Atmospheric correction is an important preprocessing step for hyperspectral remote sensing images. The atmospheric correction quality determines the application degree of hyperspectral remote sensing to a certain extent. First, this study analyzed the influence of the atmosphere on radiative transfer and summarized the inversion methods of aerosol optical thickness and water vapor in the atmosphere, indicating the main atmospheric factors affecting the quality of hyperspectral remote sensing images. Then, the influence of the atmosphere was demonstrated theoretically by clarifying the derivation process of the radiative transfer equation and the action mechanism of relevant parameters, indicating the main aspects of hyperspectral atmospheric correction. Furthermore, this study summarized the hyperspectral atmospheric correction methods formed in recent years, including methods based on empirical statistics and radiative transfer, and analyzed the study advances and development trends of hyperspectral atmospheric correction. Finally, this study forecasted the development of atmospheric correction of hyperspectral remote sensing images. This study will provide a certain reference for the engineering application and study of hyperspectral remote sensing.
As a major form of soil degradation, soil salinization can greatly harm agricultural production and ecological environment. Remote sensing methods can acquire soil spectral characteristics in a rapid, macroscopic, and timely manner. Based on this, remote sensing monitoring models can be built for a wide range of soil salinization monitoring and assessment. Thus, summarizing and discussing the building methods for remote sensing monitoring models of soil salinization is of great significance to improve the precision of remote sensing monitoring of soil salinization and to monitor and control salinized soil. This study reviewed the recent literature related to remote sensing studies concerning soil salinization at home and abroad. Then, it summarized the steps such as factor selection, model building, and precision verification in the building of remote sensing monitoring models of soil salinization. Focusing on the current hot research topic, this study discussed the limitations and development trends. The main conclusions are as follows. The remote sensing monitoring models of soil salinization are important means for monitoring and forecasting salinized soil. In recent years, the hot research topic in this field is to improve the model precision using new data sources and models. Differences exist in the use of remote sensing data sources among different studies, but the modeling factors are all optimized from spectral sensitive bands, prior spectral indices, and remote sensing-derived data. The remote sensing monitoring models of soil salinization mainly include the linear regression model and the machine learning model. The remote sensing models built for different regions have different precision and applicability.
Vehicle detection is a hot research topic in the fields of computer vision, photogrammetry, and remote sensing. With the continuous development of deep learning technology, vehicle detection based on remote sensing images has been applied in fields such as smart city construction and intelligent transportation. This study systematically summarized existent vehicle detection algorithms based on remote sensing images and deep learning models and highlighted the classification, analysis, and comparison of one-stage and two-stage vehicle detection algorithms. Moreover, this study summarized the key technologies of vehicle detection in large-scale and complex backgrounds and analyzed the advantages and disadvantages of mainstream deep learning models of vehicle detection based on remote sensing images. Experiments were conducted to evaluate the YOLOv5, Faster-RCNN, FCOS, and SSD algorithms using DOTA and DIOR datasets. The vehicle detection precision based on the DOTA dataset was 0.695, 0.410, 0.370, and 0.251, respectively and that based on the DIOR dataset was 0.566, 0.243, 0.231, and 0.154, respectively. The experimental results show that the small target scale is still the main factor restricting the vehicle detection performance based on remote sensing images and that the application of deep learning models to the detection of small targets is to be further improved. Finally, based on public datasets and the analysis of existing algorithms, this study proposed the solution and development trend of vehicle detection based on remote sensing images in large-scale and complex backgrounds.
The mariculture industry occupies an important position in the marine economy of Guangdong Province. Timely and accurate knowledge of the spatial distribution and area changing trends of mariculture areas can greatly promote the sustainable development of the mariculture industry. Conventional interpretation methods for remote sensing images have problems of poor repeatability, low applicability, and high subjective arbitrariness. By contrast, the U-Net convolutional neural network, which belongs to the deep learning network model, can better extract the features of the object with higher extraction precision. Therefore, based on the multi-temporal Landsat TM/OLI remote sensing images, this study identified the mariculture areas (enclosed-sea and open-cage aquaculture areas) in Guangdong from 1998 to 2021 using the U-Net model as the interpretation model. The area trend analysis of mariculture areas was made. The changing characteristics of mariculture areas in terms of spatial distribution patterns were studied. The results are as follows. Compared with network models such as K-Means cluster analysis and DBN, the U-Net model with higher interpretation precision is more suitable for the interpretation of mariculture areas in Guangdong. The mariculture areas in Guangdong are mainly distributed in the western portion of Guangdong, such as Zhanjiang, Jiangmen, and Yangjiang. The mariculture areas in Guangdong can be classified into three levels in terms of area. They have small changes and keep a relatively stable state. The mariculture areas in Guangdong showed a spatial trend of outward expansion from 1998 to 2014 and inward contraction from 2014 to 2021. This study will provide data and technical support for the scientific management of the mariculture areas in Guangdong.
