Snow depth and snow water equivalent are critical elements for snow cover observation and are greatly significant in fields such as cryosphere, global climate change, and water resource surveys. Microwave remote sensing is superior to both visible-light and near-infrared remote sensing in snow cover observation. This study systematically summarized the research results of the passive microwave remote sensing in the inversion of snow depth and snow water equivalent. It organized three types of snow cover observation methods, i.e., field surveys, long-term observations at ground stations, and regional observations based on satellite remote sensing, as well as major snow cover parameters to be observed. Furthermore, it summarized and evaluated three inversion algorithms, i.e., semi-empirical method, physical model, and machine learning. Finally, this study presented the results of the snow cover in the Qinghai-Tibet Plateau observed using passive microwave remote sensing, predicted the future development trend of remote sensing-based inversion of snow cover parameters, and put forward scientific suggestions for the in-depth implementation of the inversion of snow depth and snow water equivalent passive microwave remote sensing.
Determining the present distribution of historically abandoned mines nationwide and carrying out orderly ecological rehabilitation of these mines are important parts in the preparation of mine ecological rehabilitation planning and serve as the main bases for the deployment of ecological rehabilitation engineering. This study proposed the technical process and method for determining the historically abandoned mines according to the definition of historically abandoned mines and the public management requirements. This technical method was proven effective through tests.
Eliminating impulse noise of high-quality remote sensing images is of great significance for applied research. It has always been a challenge to eliminate high-density impulse noise while remaining detailed information on edges in original remote sensing images. This study concluded that uncertain changes will appear when a remote sensing image is corrupted by impulse noise. Given this, an uncertainty model based on the evidence theory was constructed using multiple features of impulse noise. The BJS divergence and the reliability entropy were fused into the model to obtain new weights and a new probability assignment. Then, the classification between noise and signals was given according to fusion rules and probability transformation, thus effectively reducing the possibility of high-level conflicts. The experimental results show that the classification method proposed in this study is effective even when the noise density is up to over 90% and can well maintain detailed information on different ground objects in the denoised remote sensing images.
The local convolution operation in convolutional neural networks cannot fully learn the global semantic information in hyperspectral images. Given this, this study designed a novel deep network model based on Transformer in order to further improve the classification precision of hyperspectral images. Firstly, this study reduced the dimensionality of hyperspectral images using the principal component analysis method and selected the neighborhood data around pixels as input samples to fully utilize the spatial-spectral information in the images. Secondly, the convolutional layer was used to transform the input samples into sequential characteristic vectors. Finally, image classification was conducted using the designed deep Transformer network. The multi-head attention mechanism in the Transformer model can make full use of the rich discriminative information. Experimental results show that the method proposed in this study can achieve better classification performance than the existing convolutional neural network model.
This study developed an automatic cloud detection method based on the naive Bayes algorithm for the cloud detection of the advanced geosynchronous radiation imager (AGRI) aboard the FY-4A satellite. In this method, the naive Bayes algorithm serves as the core structure, and appropriate infrared channels are selected as the parameters of the characteristic classifier according to the basic cloud detection principle of optical payload to ensure the consistency of cloud detection between day and night. After the classified training and construction for different surface types and different months, a cloud detection model based on the naive Bayes algorithm was finally established. Moreover, the classifier for FY-4A/AGRI data used in the method was established considering seven typical cloud detection characteristics and one characteristic based on the infrared composite images. As indicated by the learning tests and verification using the business cloud detection product of the National Satellite Meteorological Center (NSMC) in 2019, the classifier yielded a probability of detection (POD) greater than 98% for land, desert, shallow water, and deep sea, greater than 80% for snow cover, and greater than 80% for North and South poles. The comparison between the cloud detection results of this study and those obtained using the NSMC business system showed that the cloud detection results of this study had an average monthly POD of the whole year greater than 98%, a false alarm ratio (FAR) less than 5%, and all Kuiper’s skill scores (KSSs) greater than 90%.
