With the launch of high-resolution satellites, their applications in geological survey become more prominent. Based on the introduction of various kinds of high resolution data which include high spatial resolution satellite remote sensing, high temporal resolution satellite remote sensing, high spectral resolution satellite remote sensing and high radiation resolution satellite remote sensing, this paper gives a review of the application of optical high resolution images to geological survey, mainly in the aspects of dynamic monitoring of land use, basic geological and resources survey, mineral resources development and ecological monitoring, ecological environment investigation, geological disaster and emergency investigation. It is found that high resolution remote sensing images have great potential in natural resources survey,and collaborative application of multi-source multi-scale high resolution remote sensing data will play an increasingly important role in natural resources survey in the future.
In view of the low resolution in direction of wavelet transform,the rich geometric structure of high spatial resolution remote sensing imagery (HSR) and the existence of edge in various directions which causes shortcomings in edge extraction of wavelet transform on objects with complex geometric structure, the authors proposed a HRS image edge extraction method of multi-direction wavelet transform based on Directionlet theory and modulus maximum method. The method first decomposes original image based lattice to obtain one-dimensional line set, then carries out wavelet transform and obtains the high frequency directional sub-band by restore image format. The edge result is obtained by using improved module maximum and the dual threshold method. Finally, the mathematical morphology is used to refine and connect edge results. Experiment result shows that the proposed method can get more complete edge compared with traditional method and standard two-dimensional wavelet transform.
In view of the fact that different kernel functions have greatly different performance on the same feature, the authors propose a new method of change detection of multi-feature hybrid kernel support vector machine (SVM) model. According to the different characteristics of the change detection, the authors extract image features, make use of the multi-kernel function of several features, give the methods of constructing multi-feature and mixed-kernel function, construct change detection model of multi-feature mixed-nuclear support vector machine, and fully tap the integrity and accuracy of the varying target. The experimental results show that this method makes use of the information of various features. The detection precision is obviously higher than that of the single feature. The method not only takes advantage of extracting change information of small samples, but also avoids the complexity and uncertainty of the old detection method for determining the change threshold.
In order to remove the noise in the hyperspectral image effectively, strengthen the spatial structure, make full use of the spatial context information of the object, and improve the classification accuracy of hyperspectral image, the authors put forward recursive filtering and k-nearest neighbor (KNN) method for hyperspectral image classification. The main steps are as follows: Firstly, the principal component analysis (PCA) is used to perform feature dimension reduction of hyperspectral images. Next, the recursive filtering is used to filter the principal component image. Then, the Euclidean distance between the test sample and the different training samples is calculated by the KNN algorithm. Finally, according to the comparison of average values of k minimum Euclidean distances, the classification of test samples is achieved. Experimental results are based on several real-world hyperspectral data sets, and the influence of different parameters on the classification accuracy is analyzed. Experimental results show that, with recursive filtering, the noise can be effectively removed, and the image outline can be strengthened. Compared with other hyperspectral image classification methods, the proposed method is outstanding in classification accuracy.
With the large quantities of redundant information in the hyperspectral imagery, the traditional anomaly detection algorithm using the overall hyperspectral spectrum should consume a larger amount of computing time. Based on the linear prediction and learning dictionary, the authors put forward a novel algorithm. Compared with other low rank representation methods, the linear prediction method with the similarity of the band is utilized to find the least similar band subsets, and then the learning dictionary is implemented to obtain the learning dictionary which can represent the background information of the imagery. In addition, the imagery is divided into low rank matrix and sparse matrix via the low rank and decomposition. Finally, the traditional RXD (Reed-X detector) detection algorithm is utilized to detect the sparse image anomaly. Compared with other methods, the proposed method performs better with lower computational cost. Experimental results demonstrate that the selection of some bands including original information can achieve a good performance without corrupting the original information. It is a fine technique to apply to the hyperspectral imagery anomaly detection.
To tackle the incomplete extraction problem faced by most remote sensing images shadow extraction algorithms for extracting shadow,this paper purposes an automatic expansion extraction algorithm of remote sensing images shadow. Firstly, based on the characteristics that there is a peak of the rate of change of pixel values at the shadow boundary of the near infrared band and the rate of change of pixel values is stable inside the shadow, the authors established the criteria of shadow boundary judgment to determine whether the pixel is located in the shadow boundary. Second, on the basis of initial shadow extraction, each shadow is expanded by the criterion from the inside outward, which not only can take into account a single shadow area, but also is no longer confined to the global image features or local features of remote sensing images, so that shadow is extracted more completely. Experimental results show that the algorithm can effectively improve the accuracy and efficiency of shadow extraction.