To control the negative effects of the disorderly development of aquaculture ponds and promote the further development of the aquaculture industry, the top priority is to realize rapid and accurate identification and extraction of information on aquaculture ponds. Aquaculture ponds are special net-like water bodies divided by complex roads and dikes. Simple spectral features or spatial texture features are not enough for accurate information extraction. Moreover, the mixed feature rule set gets more demanding on computer performance. Therefore, based on the Landsat image sequence and the Google Earth Engine (GEE) platform, this study proposed an automatic extraction method for coastal aquaculture ponds, which combined the image spectral information, spatial features, and morphological operation. In this method, dual characteristic water spectral indices, that is, the modified combined index for water identification (MCIWI) and the modified normalized difference water index (MNDWI), were employed to highlight the grid characteristics of large water bodies and aquaculture ponds. Then, the low-frequency filtering spatial convolution operation was used to stretch the differences between aquaculture and non-aquaculture water bodies. Finally, the information on aquaculture pond areas as a whole was identified and extracted accurately and quickly. The results are as follows. ① This method has overall precision of 93% and a Kappa coefficient of 0.86. According to the test process verification of typical area superposition comparison, the overlapping proportions between the extraction results and the actual results were all more than 90%, averaging 92.5%, reflecting the high precision and reliability of this extraction method. ② In 2020, the coastal aquaculture ponds in Fujian Province had a total area of 511.73 km2and were mainly distributed in Zhangzhou, Fuzhou, and Ningde cities. ③ The kernel density analysis suggested that Zhangzhou had a high concentration of aquaculture ponds and thus had high pressure in the management of aquaculture ponds. This method can realize automatic information extraction of coastal aquaculture ponds. Thus, it is of great significance to promote the orderly management and scientific development of fishery aquaculture.
Aquaculture is an important way for humans to obtain food, and aquaculture ponds are a major production mode of aquaculture. The Pearl River Delta, as an important aquaculture base in southern China, has undergone great changes in its spatial distribution in the past 30 years. This study investigated Zhongshan City and its adjacent areas. First, the mixed pixels of Landsat and Sentinel-2 remote sensing data were decomposed using the linear mixed pixel decomposition method. Then, the NDWI threshold range corresponding to the water abundance of 70% and above was selected through visual comparison and analysis. Finally, the spatio-temporal distribution of typical aquaculture ponds from 1990 to 2021 was obtained. The study results show that the aquaculture ponds in Zhongshan City and its adjacent areas have experienced a process of first increasing and then decreasing since 1990. Specifically, the area of aquaculture ponds nearly doubled from 1990 to 2000, tended to be stable from 2000 to 2010, but decreased by nearly 50% from 2010 to 2021. This study can reduce the impact of mixed pixels on the monitoring of aquaculture ponds and support the scientific aquaculture and sustainable development of fisheries in the Greater Bay Area.
Intertidal zones, as important parts of coastal wetlands play a significant role in ecological and economic development. However, the dynamic interaction between seawater and land makes it difficult to accurately determine the tidal flat area using the remote sensing information extraction method based on instant remote sensing images. To solve this problem, this study developed an intertidal information extraction method based on Google Earth Engine (GEE) platform and remote sensing index. This proposed method was applied to study the coastal zone of Zhoushan Islands. First, a decision tree algorithm based on the fusion of the digital elevation model (DEM) data was built using the Landsat8 time series image data in 2021. Then, a multi-layer automatic decision tree classification model was formed using the maximum spectral index composite (MSIC) and the Otsu algorithm (OTSU). Based on this, the DEM data were fused to extract and calculate the area of the intertidal zone in Zhoushan Islands. The results show that the area of the intertidal zone in Zhoushan Islands is 35.19 km2 in 2021. The evaluation based on the Google Earth high-resolution images shows that this proposed method has a general precision of 97.7% and a Kappa coefficient of 0.95, indicating good extraction precision and practical effects. This method can provide data support for sustainable management and utilization of coastal zone resources through automatic and rapid extraction of intertidal information, thus promoting regional high-quality development.
Coastal tidal flats, as a major type of coastal wetlands, have significant ecological value in maintaining biodiversity and influencing global climate and environmental change. Since they are only exposed in their entirety at the lowest tide, the previous remote sensing interpretation results showed significant omissions and misclassifications of the tidal flats. Based on the Google Earth Engine (GEE) platform and the Landsat series satellite data, this study constructed high-quality dense time series image stacks for four time periods, i.e., 1990, 2000, 2010, and 2020. Then, these image stacks were combined with the maximum spectral index composite (MSIC) algorithm and the Otsu algorithm (OTSU) for rapid and automatic extraction of coastal tidal flats resources in Laizhou Bay of China. Furthermore, the land cover around the coastal tidal flats was delineated based on the object-oriented analysis technology and the fuzzy-based segmentation parameter (FbSP) optimal scale. Finally, the spatial and temporal evolution patterns of the coastal tidal flats were analyzed. The results are shown as follows. During 1990—2020, the coastal tidal flats in Laizhou Bay gradually decreased, with an area of 822.38 km2 in 2020, a reduction of about 40% compared with that in 1990. The largest reduction was 304.78 km2 during 2000—2010. The coastal tidal flats near the Yellow River estuary showed a seaward migration, while the coast tidal flat patches in other regions of Laizhou Bay showed a landward migration. Human activities were the dominant factors in the changes in tidal flats in Laizhou Bay in the past 30 years. Among them, the expansion of aquaculture ponds/salt fields directly encroached on 414.20 km2 of coastal tidal flats.
Changes in urban expansion and urban land cover structures greatly affect the urban ecological environment and even ecological security. The highly concentrated urban population and economic activities and the resultant rapid urbanization have caused dramatic changes in urban expansion and urban land cover structures in China’s coastal zone. However, previous study data are insufficient for a clear understanding of the urban expansion and urban land cover structures in China’s coastal zone. In light of this, this study analyzed the spatio-temporal changing characteristics of the abovementioned aspects from multiple perspectives using the global urban land use/cover composites with 30 m spatial resolution (GULUC-30). The results are as follows. The urban land area in China’s coastal zone increased from 16 600 km2 in 2000 to 48 000 km2 in 2020, with an expansion intensity of 9.41%. The regional cities in high-density expansion accounted for 41.03% and were mainly located in the north-central portion of the coastal zone. Cities in China’s coastal zone were expanding rapidly at a rate of 1 260.04 km2/a from 2000 to 2010 and 1 871.74 km2/a from 2010 to 2020. The urban impervious surface area had continued to increase over the past two decades. However, the proportion of the urban impervious surface decreased while that of urban green space increased. In 2000, the areas of urban impervious surface and urban green space in China’s coastal zone accounted for 69.49% and 20.81% of the urban land area, respectively. By 2020, they accounted for 63.70% and 26.72%, respectively. The urban expansion of 83.33% of the cities in China’s coastal zone was ahead of the urban population growth. The small cities have large GDP per urban land and high land use efficiency. The study can provide an important scientific basis and decision support for regional urban planning and sustainable development of China’s coastal zone.