An effective unmixing method of hyperspectral mixed pixels can improve the precision of mineral information extraction. To further study such unmixing methods, this study explained the imaging mechanism of hyperspectral images using a linear spectral mixing model. The linear combinations of different mineral endmembers were used to express mixed pixels. The expected maximum (EM) algorithm was used to estimate the endmembers and abundance of mixed pixels under the framework of maximum likelihood estimation. A robust K-P-Means algorithm based on a random sampling consensus algorithm was proposed to improve the endmember optimization process, aiming to resist the impacts of anomalies on endmember extraction. The spectral angular distance and the spectral information divergence were used to assess the consistency between the estimated endmembers and the real endmembers. To obtain the similarity between the image and the original image, the structural similarity and the peak signal-to-noise ratio were used to measure the estimated abundance and endmembers. Various simulation data indicators show that the optimized model can obtain more precise estimations of endmembers and abundance. The mineral types were determined by matching the extracted endmembers with the mineral spectrum curves provided by the USGS spectral library. The actual data originated from the Cuprite data set of the AVIRIS hyperspectral sensor for the Nevada copper mining area. The results of mineral extraction showed that the model proposed in this study yielded satisfactory recognition results for eight types of main minerals including chlorite, which showed significant mineral aggregation and were consistent with the actual situation. Therefore, the method proposed in this study can extract precise mineral information while effectively resisting the impacts of noise.
The high spatial resolution of GF-2 images helps to obtain more accurate water distribution information. This study constructed a water index based on GF-2 images and verified it, aiming to solve the problem that the salt and pepper noise is prone to occur when the existing water indices are used to extract information on water bodies in complex environments from high-resolution remote sensing images. Firstly, this study established a comprehensive water index (CWI) by analyzing the spectral information of surface coverings and verified its precision. Secondly, information on water bodies was extracted through image segmentation combined with the CWI, and the extraction precision was verified. Then, to fully utilize the spectral information and the advantages of a classifier, the spectral information on the segmented homogeneous objects and the CWI were combined as the input data of the classifier to extract information on water bodies and verify the extraction precision. Finally, this study verified the applicability of the CWI in both WorldView-2 and GF-1 images. The results are as follows. ① The newly constructed CWI can effectively suppress the impacts of surface coverings, such as shadow, buildings, roads, vegetation, and bare soil, thus significantly improving the extraction precision. ② Extracting information on water bodies through image segmentation combined with the CWI can effectively inhibit the occurrence of the pepper and salt noise. ③ A classifier combined with a water index can effectively improve the information extraction precision of water bodies. ④ The CWI is applicable to both WorldView-2 and GF-1 images. In sum, the CWI can be used to effectively extract information on water bodies and applies to the information extraction and renewal of rivers and lakes and the surveys of the cultivation area of pounds and thereby is a high-precision method for extraction information of water bodies.
Given the limitations in the existing vector geographic information acquisition based on remote sensing images, this study proposed a new method, in which the vector geographic information is orthorectified synchronously with remote sensing images. Firstly, the original remote sensing images are no longer processed using high-precision orthorectification, and vector geographic information acquisition is directly carried out based on the original remote sensing images. The original remote sensing images are processed using high-precision orthorectification after the vector geographic information acquisition. Moreover, the vector geographic information is synchronously corrected using the same model based on the original remote sensing images, thus achieving the consistency and synchronization between the remote sensing images and vector geographic information. This method can eliminate the potential risks in data security in the process of field investigation and can help optimize the existing production process and improve the timeliness of vector geographic information acquisition. Taking WorldView-2 remote sensing images as the data source, this study performed the vector geographic information acquisition of two selected typical types of terrain, i.e., plain and mountain, using this method. The results show that this method can ensure that the spatial positioning accuracy of the results can roughly meet relevant requirements and can effectively address the problems of the feature intersection and gaps possibly occurring in the existing improved techniques and methods.
Uneven brightness and inconsistent colors are prone to occur inside and between captured images in the process of remote sensing imaging. However, the manual color conditioning combined with image processing software can no longer meet the color matching demand of geometrically increasing remote sensing images. Given this, this study proposed a kind of unsupervised channel-cycle generative adversarial network (CA-CycleGAN) integrated with the attention mechanism suitable for ground objects in complex urban areas with a high land utilization rate. Firstly, the sample data set used for color reference was manually made through histogram adjustment and Photoshop, and the appropriate urban images were selected as the sample set to be corrected. Then, the two kinds of images were cut respectively to obtain the preprocessed image sample sets. Finally, the preprocessed image set to be corrected and the image set for color reference were processed using the CA-CycleGAN. Because the attention mechanism has been added to the generator, the generated focuses can be distributed into key areas using the attention feature map in the training process of the confrontation between the generator and the discriminator, thus improving the image effects and obtaining the color correction model based on urban images and the images after color correction. Both the image correction effect and the loss function diagram show that the proposed method is optimized based on the CycleGAN and that the comprehensive performance of the CycleGAN integrated with the attention mechanism is better than that without the attention mechanism. Compared to conventional methods, the method proposed in this study greatly reduced the time for color correction and achieved more stable image color correction effects than manual color matching. Therefore, the method proposed in this study enjoys significant advantages in the color dodging of remote sensing images and has a good application prospect.
