With the substantial improvement in spatial resolution, spectral resolution, temporal resolution, and data coverage, domestic satellites have been widely used in natural resources supervision and geological surveys. Taking ZY-1 02C, GF-1, GF-2, and ZY-3 satellites as examples, this paper explores and studies their interpretation applications in basic geography, basic geology, land resources, mineral resources development, hydrogeology, engineering geology, and geological disasters. Furthermore, this paper compares, assesses, and summarizes the ability of the domestic satellites in the interpretation of geological survey elements. All these will provide guiding suggestions and scientific references for more extensive and in-depth applications of domestic satellites.
The researches on the effects of band parameters on the biophysical parameters of vegetation estimation using the normalized difference vegetation index (NDVI) have great significance for the improvement in the application accuracy of NDVI in vegetation dynamic monitoring. Based on the hyperspectral images of artificial grassland obtained from a Resonon, Inc. Pika XC2 Hyperspectral Imaging Camera loaded by an unmanned aerial vehicle (UAV), this study analyzes the effects of the positions and width of red and near-infrared bands on NDVI and assesses the sensitivity of NDVI to fractional vegetation cover and the estimation accuracy. The results are as follows. When band positions were fixed, the width expansion of red and near-infrared bands had little effects on NDVI and its sensitivity, and the accuracy of fractional vegetation cover estimated using narrowband NDVI is higher than the accuracy based on broadband NDVI. When the red and near-infrared bands moved towards long waves, the NDVI and its sensitivity were affected to different extents. With an increase in the sensitivity, the anti-disturbance performance of NDVI decreased, and the estimation accuracy of fractional vegetation cover decreased. The sensitivity coefficient of narrowband NDVI and the R2 determined by the linear fitting of the sensitivity coefficient and the fractional vegetation cover greatly fluctuated, and the estimated fractional vegetation cover at various locations was unstable. High estimation accuracy of fractional vegetation was obtained at different locations using the 10 nm NDVI, with the maximum R2 value of 0.83. The broadband NDVI calculated using four popular satellite images can be well applied in the inversion of the fractional vegetation cover in areas with high vegetation cover. However, its inversion accuracy of fractional vegetation cover still suffered some attenuation compared with narrowband NDVI (10 nm). These results will serve as scientific references and bases for accurate inversion of vegetation parameters using NDVI.
The temporal resolution of high spatial resolution remote sensing data can be effectively improved by spatio-temporal fusion of remote sensing data. However, the most widely used spatial and temporal adaptive reflectance fusion model (STARFM) fails to achieve highly accurate prediction effects for areas with abrupt changes at present. Given this, this paper proposed a hierarchical spatial-temporal fusion model (H-STFM). In this model, the target pixels to be predicted are divided into pixels with phenological change and pixels with abrupt changes, which are predicted using linear regression and weighted filtering methods, respectively. Then the prediction results of the two types of pixels are fused using an optimized time weighted function to form the final prediction map. The H-STFM proposed in this paper was qualitatively and quantitatively assessed using two sets of medium-resolution remote sensing images from moderate resolution imaging spectrometer (MODIS) and Landsat satellite. As indicated by the experiment results, H-STFM is significantly superior to STARFM in terms of structural similarity and relative dimensionless global error.
Based on FY-3C VIRR LST and FY-3D/MWRI L1B brightness temperature data of February 1, 2020 and taking the area with geographical coordinates of 18°~54°N, 73°~135°E as an example, the LST reversion and downscaling based on the FY-3D/MWRI L1B data were studied using a statistical regression model and a hierarchical Bayesian fusion model. As a result, two models were constructed, namely a LST binary linear regression inversion model based on FY-3D single-channel horizontal and vertical polarization-corrected brightness temperature data and a hierarchical Bayesian fusion downscaling model based on FY-3D retrieved LST and FY-3C VIRR LST. They were verified with the LST on the day of MYD11A1 as reference data, obtaining the following results. As for the reversion statistical model, the mean bias, error standard deviation, and root mean square error were -1.28 K, 8.85 K, and 8.85 K, respectively for the FY-3D descending data and were -0.81 K, 6.74 K, and 6.78 K, respectively for the FY-3D ascending data. As for the hierarchical Bayesian fusion downscaling model, the mean bias, error standard deviation, and root mean square error were 0.50 K, 5.45 K, and 5.41 K, respectively for the FY-3D descending data and were 0.25 K, 5.54 K, and 5.54 K, respectively for the FY-3D ascending data. This study will provide a novel idea for the LST inversion and downscaling of passive microwaves.
For the classification of remote sensing images, traditional feature extraction methods frequently ignore their intrinsic properties and the multi-scale local characteristics of the images. As a result, only a small amount of image information can be acquired. Given this, this study proposed a model of multi-scale gray level and texture feature fusion (Ms_GTSFF ) for the feature extraction of remote sensing images, and the extraction steps are as follows. Firstly, extract the gray-level features of the images at different scales. Then obtain the local texture features of the images using the local binary pattern (LBP) algorithm and meanwhile, obtain the image features of a larger receptive field using a multi-scale method. Afterward, obtain the gray-level attributes corresponding to various codes using the obtained multi-scale LBP histograms. Finally, code and fuse multi-scale feature information obtained from the above steps to constitute the Ms_GTSFF feature extraction model, to which multiple machine learning classifiers are connected for classification and recognition. Taking the aerial hyperspectral remote sensing images of Xiongan New Area (Matiwan Village) as the test dataset, the feature extraction and classification tests were performed following the data preprocessing by blocks. The classification accuracy was up to 99.44%, indicating a great improvement in the recognition capability compared with traditional methods. This verified the effectiveness of the proposed model in enhancing the feature extraction capability and improving the classification and reorganization performance of remote sensing images.