Dynamic shoreline monitoring is greatly significant for the scientific management of coastal zones and the rational utilization of marine resources. Based on the Landsat remote sensing images of four periods i.e., 1990, 2000, 2010, and 2020, this study extracted the changes in the shorelines and the coastal zones within the 2 km of the buffer zone in the north of Jiaodong Peninsula from 1990 to 2020 by making comparison and using an object-oriented method. By combining the calculation method for shoreline change intensity, this study analyzed the changing rate and temporal-spatial distribution characteristics of the shorelines using the digital shoreline analysis system (DSAS). Then, this study conducted a driving analysis of changes in the shoreline by constructing a human activity intensity index (HAII) model. The results are as follows. The shorelines of the study area generally showed an upward trend and advanced slowly to the seaside. The overall length of the shorelines increased by 183.13 km. The highest increased and decreased amplitude occurred in artificial shorelines and sandy natural shorelines, respectively. The shoreline changing rates showed uneven temporal-spatial distribution. The maximum growth rate of 94.59 m/a occurred in the Jiaolai River - Jiehe River section, while the maximum erosion rate of -49.01 m/a occurred in the Jiehe River - Dagujia River section. The changes in offshore human activities were the main contributor to the temporal-spatial changes of coastlines in the study area. The lengths and types of shorelines were mainly affected by human activities through sea reclamation and port construction.
With the rapid development of China’s aerospace remote sensing industry, the types of Chinese civilian optical remote sensing satellites have continuously increased. Consequently, the data volume of optical images shows a leapfrogging growth. This brings huge challenges to the daily quality inspection of the calibration products for optical remote sensing image sensors. The inspection of image radiation anomalies is a key step in image quality inspection. However, the inspection faces many problems such as a lack of automated inspection technical capabilities, high manual participation, and low efficiency. To address the above problems, this study proposed a deep learning network model that integrates multi-scale features for the classification and detection of radiation anomaly data. This network model employed a hollow space convolutional pooling pyramid based on the EfficientNet-B0 model. The features of radiation anomaly data on different scales were collected by setting different expansion rates and then processed through channel splicing, pooling, and convolution. Furthermore, they were merged with the features extracted using the EfficientNet-B0 model to improve the precision of the classification and detection model. The experimental results show that the proposed classification and detection model has a higher classification precision for the detection and classification of radiation anomaly data of optical images than other models. Therefore, this study will help to improve the automation level of radiation quality inspection of remote sensing images.
Compared with common dual-temporal satellite images, satellite time series images contain richer surface information and can alleviate the impact of foreign objects with the same spectrum and the same object with different spectra. Therefore, they play an important role in change detection. However, the change detection methods for satellite time series images are mostly based on pixels and ignore the spatial relationship between pixels and their surroundings. This causes noise in the change detection result. Accordingly, this study proposed a method of change detection based on spatial-temporal-spectral features(CDSTS) for satellite time series images. First, the temporal, spatial (textural and statistical), and spectral features of each pixel were extracted from Landsat time series images using a gray-level co-occurrence matrix and local statistical calculation methods. Then, anomalies of time series features were automatically screened according to the time series performance regularity of each pixel in different bands. These anomalies were then fused with the detection results of the continuous change detection and classification method (CCDC) to obtain high-precision changed/unchanged training sample points. Finally, the SVM classifier was trained using the training sample points and their corresponding spatial-temporal-spectral features for full graph classification. The results show that the CDSTS algorithm significantly outperforms the commonly used time series change detection algorithms CCDC and COLD (continuous monitoring of land disturbance) in terms of change detection precision, with the overall precision improved by 4.8 to 11.7 percentage points.
Using remote sensing to detect changes in urban buildings can obtain the change information of building coverage quickly and accurately. However, it is difficult to detect 3D changes quickly and accurately based on image data alone. Moreover, conventional point cloud-based methods have low automation and poor precision. To address these problems, this study used the airborne LiDAR point clouds and employed the RandLA-Net’s point cloud semantic segmentation method to improve the accuracy and automation of change detection. Meanwhile, the failure in differentiating two-period data due to point cloud disorder was overcome through point cloud projection. The standard RandLA-Net method, with the location and color information of points as features, is mainly used for semantic segmentation of street-level point clouds. In this study, urban large-scale airborne point clouds combined with the inherent reflection intensity and the spectral information of point clouds given by images were used to explore the influence of different feature information on the precision of the results. Furthermore, it was found that in addition to the point cloud intensity and spectral features, the coordinate information of points is equally important and can be converted into relative coordinates to significantly improve the result precision. The experimental findings show that the results obtained using RandLA-Net are significantly better than those using conventional methods for building extraction and change detection. This study also verified the feasibility of using deep learning methods to process LiDAR data for building extraction and change detection, which can realize reliable 3D building change detection.
