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The yellow river basin (YRB) plays a crucial role in maintaining national ecological security, and controlling desertification is a critical factor in strengthening the foundation for the high-quality development of the basin. However, there are fewer studies on the spatiotemporal patterns of desertification change in different regions of the YRB, and the mechanisms affecting desertification change are not yet clear. The objective of this article was to construct a desertification difference index (DDI) to characterize the degree of desertification in the YRB based on surface Albedo and normalized difference vegetation index. We analyzed the spatiotemporal patterns of desertification in different regions of the YRB. We used geographical detector and correlation analyses to screen out the main drivers affecting desertification change. We found that the DDI increased significantly at a rate of 0.5 × 10−3/a from 2001 to 2021, indicating a slowing down trend of desertification in the YRB. The spatial concentration of desertification was a high degree of agglomeration and significant spatial autocorrelation. Precipitation (PRE) and surface radiation were the primary factors influencing desertification in the YRB. The most important factor influencing desertification in the upstream of the YRB and midstream of the YRB areas was PRE. However, human footprint has exacerbated desertification in the downstream of the YRB. This article aims to reveal the driving mechanisms behind desertification in the YRB. The objective is to provide a reference for combating desertification and improving the ecological environment in the region.

The Yellow River basin (YRB) is a major ecological functional area in China, and its ecological safety development and change have extremely significant impacts on the natural environment and human society. However, existing studies on the YRB lack spatiotemporal characteristics analysis and prediction of ecological safety with vegetation as the core. Therefore, this study proposes to construct an ecological safety index (ESI) of the YRB based on the comprehensive multidimensional ecological safety evaluation system “vigor-pressure-state-response,” using the normalized difference vegetation index, vegetation carbon sink indicator parameters, temperature, precipitation, the digital elevation model, population density, and the per capita gross domestic product from 2000 to 2020. The spatiotemporal characteristics of the ESI were then analyzed for the YRB, and a long-term and short-term memory network model was constructed to predict the ESI trend of the YRB over the next 10 years. According to the results, from 2000 to 2020, the ESI of the YRB showed a fluctuating upward trend, and the annual average of the ESI changed abruptly in 2015 due to drastic changes in hazardous areas. The ESI in most areas of the YRB showed a significant upward trend, the stability of ESI changes was weak in some areas, and the overall spatial distribution showed significant positive spatial agglomeration characteristics. Further, the response of ESI to landscape complexity in different reaches of the YRB varied. Most of the middle reaches were positively correlated with landscape complexity, while most of the upper and lower reaches were not significantly or negatively correlated. Notably, over the next 10 years, YRB's ESI growth will slow down, with areas with degradation increasing, areas with significant growth decreasing, and areas currently showing stability improvement.

Accurate surface deformation (SD) predictions are critical for early warning and timely remediation of infrastructure damage. However, the current SD prediction models do not integrate the multiscale features of InSAR SD and environmental factors (EFs), which make their prediction results inaccurate. To address these limitations, we proposed a bidirectional gated recurrent unit (BiGRU) multioutput SD prediction network (GLER-BiGRUnet), which mainly included global–local feature extraction (GLFE), multifactor cross-attention residual (MCAR), and local residual module embedded in self-attention mechanism (RCSA) modules. Specifically, dense and one-dimensional convolutional layers were concatenated in the GLFE module to extract global–local SD features. The long time-series dependence between EFs and SD was learned in the MCAR module using the multihead cross-attention mechanism to obtain the corresponding attention weight feature matrix. The residual connection and self-attention mechanisms were used in the RCSA module to merge the multiscale features and enhance the model fitting ability. We chose four typical regions in the permafrost area of Qinghai–Tibet Railway as the scene for the experiment. The spatial distribution and local profile exhibited relatively small discrepancies between the prediction results of the GLER-BiGRUnet model and the InSAR SD. Meanwhile, the average root-mean-square error of the GLER-BiGRUnet model in the four typical regions was 0.19 mm, and the proposed model had the best evaluation index compared with other SD prediction models. Additionally, the prediction trend of SD of the proposed GLER-BiGRUnet model was consistent with the original InSAR SD, and the prediction results were more stable than those of the other prediction models. The SD prediction model proposed in this article contributes to early warning of SD.