With the inland waters in Chongming Island, Shanghai as the study area, this study researched the color changes of waters and the identification of suspected polluted waters using unmanned aerial vehicle (UAV) hyperspectral remote sensing images. First, reflectance calibration was carried out for the radiance signals detected by the hyperspectral sensor carried by UAVs. Compared with on-site observations, this calibration method was more accurate, the average unbiased absolute percentage differences of various bands were 13.34% on average and the average determination coefficient R2 was 0.83. Afterward, the inversion of hue angle and apparent visible wavelength (AVW) was conducted using the hyperspectral reflectance of the inland waters according to the CIE-XYZ color space and weighted harmonic mean. Then an inversion model of water quality parameters was constructed based on measured data, and the water colors in the study area were classified by setting the threshold of hue angle. As indicated by the results, there exist many abnormal yellowish-brown inland waters in the Chongming Island in dry seasons and it is necessary to strengthen the supervision and governance of the aquatic environment of major shipping rivers. Finally, the suspected polluted inland waters were identified and analyzed by integrating the inversion results of the parameters of water color and water quality. This study shows that UAV hyperspectral remote sensing can be used to obtain the inversion results with high temporal-spatial continuity of the parameters of water color and water quality, which will provide credible technical support for the aquatic environment investigations of inland waters while saving costs.
The distribution of wind farms is an important basis for the monitoring and early warning of wind power investment, the analyses of land occupation, and the assessment of clean energy consumption capacity. Remote sensing technology serves as an effective method for extracting wind farm distribution on a large scale. As the remote sensing interpretation marks of wind farms, wind turbine towers are a kind of multi-scale targets in high-resolution images. However, their characteristics greatly differ due to the effects of image acquisition time, illumination conditions, and surface coverage. Therefore, it's difficult to automatically detect wind turbine towers in remote sensing images. Aiming at the above problems, this paper proposed an automatic detection method of wind turbine towers based on the YOLOv3 model, and the steps are as follows. Firstly, determine the sample construction conditions and the target scale of wind turbine towers according to the analyses of the remote sensing characteristics of a wind farm. Secondly, optimize the depth of the feature extraction network of the YOLOv3 model to improve the characterization capacity of multi-scale targets. Finally, suppress false detections using the DBSCAN density clustering algorithm according to the density difference between noise and wind turbine towers. The experimental results show that the proposed method exhibits superiority over the benchmark models such as Faster R-CNN and FPN. With a detection accuracy rate of 96%, a recall rate of 94%, and F1 of 95%, the proposed method has good effects for the detection of small targets in the remote sensing images with complex background.
With the rapid development of remote sensing technology, the research on the classification methods of hyperspectral remote sensing images has received widespread attention. However, existing studies on the classification of hyperspectral remote sensing images conduct image segmentation using a single-scale superpixel method. As a result, the optimal superpixel number cannot be determined, image details are liable to be omitted, and a single kernel matrix cannot characterize multiple feature information, thus leading to a decrease in the classification precision. Therefore, this study proposes to perform multiscale superpixel segmentation of the first principal component of hyperspectral images. Then it conducts hyperspectral image classification using the composite kernel obtained by coupling the multiscale spatial-spectral kernel with the original spatial-spectral kernel according to weights. Finally, it tests and analyzes the proposed method using the hyperspectral images of the National Mall in Washington, D.C. as experimental data. The test results show that the effective classification precision of this method is 6.93% higher than that of the compared methods. As proved by the results, this method can be used to effectively solve the problems such as the lack of self-adaption of image spectra and incomplete spectrum information acquired, thus significantly improving the classification accuracy of hyperspectral images.
The crop planting structure consists of information such as crop species, quantity structure, and spatial distribution characteristics, and it serves as the basis for agricultural scientific management. Taking the Shijin irrigation area, Hebei Province as the study area and on the premise of not considering the optimal window period of crop time series, this study calculates and analyzes the ability of texture features in crop classification and identification based on GF-1WFV images. Meanwhile, the vegetation index is introduced into the time phase in which the classification effects based on texture features are poor, in order to make up for the defects of texture in the expression of crops. According to the comparison of the classification results of various groups, the classification accuracy of individual texture features reached greater than 80% in April and August when the crop structure is obvious but was still less than 80% in May, June, July, and September when crops are the most complex. After combining the texture features with the vegetation index, the classification results of the crops in these four months were greatly improved. In detail, the overall classification accuracy was greater than 80%, which basically meets the need for agricultural dynamic monitoring. Meanwhile, the accuracy was improved by 2.27%~9.75 % and the Kappa coefficient was increased by 0.02~0.16 compared to the individual texture features. As verified using summer maize samples, the recognition accuracy reached up to 98%, the recognition effects were relatively complete, the fragmentation degree was the minimum, and the optimal discrimination from other crop categories was achieved. Meanwhile, it also proved that the texture features based on GF-1WFV images can be applied to the extraction of the crop planting structure, especially in the months when the crop structure is relatively obvious, and they can provide some effective information for the information extraction of crops base on images.