Timely and accurate acquisition of the spatial pattern evolution of land use can effectively support urban ecological environment protection and scientific management. In this study, the spatial characteristics of land use in multiple periods were extracted using a convolutional neural network. Then, they were combined with multiple spatial driving factors to build the long short-term memory network - cellular automata (LSTM-CA) model. Based on the data of land use classification, terrain, and urban traffic of the Zhangjiakou central urban area in 1995, 2000, 2005, 2010, and 2015, this study investigated the simulation methods for urban land use in 2020. By comparison with the precision of the multi-layer perceptron - cellular automata (MLP-CA) model, the proposed method has a Kappa coefficient of over 0.90 and FoM of 0.39. All indices are better than those of the MLP-CA model. Therefore, the LSTM-CA model can fully explore the internal relationships between the changes in historical land use and effectively improve the simulation precision.
Water information extraction is an important study direction in the application of high spatial resolution remote sensing images. Conventional recognition methods only focus on the shallow features of water. Therefore, to further improve the robustness of water information extraction algorithms and increase the segmentation precision by extracting more deep information from remote sensing images, this study proposed a water classification method using the semantic segmentation model based on deep learning. First, deep neural networks were used to mine the information from high-resolution remote sensing images. Then, attention modules were used to integrate the deep information with the shallow features such as shape, structure, texture, and hue. Based on the integrated information, a new deep semantic segmentation model with higher precision and prediction efficiency than existent models was built. Finally, the ablation experiment was conducted to compare with conventional recognition methods and common semantic segmentation models. The experiment demonstrates that the proposed algorithm model yields higher overall precision and efficiency than previous methods and that the algorithm parameters are easy to set and less human intervention is required in the model. This study proved the accuracy and efficiency of deep learning and attention mechanism on water information extraction from high-resolution remote sensing images. Moreover, this study provided a possible solution for the segmentation of high-resolution remote sensing images using the deep learning method and explored the future prospect of the solution.
Soil salinization is the most severe environmental risk in arid and semi-arid areas. The remote sensing method that constructs a characteristic space based on characteristic parameters provides an effective and economical tool and technique for the timely monitoring and inversion of soil salinization. Presently, the normalized difference vegetation index (NDVI) and the salinity index (SI) are mainly selected as the characteristic parameters for salinization inversion, while refined analysis and regional applicability are lacking. This study investigated Urad Front Banner in Inner Mongolia based on the Landsat8 OLI data. The ENDVI-SI3 characteristic space was constructed using the enhanced normalized difference vegetation index (ENDVI) that introduced the shortwave infrared band and the salinity index 3 (SI3) with the best inversion effect for semi-arid areas. Accordingly, the improved salinization monitoring index (ISMI) model was built. The results show that the correlation coefficient between ISMI and soil salt content was up to 0.82, and the inversion precision of the ISMI model was higher than that of NDVI, EDNVI, and SI3 (-0.66, -0.70, and 0.75, respectively). Based on the ISMI, this study achieved the quantitative inversion analysis and risk assessment of soil salinization in Urad Front Banner. This study provides an approach for selecting the optimal characteristic parameters of the characteristic space in the salinization inversion of semi-arid areas.
The eastern agricultural areas of Qinghai are located in the transitional zone from the Loess Plateau to the Qinghai-Tibet Plateau. In this transitional zone, the loess hills feature various landforms, large fluctuations, and fragmentation. With the acceleration of urbanization in recent decades, the shortage of available rural labor force has aggravated land abandonment. Therefore, ascertaining the distribution of abandoned land in the eastern agricultural areas of Qinghai is very crucial to protecting cultivated and ecological land. This study investigated Minhe County in Qinghai Province based on the GEE cloud platform. According to the phenological characteristics of crops, both Sentinel-2 MSI and Sentinel-1 SAR satellite images covering the growth and planting periods of crops were selected as the main data source. With the aid of the DEM and by combining the characteristics of spectra, terrain, polarization, and tasseled cap, this study automatically classified the land cover from 2018 to 2020 in the study area using the random forest method, obtaining the three-year land cover data of the study area. Then, this study built a decision tree based on the determination rules for abandoned land and extracted and verified the abandoned land information using the decision tree. The study results are as follows. The overall classification precision of land cover in 2018, 2019, and 2020 were 86.93%, 87.36%, and 88.54%, respectively. The area of abandoned land in Minhe County in 2020 was 43.17 km2, accounting for 2.28% of the total study area. The abandoned land was mainly distributed in areas with an altitude of 2 200~2 600 m, a slope of 6°~25°, and a shady slope direction. The integration of the polarization characteristics of Sentinel-1 SAR images into Sentinel-2 MSI multi-season images can effectively improve the land cover classification precision and yield accurate information on the abandoned land. This study will provide a reference method and basis for the information extraction of abandoned land in areas with similar terrain.
Karst mountainous areas are influenced by complex cloudy and rainy weather. This brings great difficulties to the extraction of planting structure information using the remote sensing technology. Sentinel-1-based crop identification has unique advantages in precision agriculture. It can obtain the information on regional main crops in time and accurately, thus playing a significant role in formulating agricultural policies and guiding agricultural production. This study investigated Guanling County based on Google images in 2020, Sentinel-1 time series data from April to August, and UAV remote sensing data. First, the plots were extracted using the D_LinkNet model. Then, the planting structures were classified based on the LightGBM module. Finally, the spatial differentiation characteristics of main crops and the influencing mechanism of planting structures in the study area were explored combined with geographic detectors. The results are as follows. ① The crops in Guanling County showed an uneven spatial distribution pattern of more crops in the northwest and less crops in the southeast. ② The influence of factor interaction was greater than that of single factors. The distribution of cultivated land was mainly influenced by traffic location and drainage capacity, followed by factors such as elevation and traffic location. ③ The extraction results of crop planting structures are consistent with the proportions shown in the statistical yearbook, with confusion-matrix overall precision of 0.87 and Kappa coefficient of 0.83. The results can help understand the formation mechanisms and differences in the spatial differentiation of different crop planting structures in Karst mountainous areas. Therefore, this study can provide a scientific basis for the optimization and adjustment of planting structures and the analysis of influencing factors.
