The Three Gorges reservoir area is an important part of the the upper Yangtze River economic belt. It is very necessary and important to carry out comprehensive, multi-level and regular systematic geological survey. This paper gives a review on the development and progress of geological survey in the Three Gorges Reservoir area based on aerial remote sensing technology conducted in the past forty years, briefly describes the principle and characteristics of the aeronautical remote sensing technology that has been proved to be successive, and sorts out the results obtained by using different technical methods,affirms the important role of aerial remote sensing technology in the field of geological survey, summarizes the problems encountered in practice, and predicts the application prospects of aerial remote sensing technology in the geological survey of the Three Gorges reservoir area. The application and research results show that the aerial remote sensing technology has played an important role in geological surveys such as disaster body identification, ecological environment monitoring and resource exploration on the basis of its advantages such as flexibility, efficiency, detailed survey, and accuracy.
The Tibetan Plateau has the largest ice avalanche in China. Since the 20th Century, with global warming, more and more ice avalanches have occurred in this region, which makes serious loss of life and property of the local residents. Further researches and investigations on ice avalanches have important practical significance for preventing and reducing the disasters. On the basis of the analysis and summary of the disaster mode, the avalanches movements and influence factors, in combination with the typical characteristics of the ice avalanche in alpine valley region, the authors elaborated the definition of the ice avalanche disaster. It is considered that ice avalanche disaster should not only contain the formation of the disasters directly induced by ice avalanche but also include the chain-type disaster induced by ice avalanche. Based on the definition, the authors also divided ice avalanche disaster into three types, i.e., ice avalanche direct hazard, ice avalanche induced glacier lake outburst flood hazard and ice avalanche induced dammed lake outburst flood hazard. The authors want to establish some universal and practical classification criteria which could provide the theoretical and scientific basis for the further study about disaster prevention, mitigation and relief of ice avalanche in the Tibetan Plateau.
The traditional statistics-based change detection method requires the prerequisite that the dataset should obey the Gaussian distribution, such as the iterative chi-square test based change detection method. However, the dataset does not strictly obey the Gaussian distribution, and hence the result is not ideal. A novel change detection method is proposed in this paper, which does not need any assumptions and can take change detection by its own structure. First, an incremental segmentation method is adapted to get objects. After that, spectral and contextual features are combined to calculate its cosine value. Finally, changed objects are found by the box-plot. High-resolution remote sensing images of GF-1 are used as the experimental data. The results are much better than the results of the traditional statistical object-based change detection.
Mechanical damaged surface tends to cause soil erosion, secondary geological hazards and other ecological environment problems, but there is still a lack of effective extraction methods based on remote sensing images. Based on the GF-2 remote sensing image, the authors studied the object-oriented extraction method based on texture features in Tanglangchuan watershed with densely distributed mechanical damage surface. According to the seven types of features, the classification rules were established. On the basis of the optimal scale segmentation, the decision tree A based on spectral features and the decision tree B based on "spectral + texture" features are classified in object-oriented way. Precision evaluation and analysis show that, compared with the traditional supervised classification method and the spectral-based object-oriented classification method, the classification method improves the Kappa coefficient and the total accuracy to 0.82 and 86.25%, respectively, and also effectively improves the extraction accuracy of mechanical damage surface.
In order to improve synthetic aperture Radar (SAR) target recognition performance, the authors propose a method based on variational mode decomposition (VMD). First, the bidimensional VMD (BVMD) is employed to decompose SAR images, thus obtaining multi-mode representations. Afterwards, the joint sparse representation is employed to represent the multiple modes. Finally, the target label is determined based on the minimum reconstruction error. The proposed method was tested on the MSTAR dataset. It could achieve a recognition rate of 99.24% on 10 classes of targets under the standard operating condition (SOC). In addition, its performance outperforms some other SAR target recognition methods under configuration variance, depression angle variance, and noise corruption. The results have confirmed the validity of the proposed method.
Aimed at tackling the problems that there are many mismatched points in the bundle adjustment of unmanned aerial vehicle (UAV) images, the variance loss function may cause the distortion of the solution parameters, and there exists large deviation from the true value which even causes impossibility of converge, the authors applied a robust bundle adjustment method considering the reliability of the observation value. This method uses loss function as a strategy to suppress gross errors, and is a variant designed on the basis of Cauchy loss function. The main idea of this method is adjusting the total loss function adaptively according to the mean value and variance of overlap degree and the residual of feature points, so as to overcome the influence of mismatched points on the computation of image parameters. Correspondingly, a practical accuracy evaluation method independent of ground control point (GCP) was designed. Experiments show that the method can still get robust adjustment results with high mismatch rate, and hence it is practical.