Tidal flat topography is closely related to the structure and function of the ecosystem in intertidal wetlands. Therefore, it is significant for the analyses of tidal flat dynamics and the monitoring of the diffusion process of saltmarsh vegetation to obtain high-precision topography data. However, owing to limited ebb time, muddy tidal flats, and saltmarsh vegetation, traditional geographic observation techniques suffer the shortcomings such as low accuracy and efficiency, high cost, and limited coverage. In this study, unmanned aerial vehicle (UAV) low-altitude remote sensing was employed to obtain aerial images and their band information. Then the 3D and spectral information with precise coordinates were extracted based on the structure obtained using motion technology. They were used to construct a high-precision digital surface model (DSM) of the study area. The DSM of bare flats can be directly used as the digital elevation model (DEM) of the tidal flat. In the areas with saltmarsh vegetation, the information of red, green, and blue bands was used to calculate the visible-band vegetation index (VDVI), which was combined with field surveys to build an inversion model for vegetation height. Finally, vegetation was filtered out from the DSM using the height inversion model to obtain accurate DEM. In this way, the elevation of the vegetation zone in the tidal flat can be reflected. As indicated by the results of this study, the method that combines UAV low-altitude remote sensing with field surveys can realize precise inversion of tidal flat topography. The root mean square error (RMSE) of the topography in bare flat obtained using the method was 0.07 m and the accuracy was close to the terrestrial laser scanner (TLS). For areas with saltmarsh vegetation, the RMSE was reduced to 0.14 m and the geographical accuracy can be improved by 60% after the vegetation was filtered out. Therefore, the method is superior to traditional point cloud filtering. Overall, this study provided an inversion method of tidal flat topography based on UAV remote sensing and field surveys, which can effectively monitor large-scale natural tidal flat systems. The method can be applied to other similar natural tidal flat systems or coastal areas, providing important technological support for the protection and management of coastal tidal flat wetlands.
Owing to the complex surface features in the high spatial resolution (HR) remote sensing images, traditional change detection methods suffer the shortcoming of a high omission rate. Given this, this paper proposed a change detection method based on multi-temporal remote sensing images based on the fusion of co-saliency difference images. In this study, three groups of dual-temporal HR remote sensing images were selected to carry out the experiment according to the following steps. First, develop difference images based on the dual-temporal HR remote sensing images using the methods of change vector analysis (CVA) and spectral gradient difference (SGD). Then obtain a co-saliency map of two difference images using the cluster-based co-saliency detection. Finally, obtain the co-saliency difference map by fusing two co-saliency maps, and then conduct threshold segmentation and closing operation of the co-saliency difference map using the OTSU method. In this way, the final change map was obtained. As indicated by the experiment results, this method is superior to traditional methods in terms of overall accuracy (OA), Kappa coefficient, and F-measure accuracy and thus is highly reliable and accurate.
Aiming at the omission in the ship target detection from remote sensing images with complex background caused by the arbitrary and dense arrangement of ships, this study, based on the rotation region generation network, proposes a ship target detection algorithm using the multi-scale feature enhancement of remote sensing images. The detailed steps are as follows. Firstly, improve the feature pyramid network using the receptive field module with dense connection at the feature extraction stage. Then obtain the characteristics of multi-scale receptive fields using the convolution of different dilate rates. In this way, the expression of high-level semantic information can be enhanced. Then design a feature fusion structure based on attention mechanisms to restrain noise and highlight the target characteristics. Afterward, fuse all layers according to the spatial weight value of each layer to obtain a feature layer that takes both semantic and position information into account. Then conduct attention enhancement to the features of this layer, and integrate the enhanced features into the original feature layer in the pyramid network. Consequently, pay more attention to target locations by increasing attention loss and optimizing the attention network according to the classification and regression loss. As indicated by the experiment results of DOTA remote sensing dataset, the average precision of this algorithm is as high as 71.61%, which is higher than the latest ship target detection algorithm based on remote sensing images. In this manner, the omission in ship target detection can be effectively solved.
Aiming at the time-consuming problems in the remote sensing image processing for flood and drought monitoring, the authors analyzed related workflows and algorithms including radiometric correction, geometric correction, and the calculation of remote sensing indices. Based on the storage structure and program design model of compute unified device architecture (CUDA), the remote sensing image processing was divided into several modules, including data reading, histogram statistics, grid partition, band calculation, resampling, and data output. Among them, parallel processing schemes were designed for the modules of band calculation and resampling, and the optimal cell sizes were determined for the module of grid partition. Meanwhile, the data transfer efficiency was increased through the grid data mapping based on a graphics processing unit (GPU). Finally, a parallel processing algorithm based on CPU-GPU collaboration in CUDA was proposed. The experiment results are as follows. The modules of radiometric correction and band calculation of remote sensing indices showed a 58.9% saving in time. Meanwhile, the geometric correction module enjoyed the most significant time-saving effects, and the final speedup ratios of the resampling methods of nearest neighbor and bilinear interpolation reached up to nine and seven times, respectively.