Frequent disasters continue to plague many rural areas, and the precise identification of the comprehensive disaster risk in rural areas is critical to disaster prevention and mitigation. With 232 villages in Huayuan County, Hunan Province as a case study, this study defined the comprehensive disaster risk index (CDSI) and constructed an assessment system reflecting the dynamics of disaster-inducing environment based on the three elements of risk formation stated in the regional disaster system theory. Then, this study investigated the comprehensive disaster risk in rural areas by comparing four models, namely the analytic hierarchy process - technique for order preference by similarity to ideal solution (AHP-TOPSIS), the entropy- TOPSIS, AHP, and the entropy weight method. The conclusions are as follows. The multi-model evaluation results show a positive correlation, with a CDSI ratio of 1:0.877:0.740:0.539. The entropy-TOPSIS model is the optimal model for the assessment of comprehensive disaster risk in the study area. The CDSI of the study area has a Moran’s I value of 0.74, a strong spatial autocorrelation, and spatial distribution characteristics of being high in the west, low in the east, and significant locally. This study deepens the assessment of comprehensive disaster risk in rural areas. It will provide practical experience and a theoretical basis for scientifically guiding rural disaster prevention and mitigation and ensuring the safe implementation of the rural revitalization strategy.
Land use can cause carbon stock changes by affecting the structural layouts and functions of terrestrial ecosystems. Therefore, research on the relationship between land use changes and carbon stock is greatly significant for optimizing regional land use patterns and making sensible ecological decisions. This study predicted the spatial-temporal changing characteristics of land use and carbon stock in Xi’an under different scenarios in the future using the PLUS and InVEST models and investigated the impact of land use changes on carbon stock. The results are as follows. From 2000 to 2015, the expansion of construction land and the transfer of high-carbon-density land reduced the carbon stock of Xi’an by 2.49×106 t. From 2015 to 2030 the carbon stock continuously declined by 2.14×106 t in the natural growth scenario, and the carbon stock of Xi’an will increase by 6.92×105 t in the ecological protection scenario due to the measures taken for land protection and transfer control. In the cultivated land protection scenario, the cultivated land will be protected, but the high-carbon-density land such as woodland and grassland will be affected by the expansion of construction land during 2015—2030, reducing the carbon stock to 1.60×108 t. As indicated by the analysis of carbon density change, ecological protection measures can increase the changing rate of carbon density. Compared with the natural growth scenario, the ecological protection scenario will increase the proportion of areas with increased carbon density (mainly high-increase areas) from 0.05% to 1.57%. By contrast, under the cultivated land protection scenario, the carbon density will decrease, and high-increase areas will be transformed into moderately-high-increase areas. Based on cultivated land protection, it is necessary to take proper ecological protection measures in the future land use planning of Xi’an to control the rapid expansion of construction land from cultivated and forest land. Optimizing land use patterns can effectively reduce the loss of carbon stock, improve the level of regional carbon stock, and achieve regional sustainable development.
Land subsidence is a natural geological phenomenon in which the surface elevation drops. It can severely destroy urban infrastructure and threaten urban safety if it occurs in densely populated cities with a high social development degree. The analysis of the evolution characteristics of land subsidence can reflect the degree of the influence of land subsidence on the ground infrastructures, and building an efficient land subsidence prediction model is of great significance for preventing and controlling land subsidence and protecting urban safety. This study obtained the spatial-temporal information on land subsidence using the persistent scatterer interferometric synthetic aperture Radar (PS-InSAR) method first and then verified the information using leveling to get high precision. Then, this study analyzed the general spatial-temporal characteristics of the land subsidence field using an empirical orthogonal function. The analysis results are as follows. Spatial modal No. 1 had a high variance contribution rate, almost representing the general spatial evolution of the study area. Its corresponding time coefficient showed a significant linear trend. By contrast, spatial mode No. 2 had a low variance contribution rate and a seasonally significant time coefficient. Finally, the time series of the regional land subsidence were predicted using both long short-term memory (LSTM) and Attention-LSTM models. The prediction results indicate that the Attention-LSTM model was superior to the LSTM model, with the mean square error loss (MSE-loss) of as low as 0.01. This prediction method expands the application of deep learning in the study of land subsidence.
The deployment of video monitoring points for epidemic prevention in border areas is an important measure to deal with emergencies and has great significance for regional public health security. The deployment of video monitoring points mainly focused on cities in the past. Few studies concerned the deployment of video monitoring points based on the characteristics of border areas and emergency response needs. This study constructed a framework for the deployment of video monitoring points for epidemic prevention in the border area of Xishuangbanna Dai Autonomous Prefecture. The suitability and limiting factors of monitoring points were determined for the multi-round selection of monitoring points. More monitoring points were deployed properly in areas with low monitoring efficiency. Finally, the appropriate deployment sites for epidemic prevention monitoring points in Xishuangbanna were determined. The results are as follows. With the video monitoring points for epidemic prevention determined in this study, 93.3% of the area within 5 km of the Xishuangbanna border can be observed. Thus, information on people flow at the border can be comprehensively obtained. Compared with the conventional site selection methods using single-dimensional suitability, mathematical modeling, and algorithms, the proposed deployment method of video monitoring points for border epidemic prevention is more suitable for the actual situation of border areas and can give full play to the overall coordination level of the deployment of monitoring points. Besides, this proposed method avoids the complex application of conventional methods. Therefore, the site selection method of video monitoring points for epidemic prevention proposed in this study provides theoretical reference and technical support for current COVID-19 prevention in border areas, so as to ensure regional public health security and national sustainable development.
