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Soil salinization is a significant global threat to sustainable agricultural development, with soil salinity serving as a crucial indicator for evaluating soil salinization. Remote sensing technology enables large-scale inversion of soil salinity, facilitating the monitoring and assessment of soil salinization levels, thus supporting the prevention and management of soil salinization. This study employs multi-source remote sensing data, selecting 8 radar polarization combinations, 10 spectral indices, and 3 topographic factors to form a feature variable dataset. By applying a normalized weighted variable optimization method, highly important feature variables are identified. AdaBoost, LightGBM, and CatBoost machine learning methods are then used to develop soil salinity inversion models and evaluate their performance. The results indicate the following: (1) There is generally a strong correlation between radar polarization combinations and vegetation indices, and a very high correlation between various vegetation indices and the salinity index S3. (2) The top five feature variables, in order of importance, are Aspect, VH2, Normalized Difference Moisture Index (NDMI), VH, and Vegetation Moisture Index (VMI). (3) The method of normalized weighted importance scoring effectively screens important variables, reducing the number of input feature variables while enhancing the model's inversion accuracy. (4) Among the three machine learning models, CatBoost performs best overall in soil salt content (SSC) prediction. Combined with the top five feature variables, CatBoost achieves the highest prediction accuracy (R2 = 0.831, RMSE = 2.653, MAE = 1.034) in the prediction phase. This study provides insights for the further development and application of methods for collaborative inversion of soil salinity using multi-source remote sensing data. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The movement of glaciers plays a crucial role in environmental and geological processes, significantly influencing the formation and dynamics of ice bodies. This study leverages feature tracking technology to analyze optical and Synthetic Aperture Radar (SAR) remote sensing imagery, specifically GF-1 optical images and GF-3, Sentinel-1 SAR images, captured during the 2020 to 2021 ablation season in Gongar. The aim was to quantify glacier surface velocities and to evaluate the comparative effectiveness of different remote sensing modalities in capturing these dynamics. Our findings indicate a strong consistency in the spatial distribution of glacier surface velocities derived from diverse remote sensing data sources, with high-precision optical imagery (GF-1) yielding the most accurate velocity measurements, followed by Sentinel-1 SAR data. Notably, large glaciers in Gongar exhibited rapid movements, with an average velocity of 0.16 m/d, primarily at elevations between 4,500 and 6,500 m. The fastest velocities were recorded at approximately 4,500 m elevation. Glaciers with inclines ranging from 10° to 60° displayed the highest velocities within the 20°-30° slope range. It was observed that glaciers on the southeast slope moved faster, exhibiting the highest average surface velocity, in contrast to those on the west slope, which moved more slowly. The surface velocity of the ice tongue region of Krayaylak Glacier that the largest glacier in Pamir, was observed to be lower than 0.6 m/d, indicating a slow movement speed. The study also reveals that the effectiveness of different remote sensing data in detecting glacier velocity in Gongar, with high-resolution data more accurately capturing surface velocities in melting areas or those with slower movement. This study underscores the importance of multi-source remote sensing data in understanding glacier dynamics and contributes valuable insights into the mechanisms driving glacier movements. [ABSTRACT FROM AUTHOR]

Large-scale exploitation of underground mineral resources causes surface collapse, reduces land use efficiency, and brings a series of ecological and environmental problems. This is significantly important for the ecological restoration work of mining areas to accurately extract the subsidence range and depth of coal mine surface and formulate the regulation model suitable for coal mine subsidence areas. In this research, we used Differential Interferometric Synthetic Aperture Radar (D-InSAR) technology to extract the subsidence range of the Zhaogu Mining Area in Henan Province based on multi-source remote sensing data. We constructed the Spectral-Spatial Residual Network (SSRN) to classify the land use information within the subsidence range. Finally, we constructed a fuzzy comprehensive evaluation model based on the improved G1 method that assesses the extent of land damage in the subsidence area. Additionally, a suitable governance model for the subsidence area in the Zhaogu Mining Area is proposed. The results can provide technical support and data reference for the comprehensive treatment of subsidence in the Zhaogu Mining Area. [ABSTRACT FROM AUTHOR]

With the acceleration of global warming and the intensification of anthropogenic activities, numerous lakes worldwide are experiencing reductions in their water surface areas. Yilong Lake, a typical shallow plateau lake located on the Yunnan–Guizhou Plateau in China, serves as a crucial water resource for local human production, daily life, and ecosystem services. Hence, long-term comprehensive monitoring of its dynamic changes is essential for its effective protection. However, previous studies have predominantly utilized remote sensing data with limited temporal resolution, thus failing to reflect the long-term variations in Yilong Lake's water body. This study employs high temporal resolution monitoring, utilizing multi-source satellite data (e.g., KeyHole, Landsat, HJ-1 A/B) images spanning from 1965 to 2022 to investigate the changes in Yilong Lake's surface area, analyzing the influencing factors and ecological impacts of these changes. The results indicate that from 1965 to 2022, Yilong Lake's water surface area decreased by 8.33 km2, with a maximum surface area of 40.49 km2 on 7 January 1986, and a minimum surface area of 10.64 km2 on 20 April 2013. These changes are characterized by three significant phases: (1) a rapid shrinking phase (1965–1979); (2) a fluctuating shrinking period (1986–2016); and (3) an expanding recovery phase (2016–2022). Spatially, the most significant shrinkage was observed along the southern and southwestern shores of the lake. The driving factors varied across different periods: sunshine duration was the dominant influence during the rapid shrinking phase (1965–1979), accounting for 82% of the changes; population and cropland area were the main drive factors during the fluctuating shrinking period (1986–2016), accounting for 56% of the changes; and during the expanding recovery phase (2016–2022), the population accounted for 75% of the changes in the lake's surface area. Currently, the protection of Yilong Lake depends on water supplementation and strict regulation of outflow, resulting in the lake exhibiting characteristics similar to a reservoir. This long-term investigation provides baseline information for future lake monitoring. Our research findings can also guide decision-makers in urban water resource management and environmental protection, ensuring the scientific and rational use of watershed water resources, effectively curbing the shrinkage of Yilong Lake, and achieving long-term sustainable restoration of the lake's ecology. [ABSTRACT FROM AUTHOR]

Geohazards that have developed in densely vegetated alpine gorges exhibit characteristics such as remote occurrence, high concealment, and cascading effects. Utilizing a single remote sensing datum for their identification has limitations, while utilizing multiple remote sensing data obtained based on different sensors can allow comprehensive and accurate identification of geohazards in such areas. This study takes the Latudi River valley, a tributary of the Nujiang River in the Hengduan Mountains, as the research area, and comprehensively uses three techniques of remote sensing: unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR), Small Baseline Subset interferometric synthetic aperture radar (SBAS-InSAR), and UAV optical remote sensing. These techniques are applied to comprehensively identify and analyze landslides, rockfalls, and debris flows in the valley. The results show that a total of 32 geohazards were identified, including 18 landslides, 8 rockfalls, and 6 debris flows. These hazards are distributed along the banks of the Latudi River, significantly influenced by rainfall and distribution of water systems, with deformation variables fluctuating with rainfall. The three types of geohazards cause cascading disasters, and exhibit different characteristics in the 0.5 m resolution hillshade map extracted from LiDAR data. UAV LiDAR has advantages in densely vegetated alpine gorges: after the selection of suitable filtering algorithms and parameters of the point cloud, it can obtain detailed terrain and geomorphological information on geohazards. The different remote sensing technologies used in this study can mutually confirm and complement each other, enhancing the capability to identify geohazards and their associated hazard cascades in densely vegetated alpine gorges, thereby providing valuable references for government departments in disaster prevention and reduction work. [ABSTRACT FROM AUTHOR]