In the high-resolution remote sensing image retrieval, it is difficult for hand-crafted features to describe the images accurately. Thus a method based on aggregating convolutional neural network(CNN) features is proposed to improve the feature representation. First, the parameters from CNN pre-trained on large-scale datasets are transferred for remote sensing images. Given input images with different sizes, the CNN features which represent local information are extracted. Then, average pooling with different pooling region sizes and bag of visual words (BoVW) are adopted to aggregate the CNN features. Pooling features and BoVW features are obtained accordingly. Finally, the above two aggregation features are utilized for remote sensing image retrieval. Experimental results demonstrate that the input image with reasonable size is capable of improving the feature representation. When the pooling region size is between 60% and 80% of the feature map, the vast majority of the results of pooling features are superior to those of the traditional average pooling method. The optimal average normalized modified retrieval rank values of pooling feature and BoVW feature are 27.31% and 21.51% lower than those of hand-crafted feature. Therefore, both the average pooling and BoVW can improve the remote sensing image retrieval performance efficiently.
In the object-oriented multispectral image segmentation, the initial object feature may not reflect the global feature of the whole region and can lead to an incorrect merge. To solve the problem, this paper proposes a method that combines the result of the simple linear iterative clustering(SLIC) super pixel and the rough segmentation result of structure tensor. First, the SLIC process is executed to get an over-segmentation result. Then, make sure the feature of the initial object of the fractal net evolution approach can reflect the real distribution of the whole region, and do the pre-merging between the super pixels under the control of the rough segmentation result of the structure tensor in the scale space. This process can enhance the anti-noise capability of the following merging process. Finally, the final results are given; compared with the results of the traditional fractal net evolution approach(FNEA), the result shows that the method proposed in the paper has better anti-noise capability, and can get better segmentation results even in handling the complex city multispectral images.
The extraction method based on the edge features is widely used in the road recognition of remote sensing image. However, the traditional methods are not good at eliminating noise, and tend to cause the misjudgment and leak-judgment of the edge. Therefore, based on the idea of the canny edge detection algorithm, the authors firstly adopt a smoothing and self-adapting Gaussian filter to reduce the noise of remote sensing image, reduce the noise interference and reserve the edge and details. Then, in the edge judgment of the dual threshold, the authors select the high and low thresholds on the basis of local characteristics within the object scale of the pixel point and enhance the exact judgment performance of the edge. The experiment results show that the new method can effectively improve the accuracy and positioning accuracy of the edge detection, obviously reduce the misjudgment of road edge extraction and remarkably increase integrity and consecutiveness, with high automation.
Building detection plays an important role in urban planning, change detection, surface coverage and so on. However, in high resolution remote sensing images, buildings vary in shape, color, and size, which makes building detection a difficult problem. Therefore, this paper proposes a method based on multi-scale and multi-feature to automatically extract buildings in high resolution images: Firstly, down sampling images are used to construct Gauss pyramid model, while fixed size windows in different layers of pyramid image represent different ground areas. Then multi features are calculated which describe building characteristics by sliding windows, and multi features are fused to evaluate the saliency of building in different scales. Then the saliency of superpixels is calculated, and Otsu algorithm is used to automatically determine the threshold, and furthermore, some constraints such as the aspect ratio were combined to extract buildings accurately and automatically. Experiments were made by 0.5 m and 0.2 m high resolution remote sensing images in comparison with the markov random field model based on color and texture modeling algorithm for qualitative and quantitative comparison. The results show that the method suggested in this paper can obtain more satisfactory precision and has higher effect on building detection from high-resolution remote sensing images.
Atmospheric water vapor content plays an important role in the water cycle between sea and land as well as the formation of aerosol and cloud. Therefore, it is crucial to investigate its spatial-temporal change and influence factors. However, the impact of land use and landform types on it still needs further study. In this paper, the authors selected Heilongjiang Province as the study area, and used two - channel and three - channel ratio methods to retrieve the atmospheric water vapor content and validate the retrieval precision based on MODIS data and sounding real-measure data. Then, the authors analyzed its spatial-temporal change and the relationships between land use types, landform types and atmospheric water vapor content. Some conclusions have been reached: ①The performance of two-channel approach is better than that of three-channel approach; ②The water vapor content in the northwestern and southeastern of Heilongjiang Province is low, but it is higher in the eastern and western parts from April to July; ③The atmospheric water vapor content presents a rising trend in its totality; ④The impacts of land use types and landform types on water vapor content are obvious.