This study proposed a K-means clustering-guided threshold-based approach to classifying the high-resolution remote sensing images obtained using unmanned aerial vehicles (UAVs). The steps of the approach are as follows. First, calculate the average silhouette of the UAV remote sensing image dataset as the optimal number of clusters in the K-means clustering. Then perform K-means clustering on the original images, and manually remove non-target areas in the initial segmentation results. Afterward, perform threshold-based segmentation and image optimization on the new objects obtained to extract objects. Finally, combine all the feature tags obtained to realize the recognition and classification of remote sensing images. The abovementioned processing steps were integrated using the MATLAB/GUI platform. Based on this, a classification processing system of UAV remote sensing images was developed. It can quickly process UAV remote sensing images and achieve semi-automatic interpretation. The accuracy of the classification results was verified, obtaining an overall accuracy of 91.09% and a Kappa coefficient of 0.88. This indicates that the approach proposed in this paper can obtain high-quality segmentation results of UAV remote sensing images.
The occurrence and development themselves of loess have recorded abundant historical information, and the macro element content of loess can accurately reflect the environmental evolution. Hyperspectral remote sensing technology enjoys the advantages of being multi-band, continuous, and high-resolution. Therefore, it can be used to detect subtle differences in soil attributes and thus provide technical support for the fast and effective acquisition of basic loess information. In this paper, the loess profile of Zaoshugou Village, Zhengzhou City is studied. Combining the hyperspectral technology, the correlation between the spectral data and the macro elements of the loess was analyzed according to smoothed original spectra, first-order differential (FD), second-order differential (SD), de-envelope (CR), and reciprocal logarithm (Log(1/R). A partial least square regression (PLSR) model was established using the wave band with a larger correlation coefficient R as the characteristic band. The main conclusions are as follows. The variations in Ga, Fe, and Mg elements in the loess profile indicate that the study area has experienced a cold dry - warm wet - cold dry climate cycle since the Middle Holocene about 5400 aBP. The reflectance spectra of the loess in different stratigraphic units show the characteristics with similar trends. However, their spectral reflectance is in the order of L0-2>L0-1>Lt>S0-1>TS. According to the method of partial least squares, the optimal inversion models of Fe2O3, CaO, and CaO/MgO are the PLSR model with FD spectral transformation as the independent variable, while the best inversion model of MgO is the PLSR model with CR spectral transformation as the independent variable. The optimal inversion model of Fe2O3, CaO, and CaO/MgO can effectively distinguish different climate zones and indicate palaeoclimate cycle changes in the region where the study area falls. The optimal inversion model of MgO can better indicate the palaeoclimate evolution law of the region where the study area falls and thus has a certain reference value.
The water level of the Taihu Lake from January 2003 to April 2019 was monitored using the waveform retracking method based on the altimetry data of Envisat and Cryosat-2 satellites. Through gross error elimination and system error correction as well as the boundary extraction of Taihu Lake using MODIS remote sensing images, the long time series of the water level of the Taihu Lake were obtained. Based on these as well as weather observation data and the data on urban population changes, the variation pattern of the water level and its response to climate change and human activities were discussed. The results are as follows. The water level of the Taihu Lake showed an upward trend (0.036 m/a) during 2003—2009 and a downward trend (-0.014 4 m/a) during 2010—2019. It was affected by the ground surface temperature and precipitation in a periodic manner, especially the precipitation. In addition, as the urbanization in the cities around the Taihu Lake accelerated, the population growth rate in the cities had increased and the water demand had notably increased accordingly from 2009. This resulted in a distinct downward trend in the water level of the Taihu Lake since 2009, indicating that human activities affected the water level of the Taihu Lake over.
Remote sensing interpretation of the Siliguri Corridor, West Bengal, India was carried out based on 33 scenes of multispectral remote sensing images from GF-1 and GF-2 satellites, which cover an area of 154 814 km2. As a result, the mileage, density, and distribution of highways at all levels in the Siliguri Corridor were obtained, and the overall characteristics of the transportation in the area were ascertained. Then this paper assessed the trafficability in the selected key areas using the weighted scoring method from the aspects such as landform, lithology, geologic disasters, and road conditions. Furthermore, the factors such as the variation and relative decrease rate of whole network’s efficiency (ΔE and e) of 19 pivotal nodes were calculated using the complex network theory. They can be used to characterize the importance of pivotal nodes relative to the overall trafficability of the road network. For the four most important pivotal nodes, the geological environment characteristics (i.e., important targets, slope, and engineering rock and soil masses in the peripheries of the nodes) were analyzed and potential disasters and risks were proposed.