The Awula-Chagan lead-zinc-silver mine in Xin Barag Right Banner of Inner Mongolia is located in the abdomen of Hulunbuir grassland. Its semi-arid climate makes tailings ponds, solid waste piles and ore piles easily generate dust, polluting surrounding grassland. The traditional chemical sampling and spectral analysis investigate the high precision of the mining area but they are time-consuming and labor-intensive. It is convenient to use the time-series remote sensing method to monitor the dust pollution in the mining area. In this paper, GF-1 satellite data in 2018 were used to extract the information of the mining area in the study area. Based on an analysis of the wind field and the best observation month in the study area, the authors used the five-phase Landsat satellite data to adopt the end-element decomposition model of “dust accumulation-vegetation-water and shadow”, and used the method of semi-automatic elimination of road interference by manual intervention to remove the effects of roads. Compared with NDVI index analysis method, the proposed method considers the vegetation spectral information and takes into account the spectral information of the dust, thus making the monitoring effect more objective. A comparative study of 5 remote sensing image aeolian dust extractions found that, as of 2018, the mining area 1 km buffer aeolian dust contamination area expanded to 190.57 hm2, of which annual average growth area in 2000—2010 and 2010—2018 were 14.72 hm2 and 0.64 hm2, respectively. The monitoring results show that the prevention and control measures adopted in the mining area can significantly improve the pollution of dust and wind accumulations; nevertheless, with the further development of the mining area, ecological restoration and management should also be conducted in time.
Extracting rivers from high-resolution satellite images has many important implications. At present, most methods are devoted to extracting rivers from the spectral characteristics or texture analysis of rivers. But for the image which is with the phenomenon of the same object with different spectra or different objects with the same spectra, or has serious noise, or is hard to determine the scale of texture analysis, the method based on water spectrum analysis or texture analysis is not very suitable. The rivers in high-resolution satellite images are generally irregular in structure, and it is more likely that the rivers have different spectral features and texture features due to various reasons. However, in some satellite images, rivers may have approximately uniform width over a wide range. In view of such a situation, a river extraction method combining stroke width transform and geometric feature set is proposed innovatively. Firstly, the Canny edge detector is used to extract the edge of the image, and the edge map is used as the input of the stroke width transform algorithm to obtain the stroke width map. Then, the connected pixels are grouped by using the connected component algorithm, and next, the connected components obtained after the grouping are filtered according to the geometric feature set, and finally the remaining connected components experience the process for filling holes. Experiments using the GF-1 satellite images show that the method can suppress the noise well while extracting the target river. At the same time, compared with the Multiplicative Duda operator and the region growing algorithm, the proposed method has obvious advantages in the aspects of extraction effect and algorithm stability.
The structural similarity region search algorithm is used to realize the automatic extraction of TM image narrow rivers, which is of great value for disaster assessment and soil and water resources management. The discontinuity of narrow river extraction is the main problem which causes the difficulty in accurate obtaining of information about rivers. Many experts have studied various characteristic properties of water bodies to avoid the phenomenon of river information leakage during extraction. However, due to the complex flow of narrow rivers and the vulnerability to environmental disturbances, it is difficult to achieve complete extraction of river information. Combining structural similarity and heuristic search algorithm, this paper proposes a new method for accurately connecting faulted rivers. The specific process of the method is as follows: Firstly, according to the reflection characteristics of the ground objects, the water body extraction model is used to distinguish the narrow rivers from the irrelevant information. Then, the difference between the gray values of the water bodies on different bands is used to set different thresholds for unrelated noise removal. Third, the discontinuous rivers are evaluated by searching. The area is used to determine the breakpoints to be connected to the river. Finally, the heuristic automatic search connection is realized by using the structural similarity between the 5, 4, and 3 bands of river pixels in the TM image. A comparison with several algorithms shows that the proposed method can solve the problem of river extraction fracture of traditional algorithms and realize the precise connection of discontinuous narrow rivers.
Plantation in southern China is growing rapidly, and rotation cutting period is short. To explore the forest change detection method used to update the forest resource database effectively and to monitor the dynamic changes in forest harvesting and renewal in a short period, the authors chose the plantation area of Shangsi County in Guangxi as the study area, where the plantation area changes frequently and rapidly and the change patterns are numerous and small. The GF-2 remote sensing images of two phases were used as data sources. Multi-scale segmentation and spectral difference segmentation were used to segment the two-phase images. The change areas and change types were extracted from the NDVI difference of the objects and the threshold value was determined based on the distribution function, so as to realize the rapid detection of forest change. In addition, the same method was adopted for pixel-based processing in comparison with object-oriented NDVI difference method. The results show that the overall accuracy of the object-oriented NDVI difference method is 87.12%, and the Kappa coefficient is 0.81. The accuracy and extraction effect are better than those of the pixel-based NDVI difference method, indicating that the object-oriented NDVI difference method can better depict the shape and boundary of the change spots and can also more accurately detect the small change area. This method can be adapted to detect the changing characteristics of plantation in south China and can also be used to update the forest resource database for the purpose of rapid change detection.
