The urban functional areas (UFA) are the basic spatial units of a city, identifying their category information and spatial distribution is of great significance for studying urban spatial structure and formulating scientific and reasonable urban planning policies. However, traditional classification systems are still limited due to the data processing costs and time consuming. With the development of high-resolution satellite, the two-dimensional image has been able to identify more ground object information on a fine scale, but it lacks the application of three-dimensional characteristics and social attributes. At the same time, machine learning and deep learning have shown great utility in extracting features, and they should be applied more to the classification of urban functional areas to improve efficiency. To solve these problems, we propose an efficient and accurate framework for mapping UFA using multi-source geospatial data. It can grasp the dynamic changes in the city more accurately, and provide reference for the study of urban functional areas. An improved frequency density (IFD) model was proposed to improve the overall classification accuracy by 4.4%. Besides, the nighttime light (NTL) data from SDGSAT-1 satellite glimmer imagers are also used to classify UFA. By combining NTL and urban building height data, a sky view enhanced nightlight index (SVENI) was proposed, which can improve the overall classification accuracy of UFA by 5.8%. This study systematically clarifies the role of data sources, methods, and automatically integration models in the classification framework for UFA, which is of great significance for urban planning and sustainable development.

Bare land, as a significant land cover type on the Earth’s surface, plays a crucial role in supporting land-use planning, urban management, and ecological environmental research through the investigation of its spatial distribution. However, due to the diversity of land-cover types on the Earth’s surface and the spectral complexity exhibited by bare land under the influence of environmental factors, it is prone to confusion with urban and other land features. In order to extract bare land rapidly and efficiently, this study introduces a novel bare land extraction index called the Bare Land Extraction Index (BLEI). Then, considering both Ganzi Tibetan Autonomous Prefecture and Urumqi, China as the study areas, we compared BLEI with three presented indices: the Bare-soil Index (BI), Dry Bare Soil Index (DBSI), and Bare Soil Index (BSI). The results show that BLEI exhibits excellent efficacy in distinguishing bare land and urban areas. It gets the most outstanding accuracy in bare land identification and mapping, with overall accuracy (OA), kappa coefficient, and F1-score of 98.91%, 0.97, and 97.89%, respectively. Furthermore, BLEI is also effective in distinguishing bare land from sandy soil, which can not only improve the mapping accuracy of bare land in soil-deserted areas but also provide technological support for soil research and land-use planning.

In karst regions, rocky desertification (RD) represents an extreme form of land degradation, posing significant threats to both the ecological environment and socio-economic development. Extracting the spatial distribution of rocky desertification land (Roc) is crucial for RD management, resource allocation, and sustainable development. However, current research primarily focuses on RD degree classification, with lack of studies distinguishing Roc from bare soil land (Bar) and other land types, neglecting the dynamic degradation characteristics of Roc in single-temporal data, and facing challenges in effectively integrating high-dimensional nonlinear features with machine learning (ML) methods, leading to confusion between Roc and Bar and inaccurate Roc identification. To address these challenges, this study first constructs a time-series-based Spectral-Texture-Scattering-Terrain (STST) feature set and applies multi-scale segmentation (MS) processing. Subsequently, by incorporating an improved spatial-channel attention mechanism and a gating mechanism-based classification module, a Multi-Scale Dilated Novel Attention 1-Dimensional Convolutional Neural Network (MDNA-1DCNN) is designed for object-oriented classification (OOC) in Ziyun County, Guizhou Province, and Longlin County, Guangxi Province, China. This method does not require cutting the images into small patches for network input, enabling more efficient large-scale Roc extraction. Experimental results show that MDNA-1DCNN achieves an overall accuracy (OA) of 97.67% and a Kappa coefficient of 97.07% in multi-class tasks, with F1-score (F1) and Intersection Over Union (IoU) for Roc extraction reaching 97.02% and 94.21%, respectively, outperforming other advanced deep learning (DL) networks and ML methods. Moreover, the STST feature set, which encompasses multiple attribute features from various perspectives, is more effective in improving Roc extraction accuracy compared to using only 1-3 of these attributes. This study provides effective technical support and reference for land monitoring, RD prevention, and ecological restoration in karst vulnerable regions.