This study aims to explore the differences and characteristics of the impacts of coal mining activities at different stages on various land use types in mining areas. Taking Yijin Huoluo Banner-a major coal-producing area in China-as the study area and multi-stage Landsat remote sensing images of nearly 30 years during 1990-2019 as the main data source, this study extracted land use distribution information using the random forest classification method on the Google Earth Engine platform. Based on this as well as coal mining statistical data, this paper analyzed the characteristics of land use changes at three stages of coal mining using the intensity analysis theory. The results are as follows. ① The intensity change theory can be used to comprehensively analyze the land use change from the aspects of intervals, categories, and transformation and to more systematically exhibit the characteristics of land use changes and the impacts of human activities in the study area. These are greatly significant for the in-depth understanding of the land use change process. ② Coal mining produces different impacts on different types of land, and it primarily affects the vegetation, water areas, and bare land. ③ Coal mining imposes different impacts on various types of land at different stages. It produces slight impacts on various types of land at the initial stage. It produces increasing impacts on various types of land at the high-speed development stage, during which it mainly affects vegetation, bare land, and water areas in and around the mining area. Then the impacts decrease at the steady development stage of coal mining. The results of this study can serve the implementation of precise protection plans for different types of land at different coal mining stages and provide a scientific basis for the protection of the ecological environment in the mining area.
The identification and extraction of mineralized alteration information play an important role in the ore prospecting using remote sensing technology. Taking the Beiya gold polymetallic deposit as an example, this study designed an alteration information extraction scheme using the principal component analysis technique according to Landsat8 OLI data and the spectral characteristics related to mineral alteration. Specifically, the extraction scheme consists of the removal of interference information (vegetation, water, and shadows), extraction of abnormal information, anomaly gradation, median filtering, and anomaly screening successively. According to the anomaly information extracted, as well as geological interpretation of remote sensing data (lithology and structures) and field surveys, three prospecting areas were delineated in the study area. This will provide basic data and decision-making bases for the ore prospecting in the Beiya area.
The theories on population spatialization tend to be mature in recent years. However, the spatial stability of the variables and parameters used in population spatialization modeling has been scarcely focused on. With the land use data, night-time light data, and demographic data as the data sources, this study proposed a novel precise population spatialization method based on a semi-parametric geographically weighted regression model (S-GWR). Then a permanent population spatialization model on a county scale was built using the method proposed in this study and then was verified using the Sichuan Province as the study area. In this study, the spatial stability of parameters and variables were obtained using the S-GWR model while the characteristics of the variables were analyzed, in order to improve the accuracy of population estimation. Finally, the population spatial distribution map (SDP) with a resolution of 1 km of Sichuan Province in 2010 was formed. The results show that the coefficient of determination coefficient of the S-GWR model was 0.903, which is higher than that of traditional regression models and indicates better fitting effects. The S-GWR model was verified using two commonly used population datasets, and the verification results are as follows. At a county level, the overall average error of the study area and the relative error of each district and county in the SDP all approximated to 0, and thus the SDP was more precise than the other two datasets. At a township level, the mean relative error, mean absolute error, and root mean square error of SDP were 34.54%, 5 715.703, and 12 085.932, respectively, which were all lower than those of the other two datasets. Meanwhile, the SDP showed more favorable dispersion effects than the other datasets. Furthermore, the number of the towns whose population was accurately estimated was 185 in the SDP, which was higher than that in the other two datasets. Therefore, the accuracy of population spatialization can be improved by considering the spatial stability of parameters.
This study focuses on the surface albedo characteristics of different land use types in the Junggar Basin, aiming to provide a scientific basis for the revealment of the biogeophysical mechanisms of different land use types on a regional scale. Based on the surface albedo data during 2000-2018 obtained through remote sensing inversion and the land use data of 2000, 2010, and 2018, this study analyzed the temporal and spatial variation characteristics and interannual variation trend of the surface albedos for short wave (0.3~2.5 μm), near infrared (0.76~3.0 μm) and visible light (0.35~0.76 μm) of different land use types in the Junggar Basin. It will provide a scientific basis for the understanding of the albedo characteristics of different land use types and reveal the impacts of cover change on climate change on a regional scale. The results are as follows. The surface albedos of different land use types have distinctly different characteristics for different wavebands. The surface albedos of the first- and second-level land use types are in the order of near infrared > short wave > visible light, except for the second-level land use types of lakes and reservoirs. For the interannual change trend, the surface albedos of different land use types for the three bands during 2010—2018 are slightly higher than that during 2000—2010. Moreover, all the first-level land use types in the short waveband during 2010—2018 passed the significance test of p=0.05. The interannual variations of surface albedos of land use types in the Junggar Basin over the past 18 years showed a weak growth trend in terms of the variation rate and were slight and stable on the whole in terms of the rate variation. The results of this study will lay a foundation for the research into the surface spectral radiation and energy balance of the study area.