Based on the NPP/VIIRS nighttime lighting data, the authors constructed a panel regression model to estimate the county GDP of some counties where the new high-speed railway was located in 2013—2018. In this paper, the NPP/VIIRS data was firstly based on the maximum estimation of the correction process, and the night light-GDP panel regression model was established for GDP estimation. The results show that, among the 25 counties, 16 counties have a correlation coefficient R2 of 0.9 or more. The R2 of the six county-level regions is between 0.85 and 0.9, which confirms that the NPP/VIIRS satellite nighttime lighting data changes and the economic growth of the county where the new high-speed railway station is located shows a good and long-term stable positive correlation. At the same time, the authors made a brief analysis of the impact of high-speed rail and county-level economic development, and argued that it is feasible for the panel data model to fit the NPP/VIIRS satellite nighttime lighting data and to estimate the GDP of the counties where the new high-speed railway is located.
In order to improve the polarimetric synthetic aperture Radar (SAR) images classification accuracy by fully extracting variety of useful information, this paper proposes integrating visual features in SAR images classification. Firstly the authors constructed the polarimetric decomposition feature vector, then extracted texture parameters with Grayscale symbiosis matrix, and finally extracted color feature parameters by pseudo-color image. Based on constructing visual vector with texture and color parameter, the authors integrated the visual vector with the polarimetric feature vector to combine the new feature vectors. Using different feature vectors for classification of full PolSAR image, the authors made a comparative study of the classification results. The results show that the combination of visual features can effectively improve the classification accuracy of fully polarimetric SAR image.
In view of the defects of the existing road obstacle detection methods, such as requirement for high registration accuracy, influence by imaging conditions , low-level automation and the need for professional operation, this paper proposes a road obstacle detection method based on reverse feature matching and accessibility evaluation method. On the basis of SIFT feature extraction algorithm, this method detects obstacles by acquiring the set of feature points that are not matched in the road buffer area of the disaster image, then obtains the distribution range and shape of obstacles by using a variety of sub-point region growing algorithms, and finally evaluates the road accessibility by overlapping analysis with road vector data. The experimental results show that this method can effectively extract the position information and shape information of obstacles.
It is important to reduce the amount of unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) data effectively based on point cloud simplification method, and this is of great significance for later point cloud storage and fast processing. The authors used K-means++ method to cluster point cloud normal vectors so as to achieve point cloud simplification. Firstly, the echo point cloud was removed by using the echo number. After that, the zero-mean normalization method was used to normalize the point cloud attribute information, and the KD tree (K-dimension tree) was used to establish the point cloud index so as to construct the point cloud K neighborhood. Then, the principal component analysis method was used to estimate the point cloud normal vector, and the optimal number of clusters was determined by the elbow method. Finally, the point cloud simplification was achieved by K-means++ clustering method. The simplified result was generated into a Delaunay triangulation and converted into raster data, and the validity of the method was verified by the correlation coefficient. The results show that this method can remove 7 722 points of multiple echo point clouds for 69 544 point cloud data in the study area; as for K-means++ clustering with a cluster number of 8 for the point cloud normal vector, the corresponding simplification rates were 81.389%, 81.833% and 85.369%, respectively. The time to generate the Delaunay triangulation after streamlining was much lower than that before the simplifying, with the simplification process being 81.833%, and the highest correlation coefficient was 0.890. This method can provide a reference for point cloud reduction.
Remote sensing images with high spatiotemporal resolution offer a reliable way to the monitoring of flood disasters. However, the application of high spatial resolution images is restricted by satellite revisit period and extreme weather. Therefore, this paper proposes a method that can blend Landsat and MODIS images to generate high spatiotemporal images for monitoring flood disaster. Selecting Gwydir and the New Orleans as study areas, the authors performed fusion of MODIS and Landsat TM based on three major spatiotemporal fusion algorithms, i.e., the spatial and temporal adaptive reflectance fusion model (STARFM), the spatial and temporal reflectance unmixing model (STRUM) and the flexible spatiotemporal data fusion (FSDAF), which led to the formation of a new TM image. Meanwhile, classified flood information was extracted by applying support vector machine (SVM) to the new TM image. The results show that three spatiotemporal fusion algorithms can monitor flood disasters effectively, with FSDAF playing a more superior role in fusion accuracy and flood information extraction. Evaluation of flood classification shows that, in Gwydir, the overall accuracy of STARFM, STRUM and FSDAF is 0.89, 0.90, 0.91, and the Kappa coefficients are 0.63, 0.64, 0.67, respectively. In the New Orleans, the overall accuracy of three fusion algorithms is 0.90, 0.89, 0.91, and the Kappa coefficients are 0.77, 0.76, 0.81, respectively. This study shows that spatiotemporal fusion algorithms can be effectively applied to flood monitoring.