Accurate estimation forest biomass by remote sensing is an important basis for assessing the potential of forest carbon sinks. We explored the potential of synergistic estimation of above-ground biomass (AGB) in bamboo forests using different optical and LiDAR point cloud data, by comparing the differences between airborne multispectral and LiDAR data, respectively. In this study, the plantation forest of Acidosasa edulis, a characteristic bamboo species in Fujian Province, was used as the research object. Unmanned airborne multispectral imagery and LiDAR data were collected synchronously to extract and select the preferred feature set of a single LiDAR and multispectral imagery data each, the preferred feature set of the combined airborne LiDAR and multispectral imagery, and the combined feature set of the combination airborne LiDAR and multispectral imagery, using multivariate linear regression model (MLR) and support vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost) to fit the bamboo forest AGB estimation model. Results showed that (1) the height and density features of LiDAR data are important for bamboo forest AGB estimation. The feature set based on multi-source heterogeneous remote sensing data was better than single data for bamboo forest AGB estimation. (2) The nonparametric model based on machine learning algorithms was better than the MLR model for bamboo forest AGB estimation; the AGB estimation model integrating XGBoost and multi-source feature set was the best fit for Acidosasa edulis bamboo, with R² of 0.64 and Erms of 9.90 t·hm-2. Bamboo forests have a special canopy structure, and applying LiDAR data can effectively obtain information of the vertical structure of the forest stand. Moreover, the combination of multi-source isomorphic data can improve the accuracy of the biomass estimation model to provide a reference for the application of the biomass survey of the fixed sample plots of bamboo forests in subtropical areas and the estimation of carbon sinks. [ABSTRACT FROM AUTHOR]

On August 12, 2015, a catastrophic landslide occurred in Shanyang County, Shaanxi Province, China, resulting in 7 deaths and 53 missing. This study investigates the lifecycle evolution and failure mechanism of the Shanyang landslide with multi-source remote sensing data, emphasizing the critical role of locked segments in the Shanyang landslide. Differential interferometric analysis and deformation decomposition were utilized to reveal the pre-failure deformation pattern of the Shanyang landslide. Creeping deformation was found along the underlying soft layer 4 months prior to the landslide, with the deformation mainly occurring downslope and controlled by the locked segment at the front edge of the slope. The integration of a 1:1000 pre-failure topographic map and a high-precision post-failure digital elevation model determined the landslide volume to be 1.60 × 106 m3 and revealed a maximum travel distance of 500 m. Combining engineering geological zoning with deformation data, the Shanyang landslide was classified as a typical locked-segment-dominated slide in soft-hard interbedded strata, with rainfall as a key deformation influence factor. Finally, using the time series deformation from SBAS-InSAR, the post-failure stability of the landslide area was analyzed. This study demonstrates the potential of integrating multi-temporal remote sensing techniques to identify the entire deformation and destruction process of landslides and their influencing factors, which offers valuable insights for improving early landslide warnings and hazard assessments. [ABSTRACT FROM AUTHOR]

Air quality degradation has triggered a large-scale public health crisis globally. Existing machine learning techniques have been used to attempt the remote sensing estimates of PM2.5. However, many machine learning models ignore the spatial non-stationarity of predictive variables. To address this issue, this study introduces a Flexible Geographically Weighted Neural Network (FGWNN) to estimate PM2.5 based on multi-source remote sensing data. FGWNN incorporates the Flexible Geographical Neuron (FGN) and Geographical Activation Function (GWAF) within the framework of Artificial Neural Network (ANN) to capture the intricate spatial non-stationary relationships among predictive variables. A robust air quality remote sensing estimation model was constructed using remote sensing data of Aerosol Optical Depth (AOD), Normalized Difference Vegetation Index (NDVI), Temperature (TMP), Specific Humidity (SPFH), Wind Speed (WIND), and Terrain Elevation (HGT) as inputs, and Ground-Based PM2.5 as the observation. The results indicated that FGWNN successfully generates PM2.5 remote sensing data with a 2.5 km spatial resolution for the contiguous United States (CONUS) in 2022. It exhibits higher regression accuracy compared to traditional ANN and Geographically Weighted Regression (GWR) models. FGWNN holds the potential for applications in high-precision and high-resolution remote sensing scenarios. [ABSTRACT FROM AUTHOR]

In order to explore the advantages and complementarity of optical data and synthetic aperture radar( SAR) data in for‐ est type classification, this study focused on the overlapping area of Landsat8 data and ALOS2 data from one scene SAR image in Simao District, Puer City, Yunnan Province, China, and used hierarchical classification technology for forest type classification re‐ search. Three feature sets were constructed： optical feature set( spectral + vegetation + texture + terrain features), SAR feature set( backscattering + polarization decomposition features), and optical-SAR fusion feature set( spectral + vegetation + texture + terrain + backscattering + polarization decomposition features) . Recursive Feature Elimination( RFE) was employed to perform stratified fea‐ ture selection on the three feature sets, and random forest( RF) and support vector machine( SVM) were used for forest type classifi‐ cation. The SVM classification with the fusion of optical images and SAR data achieved the best results. The results showed,1) In the first layer( vegetation/non-vegetation) classification, the overall accuracy was 98. 57%, the Kappa coefficient was 0. 971. 2) In the second layer( forest/non-forest) classification, the overall accuracy was 92. 14%, the Kappa coefficient was 0. 826. 3) In the third layer( coniferous/broad-leaved/mixed forest) classification, the overall accuracy was 83. 47%, and the Kappa coefficient was 0. 743. The fusion data showed an improvement of 6. 74% in accuracy compared to optical data feature set classification and 29. 24% com‐ pared to SAR classification. 4) In the classification of the third layer using fusion data, the influence of different window sizes( 3×3, 5×5,7×7,9×9) of texture features in optical images was compared, and the highest accuracy was achieved with a 7×7 texture win‐ dow. Results shows that, the accuracy of forest type classification using multi-source data is higher than that using a single data source. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Underground coal fires have strong concealment and great harm, not only damaging vegetation and ecological environment, causing serious air pollution, but also easily inducing geological disasters, threatening the safety of life and property of surrounding people, as well as the safety of coal production. Therefore, accurate identification and monitoring of underground coal fire disasters is of great significance. To address the issue of difficulty in accurately identifying underground coal fires using a single remote sensing method, multiple Landsat-8 and Sentinel-1 A images from 2017 to 2019 were used. Long term surface temperature and surface deformation of Shuixigou coalfield were obtained using generalized single channel algorithm and DS–InSAR （Distributed Scatterer Inter fabric Synthetic Aperture Radar） technology, respectively. On this basis, research on multi-source remote sensing recognition of underground coal fires by integrating surface temperature and deformation was carried out based on methods such as threshold segmentation and spatiotemporal coupling superposition analysis. The results indicate that the spatiotemporal collaborative analysis of surface long-term temperature and deformation can effectively overcome the impact of complex abnormal signals such as non coal fire high temperature or deformation, and basically accurately restore the response characteristics of underground coal fire signals in the two dimensions of surface temperature and deformation. Moreover, subtle differences were found in the spatial distribution patterns of surface temperature anomalies and deformation anomalies in underground coal fire areas. The deformation anomaly benefits from the resolution of SAR images and fewer external interference factors, which have a stronger indicating effect on underground coal fire identification. However, the range of coal fire areas correctly identified based on temperature anomalies has better spatial consistency with the actual coal fire boundaries. In addition, there is a small deviation between the temperature and deformation peak spatial position of underground coal fire disasters. However, there is a clear consistency in the response between temperature and deformation in the time dimension, which is characterized by stable abnormal high temperatures and continuous subsidence in the coal fire area. It can be seen that compared to a single remote sensing method, the method of integrating temperature and deformation can more accurately identify underground coal fire areas, providing good technical support for the wide area survey and timely prevention and control of underground coal fire disasters.