Gross Domestic Product (GDP) is commonly regarded as the best measure of a country's economic health. In 2020, China suffered from a relatively serious COVID-19 epidemic, which had a huge impact on economic development. This paper aims to accurately analyze the spatial and temporal variation pattern of the GDP contributed by the second and tertiary industries in Guangdong Province, China in the first quarter under the background of the epidemic. To this end, the remote sensing data of night-time light was taken as an indicator of GDP contributed by the secondary and tertiary industries (GDP 23). By combining the real-time monitoring data of the epidemic and point of interest (POI) data of Guangdong Province, the authors firstly determined that the epidemic was the factor that caused the decrease in urban total night light intensity (TNLI). Then they analyzed the fitting of various night light indices and different regression models to the GDP 23 of Guangdong Province. Based on this, the optimal index and model were selected for the spatial grid partition of GDP 23 and the comparison of GDP 23 with that in 2019. Afterward, the authors analyzed the impacts of COVID-19 on GDP 23 of Guangdong Province in the first quarter and the reasons from spatial-temporal perspectives according to the spatial simulation results of GDP 23. For the cities most affected by the epidemic, the most affected industries were obtained through the statistical analysis of POI data, aiming to scientifically guide the precise resumption of work and production in Guangdong Province. The results are as follows. The spatial distribution of GDP 23 in 2019 was highly consistent with that in 2020, and the heart of Guangdong's economic development consisted of Guangzhou, Shenzhen, Dongguan, and Foshan cities in the two years. In terms of temporal distribution, 21 cities in Guangdong Province were affected by COVID-19 at different degrees in 2020 compared to 2019. Among them, the cities with relatively developed economies were affected the most, including Shenzhen, Guangzhou, Dongguan, and Foshan. According to POI data and the spatial distribution difference of GDP 23 between 2019 and 2020, the cities having suffered the biggest economic impacts were Guangzhou and Zhongshan, where the leading industries included shopping, real estate, and enterprises and companies, while the cities with the highest increased amplitude of GDP 23 included Shaoguan and Shenzhen, where the leading industries consisted of finance, real estate, and shopping. Therefore, the provincial and municipal governments should formulate corresponding policies on the financial industry, life service industry, and shopping consumption in Guangzhou and Zhongshan cities, in order to accurately assist enterprises and companies to early resume work and production.
The monitoring of surface subsidence in mining areas can provide key information for local production safety protection and mining planning and management. Based on the Sentinel-1A images from September 2018 to October 2019, this study characterized the surface subsidence in the mining area of the Shadunzi Coal Mine in Hami City, Xinjiang, China using the combined small baseline subset (SBAS) and interferometric synthetic aperture radar (InSAR) analysis. The InSAR measurement results revealed a subsidence funnel with a maximum subsidence rate of about -150 mm/a to the northwest of the main shaft of the coal mine. As indicated by the displacement time series, the subsidence funnel showed a significant linear subsidence pattern from September 2018 to June 2019 and gradually stabilized thereafter. Then the surface deformation inversion was conducted using the Okada rectangular dislocation model to obtain the parameters of the working face of the coal mine. The modeling results showed that the working face had a depth of about 349.89 m, a length of about 1 001.27 m, and a width of about 211.80 m. Based on the inversion results as well as the apparent density of the coal seams, the annual mining capacity of the coal mine was estimated to be about 3.18 Mt during 2018—2019, which is consistent with the reported annual production capacity of the coal mine. This paper provides a feasible way to conduct the parameter inversion of coal mine working face under the constraints of InSAR measurements and to infer the relationship between the working face parameters and the mining capacity according to the apparent density of coal seams.
Existing drought monitoring technologies are liable to be affected by the environment and suffer poor timeliness. Given this, this study utilized the MODIS_TVDI and GNSS_PWV data to investigate the spatial-temporal changes in the drought characteristics in spring from 2016 to 2020 in Yunnan province through correlation analysis and regression analysis. The research results are as follows. The TVDI inversion results can accurately reflect the spatial-temporal changes in the regional drought characteristics during 2016—2020. In space, the drought showed the trend of increasing from northwest to southeast in Yunnan. In terms of time, the drought increased first and then alleviated in spring, especially from March to April. In addition, there was a strong correlation between PWV and TVDI according to Pearson correlation analysis. The correlation coefficient was largely greater than 0.5 on a quarterly scale. On a monthly scale, the variation trend of PWV was roughly consistent with that of TVDI, except that the variation of TVDI showed a certain time delay. On a daily scale, the variation amplitude of PWV was highly consistent with that of TVDI, especially during rainfall, and both of them showed certain signals of drought characteristics. Therefore, PWV can serve as a new technical means for drought monitoring.
The Yellow River basin is an important ecological safety barrier, an agglomeration area of resource and energy, and an area with highly intensive production activities in China. Therefore, its ecological change directly affects the sustainable development of the ecological environment and economy in the basin. This paper aims to quantitatively assess the ecological vulnerability and analyze the spatial heterogeneity in the Yellow River basin. To this end, an evaluation system was established using the partition-integration assessment method by selecting indicators such as water resources, climate, soil, vegetation, and human activities. Meanwhile, a multiplication model was introduced. The assessment results are as follows. The overall ecological environment in the basin is moderately vulnerable, with moderately vulnerable areas accounting for 42.37% of the total area of the basin. Meanwhile, the areas with a highly vulnerable ecological environment in the basin are mainly distributed in the urban economic belt along the upper mainstream of the Yellow River. From 2000 to 2018, the ecological vulnerability of the basin first decreased and then increased. During this period, ecological problems were the most notable in 2000 and ecological vulnerability was the lowest in 2015, with the Comprehensive Vulnerability Index (CVI) of 2.28 and 2.00, respectively in 2000 and 2015. The ecological vulnerability and its evolution trend in the basin significantly varied in space. In detail, the ecological vulnerability notably increased in the plateau areas in the upper reaches, slightly changed in the urban belt along the river, and significantly decreased in the middle and lower reaches.