Surface temperature is an important indicator that reflects the regional natural environment and climate changes. High-quality data are very valuable for the study of the temporal and spatial changes in regional surface temperature. In recent years, North America has witnessed relatively abnormal climate changes, thus the surface temperature in this region has great study significance. Based on the MODIS surface temperature data, this study reconstructed the remotely sensed surface temperature data set of North America from 2002 to 2018 and analyzed the spatial and temporal changes in surface temperature over the past 17 years. The reconstructed surface temperature data cover all land surfaces of North America and guarantee precision of about 1 ℃. The analysis results are as follows. North America had a fluctuating temperature increase at an average rate of 0.02 ℃/a in the past 17 years. A historical peak in surface temperature increase occurred in 2016, followed by a sharp drop in the following two years, which was closely related to El Nino. In North America, the temperature increase was greater in spring and autumn than in winter and summer. In recent years, northern Alaska and the Baja California peninsula have experienced significant warming. Vegetation and atmospheric water vapor significantly affect the change in surface temperature. Vegetation and atmospheric water vapor are positively correlated with surface temperature in the north of 40°N, while they are negatively correlated in the south of 40°N. The general changing trend of surface temperature in the next 1~2 years can be predicted to a certain degree of reliability according to the periodic fluctuation trend of the average surface temperature in North America and the influence of El Nino.
This study aims to achieve the dynamic and continuous monitoring of drought in Xinjiang. Based on the temperature vegetation dryness index (TVDI), as well as the Sen’s slope trend analysis, R/S, and partial correlation analysis, this study analyzed the spatial and temporal dynamics, changing trends, and future sustainable state of TVDI and the influences of seasonal precipitation and temperature on TVDI in Xinjiang during the period from 2001 to 2020. The results are as follows. ① The northern Tianshan Mountains and the Kunlun Mountains showed minimum TVDI values of less than 0.57, indicating light drought. The Tarim and Junggar basins showed TVDI values of greater than 0.86, indicating extraordinary drought. ② The TVDI values in spring decreased at a rate of 0.001 3/a. By contrast, the TVDI values in summer, autumn, and winter increased at a rate of 0.001 4/a, 0.002 0/a, and 0.000 8/a, respectively. Therefore, the increased amplitude of the TVDI values was the highest in autumn and the lowest in winter. ③ In the near future, the TVDI values in most regions of Xinjiang will increase in spring and winter, while the pixel quantity of most TVDI values will increase in summer and autumn. ④ The TVDI values were mainly negatively correlated with precipitation in spring and winter and were positively correlated with precipitation in summer and autumn. The TVDI values were mainly positively correlated with temperature in spring and were negatively correlated with temperature in autumn and winter. Moreover, the TVDI values in summer had a decreased correlation with temperature from west to east, with the correlation gradually changing from a negative to a positive correlation.
Vegetation phenology shows non-linear and regionally different responses to global changes. Typical differences exist in the climates between the north and the south of the Qinling Mountains. Accordingly, this study investigated the Qinling Mountains - Huanghuai Plain ecotone zone. Based on the MOD09Q1 remote sensing data from 2002 to 2020, this study extracted key parameters of the phenological characteristics of the Qinling Mountains-Huaihe Plain ecotone zone using the adaptive dynamic threshold method. Then, it described in detail the spatio-temporal change process of vegetation phenology in the study area to reveal the spatio-temporal differentiation characteristics. Furthermore, the responses of vegetation phenology to climate changes in the study area were analyzed by combining the temperature data. The study results show that: Significant spatial differentiation characteristics of vegetation phenology existed in the Qinling Mountains - Huanghuai Plain ecotone. Both the start of the growing season (SOS) and the end of the growing season (EOS) of the forest vegetation were later than those of farmland vegetation. Specifically, the SOS and EOS were Day 67-Day 116 and Day 280-Day 340 for forest vegetation and were Day 49-Day 92 and Day 195-Day 328 for farmland vegetation. The length of the growing season (LOS) was 215~262 days for forest vegetation and was 147~261 days for farmland vegetation. In addition, the forest vegetation phenology was affected by altitude. A higher altitude corresponds to a later SOS and an earlier EOS. From 2002 to 2020, the Qinling Mountains-Huaihe Plain ecotone zone generally had early SOS and EOS and shortened LOS. The changing trends of SOS and EOS were -0.14 d·a-1and -0.78 d·a-1, respectively for forest vegetation and 0.1 d·a-1 and -1.43 d·a-1, respectively for farmland vegetation. The vegetation phenological characteristics of the Qinling Mountains-Huaihe Plain ecotone were significantly correlated with regional temperature, especially the temperatures in March and September. The analysis of the data from the existent observation sites shows that the rising temperature advanced the regional phenophases.
This study aims to determine the impacts of human activities on the ecosystems of Henan national nature reserves in an objective, timely, and accurate manner, so that problems existent in management and protection can be identified and evaluated in time. Based on Chinese high-resolution remote sensing images from 2016 to 2018, this study extracted data on land cover types of national nature reserves in 2016 and human activities in 2016, 2017, and 2018. Then, the source, change type, distribution pattern, and temporal-spatial transformation of new human activities in the reserves were ascertained using the transition matrix. Furthermore, the changing characteristics of human activities in different types of reserves were analyzed. Finally, the impact degree and change patterns of human activities on the reserves were evaluated using the impact intensity index of human activities. The results are as follows. Human activities dominated by agricultural land and residential areas were widespread in Henan national nature reserves in 2016. They were mainly distributed in inland wetlands and paleontological relic reserves. From 2016 to 2018, new human activities were mainly distributed in inland wetland reserves, which were mainly transformed from agricultural land, forest land, grassland, and wetland. From 2016 to 2018, the human activities in the reserves had an impact intensity index range of 0.045~4.735. The impact degrees of human activities on reserves of forest ecological type, inland wetland type, wildlife type, and Paleozoic relic type were slight, significant, general, and severe, respectively. Therefore, the spatial distribution, types, intensity, and dynamic changes of human activities in the reserves can be accurately identified using the remote sensing technology and the impact intensity assessment model. This study can be used as an important guide for scientific assessment and improvement of the management of the reserves.