The efficient and accurate extraction of urban green space (UGS) is of great significance to land planning and construction. The application of deep learning semantic segmentation algorithm to remote sensing image classification is a new exploration in recent years. This paper describes a multilevel architecture which targets UGS extraction from GF-2 imagery based on DeepLabv3plus semantic segmentation network. Through Atrous Spatial Pyramid Pooling (ASPP) and other modules of the network, high-level features are extracted, and data set creation, model training, urban green space extraction and accuracy evaluation are completed relying on the architecture. The accuracy evaluation shows that DeepLabv3plus outperforms the traditional machine learning methods, such as maximum likelihood (ML), support vector machine (SVM), random forest (RF) and other four semantic segmentation networks (PspNet, SegNet, U-Net and DeepLabv2), allowing us to better extract UGS, especially exclude interference of farmland. Through accuracy evaluation, the proposed architecture reaches an acceptable accuracy, with overall accuracy being 91.02% and F Score being 0.86. Furthermore, the authors also explored the portability of the method by applying the model to another city. Overall, the automatic architecture in this paper is capable of excluding farmland pixels'interference and extracting UGS accurately from RGB high spatial RS images, which provides reference for urban planning and managements.
Vegetation index is an important approach in vegetation monitoring and investigation. SAR data are free with weather condition observing data day and night. Building relationships between SAR data and vegetation indices can contribute to fusing two data to improve temporal monitoring in forest of mountain areas. Therefore, the authors made a statistical analysis between vegetation indices including NDVI, EVI, GVI, NDWI and C band SAR data and then made a comparison about difference of correlation between NDVI, NDWI and X, C, L band SAR data in different forest disturbances in Genhe forest region of Da Hinggan Mountains in Inner Mongolia. The results are as follows: ①PR and interferometry coefficients both have significant negative correlations with optical vegetation indices, PR has strong linear correlations with NDVI, EVI, GVI (R2=0.40~0.49), and interferometry coefficients have strong linear correlations with all optical indices (R2=0.43~0.51). ②Ground cover can affect linear regression between VH and NDVI. Scrub-grass land and fires scars with thick vegetation layer and forest land have a strong linear correlation with NDVI (R2=0.64~0.76). ③The correlations are different for different forest disturbances: In fires scars, NDVI has significant negative correlations with X- band HH, and C band PR and NDWI have a significant positive correlation with C band VH. In deforestation areas, L-band PR has significant negative correlations with NDVI, and L band VV and VH have significant positive correlations with NDWI. In undisturbed forest land, C-band PR has significant negative correlations with NDVI and NDWI.
Based on DEM and remote sensing data, this paper proposes a new method for extracting alluvial fan by determining the fan apex point, leading edge points and side edge points. Firstly, the fan apex point is determined by hydrological analysis. Secondly, the fan leading edge points are determined by DEM elevation fitting curve according to the obvious slope break at the boundary of alluvial fan and alluvial plain. Finally, the side edge points are interpreted by DEM elevation fitting curve and remote sensing data. Taking the eastern foot of Helan Mountain as a study case, the authors detected that the alluvial fans extracted by the proposed method are more objective and stable than visual interpretation from available studies. Moreover, this method can extract the alluvial fan more effectively under the complex condition with vegetation coverage and human activities, which is difficult for visual interpretation. The method is significant for alluvial fan extraction and also for the research on landform and sedimentary characteristics of alluvial fan.