ABSTRACTMarine aquaculture is one of the most important marine resource utilization types, widely distributed in seashore areas. Because mass marine aquaculture development will lead to the eutrophication disaster of macroalgae blooms (green tide), it is imperative to discover the latent relationship. Inspired by the big data analysis theory, the spatiotemporal correlation change in 2017 and 2019 of the marine aquaculture and macroalgae blooms in Jiangsu Shoal in the West of the Yellow Sea and its adjacent seas were monitored and extracted respectively based on multi-source remote sensing images. Through optical and Synthetic Aperture Radar (SAR) images, the imaging characteristics of marine aquaculture in different growth cycles and sea conditions are analyzed. The relationship between the green tide and marine aquaculture harvest was explored from the early green algae distribution and spatiotemporal distribution changes. The earliest harvest area of marine aquaculture is the exact location of the outbreak of green tide. Along with the rapid reduction of aquaculture area, the distribution range of green tide gradually increased. The two showed opposite trends through many multi-source remote sensing data statistical analyses. Therefore, they have a strong correlation, which can be utilized to offer a significant value for the balance of marine environmental protection and resource development.

The continuous downward movement exhibited by the Lashagou landslide group in recent years poses a significant threat to the safety of both vehicles and pedestrians traversing the highway G310. By integrating geomorphological interpretation using multi-temporal optical images, interferometric synthetic aperture radar (InSAR) measurements, and continuous global navigation satellite system (GNSS) observations, this paper traced the formation period of the Lashagou landslide group, and explored its kinematic behavior under external drivers such as rainfall and snowmelt. The results indicate that the formation period can be specifically categorized into three periods: before, during, and after the construction of highway G310. The construction of highway G310 is the direct cause and prerequisite for the formation of the Lashagou landslide group, whereas summer precipitation and spring snowmelt are the external driving factors contributing to its continuous downward movement. Additionally, both the long-term seasonal downslope movement and transient acceleration events are strongly controlled by rainfall, and there is a time lag of approximately 1–2 days between the transient acceleration and heavy rainfall events. This study highlights the benefits of leveraging multi-source remote sensing data to investigate slow-moving landslides, which is advantageous for the implementation of effective control and engineering intervention to mitigate potential landslide disasters. [ABSTRACT FROM AUTHOR]

Copyright of Geology & Exploration is the property of Geology & Exploration Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Characterized by soil moisture content and plant growth, agricultural drought occurs when the soil moisture content is lower than the water requirement of plants. Microwave remote sensing observation has the advantages of all-weather application and sensitivity to soil moisture change. However, microwave remote sensing can only invert 0~5 cm of soil surface moisture, so it cannot effectively reflect the drought situation of farmland. Therefore, this study took Henan Province as the study area, used soil moisture active and passive (SMAP) satellite soil moisture data, employed NDVI, LST, and ET as the independent variables, and took the drought grade on the sample as the dependent variable. Using the 2017–2019 data as the training set and the 2020 data as the testing set, a random forest drought monitoring model with comprehensive influence of multiple factors was constructed based on the training set data. In the process of model training, the cross-validation method was employed to establish and verify the model. This involved allocating 80% of the sample data for model construction and reserving 20% for model verification. The results demonstrated an 85% accuracy on the training set and an 87% accuracy on the testing set. Additionally, two drought events occurring during the winter wheat growing period in Henan Province were monitored, and the validity of these droughts was confirmed using on-site soil moisture and the vegetation supply water index (VSWI). The findings indicated a high incidence of agricultural drought in the southwestern part of Henan Province, while the central and northern regions experienced a lower incidence during the jointing to heading and filling stages. Subsequently, leveraging the results from the random forest drought monitoring, this study conducted a time series analysis using the Mann–Kendall test and a spatial analysis employing Moran's I index to examine the temporal and spatial distribution of agricultural drought in Henan Province. This analysis aimed to unveil trends in soil moisture changes affecting agricultural drought, as observed via the SMAP satellite (NASA). The results suggested a possible significant spatial auto-correlation in the occurrence of agricultural drought. [ABSTRACT FROM AUTHOR]

The movement of glaciers plays a crucial role in environmental and geological processes, significantly influencing the formation and dynamics of ice bodies. This study leverages feature tracking technology to analyze optical and Synthetic Aperture Radar (SAR) remote sensing imagery, specifically GF-1 optical images and GF-3, Sentinel-1 SAR images, captured during the 2020 to 2021 ablation season in Gongar. The aim was to quantify glacier surface velocities and to evaluate the comparative effectiveness of different remote sensing modalities in capturing these dynamics. Our findings indicate a strong consistency in the spatial distribution of glacier surface velocities derived from diverse remote sensing data sources, with high-precision optical imagery (GF-1) yielding the most accurate velocity measurements, followed by Sentinel-1 SAR data. Notably, large glaciers in Gongar exhibited rapid movements, with an average velocity of 0.16 m/d, primarily at elevations between 4,500 and 6,500 m. The fastest velocities were recorded at approximately 4,500 m elevation. Glaciers with inclines ranging from 10° to 60° displayed the highest velocities within the 20°–30° slope range. It was observed that glaciers on the southeast slope moved faster, exhibiting the highest average surface velocity, in contrast to those on the west slope, which moved more slowly. The surface velocity of the ice tongue region of Krayaylak Glacier that the largest glacier in Pamir, was observed to be lower than 0.6 m/d, indicating a slow movement speed. The study also reveals that the effectiveness of different remote sensing data in detecting glacier velocity in Gongar, with high-resolution data more accurately capturing surface velocities in melting areas or those with slower movement. This study underscores the importance of multi-source remote sensing data in understanding glacier dynamics and contributes valuable insights into the mechanisms driving glacier movements.

The collapse of open-pit coal mine slopes is a kind of severe geological hazard that may cause resource waste, economic loss, and casualties. On 22 February 2023, a large-scale collapse occurred at the Xinjing Open-Pit Mine in Inner Mongolia, China, leading to the loss of 53 lives. Thus, monitoring of the slope stability is important for preventing similar potential damage. It is difficult to fully obtain the temporal and spatial information of the whole mining area using conventional ground monitoring technologies. Therefore, in this study, multi-source remote sensing methods, combined with local geological conditions, are employed to monitor the open-pit mine and analyze the causes of the accident. Firstly, based on GF-2 data, remote sensing interpretation methods are used to locate and analyze the collapse area. The results indicate that high-resolution remote sensing can delineate the collapse boundary, supporting the post-disaster rescue. Subsequently, multi-temporal Radarsat-2 and Sentinel-1A satellite data, covering the period from mining to collapse, are integrated with D-InSAR and DS-InSAR technologies to monitor the deformation of both the collapse areas and the potential risk to dump slopes. The D-InSAR result suggests that high-intensity open-pit mining may be the dominant factor affecting deformation. Furthermore, the boundary between the collapse trailing edge and the non-collapse area could be found in the DS-InSAR result. Moreover, various data sources, including DEM and geological data, are combined to analyze the causes and trends of the deformation. The results suggest that the dump slopes are stable. Meanwhile, the deformation trends of the collapse slope indicate that there may be faults or joint surfaces of the collapse trailing edge boundary. The slope angle exceeding the designed value during the mining is the main cause of the collapse. In addition, the thawing of soil moisture caused by the increase in temperature and the reduction in the mechanical properties of the rock and soil due to underground voids and coal fires also contributed to the accident. This study demonstrates that multi-source remote sensing technologies can quickly and accurately identify potential high-risk areas, which is of great significance for pre-disaster warning and post-disaster rescue. [ABSTRACT FROM AUTHOR]

Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurate monitoring of farmland environment and soil pollution is of great significance for food security and ecologically sustainable development in recent years. Taking Xiong'an New Area as the research area, this study aims to inverse the heavy metal content in farmland. Spectral response data of heavy metal content was extracted from multi-source remote sensing data Sentinel-2 (L2A), Zhuhai-1 (OHS), and field measurement. Pollution levels of heavy metal were evaluated in farmland soil using the one-factor and Nemero indexes. The heavy metal elements were screened to cause soil pollution in farmland, such as copper (Cu), zinc (Zn) and plumbago (Pb). Partial least squares regression (PLSR) was integrated with principal component and multiple linear regression. The quadratic inversion model was then established for the contents of the three heavy metal elements. The quantitative inversion of the heavy metal contents was realized in a wide range of soil. Sentinel-2 was used to extract seven vegetation indices, while Zhuhai-1 was to extract the original spectral reflectance of the sample points, as well as four transformed spectral reflectance of sample points. Pearson's correlation coefficient was used to analyze the correlation between spectral reflectance and vegetation index with the content of three heavy metal elements. A comparison was also made on the correlation coefficients, sensitive bands and vegetation indices. The spectral response indicators were modeled as the independent variables, while the measured parameters of the soil heavy metal contents were the dependent variables. The models were finally evaluated using the coefficient of determination (R² ), mean absolute error (MAE), and root mean squared error (RMSE). The results showed that excellent performance was achieved in the overall inversion accuracy of the three models. The R², RMSE, and MAE values of the Pb content inversion model were 0.490, 4.66, and 1.92 mg/kg, respectively. The inversion model was obtained for Cu with the R2 of 0.491, the RMSE of 16.85 mg/kg, and the MAE of 3.69 mg/kg. While, the inversion model of Zn was achieved with the R2 of 0.664, the RMSE of 20.63 mg/kg, and the MAE of 9.36 mg/kg. The spatial distribution of soil heavy metal content in farmland was mapped, according to the constructed inversion model of soil heavy metal content. The visualization of the inversion was conducive to the distribution of regional soil heavy metals from a more intuitive perspective. Among them, the Pb element content in soil in most areas was within the risk screening standard; Cu element exceeded the risk screening value of soil pollution in the southwestern and western parts of the test area; The western and southwestern parts of the test area were more seriously contaminated by Zn element, indicating the sporadic distributions in some farmlands in the southeastern part. The Cu and Zn contents in other areas were within the national risk control value of soil pollution. Therefore, the multi-source remote sensing data and spectral response can be expected to synergistically invert the Cu, Zn and Pb content of soil heavy metals. High feasibility and accuracy can also be obtained to investigate soil heavy metal pollution. At the same time, the findings can provide a theoretical basis for monitoring the heavy metal content of soil in large areas. A new idea can also be offered to construct the inverse model of heavy metal content in farmland soil. [ABSTRACT FROM AUTHOR]

The mechanism of aerosol pollution transport remains highly elusive owing to the myriad of influential factors. In this study, ground station data, satellite data, ground-based LiDAR remote sensing data, sounding data, ERA5 reanalysis and a backward trajectory model were combined to investigate the formation process and optical properties of winter aerosol pollution in Beijing and surrounding areas. The analysis of ground station data shows that compared to 2019 and 2021, the pandemic lockdown policy resulted in a decrease in the total number of pollution days and a decrease in the average concentration of particulate matter in the Beijing area in 2020. The terrain characteristics of the Beijing-Tianjin-Hebei (BTH) made it prone to northeast and southwest winds. The highest incidence of aerosol pollution in Beijing occurs in February and March during the spring and winter seasons. Analysis of a typical heavy aerosol pollution process in the Beijing area from 28 February to 5 March 2019 shows that dust and fine particulate matter contributed to the primary pollution; surface air temperature inversion and an average wind speed of less than 3 m/s were conducive to the continuous accumulation of pollutants, which was accompanied by the oxidation reaction of NO2 and O3, forming photochemical pollution. The heavy aerosol pollution was transmitted and diffused towards the southeast, gradually eliminating the pollution. Our results provide relevant research support for the prevention and control of aerosol pollution. [ABSTRACT FROM AUTHOR]

In recent years, remote sensing technology has played an important role in geological and mineral exploration, and the extraction of alteration minerals based on multi-source remote sensing data has greatly reduced the range of target areas and time cost in the work. The enhancement and extraction of mineral alteration information in Tibetan Jhule area was carried out based on Landsat-9, ASTER and Sentinel-2 remote sensing data. The feature components in the principal component analysis ( PCA) of multi-source remote sensing data were normalized by linear affiliation function and then superimposed by Fuzzy Gamma operator to map the mineral search target area. The γ parameters were set as 0. 7, 0. 75, 0. 8, 0. 85 and 0. 9 in the experiment, and finally γ = 0. 85 was determined to be relatively good after the experiment. A total of 8 target areas were circled, and the feasibility of the method in target area circling at high altitude was verified by the field survey. Meanwhile, the field investigation results show that the area has developed high sulfur type shallow-forming low-temperature hydrothermal alteration mineral assemblage, which has the potential of porphyry-shallow-forming low-temperature hydrothermal deposits [ABSTRACT FROM AUTHOR]

On October 6, 2021, a high-position landslide disaster occurred in Yanwo Village，Rongshan Town, Lizhou District, Guangyuan City,Sichuan Province, around 13: 00. The landslide resulted in the destruction of 4 houses, 3 power lines, the burial of a 170-meter-long rural road, and the blockage of a 350-meter-long river channel. By utilizing a variety of remote sensing information sources, including multi-period satellite data before and after landslide, high-precision UAV aerial images, and airborne LiDAR data, the characteristics of high landslide and its sliding mode are studied by using a four-dimensional analysis approach combining three-dimensional space with time. Starting from the development background of high landslide, the deformation and failure characteristics and sliding mode of high landslide are summarized by analyzing the deformation characteristics before sliding and the dynamic procession of the high landslide. Based on the deduced landslide progression and the stability evaluation of the residual landslide at the rear, three sliding modes for future high-position landslide events are predicted and analyzed: Firstly, the back edge of landslide continues to be disjointed and move, causing a direct shear failure of the front part of the landslide by squeezing. Secondly, the trailing edge of the landslide continues to be disjointed and move, resulting in the sliding landslide occurs along the existing landslide channel. Thirdly, the initiation of the front part of the landslide trigger a tractional landslide in the middle and rear parts of the landslide. High-position landslides are common in the southwest mountainous areas, and during early geological hazard investigations, they should be effectively identified, and disaster prevention and mitigation measures should be strengthened.

Soil salinization is a significant global threat to sustainable agricultural development, with soil salinity serving as a crucial indicator for evaluating soil salinization. Remote sensing technology enables large-scale inversion of soil salinity, facilitating the monitoring and assessment of soil salinization levels, thus supporting the prevention and management of soil salinization. This study employs multi-source remote sensing data, selecting 8 radar polarization combinations, 10 spectral indices, and 3 topographic factors to form a feature variable dataset. By applying a normalized weighted variable optimization method, highly important feature variables are identified. AdaBoost, LightGBM, and CatBoost machine learning methods are then used to develop soil salinity inversion models and evaluate their performance. The results indicate the following: (1) There is generally a strong correlation between radar polarization combinations and vegetation indices, and a very high correlation between various vegetation indices and the salinity index S3. (2) The top five feature variables, in order of importance, are Aspect, VH2, Normalized Difference Moisture Index (NDMI), VH, and Vegetation Moisture Index (VMI). (3) The method of normalized weighted importance scoring effectively screens important variables, reducing the number of input feature variables while enhancing the model’s inversion accuracy. (4) Among the three machine learning models, CatBoost performs best overall in soil salt content (SSC) prediction. Combined with the top five feature variables, CatBoost achieves the highest prediction accuracy (R2 = 0.831, RMSE = 2.653, MAE = 1.034) in the prediction phase. This study provides insights for the further development and application of methods for collaborative inversion of soil salinity using multi-source remote sensing data.