As a central city in the Huaihai Economic Zone, Xuzhou has a long way to go in terms of environmental protection. A map of land use change during 2005—2015 in Xuzhou was prepared according to the remote sensing images of this period. Based on this as well as relevant statistic yearbooks, the land use dynamic degree and land use transfer matrix of Xuzhou during 2005—2015 were calculated using the method of GIS spatial statistics. Then relevant correction coefficients were determined according to the specific conditions of the study area using the equivalent factor method, and the spatial-temporal changes in the ecosystem service values in Xuzhou were quantitatively analyzed. Meanwhile, the relationship between the land use change and ecosystem service value change was investigated. The results are as follows. ① The land use types in Xuzhou are dominated by cultivated land. During 2005—2015, the area of the cultivated land, forest land, water areas, and grassland decreased, the unused land slightly increased, and the construction land considerably increased. Meanwhile, different land use types were drastically converted. In detail, a large area of cultivated land was converted into forest land, grassland, and construction land, and the increased area of the construction land was mainly converted from the cultivated land. ② Among the second-order ecosystem services, the hydrological regulation and waste treatment services possessed the highest values, while the raw material production service showed a low value. During the study period, the value of each individual ecosystem service showed a downward trend, which led to a continuous decrease in the overall ecosystem service value of Xuzhou City. Specifically, the values of the second-order ecosystem services decreased by 2.9×109 yuan in total in the ten years. For the first-order ecosystem service types, the values of cultivated land and forest land ecosystem services decreased by 1×109 yuan and 1.43×109 yuan, respectively, the sum of which accounted for more than 80% of the total reduced value of first-order ecosystem services. ③ The sensitivity index values of the ecosystem services to various land use types were all less than 1 in different stages, indicating an inelastic relationship between the ecosystem value coefficients and the ecosystem service values of various land use types during the study period. Therefore, the ecological value coefficients and calculation method used in this paper are reasonable and reliable, and thus the calculation results are credible.
Ecological sensitivity refers to the sensitive degree of an ecosystem to the changes in the natural environment and the interference of human activities. It can be used to reflect the liable degree and possibility of the occurrence of ecological environmental problems. Taking the Yanhe River basin in the loess hilly and gully region as an example, this study selected three quantitative assessment indicators (i.e., sensitivity indices of soil erodibility, ecological risks, and biodiversity) to construct a composite environmental sensitivity index (CESI) based on spatial distance index for a river basin. Then it explored the temporal-spatial changes in the ecological sensitivity of the Yanhe River basin during 1996—2016 by combining the center of gravity model, obtaining the following results. ①From the perspective of temporal change, the ecological sensitivity in the Yanhe River basin during 1996—2016 showed a downward trend, with CESI increasing from 1.38 in 1996 to 1.41 in 2016. This indicates that the quality of the ecological environment in the Yanhe River basin improved during the period. ② From the perspective of spatial change, the spatial distribution of the ecological sensitivity in the Yanhe River basin greatly changed during 1996—2016. In detail, the areas with high ecological sensitivity were concentrated in the upper reaches in 1996 but were mainly distributed in the middle and lower reaches after 2006. ③ The center of gravity of the ecologically sensitive areas at all levels shifted toward the middle reaches during 1996—2016. Meanwhile, the ecologically sensitive areas were distributed in a concentrated way in 1996 but in a both concentrated and dispersed manner in 2016. ④ The ecological sensitivity in the Yanhe River basin was greatly affected by land use. The project of returning farmland to forest (grass) and comprehensive management project in the Yanhe River basin played a key role in the process of reducing ecological sensitivity.
This study aims to compare and analyze the effects of social control and industrial shutdown induced by the COVID-19 epidemic on the particulate matter and aerosol types in Wuhan City, Hubei Province. To this end, the aerosol optical depth (AOD) and fine mode fraction (FMF) data of Wuhan City from December 1, 2019 to April 30, 2020 were obtained based on the data of atmospheric particulate matter (PM10 and PM2.5) and the data from MODIS aerosol products. Then the models of four types of aerosols (urban/industrial, sand-dust, clean marine, and mixed types) were established, obtaining the following results. During the period of social control and industrial shutdown, the concentration of atmospheric particulate matter showed a downward trend owing to the reduction in anthropogenic emissions. Meanwhile, the proportion of urban/industrial aerosols also showed a downward trend, while the proportion of dry and clean marine aerosols increased to 13.4% in the period except for the Spring Festival holiday. In contrast, the atmospheric particulate matter and the aerosols of the above types showed opposite trends after the ordered resumption of work and production. Compared with the same period during 2017—2019, the concentration of atmospheric particulate matter and aerosol parameters were also lower during the continuous control and shutdown after the Spring Festival. It can be inferred that MODIS aerosol products can be used to effectively obtain the characteristics of regional aerosols and thus provide data for the monitoring and governance of the regional atmospheric environment.