Ecological land is connected with human social development and natural ecological succession based on the inherent correlation between land use and land cover. Studying the topographical differentiation of ecological land spatial-temporal pattern in Hebei Province can provide scientific basis for the protection and optimization of ecological land in Beijing-Tianjin-Hebei region. Based on land use data of Hebei Province obtained in 1990, 2000 and 2015, the authors used GIS technology to analyze the topographical differentiation of ecological land spatial-temporal pattern in Hebei Province. The conclusions are as follows: ① The advantageous distribution topographical types of ecological land are hills, low mountain and middle mountain, while the advantageous distribution topographical types of forest are low mountain and middle mountain, the advantageous distribution topographical types of grassland are hills, low mountain and middle mountain, the advantageous distribution topographical types of wetland are plains and plateau, the advantageous distribution topographical types of other ecological land are plains, platform and plateau. ② During the study period, the forest in the platform and the other ecological land in plain, platform, low mountain and mid-mountain landform types had extremely poor spatial stability. ③ According to the land use change TUPU, the stable type is the primary land use change type, accounting for 86.75% of the whole catchment. The low mountain and middle mountain are the advantageous distribution topographical types for the stability, while the plain, platform, hill and plateau are the advantageous distribution topographical types for the prophase kind, the plain, platform and plateau are the advantageous distribution topographical types for the anaphase kind, the plain, platform and hill are the advantageous distribution topographical types for the repetitive kind, and the plain is the advantage distribution topographical type for the constant kind. The topographical differentiation of ecological land distribution, spatial stability and TUPU are significant.
In the past, different historical periods had different important missions and, as aresult, the work of surveying and monitoring of natural resources elements of Guangxi was organized by different administrative departments. In the past, the China's natural resources were managed separately. the survey and monitoring of each element are organized and managed separately by land resources department, water resources department, agriculture department, forestry,department and marine department, etc. This led to the existence of many problems, such as multiple sources, different criteria, contradictory results and difficult share. For the purpose of solving these problems, all elements of natural resources in Guangxi need to be surveyed and monitored uniformly. The construction of a system for surveying and monitoring can provide an accurate, reliable, scientific and authoritative basic data for the registration of realestate of natural resources, the land space planning and use control, and the protection and remediation of ecological environment, etc. Starting with the theory system, the authors found the approach suitable for the situations in Guangxi and highlighted the effect of emergency monitoring. Then the architecture of cloud platform was studied and discussed, which included the multidimensional-stereoscopic observation network, the cloud computing of remote sensing big data, and the shared service of the data and results. Meanwhile, the key technologies of the system construction were analyzed. Finally, the prospect of survey and monitoring results-based applications was predicted.
Located on the northern slope of Kongur Mountains, Karayaylak glacier surged in May 2015. For the purpose of obtaining the glacier surface elevation changes of the Karayaylak glacier from 1973 to 2016, the authors used the 1973 Keyhole satellite optical remote sensing data, the US SRTM DEM data in 2000, and the TanDEM-X bistatic SAR data in 2013 and 2016. The results show that, from 1973 to 2013, there was no obvious elevation change; from 2013 to 2016, clear surface thickening was observed at the terminus of the west branch, whereas a relatively obvious glacial surface elevation thinning was detected in the upper part of the glacier. Landsat OLI image from 2013 to 2015 were employed to monitor the change of glacier surface velocities. The distributions and changes of the glacier surface velocities indicate that the western tributary of Karayaylak glacier moved faster than any other tributaries. According to the comprehensive analysis, it is considered that there is no obvious advance or retreat trend at the terminus of the glacier before glacier surge, and the flow velocity of the glacier has changed greatly. In addition, combined with the results of meteorological data, it is believed that the glacial surge has little to do with local climate change. The main reason for the surge is probably attributed to the structural change of the glacier itself.
The Yangtze River Economic Belt is an important mineral resource base in China. Ecological and environmental problems caused by mineral resources exploitation are outstanding. In order to survey the situation of the land occupied and damaged by abandoned open-pit mines in this area and study the ecological remediation countermeasures, based on the domestic high spatial resolution remote sensing images acquired in 2016—2018 as the main data source, the authors used the techniques of remote sensing interpretation and information extraction to obtain the distribution information of the land occupied and damaged by abandoned open-pit mines in the range 50 km on the both sides of the main channel of the Yangtze River, which included Jinsha River in Sichuan and Yunnan Province and Yangtze River from Yibin to the estuary and the main tributaries which included Minjiang River, Tuojiang River, Chishui River, Jialing River, Wujiang River, Qingjiang River, Xiangjiang River, Hanjing River and Ganjiang River in the Yangtze River Economic Belt, and the geological hazard and environmental pollution information related to mining in the range of 10 km on the both sides of the river. The results show that in the range of 5 km, 10 km, 30 km, and 50 km from the main stream, the areas of the land occupied and damaged by abandoned open-air mines are 4 655.14 hm2, 8 787.57 hm2, 12 207.59 hm2, 21 040.85 hm2 and 30 034.47 hm2 respectively, and that for tributaries are 5 080.04 hm2, 8 644.25 hm2, 12 345.53 hm2, 21 290.29 hm2 and 33 491.49 hm2 respectively. Based on the results of remote sensing survey, the authors analyzed the environment of the abandoned open-air mines in the range of 10 km on the both sides of the Yangtze River main channel and main tributaries. The results show that the main problem in the upper reaches of the Yangtze River is that geological disasters caused by open mining and environmental pollution caused by metal and chemical raw material mines in the middle and lower reaches of the Yangtze River are quite serious. Combined with the advanced technology of mine environmental restoration, the authors put forward the countermeasures of mine ecological environmental restoration. Methods of slope reduction, slope cutting and slope reinforcement can be adopted to eliminate the hidden danger of collapse. Different green technologies can be used to prevent soil erosion according to the slope size. Artificial barrier layer, artificial fertilizer and microbial methods can be used for soil improvement. Toxic heavy metals in soil can be degraded by tolerant plants and microorganisms. Constructed wetlands can be built for water ecological restoration. The survey results and suggestions presented in this paper would provide the scientific bases and important references for the local mining administration on the ecological environmental remediation of the abandoned open-pit mines.