The spaceborne interferometric synthetic aperture Radar (InSAR) techniques have been widely used in geological disaster monitoring at present due to their advantages of non-contact, large scope, wide space coverage, and high monitoring accuracy. With Ya’an City with dense vegetation as the experimental area, this study comparatively analyzed the identification of hidden landslide hazards based on time series InSAR techniques (stacking and SBAS). By comparing the surface deformation rate maps obtained using different time series InSAR techniques based on the Sentinel-1 data, it was found that the results of the SBAS technique were less vulnerable to various errors and achieved better monitoring results than the Stacking technique. The statistical analyses of hidden landslide hazards interpreted from the surface deformation rate map, as well as the field survey results, revealed that more hidden hazards were identified using the Stacking technique than those identified using the SBAS technique, while the SBAS technique yielded higher accuracy than the Stacking technique. Therefore, it is recommended to combine SBAS and Stacking techniques to carry out the early identification of landslide hazards in Ya’an City.
The signals of permanent scatterers can maintain high interferometric coherence for a long time. To solve the problem that manually selecting ground control points may affect the monitoring results during the orbit refinement of the SBAS-InSAR, this study combined permanent scatterers with SBAS-InSAR. Firstly, by setting the thresholds of the coherence coefficient, the amplitude dispersion index, and the surface deformation rate, this study selected robust permanent scatterers as the ground control points in the orbit refinement of the SBAS-InSAR in order to correct the accuracy of the monitoring results. Then, this study selected 20 scenes of Sentinel-1A dual-polarization images that covered Xima Town, Longli County, Guizhou Province from September 1, 2019 to April 11, 2021 as the main data source for surface deformation monitoring. Finally, this study compared the results obtained using the proposed method and those obtained through manually selecting ground control points with the displacement monitoring data of the Beidou satellite, concluding that the data obtained using the method proposed in this study were more accurate.
The indicative significance of normalized vegetation index (NDVI) and terrain factors in land classification can be applied to specific scenarios. This study extracted the land classification information of Chongqing using the AQUA/MODIS NDVI and terrain indices (height and slope) of 2002—2020 and accordingly divided the land in Chongqing into seven types, i.e., forest land, grassland, orchard, dry fields, paddy fields, waters, and residential and building land, with the former three types being economic forest land. Based on the characteristics of broken terrain caused by the staggered distribution of agricultural, forest, and grassland, as well as the need for fire prevention in Chongqing, this study categorized the economic forest land and dry fields as concern areas of forest fires and categorized paddy fields, waters, and residential and building land as unconcerned areas of forest fires. The hotspots monitored using AQUA/MODIS in 2002—2020, FY3-C/VIRR in 2014—2020, and FY3-D/MERSI in 2019—2020 individually were re-identified based on the classification results of the concern areas of forest fires. The results are as follows. The extraction accuracy of individual land types (except for orchard and dry fields) was over 64%, and that of the concern areas of forest fires was over 86%. Based on the classification results of concern areas of forest fires, the forest fire points monitored using the remote sensing techniques were re-identified. The re-identification results showed that the 46.27%, 26.47%, and 11.76% of forest fire points monitored using AQUA/MODIS, FY3-C/VIRR, and FY3-D/MERSI, respectively were in unconcerned areas of forest fires. The forest fires monitored using remote sensing techniques on May 1-2, 2021 were re-identified, and 71.4%and 81.08% of forest fire points monitored using FY3-C/VIRR and both AQUA/MODIS and TERRA/MODIS, respectively were in unconcerned areas of forest fires. Therefore, extracting land classification information in complex terrain areas using NDVI and terrain indices and applying the extraction results to the re-identification of forest fires monitored using remote sensing techniques can effectively reduce the interference to forest fire monitoring in complex terrain areas, thereby minimizing the input of manpower and properties for the verification of hotspots.