The automatic extraction technology of regional scale ground fissures based on remote sensing images has the problem of low spectral range and low geometric feature, which leads to low extraction precision. Therefore, the sequential step extraction method for ground fissures based on objects is proposed. Firstly, the image is segmented. According to the spectral and geometric characteristics of segmentation object, surface interference factors which are different from the ground fissures are removed by mask. On such a basis, the linear objects are extracted and the surface factors without linear features are removed ultimately. Finally, the fractal characteristics of linear objects are calculated to differentiate between the ground fissures and other linear surface factors and complete the automatic extraction of ground fissures. The method was applied to the extraction of ground fissures in a coal-mining region of northeastern Ordos. The results show that the method is effective in extracting the ground fissures. Its accuracy reaches 85.7%, which is better than the precision of traditional supervised classification method (57.1%) and the precision of knowledge model extraction method (71.4%) . On the basis of extraction results, this paper discusses the distribution characteristics of ground fissures. The respective relations between ground fissures and the location of goafs as well as topography are analyzed. The results show that the number of ground fissures is negatively correlated to the distance of goafs and is not clearly correlated to the topography. The research can provide the necessary technical support for the regional geological environment protection and the rational exploitation of coal resources in the mining area.
Based on 15 lakes located in different regions of China, the authors calculated the normalized difference water index (NDWI) for GF-1 satellite remote sensing images, and then employed iterative method, Otsu method and histogram bimodal method for segmentation threshold selection and water information extraction, and finally analyzed the threshold selection results and water information extraction results of the three methods. According to the results obtained, iterative method is similar to the threshold chosen by Otsu method, and the difference between the thresholds selected by histogram bimodal method is large; the iterative method is more efficient; the extraction accuracy of bimodal method is the highest, and its fitting effect is the best. This study can provide selection strategies of adaptive threshold segmentation method for extracting accurate water information from GF-1 images.
Total suspended matter is one of the important parameters to evaluate water quality. In this study, 33 data samples containing reflectance of water surface, concentration of total suspended matter and chlorophyll-a were used to conduct retrieval of total suspended matter, establish retrieval model and verify accuracy of model based on comparative analysis between field measured spectral reflectance and total suspended matter in Poyang Lake during the flood season. These models were single band, first-order differential and band ratio, respectively. The results showed that the R 2 of three models was greater than 0.9, and the best was the single band model, and R 2, RMSE and MRPE were 0.9805, 3.78mg/L and 16.99%, respectively. The single band model gave the better performance when it was applied to GF-1 satellite image data on August 3, 2015 and was validated, with R 2, RMSE and MRPE being 0.8477, 12.23mg/L and 35.22%, respectively. It was also shown that the overall level of suspended matter concentration was low and the average value was 23.26mg/L. The higher value of total suspended matter was concentrated in the northern channel area. The concentration values of suspended matter was distributed uniformly in other areas of Poyang Lake. This model was further applied to GF-1 satellite image data on October 24, 2015 and was validated using 21 data samples of total suspended matter concentration obtained on October 23 and October 24, 2015. The retrieval accuracy was close to the result of image on August 3, 2015. The results indicate that this model can be also applied to retrieving the total suspended matter concentration of other periods in Poyang Lake. By analysis of field measured spectral reflectance and application of remote sensing image data, this study can provide reference for the retrieval of total suspended matter and environment monitoring of Poyang Lake.
Using the data obtained by hyperspectral techniques to estimate the content of soil organic matter is a hotspot in recent years. For the purpose of determining the effective estimation modeling method, specific data such as reflectance obtained by hyperspectral on ground and organic matter content were used in this paper. Wavelet analysis was used to remove the noise, and continuum removal was used to extract the parameters and compress the data. Combining a variety of different data transformation methods and utilizing BP neural networks, multiple linear regression (MLR) and least squares regression (LSR), many different estimation models of soil were established. It is found that the neural network method is superior to the regression model among various data transformation methods after comparing different estimation models established by the three modeling methods. The optimal estimation model is the model established by the combination of logarithmic square transformation and neural network. The R 2 of the model is 0.933 and the RMSE is 0.069. The authors creatively carried out the data transformation at the modeling factor level and established the good estimation model. It is shown that the learning mechanism of BP + LS model is suitable for hyperspectral estimation of soil organic matter and works well. The methods, models and conclusions of this paper have some reference significance for the hyperspectral estimation of soil organic matter.