Landslide susceptibility mapping (LSM) is of great significance for regional land resource planning and disaster prevention and reduction. The machine learning (ML) method has been widely used in the field of LSM. However, the existing LSM model fails to consider the correlation between landslide and disaster-prone environment (DPE) and lacks global information, resulting in a high false alarm rate of LSM. Therefore, we propose an LSM method with graph–transformer that considers the DPE characteristics and global information. First, correlation analysis and importance analysis are employed on nine landslide contributing factors, and the landslide dataset is generated by combining remote sensing image interpretation and field verification. Second, a graph constrained by environment similarity relationship is constructed to realize the correlation between landslide and DPE. Then, the transformer module is introduced to construct a graph–transformer model that considers the global information. Finally, the LSM is generated and analyzed, and the accuracy of the proposed model is compared and evaluated. The experimental results show that the environment similarity relationship graph effectively improves the accuracy of the models and weakens the influence of environmental differences on the models. Compared with graph convolutional network, graph sample and aggregate, and graph attention network models, the area under the curve (AUC) value of the proposed model is more than 2.05% higher under the environment similarity relationship. In addition, the AUC value of the proposed model is more than 8.8% higher than that of traditional ML models. In conclusion, our proposed model framework can get better evaluation results than most existing methods, and its results can provide effective ways and key technical support for landslide disaster investigation and control.

Landslide susceptibility mapping (LSM) is of great significance in geohazard early warning and prevention. The existing LSM methods mostly used traditional static landslide conditioning factors (LCFs), which only considered the spatial features of single-pixel neighborhoods and could not extract the time-series dynamic features of developing landslides, resulting in low accuracy and insufficient reliability of LSM. To solve this problem, this study proposes to introduce time-series rainfall factors based on the traditional static factors and construct an integrated time-series dynamic neural network (TSDNN) model for LSM. A convolutional neural network (CNN) adding time-distributed convolution and a bidirectional long and short-term memory neural network is utilized to extract time-series rainfall features, and a multiscale convolutional neural network (MSCNN) is utilized to extract static features of the static LCFs. In this study, multicollinearity analysis and geodetector are utilized to analyze the LCFs. Multiple evaluation metrics are utilized to analyze the proposed model performance. The results indicate that the overall accuracy has improved by introducing time-series rainfall factors, and the susceptibility area of actual predicted is more refined. The study indicates that significant advantages of the proposed TSDNN model are over models [CNN, MSCNN, random forest (RF)] when processing combined static and rainfall data. This is notably evident that the accuracy is enhanced by 12.9%, 10.7%, and 11.4% compared to CNN, MSCNN, and RF models in the receiver operating characteristic curve analysis, respectively. Moreover, two typical areas containing three recent landslide events validate the reliability of the proposed TSDNN model. The proposed network model framework for LSM considering time-series rainfall factors can provide new ideas and key technical support for landslide disaster prevention.

Surface deformation of alpine peatland in China has an important effect on runoff and is of great significance for wetland ecosystem protection. However, spatio-temporal characteristics of alpine peatland surface deformation in China lack systematic studies, and the driving mechanism is not yet clear. In this study, we selected the alpine peatland of Maduo County in China as the research object, surface deformation of peatland based on the small baseline subset radar interferometry technique was obtained, we analyzed spatio-temporal deformation characteristics and patterns of peatland, explored the driving mechanism of the peatland surface deformation with single-factor and multifactor combinations of Geo-detector, respectively. The results showed that the overall subsidence rates of peatlands in Maduo County, China slowed down year by year from 2018 to 2020, but there was seasonal freezing and thawing, subsidence rates of peatlands at high elevation and high slopes were stable, peatlands at low elevation and low slope were vulnerable to disturbance, subsidence rates are largest. Maliecuo, Bailongqu, and Gaerlawangzang regions were serious subsidence, the maximum subsidence rate was 159 mm/year. Meteorological factors and geological conditions were the main reasons for the surface deformation of alpine peatland in Maduo County, China. This study provides a theoretical basis for the conservation and restoration of peatland ecosystems in the alpine regions of China.

The ancient Suoertou landslide seriously threatens the surrounding population's lives and property. Monitoring and predicting this landslide is crucial to ensure the affected areas’ safety. The previous research on the landslide's displacement characteristics and mechanisms has lacked detailed analyses. In addition, its future development trends must be understood. Therefore, we conducted a detailed analysis of the ancient Suoertou landslide's displacement characteristics and mechanisms using small baseline subset interferometric synthetic aperture radar monitoring results from 2018 to 2023. Furthermore, by applying a gated recurrent unit prediction mode that incorporates the refined displacement characteristics and mechanisms, we forecasted the landslide's displacement trends. The results show that this landslide is currently undergoing overall slow displacement with violent fluctuations in localized areas. Tectonic movement, precipitation, human activities, river erosion, and other factors interact, forming a vicious development displacement mechanism. According to our prediction, the displacement of this landslide will be in a trend of fluctuating increase from June 2023 to June 2024. In particular, the local area will undergo abnormal displacement acceleration. The results of the current research provide a scientific basis upon which to monitor landslides, promote their management, and reduce the risk of losses due to landslide disasters.