In the past 30 years, the land use in Shenzhen City has changed dramatically until it is almost saturated now. Using the morphological spatial pattern analysis (MSPA) and the graph theory, this study quantitatively analyzed the landscape connectivity of ecological land in Shenzhen based on ten phases of remote sensing images for land cover or use from 1988 to 2015. The results show that the cultivated land was the main land source in various periods of Shenzhen’s rapid urbanization, while the proportion of forest land used for urban development had risen since 2005. For Shenzhen’s landscape connectivity from 1988 to 2015, the equivalent connected area (ECA) of the ecological land decreased by 1 175.4 km2, and the degree of network connectivity (DOC) decreased by 43.51%. Since the delineation of Shenzhen’s basic ecological control boundary in 2005, the pace of urban habitat degradation has slowed down but the ECA of the ecological land had still been gradually eroded at a rate of 11.9 km2 per year. The analysis of the importance of ecological patches shows that areas like the Yangtai Mountain and Tanglang Mountain are key ecological nodes for landscape connectivity and should be protected with greater efforts.
Urban surface subsidence has increasingly severe impacts on human life, making it particularly important to study the methods for effectively monitoring surface subsidence. To monitor the surface subsidence in Shanghai, this study processed 24 scenes of 2019—2020 Sentinel-1A data covering the city using the PS-InSAR technique. After treatment using the permanent scatterer interferometry technique, the residual phase correction was performed using SRTM1 DEM, and the surface subsidence results of the two years were extracted. The analysis of the subsidence rate and cumulative subsidence amplitude in the monitoring results shows that the urban area mainly shows uneven surface subsidence with multiple subsidence funnels, some of which correspond to the historical subsidence data. As shown by time-series surface subsidence data of seldomly selected ground characteristic points, the surface subsidence at these points basically had the same deformation amplitude at different times and highly consistent changing trends, verifying the reliability of the PS-InSAR monitoring method. The results of this study will provide data and decision-making bases for geologic disaster prevention and control in Shanghai.
Based on the thermal infrared data from the Landsat8 satellite and a UAV, this study obtained the spatial distribution of the temperature of the sea area near the Fuqing Nuclear Power Plant through inversion. Then, this study verified the reliability of the inversion results using the measured temperature data and investigated the distribution and variation characteristics of the temperature field in the sea area near the power plant. The results are as follows. The inversion results of the temperature are strongly correlated with the measured offshore temperature data and thereby are reliable. Before the nuclear power plant was put into operation, the temperature of the sea area near the nuclear power plant was relatively uniform, without significant temperature differentiation or temperature rise. By contrast, after the nuclear power plant was put into operation, significant temperature differentiation occurred in the surrounding sea area because of the thermal discharge. Moreover, the spatial distribution of thermal discharge and its scale varied greatly under different tides and seasons. Generally, the temperature rise range was wider under ebb tides than under flood tides and was wider in summer than in winter.
Besides numerous casualties and economic losses, wars may cause damage to the environment. Using a long time series of satellite remote sensing data from 2001 to 2018, this study explored the response of vegetation growth to the environmental changes in Syria caused by the Syrian Civil War. The results are as follows. The vegetation index significantly decreased in regions that experienced the most intense conflict in the war. The land types changed slightly from 2011 when the war started to 2015 but changed significantly from 2015 to 2018, with the grassland area decreasing by 10.08% and the crop planting area decreasing by 21.87%. This study further explored the impacts of human activities on the vegetation status, revealing that both sides of the Euphrates River in the east and their extensional areas are most significantly affected by human activities. This study discovered the negative impacts of the war on vegetation growth and can be utilized as a reference for the research and strategy formulation on food security in areas with military conflicts.
This study constructed a stand mean height inversion model based on LiDAR data, aiming to provide dynamic monitoring of the growth of Sonneratia apetala. Taking the mangrove wetland of Sonneratia apetala in the Maowei Sea of Beibu Gulf as the study object and based on the height and intensity parameters extracted using airborne LiDAR data, this study compared three models, namely random forest, support vector machine, and neural network, based on the coefficient of determination (R2), root mean square error (RMSE), Akachi information criterion (AIC), and Bayesian information criterion (BIC) and then estimated the mean height and spatial distribution of Mangrove in the study area using the optimal model. The results are as follows. The stand mean height of Sonneratia apetala in the study area is 3.90~11.58 m, and Sonneratia apetala with a higher tree height and a larger diameter at breast height is mainly distributed near the tidal trench and the middle part of the study area. In the estimation of the stand mean height of Sonneratia apetala, the maximum percentile height (hmax) had the highest contribution rate, followed by 75%~99% percentile height. The random forest regression model yielded the highest precision (R2=0.938 1,RMSE=0.58 m,AIC=80.50, and BIC=49.05), followed by the support vector machine model (R2 = 0.766 5 and RMSE = 1.27 m in the test stage), and the neural network regression model yielded the worst fitting effects. Overall, the random forest model is the optimal model for the inversion of the stand mean height of Sonneratia apetala in the study area.