The studies on the hyperspectral inversion of salt lakes are still scarce due to the limitations of geographical conditions at present. This study explores the inversion ideas and methods of the water quality parameters of salt lakes by taking the dissolved oxygen inversion of a salt lake as an example. Based on the analyses of the hyperspectral data of the Chaerhan Salt Lake in Qinghai Province and the hyperspectral inversion technology of water quality parameters, this study determined the hyperspectral inversion model of the dissolved oxygen in the salt lake by means of waveband combination using the unique spectral information of the water body of the lake. The results show that the correlation coefficient between various wavebands of the original spectrum curve and the dissolved oxygen content was less than 0.3, while that between the band combination data in the unique spectral information of the water body and the dissolved oxygen content was greater than 0.75. According to the precision verification of the finally established band ratio model using the measured value, the inversion result of the dissolved oxygen content was roughly consistent with the measured value. It is impossible for the water quality parameters to significantly change with time owing to the relatively stable nature of the water body of the salt lake. Therefore, the verification using the measured data of November 2019 can also indicate that the waveband ratio model established based on the spectral characteristics of the salt lake enjoys high precision for a long term. Therefore, the hyperspectral inversion model can meet the precision requirements for the large-area monitoring of the dissolved oxygen in the lake area. Meanwhile, this study also proposed a new idea for the establishment of the inversion model of plateau salt lakes, which lays a foundation for the establishment of the monitoring system of plateau lakes in the future.
An impervious layer is an important indicator of human activities. Timely and accurate information of impervious layers is of great significance for the protection of the ecological environment. Taking the Yellow River Delta (Dongying City) as the study area, this study explores a novel extraction method of impervious layers by combining the random forest classification with Sentinel-1/2 data. According to comparative experiments, the confusion between dark and light impervious layers and bare soil can be reduced through the combination of the random forest algorithm with surface reflectance characteristics, texture characteristics, and backscatter coefficient, thus effectively improving the estimation accuracy of impervious layers (overall accuracy: 93.37%, Kappa coefficient: 0.925 8). The results of this study reveal that the random forest algorithm combined with Sentinel-1/2 data is a promising approach in the information extraction of impervious layers, which will provide a reference for the remote sensing monitoring of the Yellow River Delta through the integration of multi-source data.
The quantitative characterization of water body color can provide important reference data for the comprehensive water quality assessment of inland lakes and reservoirs. The Guanting Reservoir is a large inland lake in North China. Based on FUI inversion using the seasonal-scale Sentinel-2 and Landsat 8 OLI reflectance data during 2016—2020, this study quantitatively analyzed the heterogeneous characteristics of Forel-Ule Index (FUI) of the Guanting Reservoir on the spatial, intra-annual, and inter-annual scales. To explore the coupling relationship between the FUI and the nutrient status of the water body, models were built using both hue angle α and FUI and the trophic status index (TSI). Moreover, this study demonstrated the comparability of FUI among different sensors and its application potential. The results are as follows. ① On the spatial scale, the FUI value was low at the center but high on the edge of the reservoir. ② On the seasonal scale within a year, the FUI value showed a trend of reaching the highest in winter, slightly decreasing in spring, reaching the lowest in summer, and rising again in autumn. ③ On the interannual scale, the FUI value in the latest three years was lower than that in the first two years during 2016—2020 and the water color changed accordingly from yellowish brown to yellowish green. These may be attributable to the effective governance of the Guanting Reservoir by the Beijing Municipal Government. ④ The Pearson correlation coefficient between TSI and α and that between TSI and FUI were -0.85 and 0.80, respectively, indicating a strong correlation between FUI and TSI. ⑤ The FUI values obtained through the inversion based on the Sentinel-2 and Landsat 8 OLI images of the same day were very approximate and were 13.04 and 13.16, respectively. This indicates that FUI is comparable between the images from different sensors. Therefore, the inversion of FUI can be achieved using the long time-series remote sensing data from multiple sensors. Meanwhile, FUI possesses notable application potential and advantages in the assessment of water quality and trophic status.
Urban villages refer to the rural settlements where the homesteads are surrounded by cities after the farmland in the settlements is expropriated. Given the lack of data support and quantitative analyses in the current researches on urban villages, this study aims to extract the boundaries of urban villages using the deep learning tools in ENVI based on multiple spatial data such as high-resolution remote sensing images, building outlines, and points of interest (POI). The study area is the main urban area of Guangzhou City-the capital of Guangdong Province, for which the initial correct recognition rate of urban villages was 64.31%. To overcome the confusion between the urban villages and some old residential areas and industrial areas in the extraction results, high-resolution remote sensing images were further segmented using the road network to produce label data of urban villages. Then the outlines of the urban villages in the city were extracted using a support vector machine classifier based on machine learning classification algorithms, obtaining precision of up to 90.19%. Therefore, this study can serve as a reference for the reconstruction of urban villages and urban planning and design in the study area to some extent.
Criteria are the greatest achievements of social development and the most effective way to promote the development of social productivity and management ability. Satellite remote sensing technology has always played an important role in geological surveys. With the continuous development of domestic satellites, the application of the remote sensing technology is increasingly mature in geological surveys, and a large number of geological products have been developed. However, the lack of the criteria for the summary and normalization of the geological survey products leads to some problems, such as non-standard products or the disagreement between producers and users of the products. These problems have produced severe impacts on the management of the products. Given this, this study focuses on the classification of the geological information products through the summary of interpretation elements and geological products. It is proposed that geological information products based on satellite remote sensing can be divided into basic products and application products, which consist of several products each. In this manner, the classification system of element - basic products - application products has been established. It will provide the technical support for the standardization of the storage of geological survey achievements.