Ecosystem services are the bridge between ecology and human well-being, hence rapid and accurate assessment of their changes is very important for regional sustainable development. Sustainable Development Goal 15 (SDG15) also provides a new direction for the calculation of ecosystem services. The Qianjiangyuan is the origin of the Qiantang River and the only pilot national park in the Yangtze River Delta region at present. However, it is necessary to further clarify the spatiotemporal distribution and changes of the ecosystem services in Qianjiangyuan. Based on the 2010—2020 remote sensing monitoring data of land cover in the pilot area and oriented to the SDG15 indicators, this study quantitatively assessed the spatiotemporal variation characteristics of ecosystem services in Qianjiangyuan National Park using the InVEST model and the univariate and bivariate Moran’s I spatial autocorrelation methods. The results are shown as follows. ① From 2010 to 2020, the values of ecosystem services gradually increased by 15.21%. Specifically, cultural services grew the fastest by 19.70%, regulating services grew by 16.27%, and supply services decreased by 1.72%. ② The spatial distribution of ecosystem service values showed a gradually decreasing trend from the northeast and the southwest to the center. ③ The spatiotemporal changes in land cover types slowed the growth of ecosystem services under the land development restriction. The reduced areas of wetlands and water bodies and ecological quality led to declined spatial aggregation and value of some ecosystem services. ④ Ecosystem services can be regarded as an indicator of SDG15. The study results are expected to provide a theoretical basis and technical support for the construction of Qianjiangyuan National Park and support the sustainable development of the regional ecological environment.
This study investigated the causes of the frequent occurrence of geologic hazards in the Huangshui River basin of Qinghai Province in recent years mainly using the GF-1 and GF-2 satellite remote sensing data. Based on the comparative monitoring of multi-source, multi-temporal, and multi-period remote sensing images and the support of geoscience knowledge, this study built a detailed and reliable spatial distribution database of geological hazards through the sorting, analysis, and screening of existent geologic hazard data of the study area, the laboratory interpretation of remote sensing images, and field investigation and verification. Then, it conducted a statistical analysis using the spatial analysis module of GIS and the parameters of geologic hazards. Finally, this study explored the relationships between the occurrence of geologic hazards and geological environment, natural factors, and human activities. The results are as follows. In 2017, 3 188 sites of geologic hazards such as collapse and landslide were discovered in the study area. A total of 233 geologic hazard sites have changed since 2009. Among the formation conditions of geologic hazards, the geological environment conditions have changed slowly, while human engineering activities and rainfall have been the most active factors, which jointly induced geologic hazards.
A series of geological disasters caused by groundwater overexploitation has severely restricted the sustainable development of the three eastern coastal urban agglomerations in China: Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD). To reveal the spatial-temporal dynamic variations and their driving factors of groundwater storage (GWS) in the three urban agglomerations, this study quantitatively inverted the GWS variations in the three urban agglomerations during 2002—2016 using the Gravity Recovery and Climate Experiment (GRACE) satellite data. Then, attribution analysis was made using the gray relational analysis method. The results are as follows. The GWS kept decreasing at a linear rate of 1.17 cm/a in BTH, was relatively stable with slight fluctuation in YRD, and continued to increase at a linear rate of 0.43 cm/a in PRD. The GWS variations in the three urban agglomerations were all dominated by anthropogenic factors. The BTH was significantly affected by agricultural water consumption; the YRD was affected by agricultural water consumption, precipitation, surface water availability, and population; the PRD was significantly affected by both agricultural and domestic water consumption. According to the comparative analysis of the GWS variations and their driving factors among the three urban agglomerations, the development of urban agglomerations promoted industrial restructuring and upgrades the secondary and tertiary industries, with water utilization efficiency and structure improved, thus playing a positive role in groundwater protection. Considering the natural resource capacity and development patterns of the eastern coastal urban agglomerations, the key to GWS protection and restoration is to scientifically plan agricultural development and further optimize industrial structure so as to improve water utilization efficiency and prevent surface water pollution.
In this study, the urban expansion of the urban agglomeration in south-central Liaoning Province from 2000 to 2016 was analyzed using the nighttime light remote sensing data. The spatial relationship between urban expansion and carbon emission was quantitatively studied based on the carbon emission data. The spatial-temporal differences of carbon emissions in the study area were analyzed. Moreover, decoupling analysis was made targeting urban expansion index and carbon emissions. The results are as follows. The annual average expansion rate of the study area increased from 3.93% to 5.48%, with the expansion intensity increased from 0.211 to 0.525. The total carbon emission in the study area increased from 63.694 billion tons to 177.246 billion tons during 2000—2016. The annual average carbon emission rate increased from 7.02% to 18.96% and then decreased to 0.96%, experiencing a process from fast to slow. The average local carbon emission showed an increasing trend but varied greatly among cities. The urban expansion of the study area contributed to but also decoupled with carbon emission. The decoupling state changed from expansion negative decoupling to weak decoupling. By 2016, 80% of the cities in the study area had been in the decoupling state. The study results have significant implications for formulating future urban planning and energy conservation and emission reduction policies.