Coal fire not only wastes a lot of coal resources but also severely damages the ecological environment of the fire area. However, conventional monitoring methods suffer disadvantages such as a small scope, low frequency, high cost, and great danger. Therefore, this study developed a monitoring method of coal field fire based on the distributed scatterer interferometric synthetic aperture Radar (DS-InSAR) technology. This method successively selects homogeneous pixels using the fast statistically homogeneous pixels selection (FaSHPS) algorithm, optimizes the phases of these pixels using the eigenvalue decomposition method, obtains the final distributed targets based on the temporal coherence, and calculates the time-series surface deformation by combining the small baseline subsets (SBAS) InSAR technique. Taking 63 scenes of Sentinel-1A images from March 2017 to April 2019 as the data source, this study obtained the time series surface subsidence in the Wuda coal field using this method and then verified the reliability of the results by comparison with the monitoring results obtained using the temporarily coherent point interferometric synthetic aperture Radar (TCP-InSAR) technology. As a result, the correlation coefficient between the two methods was 0.84, but the density of monitoring sites obtained using the method proposed in this study was 1.24 times higher than that of TCP-InSAR. The monitoring results show that the surface of the Wuda coal field deforms severely, with a maximum deformation rate of -215 mm/a, and that the deformation occurs more rapidly during autumn and winter and has multiple extensional directions and multiple subsidence centers at varying degrees.
The ground subsidence caused by continuous mining in mining areas will seriously destroy the environment. There is an urgent need to quickly identify the locations and surface deformation of large-scope mining areas in the mining area monitoring. Given this, this study carried out large-scale detection and monitoring of the subsidence of mining areas in Linfen City using the synthetic aperture Radar interferometry (InSAR) technique. Firstly, by processing and analyzing 12 scenes of Sentinel 1A ascending data using the differential interferometric synthetic aperture Radar (D-InSAR) technique, this study conducted large-scale detection of subsidence disasters in mining areas in the study area. Then, this study processed 432 scenes of Sentinel 1A ascending data from different orbits using the small baseline subset InSAR (SBAS-InSAR) and monitored the obtained key areas. The results of this study show that there are a total of 105 subsidence areas in Linfen City, all of which are located in the mountains on both sides of the faulted Linfen basin. Further time-series deformation monitoring of key subsidence areas shows that many subsidence areas are continuously deforming, with high deformation amplitude and the deformation rate up to a maximum of -381 mm/a, and have caused huge damage to the ecological environment and infrastructure on the surface. The mining points near the subsidence area were identified according to optical images, thus verifying the reliability of the large-scale detection and monitoring method based on the InSAR technology. The results of this study will provide an important basis for the prevention and control of subsidence disasters in the mining areas of Linfen.
The farmland’s soil moisture plays an important role in crop yield estimation and drought monitoring and is also a key parameter for fine-scale monitoring of farmland in karst mountainous areas. Targeting the complex environmental impacts in karst regions such as farmland fragmentation and the fact that the inversion of soil moisture is vulnerable to cloud interference, this study employed both the water cloud model (WCM) and the support vector regression (SVR) model to conduct the block-scale inversion of the soil moisture in the growth periods of tobacco using the multi-temporal Sentinel-1 synthetic aperture Radar (SAR) images and the unmanned aerial vehicle (UAV) RGB images. The results are as follows. ① In this study, conventional vegetation parameters were replaced with the visible-band difference vegetation index (VDVI), which combined with its water cloud model was highly applicable to karst mountainous areas. The co-polarization method yielded higher inversion precision, with a coefficient of determination of 0.843 and RMSE of 0.983%. These provide a convenient method for the inversion of farmland’s soil moisture in cloudy and rainy mountainous areas. ② The trend of soil moisture in the four growth periods of tobacco is consistent with that of precipitation. Farmland with rocky desertification has low soil moisture, which is closely related to the bare rocks, complex terrain, and difficulties with irrigation in the experimental area. ③ Soil moisture has significant effects on tobacco growth. Specifically, high soil moisture promotes tobacco growth and low soil moisture inhibits tobacco growth, especially during T1—T3. This study can be utilized as a reference for the fine-scale inversion of the farmland’s soil moisture in cloudy and rainy mountainous areas.