Earth observation by remote sensing plays a crucial role in granite extraction, and many current studies use thermal infrared data from sensors such as ASTER. The challenge lies in the low spatial resolution of these satellites, hindering precise rock type identification. A breakthrough emerges with the Thermal Infrared Spectrometer (TIS) on the Sustainable Development Science Satellite 1 (SDGSAT-1) launched by the Chinese Academy of Sciences. With an exceptional 30 m spatial resolution, SDGSAT-1 TIS opens avenues for accurate granite extraction using remote sensing. This study, exemplified in Xinjiang’s Karamay region, introduces the BR-ISauvola method, leveraging SDGSAT-1 TIS data. The approach combines band ratio with adaptive k-value selection using local grayscale statistical features for Sauvola thresholding. Focused on large-scale granite extraction, results show F1 scores above 70% for Otsu, Sauvola, and BR-ISauvola. Notably, BR-ISauvola achieves the highest accuracy at 82.11%, surpassing Otsu and Sauvola by 9.62% and 0.34%, respectively. This underscores the potential of SDGSAT-1 TIS data as a valuable resource for granite extraction. The proposed method efficiently utilizes spectral information, presenting a novel approach for rapid granite extraction using remote sensing TIS imagery, even in scenarios with low spectral resolution and a single data source.

Due to being lightweight, fine-grained sediments easily flow with water and thus amplify the destructive effect of debris flow hazards. In such hazards, water content and shear strength are key inter-controlled factors relating to the stability of fine-grained sediments and thus control the density, scale, and danger of debris flow hazards. Although the correlation between water content and sediment stability has been studied, there are still some issues to be solved: what is the changing trend of shear strength with increasing water content? What is the water content spatial distribution pattern of fine-grained sediments in debris flow? What is the role/impact of this pattern on debris flow hazards prevention? Therefore, the objective of this research is to show the spatial distribution pattern of water content and establish a correlation between the water content and the shear strength of fine-grained sediments to provide a scientific basis for debris flow hazard prevention. Taking the Beichuan debris flow for our study, with a length of 37.6 km, and a 341 km² study area, the results show that (1) the average water content shows an increasing trend, from 9.9% in the upstream of the river (SP01–SP05) to 21.7% in the downstream of the river (SP13–SP15). (2) When unsaturated, the correlation between the water content and shear strength is determined by combining the cohesion, normal stress, and internal friction angle; when saturated, the water content is negatively correlated with shear strength. (3) Water content and shear strength are the key inter-controlled factors relating to the stability of fine-grained sediments, and the water content distribution pattern of this research indicates the key locations that require attention: locations with high water content in the downstream river or with high curvature, which is of some significance for debris flow hazard prevention.

Fine-grained sediments in debris flows refer to Quaternary sediments with grain sizes smaller than 2 mm. Their stability is closely related to the initial water threshold that triggers the debris flows and thus controls the density, scale, and damage of the debris flows. Based on this, they play a key “probe” role in early warnings of debris flows. Studies on fine-grained sediment stability are related to the accuracy and efficiency of early warnings of debris flows and thus play an important role in ensuring the safety of people and property. There have been some studies on fine-grained sediment stability in debris flows, but no one has carried out a systematic analysis and summary of this field. Therefore, in response to the urgent need for high-precision early warnings of debris flows, firstly, we review the current research on the aspects of fine-grained sediment stability, initiation, triggering, physical properties, hyperspectral remote sensing, and early warning systems; secondly, we summarize the main problems related to high-precision early warnings of debris flow hazards; and finally, we outline the future directions of research on fine-grained sediment stability in debris flows.