With the acceleration of global warming and the intensification of anthropogenic activities, numerous lakes worldwide are experiencing reductions in their water surface areas. Yilong Lake, a typical shallow plateau lake located on the Yunnan–Guizhou Plateau in China, serves as a crucial water resource for local human production, daily life, and ecosystem services. Hence, long-term comprehensive monitoring of its dynamic changes is essential for its effective protection. However, previous studies have predominantly utilized remote sensing data with limited temporal resolution, thus failing to reflect the long-term variations in Yilong Lake’s water body. This study employs high temporal resolution monitoring, utilizing multi-source satellite data (e.g., KeyHole, Landsat, HJ-1 A/B) images spanning from 1965 to 2022 to investigate the changes in Yilong Lake’s surface area, analyzing the influencing factors and ecological impacts of these changes. The results indicate that from 1965 to 2022, Yilong Lake’s water surface area decreased by 8.33 km2, with a maximum surface area of 40.49 km2 on 7 January 1986, and a minimum surface area of 10.64 km2 on 20 April 2013. These changes are characterized by three significant phases: (1) a rapid shrinking phase (1965–1979); (2) a fluctuating shrinking period (1986–2016); and (3) an expanding recovery phase (2016–2022). Spatially, the most significant shrinkage was observed along the southern and southwestern shores of the lake. The driving factors varied across different periods: sunshine duration was the dominant influence during the rapid shrinking phase (1965–1979), accounting for 82% of the changes; population and cropland area were the main drive factors during the fluctuating shrinking period (1986–2016), accounting for 56% of the changes; and during the expanding recovery phase (2016–2022), the population accounted for 75% of the changes in the lake’s surface area. Currently, the protection of Yilong Lake depends on water supplementation and strict regulation of outflow, resulting in the lake exhibiting characteristics similar to a reservoir. This long-term investigation provides baseline information for future lake monitoring. Our research findings can also guide decision-makers in urban water resource management and environmental protection, ensuring the scientific and rational use of watershed water resources, effectively curbing the shrinkage of Yilong Lake, and achieving long-term sustainable restoration of the lake’s ecology.

Large-scale exploitation of underground mineral resources causes surface collapse, reduces land use efficiency, and brings a series of ecological and environmental problems. This is significantly important for the ecological restoration work of mining areas to accurately extract the subsidence range and depth of coal mine surface and formulate the regulation model suitable for coal mine subsidence areas. In this research, we used Differential Interferometric Synthetic Aperture Radar (D-InSAR) technology to extract the subsidence range of the Zhaogu Mining Area in Henan Province based on multi-source remote sensing data. We constructed the Spectral-Spatial Residual Network (SSRN) to classify the land use information within the subsidence range. Finally, we constructed a fuzzy comprehensive evaluation model based on the improved G1 method that assesses the extent of land damage in the subsidence area. Additionally, a suitable governance model for the subsidence area in the Zhaogu Mining Area is proposed. The results can provide technical support and data reference for the comprehensive treatment of subsidence in the Zhaogu Mining Area.

Long time series land cover classification information is the basis for scientific research on urban sprawls, vegetation change, and the carbon cycle. The rapid development of cloud computing platforms such as the Google Earth Engine (GEE) and access to multi-source satellite imagery from Landsat and Sentinel-2 enables the application of machine learning algorithms for image classification. Here, we used the random forest algorithm to quickly achieve a time series land cover classification at different scales based on the fixed land classification sample points selected from images acquired in 2022, and the year-by-year spectral differences of the sample points. The classification accuracy was enhanced by using multi-source remote sensing data, such as synthetic aperture radar (SAR) and digital elevation model (DEM) data. The results showed that: (i) the maximum difference (threshold) of the sample points without land class change, determined by counting the sample points of each band of the Landsat time series from 1986 to 2022, was 0.25; (ii) the kappa coefficient and observed accuracy of the same sensor from Landsat 8 are higher than the results of the TM and ETM+ sensor data from 2013 to 2022; and (iii) the addition of a mining land cover type increases the kappa coefficient and overall accuracy mean values of the Sentinel 2 image classification for a complex mining and forest area. Among the land classifications via multi-source remote sensing, the combined variables of Spectral band + Index + Terrain + SAR result in the highest accuracy, but the overall improvement is limited. The method proposed is applicable to remotely sensed images at different scales and the use of sensors under complex terrain conditions. The use of the GEE cloud computing platform enabled the rapid analysis of remotely sensed data to produce land cover maps with high accuracy and a long time series. [ABSTRACT FROM AUTHOR]

On October 6, 2021, a high-position landslide disaster occurred in Yanwo Village, Rongshan Town, Lizhou District, Guangyuan City, Sichuan Province, around 13: 00. The landslide resulted in the destruction of 4 houses, 3 power lines, the burial of a 170-meter-long rural road, and the blockage of a 350-meter-long river channel. By utilizing a variety of remote sensing information sources, including multi-period satellite data before and after landslide, high-precision UAV aerial images, and airborne LiDAR data, the characteristics of high landslide and its sliding mode are studied by using a four-dimensional analysis approach combining three-dimensional space with time. Starting from the development background of high landslide, the deformation and failure characteristics and sliding mode of high landslide are summarized by analyzing the deformation characteristics before sliding and the dynamic procession of the high landslide. Based on the deduced landslide progression and the stability evaluation of the residual landslide at the rear, three sliding modes for future high-position landslide events are predicted and analyzed: Firstly, the back edge of landslide continues to be disjointed and move, causing a direct shear failure of the front part of the landslide by squeezing. Secondly, the trailing edge of the landslide continues to be disjointed and move, resulting in the sliding landslide occurs along the existing landslide channel. Thirdly, the initiation of the front part of the landslide trigger a tractional landslide in the middle and rear parts of the landslide. High-position landslides are common in the southwest mountainous areas, and during early geological hazard investigations, they should be effectively identified, and disaster prevention and mitigation measures should be strengthened. [ABSTRACT FROM AUTHOR]

The Shishapangma region, situated in the middle of the Himalayas, is rich in glacial lakes and glaciers. Hence, glacial lake outburst floods (GLOFs) have become a top priority because of the severe threat posed by GLOFs to the downstream settlements. This study presents a comprehensive analysis of GLOF hazards using multi-source remote sensing datasets and designs a flood model considering the different breaching depths and release volumes for the Galong Co region. Based on high-resolution optical images, we derived the expanding lake area and volume of glacial lakes. We monitored deformation velocity and long-term deformation time series around the lake dam with Small BAseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR). The glacier thinning trend was obtained from the difference in the Digital Elevation Model (DEM). We identified potential avalanche sources by combining topographic slope and measurable deformation. We then carried out flood modeling under three different scenarios using the hydrodynamic model HEC-RAS for Galong Co, which is formed upstream of Nyalam. The results show that the Nyalam region is exposed to high-intensity GLOFs in all scenarios. The larger breaching depth and release volumes caused a greater flow depth and peak discharge. Overall, the multiple remote sensing approaches can be applied to other glacial lakes, and the modeling can be used as a basis for GLOF mitigation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As one of the world's major forestry countries, accurate forest-type maps in China are of great importance for the monitoring and management of forestry resources. Classifying and mapping forest types on a large scale across the country is challenging due to the complex composition of forest types, the similarity of spectral features among forest types, and the need to collect and process large amounts of data. In this study, we generated a medium-resolution (30 m) forest classification map of China using multi-source remote sensing images and local samples. A mapping framework based on Google Earth Engine (GEE) was constructed mainly using the spectral, textural, and structural features of Sentinel-1 and Sentinel-2 remote sensing images, while local acquisition data were utilized as the mapping channel for training. The proposed method includes the following steps. First, local data processing is performed to obtain training and validation samples. Second, Sentinel-1 and Sentinel-2 data are processed to improve the classification accuracy by using the enhanced vegetation index (EVI) and the red-edge position index (REPI) computed based on the S2A data. Third, to improve classification efficiency, useless bands are removed and important bands are retained through feature importance analysis. Finally, random forest (RF) is used as a classifier to train the above features, and the classification results are used for mapping and accuracy evaluation. The validation of the samples showed an accuracy of 82.37% and a Kappa value of 0.72. The results showed that the total forest area in China is 21,662,261.17 km 2 , of which 1,127,294.42 km 2 of coniferous forests account for 52% of the total area, 981,690.98 km 2 of broad-leaf forests account for 45.3 % of the total area, and 57,275.77 km 2 of mixed coniferous and broad-leaf forests account for 2.6% of the total area. Upon further evaluation, we found that textural and structural features play a greater role in classification compared to spectral features. Our study shows that combining multi-source high-resolution remote sensing imagery with locally collected samples can produce forest maps for large areas. Our maps can accurately reflect the distribution of forests in China, which is conducive to forest conservation and development. [ABSTRACT FROM AUTHOR]