The variation in the water levels of lakes is an important indicator for the study of changes in climate and ecological environment and water resources rating. It was previously difficult for altimetry satellites to monitor small and medium-sized lakes, but the newly launched ICESat-2 satellite can improve the monitoring comprehensiveness and precision of lakes’ water levels. Based on the data coverage of ICESat-2 satellite land observation products, the high-precision dynamic monitoring of water levels was conducted for 473 lakes covering an area greater than 1 km2 in the Qinghai-Tibet Plateau from October 2018 to April 2021. The spatio-temporal variations of water levels of these lakes were analyzed from three aspects: the overall variations in the water levels of lakes in the Qinghai-Tibet Plateau, the basin-scaled and regional variations in the water levels of lakes, and the monthly or quarterly variation trends of water levels of typical lakes. The study results are as follows. In the past three years, the water levels of lakes in the Qinghai-Tibet Plateau continuously rose, with an average annual rate of variation of 0.013 m/a. The water levels of large, medium-sized, and small lakes rose significantly, rose gently, and dropped slightly, respectively. In terms of spatial distribution, the water levels of lakes in each basin generally showed an upward trend, and most of the lakes with declining water levels had relatively high elevations. During the monitoring period, the water level of Siling Co Lake rose by 1 m and that of Kering Tso Lake declined by 1 m. This study provides the latest monitoring data on the water levels of some lakes on the Qinghai-Tibet Plateau, which are conducive to the study of dynamic variation monitoring of lakes.
Phnom Penh is a typical city in the Lancang-Mekong River basin. It has rapidly developed and expanded under the Belt and Road Initiative and has continuously invaded its surrounding wetlands. To fully understand the response of the wetland landscape to the urban expansion in Phnom Penh, this study extracted the 2000—2020 land use data of this city from five phases of Landsat images, then analyzed the spatiotemporal characteristics of the changes in wetland landscapes and the land use for urban expansion from the aspects of area change, spatial distribution, change intensity, and landscape pattern, and finally established the quantitative relationships between the wetland landscape and the land use for urban expansion using the correlation coefficient. The results are as follows. The construction land and bare land in Phnom Penh had expanded outwards from the center, and their sizes had constantly increased during 2000—2020, especially during 2001—2005. Their spatial structures were increasingly concentrated, and the shapes were more complex. The wetland area continuously decreased, and the swamps and paddy fields were converted to construction land and bare land intensively. In particular, swamps with an area of 124.06 km2 were converted to construction land and bare land. In other words, about one-third of the swamps disappeared. The wetland landscape tended to be distributed in a fragmented and regularized manner. Its connectivity degree decreased and its ecological functions such as lowering the temperature, increasing the humidity, and regulating and storing floodwater were weakened. These changes in the wetland in Phnom Penh are significantly related to urban expansion, with a correlation coefficient in terms of area greater than 0.97 (p < 0.01). There is also a strong correlation between the intensity of the urban expansion and the wetland landscape pattern. To maintain the sustainable development of the city, it is necessary to reasonably plan the spatial layout in the process of urban expansion. The urban expansion should be conducted mainly through intensive development, paddy fields should be utilized first if necessary, and the occupation and destruction of swamps and wetlands should be avoided as much as possible.
The monitoring and information extraction of paddy fields using remote sensing techniques is an important means for modern agricultural management. However, it is difficult to obtain effective optical monitoring data of south China due to the frequent cloudy and rainy weather in spring and summer in this area. To accurately extract information on paddy fields in areas subject to frequent cloudy and rainy weather, this study investigated the paddy fields in Jiangxiang Town in Nanchang County, Jiangxi Province, using multi-temporal Sentinel-1 SAR data as the data source. Specifically, this study calculated the J-M distance between paddy fields and other land types in different phenological periods, analyzed the changes in the distance based on the backscattering coefficients of various land types in key phenological periods, and then obtained the best phenological images for the information extraction of paddy fields. Afterward, this study conducted ground object classification using methods such as random forest, maximum likelihood, support vector machine, and neural network and then compared and verified the classification accuracy. The results are as follows. The combined SAR data of the different stages including booting stage (June 14), trefeil stage (April 21), transplantion period (May 3), and transplanting peried of second season late rice (July 26) is the optimal temporal combination for the information extraction of paddy fields. Higher classification accuracy of ground objects in the study area can be obtained using the random forest method, with overall classification accuracy of up to 0.943 and a Kappa coefficient of 0.932. This study conducted the mapping of paddy fields in areas with frequent cloudy and rainy weather using SAR data and will provide important references for the temporal selection and classification.
