Previous wildfire risk assessments have problems such as subjectivity of weight allocation and the linearization of statistical models, resulting in generally low robustness and low generalization ability of fire risk assessment models. Therefore, in this paper, we explored the potential of integration machine learning algorithms to build wildfire risk assessment models. Based on analyzing fire data’s spatial and temporal distribution, we selected 10 triggering factors of topography, meteorology, vegetation, and human activities, using frequency ratio (FR) to provide uniform data representation of triggering factors. Next, we used the Bayesian optimization (BO) algorithm to perform hyperparametric optimization solutions for various machine learning models: support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost). Finally, we constructed an integration machine learning algorithm to acquire a fire risk grading map and the importance evaluation corresponding to each triggering factor. For validation purposes, we selected Liangshan Prefecture in Sichuan Province as the specific study area and obtained MCD64A1 burned area product to extract the extent of burned areas in Liangshan Prefecture from 2011 to 2020. The accuracy, kappa coefficient, and area under curve (AUC) were then applied to assess the predictive power and consistency of the fire risk classification maps. The experimental analysis showed that among the three models, FR-BO-XGBoost had the best performance in wildfire risk assessment in the Liangshan region (AUC = 0.887), followed by FR-BO-RF (AUC = 0.876) and FR-BO-SVM (AUC = 0.820). The feature importance result indicated that the study area’s most significant effects on wildfires were precipitation, NDVI, land cover, and maximum temperature. The proposed method avoided the subjective weighting and model linearization problems. Compared with the previous methods, it automatically acquired the importance of the triggering factors to the wildfire, which had certain advantages in wildfire risk assessment, and was worthy of further promotion.

Glacial lakes (GLs), a vital link between the hydrosphere and the cryosphere, participate in the local hydrological process, and their interannual dynamic evolution is an objective reflection and an indicator of regional climate change. The complex terrain and climatic conditions in mountainous areas where GLs are located make it difficult to employ conventional remote sensing observation means to obtain stable, accurate, and comprehensive observation data. In view of this situation, this study presents an algorithm with a high generalization ability established by optimizing and improving a deep learning (DL) semantic segmentation network model for extracting GL contours from combined synthetic-aperture radar (SAR) amplitude and multispectral imagery data. The aim is to use the high penetrability and all-weather advantages of SAR to reduce the effects of cloud cover as well as to integrate the multiscale and detail-oriented advantages of multispectral data to facilitate accurate, quantitative extraction of GL contours. The accuracy and reliability of the model and algorithm were examined by employing them to extract the contours of GLs in a large region of south-eastern Tibet from Landsat 8 optical remote sensing images and Sentinel-1A amplitude images. In this study, the contours of a total 8262 GLs in south-eastern Tibet were extracted. These GLs were distributed predominantly at altitudes of 4000–5500 m. Only 17.4% of these GLs were greater than 0.1 km² in size, while a large number of small GLs made up the majority. Through analysis and validation, the proposed method was found highly capable of distinguishing rivers and lakes and able to effectively reduce the misidentification and extraction of rivers. With the DL model based on combined optical and SAR images, the intersection-over-union (IoU) score increased by 0.0212 (to 0.6207) on the validation set and by 0.038 (to 0.6397) on the prediction set. These validation data sufficiently demonstrate the efficacy of the model and algorithm. The technical means employed in this study as well as the results and data obtained can provide a reference for research and application expansion in related fields.

Clouds seriously limit the application of optical remote sensing images. In this paper, we remove clouds from satellite images using a novel method that considers ground surface reflections and cloud top reflections as a linear mixture of image elements from the perspective of image superposition. We use a two-step convolutional neural network to extract the transparency information of clouds and then recover the ground surface information of thin cloud regions. Given the poor balance of the generated samples, this paper also improves the binary Tversky loss function and applies it on multi-classification tasks. The model was validated on the simulated dataset and ALCD dataset, respectively. The results show that this model outperformed other control group experiments in cloud detection and removal. The model better locates the clouds in images with cloud matting, which is built based on cloud detection. In addition, the model successfully recovers the surface information of the thin cloud region when thick and thin clouds coexist, and it does not damage the original image’s information.