Copyright of Arid Land Geography is the property of Chinese Academy of Sciences, Xinjiang Institute of Ecology & Geography and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The imbalance of natural, social, and economic systems has engendered a multitude of environmental challenges, resulting in the increasingly vulnerable ecological environments. To mitigate the adverse effects of external disturbances on ecosystems, ecological vulnerability assessment (EVA) has emerged as a pivotal research domain in environmental science. Therefore, EVA of the Loess Plateau from 2000 to 2020 was conducted, utilizing a sensitivity-resilience-pressure (SRP) model based on ecological zoning, and identifying its driving factors. The findings revealed an overall ecological vulnerability index (EVI) of 0.53, signifying indicating a moderate vulnerability, and it exhibited a decreasing trend, with EVI of 0.58, 0.53, and 0.49, respectively. The EVI in the five ecological zones was: S5 (sandy desert region, 0.80) > S3 (agricultural irrigation regions, 0.66) > S1 (loess hilly and gully region, 0.52) > S4 (river valley plain region, 0.43) > S2 (Earth-rocky mountainous region, 0.38). Notably, a declining pattern in EVI was observed in S5, S1, and S3, while S2 and S4 experienced an increase. Moreover, the grade of EV reduced from the northwest to the southeast during all three periods, with a distinct positive spatial correlation and the presence of hot spots, sub-hot spots, sub-cold spots, and cold spots. Vegetation cover, humidity, and dryness were the main driving factors of EV, displaying significant interrelationships among all indicators. Finally, targeted protective strategies were proposed to enhance EV in different ecological zones of the Loess Plateau. Overall, EV in five ecological zones exhibited distinct spatiotemporal variation. This study offered a valuable framework of EVA applicable to arid areas, thereby promoting the ecological management and healthy development in vulnerable regions.

Air quality degradation has triggered a large-scale public health crisis globally. Existing machine learning techniques have been used to attempt the remote sensing estimates of PM2.5. However, many machine learning models ignore the spatial non-stationarity of predictive variables. To address this issue, this study introduces a Flexible Geographically Weighted Neural Network (FGWNN) to estimate PM2.5 based on multi-source remote sensing data. FGWNN incorporates the Flexible Geographical Neuron (FGN) and Geographical Activation Function (GWAF) within the framework of Artificial Neural Network (ANN) to capture the intricate spatial non-stationary relationships among predictive variables. A robust air quality remote sensing estimation model was constructed using remote sensing data of Aerosol Optical Depth (AOD), Normalized Difference Vegetation Index (NDVI), Temperature (TMP), Specific Humidity (SPFH), Wind Speed (WIND), and Terrain Elevation (HGT) as inputs, and Ground-Based PM2.5 as the observation. The results indicated that FGWNN successfully generates PM2.5 remote sensing data with a 2.5 km spatial resolution for the contiguous United States (CONUS) in 2022. It exhibits higher regression accuracy compared to traditional ANN and Geographically Weighted Regression (GWR) models. FGWNN holds the potential for applications in high-precision and high-resolution remote sensing scenarios.

Geohazards that have developed in densely vegetated alpine gorges exhibit characteristics such as remote occurrence, high concealment, and cascading effects. Utilizing a single remote sensing datum for their identification has limitations, while utilizing multiple remote sensing data obtained based on different sensors can allow comprehensive and accurate identification of geohazards in such areas. This study takes the Latudi River valley, a tributary of the Nujiang River in the Hengduan Mountains, as the research area, and comprehensively uses three techniques of remote sensing: unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR), Small Baseline Subset interferometric synthetic aperture radar (SBAS-InSAR), and UAV optical remote sensing. These techniques are applied to comprehensively identify and analyze landslides, rockfalls, and debris flows in the valley. The results show that a total of 32 geohazards were identified, including 18 landslides, 8 rockfalls, and 6 debris flows. These hazards are distributed along the banks of the Latudi River, significantly influenced by rainfall and distribution of water systems, with deformation variables fluctuating with rainfall. The three types of geohazards cause cascading disasters, and exhibit different characteristics in the 0.5 m resolution hillshade map extracted from LiDAR data. UAV LiDAR has advantages in densely vegetated alpine gorges: after the selection of suitable filtering algorithms and parameters of the point cloud, it can obtain detailed terrain and geomorphological information on geohazards. The different remote sensing technologies used in this study can mutually confirm and complement each other, enhancing the capability to identify geohazards and their associated hazard cascades in densely vegetated alpine gorges, thereby providing valuable references for government departments in disaster prevention and reduction work.

The continuous downward movement exhibited by the Lashagou landslide group in recent years poses a significant threat to the safety of both vehicles and pedestrians traversing the highway G310. By integrating geomorphological interpretation using multi-temporal optical images, interferometric synthetic aperture radar (InSAR) measurements, and continuous global navigation satellite system (GNSS) observations, this paper traced the formation period of the Lashagou landslide group, and explored its kinematic behavior under external drivers such as rainfall and snowmelt. The results indicate that the formation period can be specifically categorized into three periods: before, during, and after the construction of highway G310. The construction of highway G310 is the direct cause and prerequisite for the formation of the Lashagou landslide group, whereas summer precipitation and spring snowmelt are the external driving factors contributing to its continuous downward movement. Additionally, both the long-term seasonal downslope movement and transient acceleration events are strongly controlled by rainfall, and there is a time lag of approximately 1–2 days between the transient acceleration and heavy rainfall events. This study highlights the benefits of leveraging multi-source remote sensing data to investigate slow-moving landslides, which is advantageous for the implementation of effective control and engineering intervention to mitigate potential landslide disasters.

Characterized by soil moisture content and plant growth, agricultural drought occurs when the soil moisture content is lower than the water requirement of plants. Microwave remote sensing observation has the advantages of all-weather application and sensitivity to soil moisture change. However, microwave remote sensing can only invert 0~5 cm of soil surface moisture, so it cannot effectively reflect the drought situation of farmland. Therefore, this study took Henan Province as the study area, used soil moisture active and passive (SMAP) satellite soil moisture data, employed NDVI, LST, and ET as the independent variables, and took the drought grade on the sample as the dependent variable. Using the 2017–2019 data as the training set and the 2020 data as the testing set, a random forest drought monitoring model with comprehensive influence of multiple factors was constructed based on the training set data. In the process of model training, the cross-validation method was employed to establish and verify the model. This involved allocating 80% of the sample data for model construction and reserving 20% for model verification. The results demonstrated an 85% accuracy on the training set and an 87% accuracy on the testing set. Additionally, two drought events occurring during the winter wheat growing period in Henan Province were monitored, and the validity of these droughts was confirmed using on-site soil moisture and the vegetation supply water index (VSWI). The findings indicated a high incidence of agricultural drought in the southwestern part of Henan Province, while the central and northern regions experienced a lower incidence during the jointing to heading and filling stages. Subsequently, leveraging the results from the random forest drought monitoring, this study conducted a time series analysis using the Mann–Kendall test and a spatial analysis employing Moran’s I index to examine the temporal and spatial distribution of agricultural drought in Henan Province. This analysis aimed to unveil trends in soil moisture changes affecting agricultural drought, as observed via the SMAP satellite (NASA). The results suggested a possible significant spatial auto-correlation in the occurrence of agricultural drought.

Coal fire cause serious influence on environment, economy and safety of surrounding area. It is of great significance to accurately identify the scope of coal fire caused by spontaneous combustion in coal field for monitoring and controlling coal fire. Relevant scholars identified the scope of coal fire by extracting surface thermal anomaly or surface deformation information respectively, but due to the single method and means, there are many factors causing the occurrence of coal fire, so the experimental results are not accurate enough. In order to improve the accuracy of coal fire identification, the coal fire identification method combining satellite thermal infrared technology and radar technology is applied to the fire area identification of Ningwu Coalfield in Shanxi Province through practical application research. Firstly, the ASTER–TES(Temperature-Emissivity Separation) algorithm is used to retrieve land surface Temperature from ASTER thermal infrared data at night. At the same time, surface subsidence information is inverted using The Sentinel-1 data of SBAS–InSAR(Small Baseline Subset InSAR) technology, and then the abnormal high temperature area and abnormal settlement area in the study area are extracted by threshold segmentation method, and then the range of suspected coal field fire area is obtained by fusion processing. Finally, the experimental results are compared and verified by the coal fire range determined by the field survey method of measuring radon. The results show that the accuracy of the proposed method is as high as 93.78%, which is 43.29% and 62.23% higher than that of the single temperature inversion method and the settlement anomaly method. However, some fire zones have not been identified, mainly because it is difficult to obtain the threshold of identifying fire zones using surface deformation. The results show that the cooperative identification method of thermal infrared technology and radar technology can effectively overcome the deficiency of single identification method, significantly improve the identification accuracy of coal fire range, and provide a powerful reference for accurately determining the control range of fire area. In order to obtain more comprehensive and accurate range of coal fire, it is necessary to study the characteristics of surface deformation detection method in the future.