The net primary productivity (NPP) of vegetation is a vital indicator for assessing a basin ecosystem. Based on a long time series of NPP data of 2000—2019 from a moderate resolution imaging spectroradiometer (MODIS), this study analyzed the spatio-temporal variations in the vegetation NPP of the Dongting Lake basin in the past 20 years. Then, it revealed the influence characteristics and contribution of driving factors (e.g., meteorology and ground surface) on the vegetation NPP of the study area using methods including the GIS spatio-temporal analysis and GeoDetector. The results are as follows. ① The NPP values in the study area have an average of 0.65 kgC/(m2·a), with high values mainly distributed in the west and south of the basin and low values concentrated near the lake. ② During 2000—2019, the vegetation NPP of the Dongting Lake basin presented a slightly rising trend (y=0.003x+0.622 7, R2=0.437, p<0.001). It increased in the northwest and south-central parts and decreased in the northeast and southwest boundaries, and its center of gravity slightly shifted. ③ The changes in the vegetation NPP of the Dongting Lake basin was significantly affected by meteorological factors (especially temperature). By contrast, its spatial characteristics were mainly affected by land use, followed by precipitation and DEM. In addition, the results suggested significant interactions between different factors, which was mainly reflected by the bi-factor enhancement (DEM and land use, or DEM and precipitation) and nonlinear enhancement (temperature and precipitation, land use and DEM, and precipitation and land use). The conclusions of this study help to correctly understand and grasp the spatio-temporal characteristics of the vegetation NPP of the Dongting Lake basin and their internal influencing mechanisms, thus providing a scientific basis for the management and governance of the ecosystem in the basin.
The evaluation of ecosystem service value (ESV) is an important basis for formulating policies regarding ecological protection, ecological compensation, and the accounting for natural resource assets. An in-depth study of the characteristics and driving factors of the spatiotemporal changes in the ESV in Xiangxi Tujia and Miao Autonomous Prefecture, Hunan Province, China is greatly significant for ecological control and protection. This study analyzed the changes in land use based on seven phases of 1990—2018 land use data of Xiangxi and evaluated the ESV in Xiangxi using the equivalence factor method. Moreover, it analyzed the spatiotemporal characteristics of the ESV by combining a spatial statistical model and further explored the driving factors of the ESV. The results are as follows. The main land type in Xiangxi is forest land. In the past 28 years, the area of forest land and grassland decreased due to occupation by construction land, the area of construction land, wetland, and unused land increased, and the water area roughly remained unchanged. Overall, land use in Xiangxi had a moderate or low degree of activity. The total ESV successively increased, decreased, increased, and decreased, forming an M-shaped trend. Moreover, it declined overall. Spatially, the total ESV was higher in the southeast than in the northwest. The spatial self correlation analysis indicated that the ESV in the study area showed positive spatial aggregation, and the ecological spatial pattern in Xiangxi had not changed significantly over the past 28 years. The driving factors leading to the spatiotemporal differences in the ESV main included urbanization rate, population density, the gross output by forestry, and area of forest land. This study will provide a theoretical reference for the rational utilization of land resources and ecological protection in Xiangxi.
Although the COVID-19 pandemic has been contained in China presently, it remains a major threat to the international environment. The border areas of China remain at high risk of COVID-19 infection, including Ruili, an important port city on the border between China and Myanmar, which still faces great challenges in pandemic prevention and control along the border. This study analyzed the topographic, traffic, and basic factors of Ruili using the GIS technology, the remote sensing technology, and the AHP-entropy weight method and identified locations with high risks of the pandemic along the border, aiming to achieve more scientific the pandemic prevention and control. The results showed that the high-risk areas in Ruili that need major pandemic prevention and control were in the southwestern and southern zones near the border and had the following characteristics: ① gentle terrain with high fractional vegetation cover; ② convenient transportation and proximity to water systems; ③ high settlement density. To achieve a complete observation of the border, a total of 35 prevention and control points were deployed based on the set covering location model combined with the ArcGIS viewshed analysis. They were divided into 22 primary, 8 secondary, and 5 tertiary prevention and control points, of which the importance of pandemic prevention and control increased gradually. This study can provide references for improving the pandemic prevention and control capacity of border areas.
