The spatial and temporal characteristics of the expansion of urban spaces in the main urban area of Xining are obvious and are primarily spread around the central area toward tree branch shaped road, which help other cities located in river valleys better understand how urbanization progresses.

The Huangshui River Basin is one of the most densely populated areas on the Qinghai–Tibet Plateau and is characterized by a high level of human activity. The contradiction between ecological protection and socioeconomic development has become increasingly prominent; determining how to achieve the balanced and coordinated development of the Huangshui River Basin is an important task. Thus, this study used the Google Earth Engine (GEE) cloud-computing platform and Sentinel-1/2 data, supplemented with an ALOS digital elevation model (ALOS DEM) and field survey data, and combined a remote sensing classification method, grid method, and ecosystem service value (ESV) evaluation method to study the spatial correlation and interaction between land use (LU) and ESV in the Huangshui River Basin. The following results were obtained: (1) on the GEE platform, Sentinel-1/2 active and passive remote sensing data, combined with the gradient tree-boosting algorithm, can efficiently produce highly accurate LU data with a spatial resolution of 10 m in the Huangshui River Basin; the overall accuracy (OA) reached 88%. (2) The total ESV in the Huangshui River Basin in 2020 was CNY 33.18 billion (USD 4867.2 million), of which woodland and grassland were the main contributors to ESV. In the Huangshui River Basin, the LU type, LU degree, and ESV have significant positive spatial correlations, with urban and agricultural areas showing an H-H agglomeration in terms of LU degree, with woodlands, grasslands, reservoirs, and wetlands showing an H-H agglomeration in terms of ESV. (3) There is a significant negative spatial correlation between the LU degree and ESV in the Huangshui River Basin, indicating that the enhancement of the LU degree in the basin could have a negative spatial spillover effect on the ESV of surrounding areas. Thus, green development should be the future direction of progress in the Huangshui River Basin, i.e., while maintaining and expanding the land for ecological protection and restoration, and the LU structure should be actively adjusted to ensure ecological security and coordinated and sustainable socioeconomic development in the Basin.

Land cover data are important basic data for earth system science and other fields. Multi-source remote sensing images have become the main data source for land cover classification. There are still many uncertainties in the scale effect of image spatial resolution on land cover classification. Since it is difficult to obtain multiple spatial resolution remote sensing images of the same area at the same time, the main current method to study the scale effect of land cover classification is to use the same image resampled to different resolutions, however errors in the resampling process lead to uncertainty in the accuracy of land cover classification. To study the land cover classification scale effect of different spatial resolutions of multi-source remote sensing data, we selected 1 m and 4 m of GF-2, 6 m of SPOT-6, 10 m of Sentinel-2 and 30 m of Landsat-8 multi-sensor data, and explored the scale effect of image spatial resolution on land cover classification from two aspects of mixed image element decomposition and spatial heterogeneity. For the study area, we compared the classification obtained from GF-2, SPOT-6, Sentinel-2, and Landsat-8 images at different spatial resolutions based on GBDT and RF. The results show that (1) GF-2 and SPOT-6 had the best classification results, and the optimal scale based on this classification accuracy was 4–6 m; (2) the optimal scale based on linear decomposition depended on the study area; (3) the optimal scale of land cover was related to spatial heterogeneity, i.e., the more fragmented and complex was the space, the smaller the scale needed; and (4) the resampled images were not sensitive to scale and increase the uncertainty of the classification. These findings have implications for land cover classification and optimal scale selection, scale effects and landscape ecology uncertainty studies.

The unique geographic environment, diverse ecosystems, and complex landforms of the Qinghai–Tibet Plateau make accurate land cover classification a significant challenge in plateau earth sciences. Given advancements in machine learning and satellite remote sensing technology, this study investigates whether emerging ensemble learning classifiers and submeter-level stereoscopic images can significantly improve land cover classification accuracy in the complex terrain of the Qinghai–Tibet Plateau. This study utilizes multitemporal submeter-level GF-7 stereoscopic images to evaluate the accuracy of 11 typical ensemble learning classifiers (representing bagging, boosting, stacking, and voting strategies) and 3 classification datasets (single-temporal, multitemporal, and feature-optimized datasets) for land cover classification in the loess hilly area of the Eastern Qinghai–Tibet Plateau. The results indicate that compared to traditional single strong classifiers (such as CART, SVM, and MLPC), ensemble learning classifiers can improve land cover classification accuracy by 5% to 9%. The classification accuracy differences among the 11 ensemble learning classifiers are generally within 1% to 3%, with HistGBoost, LightGBM, and AdaBoost-DT achieving a classification accuracy comparable to CNNs, with the highest overall classification accuracy (OA) exceeding 93.3%. All ensemble learning classifiers achieved better classification accuracy using multitemporal datasets, with the classification accuracy differences among the three classification datasets generally within 1% to 3%. Feature selection and feature importance evaluation show that spectral bands (e.g., the summer near-infrared (NIR-S) band), topographic factors (e.g., the digital elevation model (DEM)), and spectral indices (e.g., the summer resident ratio index (RRI-S)) significantly contribute to the accuracy of each ensemble learning classifier. Using feature-optimized datasets, ensemble classifiers can improve classification efficiency. This study preliminarily confirms that GF-7 images are suitable for land cover classification in complex terrains and that using ensemble learning classifiers and multitemporal datasets can improve classification accuracy.