Climate change can induce phytoplankton blooms (PBs) in eutrophic lakes worldwide, and these blooms severely threaten lake ecosystems and human health. However, it is unclear how urbanization and its interaction with climate impact PBs, which has implications for the management of lakes. Here, we used multi‐source remote sensing data and integrated the Virtual‐Baseline Floating macroAlgae Height (VB‐FAH) index and OTSU threshold automatic segmentation algorithm to extract the area of PBs in Lake Dianchi, China, which has been subjected to frequent PBs and rapid urbanization in its vicinity. We further explored long‐term (2000–2021) trends in the phenological and severity metrics of PBs and quantified the contributions from urbanization, climate change, and also nutrient levels to these trends. When comparing data from 2011–2021 to 2000–2010, we found significantly advanced initiation of PBs (28.6 days) and noticeably longer duration (51.9 days) but an insignificant trend in time of disappearance. The enhancement of algal nutrient use efficiency, likely induced by increased water temperature and reduced nutrient concentrations, presumably contributed to an earlier initiation and longer duration of PBs, while there was a negative correlation between spring wind speed and the initiation of PBs. Fortunately, we found that both the area of the PBs and the frequency of severe blooms (covering more than 19.8 km2) demonstrated downward trends, which could be attributed to increased wind speed and/or reduced nutrient levels. Moreover, the enhanced land surface temperature caused by urbanization altered the thermodynamic characteristics between the land and the lake, which, in turn, possibly caused an increase in local wind speed and water temperature, suggesting that urbanization can differently regulate the phenology and severity of PBs. Our findings have significant implications for the understanding of the impacts of urbanization on PB dynamics and for improving lake management practices to promote sustainable urban development under global change. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Neural network models play an important role in crop extraction based on remote sensing data. However, when dealing with high-dimensional remote sensing data, these models are susceptible to performance degradation. In order to address the challenges associated with multi-source Gaofen satellite data, a novel method is proposed for dimension reduction and crop classification. This method combines the benefits of the stacked autoencoder network for data dimensionality reduction, and the convolutional neural network for classification. By leveraging the advantages of multi-dimensional remote sensing information, and mitigating the impact of dimensionality on the classification accuracy, this method aims to improve the effectiveness of crop classification. The proposed method was applied to the extraction of crop-planting areas in the Yangling Agricultural Demonstration Zone, using multi-temporal spectral data collected from the Gaofen satellites. The results demonstrate that the fusion network, which extracts low-dimensional characteristics, offers advantages in classification accuracy. At the same time, the proposed model is compared with methods such as the decision tree (DT), random forest (RF), support vector machine (SVM), hyperspectral image classification based on a convolutional neural network (HICCNN), and a characteristic selection classification method based on a convolutional neural network (CSCNN). The overall accuracy of the proposed method can reach 98.57%, which is 7.95%, 4.69%, 5.68%, 1.21%, and 1.10% higher than the above methods, respectively. The effectiveness of the proposed model was verified through experiments. Additionally, the model demonstrates a strong robustness when classifying based on new data. When extracting the crop area of the entire Yangling District, the errors for wheat and corn are only 9.6% and 6.3%, respectively, and the extraction results accurately reflect the actual planting situation of crops. [ABSTRACT FROM AUTHOR]

In recent years, there has been growing concern that vegetation changes on the Tibetan Plateau are associated with climate change (temperature and precipitation) and human activities. This study used six types of remote sensing vegetation data, including GIMMS (Global Inventory Modelling and Mapping Studies) NDVI (Normalized Difference Vegetation Index), MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI, MODIS EVI (Enhanced Vegetation Index), SPOT Vegetation (Spot-VGT) NDVI, LAI (Leaf Area Index) and NPP (Net Primary Productivity), and applied the maximum synthesis method, trend analysis, correlation analysis, and multivariate statistical analysis to investigate vegetation change processes since the 1980s. The study showed that the amount of vegetation on the TP had increased significantly since 2000 (p <.01), especially in the northeastern part of the TP. There was no significant change prior to 2000. The different vegetation data sources varied greatly. Four remote sensing indices, MODIS EVI, Spot-VGT NDVI, LAI, and NPP, showed a significant increase in vegetation from 2000, accounting for 16.18%, 44.55%, 30.44% and 8.94% of the total area, respectively (p <.05). Multiple data sources provided a more comprehensive understanding, whereas a single data source had substantial uncertainty. Human activities, such as the implementation of large-scale ecological projects, played a dominant role in increasing vegetation, while climate change played a subsidiary role. The MODIS EVI, Spot-VGT NDVI, LAI, and NPP data showed that the area of increased vegetation caused by human activities accounted for 53.51%, 45.68%, 37.52%, and 31.79% of the total area of the TP, respectively. The relative increase from climate change was 10.28%, 17.49%, 13.15%, and 8.82%, respectively. The current study applied multi-source remotely sensed vegetation data, which effectively reduced the uncertainty caused by individual data sources and provided more rigorous and scientific research conclusions. [ABSTRACT FROM AUTHOR]

It is crucial to investigate the urban agglomerations spatio-temporal evolution patterns and driving factors for analyzing the urban spatial structure-functional division and promoting the coordinated development of urban agglomerations. In this study, a novel vegetation-building-nighttime light-adjusted index (VBNAI) was established for rapid and effective mapping of urban construction land (UCL) in Central Plains Urban Agglomeration (CPUA), China during 2000–2020 based on Google Earth Engine (GEE) platform. Compared with traditional indices, VBNAI can significantly decrease the blooming effect, Normalized Difference Vegetation Index (NDVI) saturation, and soil background of nighttime light data. In addition, the urban expansion indices and standard deviation ellipse model were synthetically adopted to analyze the spatio-temporal evolution pattern of urban expansion. The gravity model and the geographically weighted regression model were employed to determine the spatial interaction forces and drivers of urban expansion, respectively. The results showed that the VBNAI index has obvious advantages in efficiency and accuracy to extract UCL with the overall accuracy of more than 91%. The UCL of CPUA had increased by 4489.84 km2 during 2000–2020 with the gravity center moving towards southeast continuously. From 2000 to 2010, the urban expansion was in a 'center-hinterland' pattern which had benefit from the favorable effect of the traffic shaft belt. During 2010–2020, the urban network structure had basically established. Urban expansion had been influenced by a variety of socio-economic and demographic factors, and the impact degree varied from region to region. This study could provide scientific references for facilitating the intensive utilization of urban resources and optimizing the spatial development pattern of urban agglomeration. [ABSTRACT FROM AUTHOR]

The collapse of open-pit coal mine slopes is a kind of severe geological hazard that may cause resource waste, economic loss, and casualties. On 22 February 2023, a large-scale collapse occurred at the Xinjing Open-Pit Mine in Inner Mongolia, China, leading to the loss of 53 lives. Thus, monitoring of the slope stability is important for preventing similar potential damage. It is difficult to fully obtain the temporal and spatial information of the whole mining area using conventional ground monitoring technologies. Therefore, in this study, multi-source remote sensing methods, combined with local geological conditions, are employed to monitor the open-pit mine and analyze the causes of the accident. Firstly, based on GF-2 data, remote sensing interpretation methods are used to locate and analyze the collapse area. The results indicate that high-resolution remote sensing can delineate the collapse boundary, supporting the post-disaster rescue. Subsequently, multi-temporal Radarsat-2 and Sentinel-1A satellite data, covering the period from mining to collapse, are integrated with D-InSAR and DS-InSAR technologies to monitor the deformation of both the collapse areas and the potential risk to dump slopes. The D-InSAR result suggests that high-intensity open-pit mining may be the dominant factor affecting deformation. Furthermore, the boundary between the collapse trailing edge and the non-collapse area could be found in the DS-InSAR result. Moreover, various data sources, including DEM and geological data, are combined to analyze the causes and trends of the deformation. The results suggest that the dump slopes are stable. Meanwhile, the deformation trends of the collapse slope indicate that there may be faults or joint surfaces of the collapse trailing edge boundary. The slope angle exceeding the designed value during the mining is the main cause of the collapse. In addition, the thawing of soil moisture caused by the increase in temperature and the reduction in the mechanical properties of the rock and soil due to underground voids and coal fires also contributed to the accident. This study demonstrates that multi-source remote sensing technologies can quickly and accurately identify potential high-risk areas, which is of great significance for pre-disaster warning and post-disaster rescue.