The radiometric calibration technique of spaceborne synthetic aperture radar (SAR) aims to establish a constant relationship between the SAR target pulse response energy and the actual radar cross-section (RCS) of the ground. It is an essential technology for achieving quantitative observations of the Earth using radar. In this article, the process of in-orbit radiometric calibration campaign for the SuperView Neo-2 SAR satellite is introduced, which is the distributed X-band SAR satellite in the new generation of Chinese SiWei commercial remote sensing satellite system. More specifically, the initial step involves deriving the total transfer function of the SAR system using the radar equation and establishing a stable calibration constant model for the SAR system. Further, by using the obtained calibration constant to invert the RCS value of the corner reflectors in the Otog Banner calibration field, while validating and analyzing the absolute radiometric accuracy. Considering that the elevation antenna pattern (EAP) predominantly affects the consistency of image radiometric correction, the method of extracting EAP and beam center bias angle from the rainforest is presented. Finally, the quality validation of impulse response function is performed over the California calibration field, which can effectively guarantee the quality of SuperView Neo-2 SAR satellite products.

Accurate and efficient landslide identification is an important basis for landslide disaster prevention and control. Due to the diversity of landslide features, vegetation occlusion, and the complexity of the surrounding surface environment in remote sensing images, deep learning models (such as U-Net) for landslide detection based only on optical remote sensing images will lead to false and missed detection. The detection accuracy is not high, and it is difficult to satisfy the demand. Synthetic aperture radar (SAR) has penetrability, and SAR images are highly sensitive to changes in surface morphology and structure. In this study, a multi-input channel U-Net landslide detection method fusing SAR, optical, and topographic multisource remote sensing data is proposed. First, a multi-input channel U-Net model fusing SAR multisource remote sensing data is constructed, then an attention mechanism is introduced into the multi-input channel U-Net to adjust the spatial weights of the feature maps of the multisource data to emphasize the landslide-related features, and finally, the proposed model is applied to the experimental scene for validation. The experimental results demonstrate that the proposed model combined with SAR multisource remote sensing data improves the perception ability of landslide features, focuses on learning landslide-related features, improves the accuracy of landslide detection, and reduces the rate of false detections and missed detections. Compared with the traditional U-Net landslide detection method based on SAR multisource remote sensing data and the traditional U-Net method that disregards SAR multisource remote sensing data, the proposed method has the best quantitative evaluation indicators. Among them, the proposed method obtained the highest F1 value (66.18%), indicating that fused SAR remote sensing data can provide rich and complementary landslide feature information, simultaneously setting up a multichannel U-Net model to input multisource remote sensing data can effectively process landslide feature information. The proposed method can provide theoretical and technical support for landslide disaster prevention and control.

Construction activities of accelerated urbanization in Shenzhen have increased the landslide risk area, which has intensified the potential threat to human and natural environment. However, the risk of landslides in Shenzhen is poorly evaluated. In this article, a landslide risk evaluation (LRE) model is constructed using landslide susceptibility map (LSM) and landslide vulnerability. In the experiment, a stacking ensemble learning (SEL) model is constructed based on convolutional neural network (CNN), multilayer perceptron, gated recurrent unit (GRU), and support vector machine regression to generate LSM by using topography, geology, human engineering activities, time-series precipitation, and time-series normalized difference vegetation index. Road network, building distribution density and annual average precipitation data are used to evaluate landslide vulnerability based on entropy weight method. In this article, multiple statistical indicators are used to evaluate the performance of the LSM model, and Interferometric Synthetic Aperture Radar (InSAR) deformation data are utilized to verify the LRE results in Shenzhen. The results show that the SEL method has more refined results for LSM, with a best overall evaluation accuracy, especially in the receiver operating characteristic curve, where the accuracy is improved by nearly 8%. In LRE of Shenzhen, very high, high, moderate, low, and very low risk areas account for 0.283%, 0.451%, 0.859%, 36.890%, and 61.517%, respectively. In most of very high-risk area, InSAR deformation results show a clear concentrated deformation trend with a large deformation rate. Research results can provide technical and data support for landslide disaster prevention in Shenzhen.

Surface deformation poses a great threat to the safety of Jinchuan mining area production activities. At present, the spatio-temporal evolution law and mechanism of surface deformation in the Jinchuan mining area are unclear, and it is difficult to obtain reliable prediction results using the existing spatio-temporal prediction methods due to the lack of model parameters or relevant data. To solve these problems, this study proposes a new unified convolutional neural network with peephole long short-term memory (CNN-PhLSTM). Small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology was used to obtain the spatio-temporal evolution characteristics of surface deformation in the period of 2014–2021. Time series InSAR deformation data are merged into a unified network model in series with a time-distributed CNN segmentation and stacked PhLSTM. The InSAR measurement results are shown to be reliable by comparison and verification with the benchmark and InSAR results of different orbits. The proposed CNN-PhLSTM model was evaluated by mean absolute error and structural similarity (SSIM) evaluation indexes, and was compared with support vector regression (SVR), multilayer perceptron (MLP) and CNN-LSTM models. The results show three continuous subsidence areas, namely the Longshou, second western and third eastern mining areas. The cumulative surface deformation continued to increase from 2014 to 2021. Faults and lithology control the spatial distribution of surface deformation in the Jinchuan mining area. The prediction results demonstrate that the surface deformation range will continue to expand and that time-series surface deformation will show a slow deceleration trend in the next two years.