It is essential to obtain a wide range of spatial distribution and dynamic change information of paddy rice timely and accurately for scientific guidance of rice production, rational utilization of water resources, and monitoring of atmospheric environmental changes. In this study, the decision tree extraction model of rice planting area in Hunan Province was constructed on the basis of time series variation characteristics of MODIS-derived LSWI and EVI in rice planting area, and the accuracy of the model was evaluated. In addition, the spatiotemporal variation characteristics of the rice planting areas in Hunan Province from 2000 to 2016 were investigated. The results are as follows: the total classification accuracy of rice planting area extraction model in the research area is 90.2%, the Kappa coefficient is 0.74 and; in comparison with agriculture statistics, the mean relative error is 13.6%. The proposed extraction model can be applied to the efficient extraction of rice planting area on a wide range and long time series basis. The average annual rice planting area in Hunan Province is 3 441.2 thousand hectares, of which 1 024.1 thousand hectares are single-cropping rice, mainly distributed in the Dongting Lake plain, and 2 417.1 thousand hectares are double-cropping rice, mainly distributed in the north-central part of Hunan Province such as Yueyang, Yiyang, Changde, Changsha, Zhuzhou, Xiangtan and Loudi City. the rice planting area in Hunan Province was reduced by 732 thousand hectares from 2000 to 2004, reached a relatively stable level in 2005—2010 and increased by 295.5 thousand hectares in 2011—2016. Overall, there existed a decreasing trend from 2000 to 2016, with the rice planting area reduced by 582.2 thousand hectares.
To address the problem that quantitative analysis of uneven subsidence is rare, the authors used the Permanent Scatterer Interferometry (PSI) to monitor land subsidence in the Beijing plain. According to the different shallow surface spatial utilizations, the authors selected 5 typical areas in the subsidence funnel region. Based on spatial autocorrelation analysis and wavelet analysis, the authors quantified the degree of spatial and annual time series uneven subsidence in each area, and studied the influence of different shallow surface spatial utilization and groundwater level variation on spatial and annual time series uneven subsidence. The results are as follows: ①Annual time series subsidence’s Moran index degrees of 5 areas are the same as those of the accumulated subsidence: I5>I3>I1>I2>I4. According to the utilization of shallow surface space, the degree of uneven subsidence of 1, 2, 5 areas are positively correlated with the complexity of space utilization, and the factors affecting the uneven subsidence degree of area 3, 4 are complicated. ②It is found that the variation and duration of groundwater level fluctuation are the main factors affecting the uneven degree of time series subsidence.
Based on the infrared data of the Landsat8 in similar tides and different time spans in the sea region near Tianwan nuclear plant,Lianyungang City,Jiangsu Province, on November 15,2013 and February 27,2017,the authors used remote sensing technology to study the thermal discharge of nuclear power plant and change along the coastal line. The relationship between the thermal discharge and change in the coastal line was analyzed. The results show that the construction of peripheral engineering of Tianwan nuclear power plant dramatically changed the coastline,which affected the size and distribution of the thermal discharge. Remote sensing technology can detect the change of coastal line near the nuclear power plant and its effect on thermal discharge distribution. It is important to monitor the change of coastline near the nuclear power plant for sea temperature monitoring.