The mechanism of aerosol pollution transport remains highly elusive owing to the myriad of influential factors. In this study, ground station data, satellite data, ground-based LiDAR remote sensing data, sounding data, ERA5 reanalysis and a backward trajectory model were combined to investigate the formation process and optical properties of winter aerosol pollution in Beijing and surrounding areas. The analysis of ground station data shows that compared to 2019 and 2021, the pandemic lockdown policy resulted in a decrease in the total number of pollution days and a decrease in the average concentration of particulate matter in the Beijing area in 2020. The terrain characteristics of the Beijing-Tianjin-Hebei (BTH) made it prone to northeast and southwest winds. The highest incidence of aerosol pollution in Beijing occurs in February and March during the spring and winter seasons. Analysis of a typical heavy aerosol pollution process in the Beijing area from 28 February to 5 March 2019 shows that dust and fine particulate matter contributed to the primary pollution; surface air temperature inversion and an average wind speed of less than 3 m/s were conducive to the continuous accumulation of pollutants, which was accompanied by the oxidation reaction of NO2 and O3, forming photochemical pollution. The heavy aerosol pollution was transmitted and diffused towards the southeast, gradually eliminating the pollution. Our results provide relevant research support for the prevention and control of aerosol pollution.

Traditional night light images are black and white with a low resolution, which has largely limited their applications in areas such as high-accuracy urban electricity consumption estimation. For this reason, this study proposes a fusion algorithm based on a dual-transformation (wavelet transform and IHS (Intensity Hue Saturation) color space transform), is proposed to generate color night light remote sensing images (color-NLRSIs). In the dual-transformation, the red and green bands of Landsat multi-spectral images and “NPP-VIIRS-like” night light remote sensing images are merged. The three bands of the multi-band image are converted into independent components by the IHS modulated wavelet transformed algorithm, which represents the main effective information of the original image. With the color space transformation of the original image to the IHS color space, the components I, H, and S of Landsat multi-spectral images are obtained, and the histogram is optimally matched, and then it is combined with a two-dimensional discrete wavelet transform. Finally, it is inverted into RGB (red, green, and blue) color images. The experimental results demonstrate the following: (1) Compared with the traditional single-fusion algorithm, the dual-transformation has the best comprehensive performance effect on the spatial resolution, detail contrast, and color information before and after fusion, so the fusion image quality is the best; (2) The fused color-NLRSIs can visualize the information of the features covered by lights at night, and the resolution of the image has been improved from 500 m to 40 m, which can more accurately analyze the light of small-scale area and the ground features covered; (3) The fused color-NLRSIs are improved in terms of their MEAN (mean value), STD (standard deviation), EN (entropy), and AG (average gradient) so that the images have better advantages in terms of detail texture, spectral characteristics, and clarity of the images. In summary, the dual-transformation algorithm has the best overall performance and the highest quality of fused color-NLRSIs.

Comprehensive use of multi-source remote sensing data, the method of combining object-oriented image analysis and visual interpretation was used to interpret the land use or cover of Huainan mining area in 2007, 2010, 2013, 2016 and 2019. Through the calculation of statistics, dynamic degree, diversity index, utilization degree index and transfer matrix for each land use or cover type area and its proportion, the spatial and temporal change characteristics of land use or cover in Huainan mining area between 2007 and 2019 were analyzed. The results show as follows. The area and proportion of cultivated land in Huainan mining area has been declining continuously, a large number of cultivated land were destroyed, resulting in soil erosion, followed by urban and industrial land, and occupied by solid waste. The order of dynamic degree of all land use or cover types was waterlogging subsidence area > lake reservoir > solid waste > industrial and mining land > forest land > bare land > urban land > road > cultivated land > rural residential land, indicating that coal mining activities have a great impact on land use in mining areas. All mine land use or cover types were disturbed by human activities, and the land use diversity index and land use degree index increased. [ABSTRACT FROM AUTHOR]

Accurate information on dominant tree species and their spatial distribution in subtropical natural forests are key ecological monitoring factors for accurately characterizing forest biodiversity, depicting the tree competition mechanism and quantitatively evaluating forest ecosystem stability. In this study, the subtropical natural forest in northwest Yunnan province of China was selected as the study area. Firstly, an object-oriented multi-resolution segmentation (MRS) algorithm was used to segment individual tree crowns from the UAV RGB imagery and satellite multispectral imagery in the forests with different densities (low (547 n/ha), middle (753 n/ha) and high (1040 n/ha)), and parameters of the MRS algorithm were tested and optimized for accurately extracting the tree crown and position information of the individual tree. Secondly, the texture metrics of the UAV RGB imagery and the spectral metrics of the satellite multispectral imagery within the individual tree crown were extracted, and the random forest algorithm and three deep learning networks constructed in this study were utilized to classify the five dominant tree species. Finally, we compared and evaluated the performance of the random forest algorithm and three deep learning networks for dominant tree species classification using the field measurement data, and the influence of the number of training samples on the accuracy of dominant tree species classification using deep learning networks was investigated. The results showed that: (1) Stand density had little influence on individual tree segmentation using the object-oriented MRS algorithm. In the forests with different stand densities, the F1 score of individual tree segmentation based on satellite multispectral imagery was 71.3–74.7%, and that based on UAV high-resolution RGB imagery was 75.4–79.2%. (2) The overall accuracy of dominant tree species classification using the light-weight network MobileNetV2 (OA = 71.11–82.22%), residual network ResNet34 (OA = 78.89–91.11%) and dense network DenseNet121 (OA = 81.11–94.44%) was higher than that of the random forest algorithm (OA = 60.00–64.44%), among which DenseNet121 had the highest overall accuracy. Texture metrics improved the overall accuracy of dominant tree species classification. (3) For the three deep learning networks, the changes in overall accuracy of dominant tree species classification influenced by the number of training samples were 2.69–4.28%. [ABSTRACT FROM AUTHOR]

With the exponential growth in the speed and volume of remote sensing data, deep learning models are expected to adapt and continually learn over time. Unfortunately, the domain shift between multi-source remote sensing data from various sensors and regions poses a significant challenge. Segmentation models face difficulty in adapting to incremental domains due to catastrophic forgetting, which can be addressed via incremental learning methods. However, current incremental learning methods mainly focus on class-incremental learning, wherein classes belong to the same remote sensing domain, and neglect investigations into incremental domains in remote sensing. To solve this problem, we propose a domain-incremental learning method for semantic segmentation in multi-source remote sensing data. Specifically, our model aims to incrementally learn a new domain while preserving its performance on previous domains without accessing previous domain data. To achieve this, our model has a unique parameter learning structure that reparametrizes domain-agnostic and domain-specific parameters. We use different optimization strategies to adapt to domain shift in incremental domain learning. Additionally, we adopt multi-level knowledge distillation loss to mitigate the impact of label space shift among domains. The experiments demonstrate that our method achieves excellent performance in domain-incremental settings, outperforming existing methods with only a few parameters. [ABSTRACT FROM AUTHOR]