Accurate and comprehensive vegetation prediction methods are essential for effective agricultural planning and budgeting. Most existing vegetation prediction methods rely on sampling points rather than on overall spatiotemporal characteristics, making it difficult to accurately forecast vegetation changes. Hence, we built a neural network model with encoding and decoding modules based on a convolution gate recurrent unit (ConvGRU) and applied it to spatiotemporal normalized difference vegetation index (NDVI) predictions for the upper Heihe river basin (UHRB) in the Qilian Mountains, China. Based on MODIS NDVI raster data for the UHRB for 2000–2020, the analysis of the region's spatiotemporal characteristics showed that the NDVI varied significantly over time. To avoid the gradient disappearance problem during ConvGRU model prediction, we proposed several numerical scaling methods for preprocessing the data before conducting training and prediction. We then constructed a spatiotemporal prediction model based on the ConvGRU, trained and predicted the numerically scaled data, and used various metrics to evaluate the model. The results showed that the grouping tanh-ln function fitting was the least erroneous, and the ConvGRU model using data scaled by this method performed well across various metrics according to the test-set results. Also, unlike traditional time series prediction methods, the model accounted for spatiotemporal correlation features, and the output data of the prediction model were continuous and intuitive. Therefore, the proposed method is suitable for predicting dynamic vegetation changes. The NDVI prediction trend analysis indicated that the vegetation in the UHRB should improve in the future.

Interferometric phase filtering is a crucial step in the interferometric synthetic aperture radar (InSAR) data processing, which is also important for improving the accuracy of topography mapping and deformation monitoring. Most of the commonly used phase filtering methods perform windowing computations based on the statistical characteristics of a single interferogram in the spatial or frequency domain. However, the difficulty in taking into account the diversity and complexity of the phase image results in filtering methods with weak denoising, limited detail preservation, and poor generalization ability. At the same time, regardless of the spatial or frequency domain, improved phase filtering performance inevitably leads to the problem of declining effectiveness. This article proposes a phase filtering method based on the deep convolution neural network with multiscale feature dynamic fusion (MSFF). Unlike the traditional feedforward neural networks, the proposed method adopts a strategy of multiscale feature dynamic fusion that accounts for the deep and shallow features of the interferometric phase while also taking into account image detail preservation and noise suppression during phase filtering. Based on both subjective and objective evaluations, the experimental results using the simulated data prove that the proposed method has better noise suppression and detail preservation than the commonly used methods and that the filtering performance is less dependent on noise level. Experiments using the real data confirm that the proposed method has better generalization ability and can meet the precision requirements of practical applications. The method presented in this article can provide a new approach for research in high-precision InSAR data processing technology while also offering technical support for practical InSAR applications.

Remote sensing monitoring of glacial lakes is an indispensable tool for identifying and preventing glacial lake disasters. At present, the existing extraction methods of glacial lakes based on Landsat remote sensing image have achieved remarkable results, but the algorithms used lack the ability to analyze glacial lake spectral and shape and texture features, and require manual design parameters to fine tune the automation of the algorithm. As a result, it cannot mine the depth features of glacier lakes in remote sensing images accurately enough. To address these challenges, this study designed a self-attention mechanism module U-net network that enhances the propagation of features, reduces information loss, strengthens the weight of glacial lake areas, restrains the weight of irrelevant features, reduces the influence of low image contrast on the model, and deals with the variety of pixel categories in glacial lakes. These features improve the performance of the model. Based on Landsat-8 images, we first extracted glacial lakes in large-scale alpine areas using a U-net network model. To make it a self-attention U-net network, we introduced the attention mechanism into the step connection part of the U-net network to adjust feature weight, focus on learning glacial lake features, and strengthen the network to extract the glacial lake features. Finally, we selected the combination of bands 3, 5, and 6 and all bands of Landsat-8 images sing the self-attention U-net network to extract glacial lakes in the study area and compared and analyzed the extraction results. The experimental results and analyses revealed that the proposed method can effectively segment glacial lakes from Landsat-8 remote sensing images. Its effectiveness was proven by different evaluation indices. Compared with a standard U-net network, the true positive for the combination of 3, 5, and 6 bands increased by 15.95% and for all bands by 5.79%. The area under curve for the whole study area reached 85.03% for all bands. The improved U-net network can, thus, meet the real time needs of glacial lake disaster information acquisition.