The scientific assessment of the overall status of the interactions between the urbanization and the ecological environment in Chengdu is of great significance for optimizing the pace and quality of urbanization and improving the quality of the ecological environment. This study adopted the nighttime light and Landsat remote sensing data obtained from the DMSP/OLS and the NPP/VIIRS. Based on these data, the normalized night light index (NNLI) characterizing the urbanization process and the remote sensing ecological index (RSEI) characterizing the ecological quality were constructed, respectively. Then, the two indices were combined into a coupling coordination degree model for evaluation of the coordination between the urbanization process and the ecological environment quality. The study results are shown as follows. The urbanization process in Chengdu had been accelerating from 2000 to 2018, with the NNLI increasing from 0.15 in 2000 to 0.81 in 2018. By contrast, the quality of the ecological environment was negatively affected by the urbanization process and showed a downward trend in some areas, with the RSEI decreasing from 0.63 in 2000 to 0.58 in 2018. The coupling coordination degree of urbanization and ecological environment in Chengdu was gradually improved. From 2000 to 2018, the coupling coordination state entered the stage of good coordination from imbalance. However, the overall urbanization process in Chengdu is ahead of the development of the ecological environment, which is lagging behind.
The overall regional poverty in China was eliminated in 2020, but the relative poverty in the country will still exist for a long time. Therefore, it is necessary to conduct a long-term measurement and development analysis of poverty in poverty-stricken areas. However, conventional measurement methods based on socio-economic data have severe limitations. With four provinces (municipalities) in southwestern China as a case study, this study built a back propagation (BP) neural network model based on the particle swarm optimization algorithm and a nighttime light (NTL) dataset of long time series from 2000 to 2019 first. Then, this study constructed the multi-dimensional poverty indices based on socio-economic and geographical data to reflect the poverty in counties. Finally, this study established a poverty measure model by combining the long-time-series NTL data with the multidimensional poverty indices and produced the nighttime light multidimensional poverty index (NLMPI). Based on the NLMPI, the measure and spatial-temporal evolution analysis of poverty in counties were carried out. The study results are as follows. The NLMPI indicates that the four provinces (municipalities) in southwestern China had significantly differentiated multidimensional poverty in 2000. However, the proportion of counties at extremely low and low levels decreased while that of moderate-level counties increased owing to the national poverty alleviation efforts. From 2000 to 2019, the NLMPI of counties in southwestern China showed a positive spatial autocorrelation and the Moran’s I index showed a downward and then an upward trend. These results indicate that poverty aggregation weakened from 2000 to 2010 and poverty alleviation dispersed thereafter in the four provinces (municipalities) in southwestern China. The local spatial autocorrelation results indicate that the multi-dimensional poverty pattern in southwestern China was alleviated but unbalanced. This pattern was reflected in the high NLMPI values surrounded by high NLMPI values (the H-H aggregation type) in Chengdu-Chongqing, Kunming, and Guiyang and in the low NLMPI values surrounded by low NLMPI values (the L-L aggregation type) in northwestern Sichuan and western Yunnan. This study highlights the application of NTL data in research on regional poverty.
This study aims at the identification and potential evaluation of the mineralization elements of calcrete-hosted uranium deposits in Saudi Arabia through the exploration of calcrete-hosted uranium deposits in the uranium exploration project of China and Saudi Arabia. Based on satellite (ASTER) remote sensing data and DEM data, the uranium metallogenic conditions of three calcrete areas were compared and analyzed using methods including visual discrimination, hydrological analysis, and principal component analysis and techniques including uranium source evaluation, source-pathway-trap system division, and ore-bearing rock identification. The results show that Area 2 has the most complete uranium metallogenic conditions in terms of uranium source and source-pathway-trap conditions, Area 1 lacks a good sedimentary basin as a drainage area, and Area 3 lacks a good uranium source. Accordingly, the following conclusions were drawn. The integrity of the source-pathway-trap system is crucial and indispensable for the metallogenesis of calcrete-hosted uranium deposits. Moreover, high-quality uranium sources and sedimentary environments are conducive to the formation of large-scale calcrete-hosted uranium deposits. The duration of uranium enrichment and accumulation directly affects the scale of calcrete-hosted uranium deposits. The favorable sedimentary environment for calcrete-hosted uranium deposits is an evaporative lake (playa) with large uranium sources in the study areas of Saudi Arabia. Therefore, this study can guide the exploration of calcrete-hosted uranium deposits in similar areas.
Flat landslides, typically characterized by crack grooves, are a common type of special disasters in southwestern China. However, the dense vegetation and complex terrain in disaster-developed areas limit the efficiency of conventional ground or remote sensing (RS) survey methods in the identification and extraction of disaster information. As one of the emerging remote sensing technologies, the airborne LiDAR technology and its data visualization analysis methods provide a new solution for the accurate identification of flat landslides. First, the high resolution digital elevation model (HRDEM) can be obtained using the UAV airborne LiDAR. Then, the HRDEM can be combined with visualization methods including sky view factor (SVF), hillshades, and 3D morphology simulation for the effective identification of flat landslides and their crack grooves. This study investigated the newly identified landslide hazard in the southern part of Nuoguzhai Village, Chunzai Town, Tongjiang County, northern Sichuan Province. The comprehensive RS identification method was used to realize the construction of landslide identification signs, the determination of the landslide boundary, the identification of crack groove position, and information extraction based on airborne LiDAR data. Combined with the results of field surveys, the effectiveness of the airborne LiDAR technology for the identification of flat landslides and their crack grooves in highly vegetation-covered areas was verified from both qualitative and quantitative aspects. The related study results can be used as a reference for the early identification, monitoring, and prevention of flat landslides.