Amplifying landslide hazards in the backdrop of warming climate and intensifying human activities calls for an integrated framework for accurately evaluating landslide susceptibility at fine spatiotemporal resolutions, which is critical for the mitigation of increasingly high landslide disaster risks. Yet, dynamic landslide susceptibility mapping is still lacking. Using high-quality data, from 14,435 landslides and non-landslides, we developed an efficient holistic framework for evaluating landslide susceptibility, considering landslide-relevant internal and external factors based on cloud computing platform and algorithmic models, which enables dynamic updating of a landslide susceptibility map at the regional scale, particularly in regions with highly complicated topographical features such as the Hengduan Mountains, as considered in this study. We compared Classification and Regression Trees (CART), Support Vector Machines (SVM), and Random Forest (RF) classifiers to screen out the best portfolio model for landslide susceptibility mapping on the Google Earth Engine (GEE) platform. We found that the Random Forest (RF) classifier integrated with synergy mode had the best modeling performance, with 90.48% and 89.24% accuracy and precision, respectively. We also found that forests and grasslands had the controlling effect on the occurrence of landslides, while human activities had a notable inducing effect on the occurrence of landslides within the Hengduan Mountains. This study highlights the performance of the holistic landslide susceptibility evaluation framework proposed in this study and provides a viable technique for landslide susceptibility evaluation in other regions of the globe.

The ecological conservation and high-quality development of the Yellow River Basin (YRB) has been declared as a major national strategy of China. Surface water availability (precipitation minus evapotranspiration, PME) poses challenges to the sustainability of ecosystems of the YRB. Noteworthy is that the Mongolian section of the YRB (IMYRB) is a critical ecological barrier in Northern China. Soil moisture (SM) changes are highly sensitive to PME and important for regional ecological security. However, SM vs. PME interactions and relevant mechanisms within the IMYRB are poorly understood. We found significant decreases in SM and PME over the east IMYRB (r = 0.7, p < 0.05). During the wet (July, August, and September) and dry (April, May, and June) seasons, as well as the whole year, decreased SM drives increased PME through land-atmosphere interactions over more than 90% of the IMYRB. Reduction in SM decreased evapotranspiration over more than 80% of the IMYRB, increased surface temperature across more than 79% of the IMYRB, boosted atmospheric vertical ascent over more than 75% of the IMYRB, and enhanced moisture convergence and PME. This study highlights the land-atmosphere interactions over the IMYRB, implicating basin-scale impacts of climatic changes on water resources.

Building extraction underpins land-use assessment, urban planning, and disaster mitigation, yet dense urban scenes still cause missed small objects, target adhesion, and ragged contours. We present High-Resolution-Mamba (HR-Mamba), a high-resolution semantic segmentation network that augments a High-Resolution Network (HRNet) parallel backbone with edge-aware and sequence-state modeling. A Canny-enhanced, median-filtered stem stabilizes boundaries under noise; Involution-based residual blocks capture position-specific local geometry; and a Mamba-based State Space Models (Mamba-SSM) global branch captures cross-scale long-range dependencies with linear complexity. Training uses a composite loss of binary cross entropy (BCE), Dice loss, and Boundary loss, with weights selected by joint grid search. We further design a feature-driven adaptive post-processing pipeline that includes geometric feature analysis, multi-strategy simplification, multi-directional regularization, and topological consistency verification to produce regular, smooth, engineering-ready building outlines. On dense urban imagery, HR-Mamba improves F1-score from 80.95% to 83.93%, an absolute increase of 2.98% relative to HRNet. We conclude that HR-Mamba jointly enhances detail fidelity and global consistency and offers a generalizable route for high-resolution building extraction in remote sensing.

《地球科学》(ISSN 1000-2383;CN42-1874/P) (原刊名《地球科学——中国地质大学学报》ISSN 1000-2383;CN42-1233/P)，创刊于1957年，是由教育部主管、中国地质大学主办的综合性地学学术理论期刊，月刊，2015年改名为《地球科学》,她以反映中国地质大学教学与科研最新的、高水平的基础地质、应用地质、资源与环境地质及地学工程技术研究成果为主要任务。

Hyperspectral remote sensing has been widely used in many fields such as ecological environment, geological survey, target detection and so on. The premise of hyperspectral remote sensing application is to obtain high-quality quantitative remote sensing data. Due to the limited number of hyperspectral satellite payloads, airborne hyperspectral remote sensing has played a leading role in hyperspectral applications because it can obtain higher spatial resolution data and higher work efficiency. This article elaborates on the important links of the hyperspectral data acquisition process and data preprocessing process, as well as the working principle and theoretical basis of the sensor. Give scientific research workers an intuitive and scientific understanding of the data production process. Research content ranging from the spectrographic imager components, laboratory calibration, installation, mission planning, engineer 's instrument operation on the plane and data preprocessing.