The evapotranspiration in the Jiangsu-Zhejiang-Shanghai Area during the 2004 and 2013 growing seasons was estimated by integrating the MODIS remote sensing data and using the Surface Energy Balance Algorithm for Land (SEBAL) model, and the model was validated using the surface observation data of the lysimeter. On the basis of summarizing the evapotranspiration time-space expansion method, the crop volatility method was used to expand the regional evapotranspiration on the monthly and seasonal scales, and the evapotranspiration of the regional monthly and seasonal scales was calculated. The results show that the SEBAL model is more suitable for evapotranspiration estimation in this region. The evapotranspiration range of the annual growing season in the Jiangsu-Zhejiang-Shanghai Area is wider, and the southern evapotranspiration is higher in the region. The average evapotranspiration in the regional growing season in 2013 was 758 mm. It was lower than the average evapotranspiration of about 930 mm in the growing season in 2004 . According to the statistics of the average daily evapotranspiration of the growing seasons, the authors have found that the natural land use evapotranspiration of the Jiangsu-Zhejiang-Shanghai Area is higher than that of the artificial land use, while in natural land use, the evapotranspiration is in order of water>forest>beach land>grassland>unused land. For artificial land use, the evapotranspiration of urban area is significantly low, while the evapotranspiration values of paddy fields, dry land and rural area are relatively high, and the difference is insignificant.
With its advantages of wide breadth, strong comprehensive coverage and short revisit period, GF-1 satellite has become one of the important data sources used in land and resources survey, remote sensing monitoring of agriculture and forestry, and major national engineering construction. In this study, the authors took the China-Pakistan Economic Corridor as an example, selected the GF-1 WFV data for comparative experiments, and analyzed two key factors that affect the application of remote sensing images, i.e., how to improve the geometric positioning accuracy of remote sensing images and the true color image synthesis method. The experimental results indicate that the block adjustment model based on rational function model technology and RGB-NIR color synthesis model have good performance in image accuracy and visual effect of imagery respectively. The X-direction residual and Y-direction residual of ortho-image were improved to 0.79 pixels and 0.83 pixels. The information entropy, average gradient, mean and standard deviation of the resulting images were improved in varying degrees. This method could not only ensure the true and natural color of the image but also keep the information details, with the image surface effect better optimized. This test method is a better strategy for large scale data application in actual production.
For the purpose of automatically obtaining a large area of winter wheat planting area, phenological information in medium spatial resolution remote sensing images based on time series curves is usually used to identify and extract. However, in actual engineering projects, if phenological information is used only, the accuracy is low. Therefore, a method based on time series curve data classification model and image segmentation is proposed for winter wheat identification. Firstly, the normalized difference vegetation index (NDVI) time series curve of multi-source data is constructed, and the NDVI time series data are smoothed and denoised by harmonic analysis of time series (HANTS) method. Then, via coordinate transformation of NDVI time series, three parameters of band mean, standard deviation and square mean are obtained to construct a new classification model so as to improve the difference between winter wheat and other crops; finally, by combining segmentation results of spatial resolution data, spatial structure information of the image is used to improve accuracy of feature boundary. Taking Jiangning District of Nanjing as an example, the authors used 21 multi-source images of GF-1, Landsat8 and Sentinel-2A from December 2017 to June 2018, and the final extraction accuracy reached 98.74%, which is better than results of other methods. This method provides agricultural management departments with accurate geographic information data on planting area and distribution of winter wheat.
In order to break through the time-consuming and laborious limitations of traditional regional economic development surveys, the authors built some regional economic development models by virtue of the advantages of remote sensing technology. First, based on multi-source and multi-temporal satellite remote sensing data, the authors obtained surface morphological changes and land use information, analyzed the correlation between land use types and regional economic indicators, optimized sensitive factors, then combined the social survey data to build a regional economic development model and finally performed an accuracy evaluation to verify the validity and applicability of the model. Zhoushan Islands were selected as the research area to carry out verification experiments. The experimental results show that the construction land area is the most sensitive factor related to various economic indicators, and the correlation coefficients with GDP, PIP, SIP and TIP are respectively 0.959 1, 0.939 0, 0.954 6 and 0.957 3. The average determination coefficient R2 of the regional economic development model built with the survey data is 0.979 5. The results obtained by the authors provide a new way of thinking for regional economic development prediction and economic data correction and also provide a possibility for humans to observe economic activities and their impact. The model built in this study is simple and clear yet with high precision, and thus is of great significance for understanding regional economic development as well as adjusting and correcting statistical data.
