Zhenqi Song

Offset tracking based on normalized cross-correlation (NCC) of synthetic aperture radar (SAR) intensity imagery serves as a critical technique for monitoring large-scale ground deformations. However, traditional NCC of SAR intensity imagery is susceptible to isolated high-intensity points, which can induce patch-like errors and compromise the reliability of the derived deformation fields. Existing suppression methods do not differentiate between isolated high-intensity points and those constituting structural features, which are beneficial for NCC, resulting in a substantial loss of valid offset measurements concurrent with errors mitigation. Regarding this, we proposed a method for suppressing patch-like errors of SAR intensity offset tracking. The new method initially employs Z-score standardization to rapidly screen high-intensity points; subsequently, Influenced Outlierness (INFLO) structural density analysis is utilized to identify isolated high-intensity points (classified as outliers), which are then replaced by the median values of their local neighborhood prior to the NCC computation. A method for detecting patch-like errors was also designed based on the spatial characteristics of patch-like errors, defined by abrupt boundary discontinuities and high internal homogeneity. On this basis, quantitative metrics including the patch-like errors removal rate and the valid offset coverage rate were further designed to evaluate the approach’s capability in eliminating patch-like errors while retaining valid offset measurements. Comparative experiments were conducted using simulated and real SAR data. Results demonstrate that the proposed method achieves patch-like errors suppression comparable to existing methods while significantly enhancing the retention of valid offset measurements and improving overall estimation accuracy. Specifically, in the real data experiments over the Amnye Machen and Central Tianshan test areas, compared to the logarithmic weighted NCC, the proposed method increased the valid offset coverage rates by 0.272 and 0.264, and improved the comprehensive quality indices by 0.191 and 0.184, respectively. This study represents a refinement of classical deformation estimation methodologies, offering a more robust option for monitoring large-scale ground deformation.

Due to over-exploitation by humans and global climate change, desertification has become an increasingly severe issue, seriously threatening the stability of ecosystems and the sustainable development of resources. Therefore, this study focuses on the Hangjin Banner region in Inner Mongolia, using satellite remote sensing and unmanned aerial vehicle (UAV) remote sensing technology. Through wide-area coverage, longterm monitoring, multi-scale analysis, and high-precision interpretation, the study demonstrates the strong synergistic effects of "multi-scale interpretation" and "data fusion applications," systematically carrying out desertification monitoring grading and refined vegetation extraction. Firstly, to address the problem that the information dimension of single index is insufficient and it is difficult to reflect the development trend of desertification, the NDVI-Albedo feature space applicable to desert environment is inversely performed based on Landsat 8 satellite images from 2009 to 2023. Then, on the basis of the feature space, the desertification difference index (DDI), which realizes the wide-area desertification monitoring grading and spatio-temporal evolution analysis of the study area, and the Hue-Saturation-Lightness Greenway Enhanced Vegetation Index (HSLGEVI), which has stronger applicability and stability in desert environments, were constructed based on the HSL color space and the hue tuning algorithm. This index can effectively overcome the limitations of the RGB vegetation index, clearly delineate the canopy edge of desert vegetation, and accurately extract surface meadow vegetation with lower chlorophyll content. To test the effectiveness of the HSLGEVI, the widely used and validated Excess Green Index (EXG), Vegetation Difference Vegetation Index (VDVI), Modified Green-Red Vegetation Index (MGRVI), and Red-Green-Blue Vegetation Index (RGBVI) were selected for comparison. The results show that the accuracy of HSLGEVI is better than that of other indices, with overall accuracy and F1-score remaining above 90%. It reduces the impact of the RGB color space vegetation index on the accuracy of vegetation extraction, effectively overcoming misclassification and omission issues, providing a reliable monitoring mechanism for desertification control in the Hangjin Banner area.

Point cloud data, known for their accuracy and ease of acquisition, are commonly used for reconstructing level of detail 2 (LoD-2) building models. However, factors like object occlusion can cause incompleteness, negatively impacting the reconstruction process. To address this challenge, this paper proposes a method for reconstructing LoD-2 building models from incomplete point clouds. We design a generative adversarial network model that incorporates geometric constraints. The generator utilizes a multilayer perceptron with a curvature attention mechanism to extract multi-resolution features from the input data and then generates the missing portions of the point cloud through fully connected layers. The discriminator iteratively refines the generator’s predictions using a loss function that is combined with plane-aware Chamfer distance. For model reconstruction, the proposed method extracts a set of candidate polygons from the point cloud and computes weights for each candidate polygon based on a weighted energy term tailored to building characteristics. The most suitable planes are retained to construct the LoD-2 building model. The performance of this method is validated through extensive comparisons with existing state-of-the-art methods, showing a 10.9% reduction in the fitting error of the reconstructed models, and real-world data are tested to evaluate the effectiveness of the method.