The Tianshui-Longnan Railway serves as an important project for guaranteeing Gansu Province’s development strategy of “consolidating the east, focusing on the west, deepening the south, and promoting the northward expansion”. This railway crosses the Qinling Mountains twice and passes through three distinct geomorphic units including the loess ridge, knoll, and gully areas, the moderately high mountainous area of the Tianshui-Xili basin, and the moderately high mountainous area of the Qinling Mountains from north to south. The complex geological tectonic setting and the intensive regional Cenozoic tectonic movements lead to environmental geological problems, such as large-scale landslide groups, Holocene active faults, and karst collapse along the railway line, which severely restrain the early-stage design of the line scheme and affect the safety and stability of the later construction and operation of the railway. By fully utilizing surveyed aerial remote sensing data, this study interpreted and analyzed various geological problems along the whole railway in detail according to high-precision stereo images and orthophoto images of realistic scenes. Moreover, this study assessed the scope, scale, stability, and possible impacts of the various geological problems on the line scheme by combining the data from field surveys. The results of this study will provide strong technical support for both the line scheme design and the field geological surveys of the Tianshui-Longnan Railway.
This study aims to further tap the remote sensing monitoring technique in monitoring the current land use of brownfields, including risk control, soil remediation, and development and construction. Firstly, this study selected 98 brownfields of Zhejiang Province that have been included in the risk control and remediation list in the national contaminated soil information management system. Then, using images of the historical period and the monitoring period from the domestic high-resolution remote sensing satellite, this study conducted the remote sensing monitoring of changes in land use through image processing and human-computer interactive interpretation on the ArcGIS platform. Finally, this study made statistics of the monitoring results by combining the reports on the surveys, risk assessment, and control and remediation effects of the brownfields, as well as the attribute information of the brownfields. The results show that the monitoring based on the remote sensing technique can be used to quickly identify the implementation of risk control, soil remediation, and development and construction of the brownfields and timely grasp the current status, dynamic trends, and issues of the use of brownfields included in the risk control and rehabilitation list. This study will provide technical support and bases for relevant public departments to carry out the access management of the redevelopment of brownfields.
This study aims to understand the variation trend of the geological environment and the ecological restoration prospect of mines in Jilin Province. Using the 2015—2019 high-resolution remote sensing data from a domestic satellite and other multi-source information, this study carried out the remote sensing-based dynamic monitoring of the geological environment and ecological restoration of mines in Jilin Province by means of automatic information extraction, human-machine interactive interpretation, in-door comprehensive research, and field surveys and verification. The analysis of the changes in the geological environment and ecological restoration of the mines based on the monitoring results allowed for basically ascertaining the current situations and variation trend of the occupation of land resources by mines, damage to land resources by mines, and the geological disasters, environmental pollution, and ecological restoration of mines in Jilin Province. The analysis results are objective and accurate, indicating good application results of remote sensing-based monitoring. The results of this study can provide references and bases for further promoting the ecological protection and restoration engineering of mountains, rivers, forests, lakes, grass, and sand in Jilin Province.
Accounting for natural resource assets is the main part in ascertaining the state-owned natural resource assets and is also a fundamental task in determining various natural resources. It is of great practical significance to design and develop an information system for the accounting treatment of state-owned land resource assets. Taking the accounting treatment of the state-owned land resource assets in Dianbai District, Guangdong Province as an example, this study designed and developed an automatic accounting system of state-owned land resource assets using the WebGIS integrated architecture based on the data from land surveys, parcels of land, and benchmark land prices and following the methods and procedures for the accounting treatment of state-owned land resource assets. The research results are as follows. The state-owned land resources in Dianbai District have a total area of 5 582.31 hm2, among which, the cultivated land has an area of 1 255.94 hm2. There is a small amount of high-quality cultivated land, with the second-class land only covering an area of 17.43 hm2. The economic value of state-owned agricultural land and construction land is approximately RMB 1 397.360 5 million and RMB 1 639.014 4 million, respectively. The results of this study will provide technical references for ascertaining the state-owned natural resource assets and preparing the balance sheets, thereby promoting the information-based management of natural resource assets.