Land surface temperature (LST) is a key parameter in the surface environment and atmospheric energy exchange system, and it plays an important role in agricultural information monitoring and agro-meteorological disaster research. Due to the interference of factors such as clouds, there are a large number of missing and low-quality pixels in the thermal infrared surface temperature data. Therefore, this study used reconstructed high-quality MODIS surface temperature data as the data source, from 2003 to 2017 year, day and night and season. The spatial and temporal distribution characteristics and long-term variation pattern of China’s surface temperature during 15 years were analyzed systematically on different time scales. The results are as follows: ①During the period of 2003—2017, the surface temperature change in China showed a slight increase in temperature, with an average annual increase of 0.011 ℃, of which 63.7% showed a trend of warming. ②In addition, China’s warming trend is significantly uneven, with the overall characteristics of “the north is greater than the south and the west is greater than the east”. The significant warming is mainly concentrated in the central and western parts of the Inner Mongolia Plateau in the northwestern region, the southern part of Tibet, and the Huanghuaihai Plain (slope k>0.07 ℃·a-1, R>0.6). In addition, the region with the largest temperature drop is concentrated in the vicinity of the Songnen Plain in the northeastern region (change slope k<-0.06 ℃·a-1, R>0.55). ③On the seasonal scale, the warming trend in winter is the fastest, with the most significant in the western region, followed by spring, while the warming trend in autumn and summer does not change much.
The impervious surface percentage is an important indicator of regional urbanization and ecological environment changes. The spatial and temporal distribution of impervious surface percentage can reveal the current and future development potential of the city, and provide a reference for urban environmental protection and green sustainable development. In this paper, Hangzhou was selected as the study area, and one Sentinel-2B satellite image was used to extract the impervious surface percentage as reference data. Based on the four-phase Landsat satellite imagery, the authors used the random forest algorithm to invert the 30 m spatial resolution impervious surface percentage datasets from 1990 to 2017 in Hangzhou. The accuracy verification results show that the mean absolute error is from 6.3% to 6.7%, and the root mean square error is from 13.40% to 14.25%, indicating that the inversion model has great accuracy and can accurately reflect the spatial distribution of the impervious surface. Based on the impervious surface weighted mean center, standard deviation ellipse and landscape pattern index, the authors analyzed the spatial and temporal patterns of impervious surface in Hangzhou. The results are as follows: The impervious surface percentage in Hangzhou was increasing from 1990 to 2017, and the annual average growing of impervious surface percentage was the fastest from 2010 to 2017, mainly concentrated in Yuhang and Xiaoshan; Due to the unbalanced development of Hangzhou between 1990 and 2017, the impervious surface weighting mean centre moved to northeast at first, then moved to south, and finally moved to north; The northwest-southeast profile was the main direction of urban growth and the gathering trend was relatively stable in Hangzhou; The change of landscape pattern shows that the impervious landscapes of all types were increasing, and were distributed in a balanced trend; The natural surface, medium density and super high density impervious landscapes became less aggregated and increasingly fragmented; The aggregation of the impervious landscapes of all types was relatively stable, the highest degree of aggregation was the natural surface, and the lowest was the medium density impervious landscape.
A great many ecological and environmental problems have been caused in the course of rapid economic development and urbanization in Sichuan Province. Evaluating ecosystem health condition is of significant importance for regional sustainable development. Based on relevant city-level data in social, economic, agricultural and ecological aspects from 2000 to 2016 as well as county-level data obtained through the downscaling model, the authors built the pressure-status-response model to make comprehensive evaluation and analysis of ecosystem health of studied regions in Sichuan Province on city-level and county-level scales. The results show that the overall score of ecosystem health in Sichuan increased from 0.610 to 0.687 in 2000—2016, but the spatial distribution shows that the eastern part of Sichuan was better than the western part. The development between studied regions is extremely unbalanced. Chengdu had outstanding advantages in comparison other regions. The results of ecosystem health assessment in each county shows that 27.9% of the total counties are “unhealthy” and “sick”, while about half of the counties are above the “normal” level. The counties whose evaluation results are “sick” are mainly concentrated in Garze and Aba prefectures. The ecological health status in northwest Sichuan deserves much more attention.
In the implementation of mine development and mine remote sensing environment monitoring, the statistical calculation of monitoring data indicators is one of the main methods for the centralized display and application of monitoring results, and is also the basis for relevant law enforcement and provides data support for the adjustment and formulation of relevant mine policies. In calculating various monitoring indicators, traditional manual statistics have many shortcomings such as low efficiency, high labor cost, large workload, wrong calculation, low accuracy, and inconsistent statistical standards. Through the project practice, the paper puts forward the automatic statistical method of mine remote sensing monitoring based on data stream filtering and multi-dimensional matrix calculation, which can quickly count the attribute information and spatial information of each mine remote sensing monitoring data, realize the calculation of each classification index according to the administrative region and mine type statistics, and complete the rapid summary of mine statistical indicators. The experimental results show that the proposed method completed the statistical summary task quickly, accurately and automatically in the remote sensing monitoring project of Qinghai Province in 2018, thus saving a lot of human resources and time and having a good application effect.