Edge optimization of semantic segmentation results is a challenging issue in remote sensing image processing. This paper proposes a semantic segmentation model guided by a block-in-block edge detection network named BIBED-Seg. This is a two-stage semantic segmentation model, where edges are extracted first and then segmented. We do two key works: The first work is edge detection, and we present BIBED, a block-in-block edge detection network, to extract the accurate boundary features. Here, the edge detection of multi-scale feature fusion is first realized by creating the block-in-block residual network structure and devising the multi-level loss function. Second, we add the channel and spatial attention module into the residual structure to improve high-resolution remote sensing images' boundary positioning and detection accuracy by focusing on their channel and spatial dimensions. Finally, we evaluate our method on ISPRS Potsdam and Vaihingen data sets and obtain ODS F-measure of 0.6671 and 0.7432, higher than other excellent edge detection methods. The second work is two-stage segmentation. First, the proposed BIBED is individually pre-trained, and subsequently, the pre-trained model is introduced into the entire segmentation network to extract boundary features. In the second segmentation stage, the edge detection network is used to constrain semantic segmentation results by loss cycles and feature bootstrapping. Our best model obtains the OA of 90.2%, 87.7%, and 81.5%, the IOU of 76.0%, 69.6%, and 61.3% on the ISPRS and WHDLD datasets, respectively.

Being built on the reclamation area, Shanghai Pudong International Airport (SPIA) has been undergoing uneven subsidence since the beginning of its operation in 1999. In order to explore the evolution characteristics of ground deformation in the SPIA reclamation area and further provide assurance for the airport’s safe operation, 141 Sentinel-1A images from October 2016 to September 2021 were selected to acquire time-series ground deformation observations by the StaMPS PSI processing procedure. We subsequently built a ground deformation prediction model using the Long Short Term Memory (LSTM) neural network for the short-term prediction of the SPIA deformation severity area. On this basis, the spatial-temporal evolution trends of SPIA ground deformation in the reclamation area were revealed concerning the influence and mode of action of geological conditions and environmental factors. Finally, we proposed targeted recommendations and strategies for the comprehensive ground deformation prevention and control needs of SPIA. The results indicated that the SPIA exhibits overall subsidence in the eastern part, with the maximum deformation rate reaching −57.29 mm/a. Meanwhile, the central and western part has a local uplift with the maximum deformation rate reaching 32.76 mm/a. The proposed LSTM ground deformation prediction model demonstrated excellent robustness in the region of uneven deformation, and the prediction results were in high agreement with the StaMPS PSI monitoring results. The time-series observations and prediction results are expected to provide references for the expansion project of SPIA and help the research of ground deformation and prevention in related fields.

Clouds are the major source of clutter in optical remote sensing (RS) images. Approximately 60% of the Earth’s surface is covered by clouds, with the equatorial and Tibetan Plateau regions being the most affected. Although the implementation of techniques for cloud removal can significantly improve the efficiency of remote sensing imagery, its use is severely restricted due to the poor timeliness of time-series cloud removal techniques and the distortion-prone nature of single-frame cloud removal techniques. To thoroughly remove thin clouds from remote sensing imagery, we propose the Saliency Cloud Matting Convolutional Neural Network (SCM-CNN) from an image fusion perspective. This network can automatically balance multiple loss functions, extract the cloud opacity and cloud top reflectance intensity from cloudy remote sensing images, and recover ground surface information under thin cloud cover through inverse operations. The SCM-CNN was trained on simulated samples and validated on both simulated samples and Sentinel-2 images, achieving average peak signal-to-noise ratios (PSNRs) of 30.04 and 25.32, respectively. Comparative studies demonstrate that the SCM-CNN model is more effective in performing cloud removal on individual remote sensing images, is robust, and can recover ground surface information under thin cloud cover without compromising the original image. The method proposed in this article can be widely promoted in regions with year-round cloud cover, providing data support for geological hazard, vegetation, and frozen area studies, among others.