With the continuous advancement of urbanization, urban renewal has become a vital means of enhancing urban functionality and improving living environments. Traditional urban renewal research primarily focuses on the macro level, analyzing regions or units, with limited studies targeting individual buildings. Consequently, the unique characteristics and specific requirements of individual buildings during urban renewal have often been overlooked. This study first identified individual buildings undergoing urban renewal in the Longgang and Longhua Districts of Shenzhen, China, from 2018 to 2023 using multisource data such as the 2018 Shenzhen Building Census. A regression analysis based on building characteristics and locational factors was conducted using a stacking ensemble machine learning model. In addition, buildings were categorized into residential, industrial, and commercial types based on their usage, enabling both overall- and category-specific predictions of building renewal. The results show the following: (1) Using the prediction results of multilayer perceptron (MLP) and eXtreme Gradient Boosting (XGBoost) base models as inputs and fusing them with an AdaBoost classifier as the final metamodel, the goodness of fit of the overall building renewal regression model increased by 2.19%. (2) The regression model achieved an overall urban renewal prediction accuracy of 89.41%. Categorizing urban renewal projects improved the goodness of fit for residential and industrial building renewal by 0.14% and 6.13%, respectively. (3) Compared with traditional macro-level evaluation methods, the experimental results of this study improved by 8.41%, and compared with single-model approaches based on planning permit data, the accuracy improved by 29.11%.

The Yellow River Basin is an important area in China’s overall development, expecially the middle and lower reaches of this river which are vital for the development of the entire basin. Therefore, it is critical to scientifically evaluate the sustainable development of the ecological environment and the social economy in these reaches of the Yellow River Basin. In this study, the level of the coordinated, sustainable development of the ecology and social economy were quantitatively analyzed based on the improved remote sensing ecological index (IRSEI) and development space social and economic curve (DSSEC). The analysis process is as follows: (1) Landsat 8 data were used to invert the surface wetness index (WET), normalized difference building and soil index (NDBSI), land surface temperature index (LST), and kernel normalized difference vegetation index (kNDVI). Then, the improved remote sensing ecological index (IRSEI) was constructed. Combining principle component analysis (PCA) and particle swarm optimization (PSO) random forest algorithm, an ecological quality evaluation model was established to evaluate the ecological environment in the middle and lower reaches of the Yellow River Basin. The dynamic temporal and spatial evolutions of the ecological environment quality and ecological sustainability were determined. (2) The relationship between the development space and two key social economy processes was quantitatively determined using the DSSEC index, in which the coefficients reflect the basic intensity and concentration of socioeconomic activities, respectively, enabling the analysis of the region’s socioeconomic sustainability. (3) The level of coordinated and sustainable ecological and socioeconomic development and its influencing factors were studied using a coupling coordination degree model and a geographical detector. The results showed that the IRSEI in the study area gradually increased from 0.392 in 2014 to 0.612 in 2020, and the ecological quality level in most areas was moderate to good. The social and economic development in the middle and lower reaches of the Yellow River Basin had regional urban centers as the core, indicating the need to emphasize the sustainable development of the central area in planning and development strategies to promote coordinated and balanced development. From 2014 to 2020, we identified an overall trend of linear growth, and the degree of coordination gradually transitioned from moderate imbalance to moderate coordination, showing a trend of increasing sustainable development. The degree of water resource development, industrial structure, and level of urbanization are the main factors driving the coordinated and sustainable development of the ecology and social economy in the middle and lower reaches of the Yellow River Basin.

Landslides are among the most prevalent geological hazards and are characterized by their high frequency, significant destructive potential, and considerable incident rate. Annually, these events lead to substantial casualties and property losses. Thus, conducting landslide susceptibility assessments in the regions vulnerable to such hazards has become crucial. In recent years, the coupling of traditional statistical methods with machine learning techniques has shown significant advantages in assessing landslide risk. This study focused on Sichuan Province, China, a region characterized by its vast area and diverse climatic and geological conditions. We selected 13 influencing factors for the analysis: elevation, slope, aspect, plan curve, profile curve, valley depth, precipitation, the stream power index (SPI), the topographic wetness index (TWI), the topographic position index (TPI), surface roughness, fractional vegetation cover (FVC), and slope height. This study incorporated the certainty factor method (CF), the information value method (IV), and their coupling with the decision tree C5.0 model (DT) and a logistic regression model (LR) as follows: IV-LR, IV-DT, CF-LR, and CF-DT. The results, validated by an ROC curve analysis, demonstrate that the evaluation accuracy of all six models exceeded 0.750 (AUC > 0.750). The IV-LR model exhibited the highest accuracy, with an AUC of 0.848. When comparing the accuracy among the models, it is evident that the coupling models outperformed the individual statistical models. Based on the results of the six models, a landslide susceptibility map was generated, categorized into five levels. High and very high landslide risk zones are mainly concentrated in the eastern and southeastern regions, covering nearly half of Sichuan Province. Medium-risk areas form linear distributions from northeast to southwest, occupying a smaller proportion of the area. Extremely low- and low-risk zones are predominantly located in the western and northwestern regions. The density of the landslide points increases with higher risk levels across the regions. This further validates the suitability of this research methodology for landslide susceptibility studies on a large scale. Consequently, this methodology can provide crucial insights for landslide prevention and mitigation efforts in this region.

Land desertification is a key environmental problem in China, especially in Northwest China, where it seriously affects the sustainable development of natural resources. In this paper, we combine high-resolution satellite remote sensing images and UAV (unmanned aerial vehicle) visible light images to extract desert vegetation data and quickly locate and accurately monitor land desertification in relevant areas according to changes in vegetation coverage. Due to the strong light and dry climate of deserts in Northwest China, which results in deeper vegetation shadow texture and mostly dry shrubs with fewer stems and leaves, the accuracy of the vegetation index commonly used in visible remote sensing image classification is not able to meet the requirements for monitoring and evaluating land desertification. For this reason, in this paper, we took the Hangjin Banner in Bayannur as an example and constructed a new vegetation index, the HSVGVI (hue–saturation–value green enhancement vegetation index), based on the HSV (hue–saturation–value) color space using channel enhancement that can improve the extraction accuracy of desert vegetation and reduce misclassification. In addition, in order to further test the extraction accuracy, samples of densely vegetated and multi-shaded areas were divided in the study area according to the accuracy-influencing factors. At the same time, the HSVGVI was compared with the vegetation indices EXG (excess green index), RGBVI (red–green–blue vegetation index), MGRVI (modified green–red vegetation index), NGBDI (normalized green–red discrepancy index), and VDVI (visible-band discrepancy vegetation index) constructed based on the RGB (red–green–blue) color space. The experimental results show that the extraction accuracy of the EXG and other vegetation indices constructed in RGB color space can only reach 70%, while the extraction accuracy of the HSVGVI can reach more than 95%. In summary, the HSVGVI proposed in this paper can better realize the extraction of desert vegetation data and can provide a reliable technical tool for monitoring and evaluating land desertification.

Spatial prediction of soil ammonia (NH₃) plays an important role in monitoring climate warming and soil ecological health. However, traditional machine learning (ML) models do not consider optimal parameter selection and spatial autocorrelation. Here, we present an integration method (tree-structured Parzen estimator–machine learning–ordinary kriging (TPE–ML–OK)) to predict spatial variability of soil NH₃ from Sentinel-2 remote sensing image and air quality data. In TPE–ML–OK, we designed the TPE search algorithm, which encourages gradient boosting decision tree (GBDT), random forest (RF), and extreme gradient boosting (XGB) models to pay more attention to the optimal hyperparameters’ high-possibility range, and then the residual ordinary kriging model is used to further improve the prediction accuracy of soil NH₃ flux. We found a weak linear correlation between soil NH₃ flux and environmental variables using scatter matrix correlation analysis. The optimal hyperparameters from the TPE search algorithm existed in the densest iteration region, and the TPE–XGB–OK method exhibited the highest predicted accuracy (R² = 85.97%) for soil NH₃ flux in comparison with other models. The spatial mapping results based on TPE–ML–OK methods showed that the high fluxes of soil NH₃ were concentrated in the central and northeast areas, which may be influenced by rivers or soil water. The analysis result of the SHapley additive explanation (SHAP) algorithm found that the variables with the highest contribution to soil NH₃ were O₃, SO₂, PM₁₀, CO, and NDWI. The above results demonstrate the powerful linear–nonlinear interpretation ability between soil NH₃ and environmental variables using the integration method, which can reduce the impact on agricultural nitrogen deposition and regional air quality.