To tackle the subjectivity of select filter parameters in Goldstein InSAR interferogram filtering, the authors adopted the experience value as Goldstein filter algorithm, and then introduced the coherent coefficient, the phase standard deviation, the pseudo coherent coefficient, the pseudo signal-to-noise ratio and the structure similarity as the adaptive filtering parameter of Goldstein interferogram filtering. After that, the authors used the simulation and the real interferometric data and carried out the detailed appraisal and the contrast analysis of the filter result. The results show that six filter parameters could suppress the noise and improve the quality of interference effectively. Among them, using the pseudo signal-to-noise as the filter parameters not only could suppress the noise and have more significant advantages in the edge information and fine pitch. Using structural similarity and pseudo signal-to-noise ratio can also achieve better filtering results. The filter result of other three kinds of filter parameter is relatively unsatisfactory.
Error precision analysis is the important guarantee of block adjustment (BA) of multi-source remote sensing data, because it not only guarantees the edge accuracy of different source data, but also improves the stability of the whole regional network adjustment. With the development of the domestic satellite, the multi-source BA has been applied to it; however, the research on precision analysis of multi-source BA and the relationship between multi-source and single-source BA is relatively deficient. According to RPC (rational polynomial coefficient), an experiment that has constructed the multi-source BA model of multi-source remote sensing data based on domestic satellite (GF-1, GF-2, ZY-3) of Chongqing area was carried out in this paper, where a series of effects of BA to correct the systematic errors and the relationship between multi-source and single-source BA were in-depth analyzed under the different plans of control points. The results show that the BA of domestic multi-source high resolution satellite images is feasible, which can also improve the accuracy for single-source BA.
The Dracaena sanderiana, as an ornamental plant, has been extensively planted in southern China, and has good economic value. In order to monitor the planting situation of Dracaena sanderiana, the authors constructed a new index-“difference enhence between net and water index”(DENWI) as a characteristic parameter based on Landsat8 OLI remote sensing image. Object-oriented classification method was used to establish the Dracaena sanderiana information extraction rule set, the Dracaena sanderiana planting information was obtained, and two kinds of traditional information extraction methods were adopted for comparative study. The results show that, compared with the traditional method, the object-oriented classification method based on DENWI can extract the information of Dracaena sanderiana, with the overall classification accuracy being 98.46% and the kappa coefficient being 0.97. Remote sensing monitoring and extraction of Dracaena sanderiana planting information is feasible and advantageous, and it can provide scientific basis for monitoring and management of Dracaena sanderiana.
The extraction of the spatial distribution of tea plantations in hilly areas of southern China is of great importance for economic development and ecological environment protection in southern China. Therefore, a method of tea plantation based on mesoscale spectrum and temporal phenology characteristics is proposed. The study used MODIS enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) data products to select the optimal time window for Landsat images. The preliminary classification results were extracted using the object-oriented method and the decision tree classification model. For extracting the distribution of tea plantation, different vegetation phenology parameters were obtained by using MODIS-EVI vegetation timing data. Verification results showed that the overall classification accuracy reached 85.71% and the Kappa coefficient reached 0.83, with the accuracy of tea plantation producers reaching 83.72% and the user precision reaching 90.00%. The extraction results are close to the open statistics of tea plantation area in Zhangzhou City and Anxi County. The results show that this method can obtain high tea plantation extraction accuracy and the classification results can provide some reference and guidance for the economic development of southern China and the government departments' regulation of the tea plantation.
In order to solve the problem that unmanned aerial vehicle (UAV) remote sensing for urban vegetation classification usually uses the spectral texture and shape features information, while the reconstruction point cloud data of image fail to be fully used, the authors put forward a new method of comprehensive reconstruction point cloud and spectral information of image to extract the vegetation. The dense cloud of the study area was reconstructed based on structure from motion(SFM), cluster multi view stereo (CMVS) and patch based multi view stereo (PMVS) algorithm, and the digital elevation model (DEM) and normalized digital surface model (nDSM) of the study area were generated based on filtering and interpolation, meanwhile in combination with the spectral information of image the urban vegetation with different heights was extracted. On the basis of object-oriented image analysis method in combination with the nDSM information and spectral information including normalized green-red difference index and visible-band difference vegetation index, the classification rules of different vegetation, such as aquatic vegetation, grassland, shrub, small tree, and tree, were established. The experimental results show that the integration of the nDSM from point cloud data of image and spectral information to extract the vegetation with different heights is feasible, and the overall classification accuracy is 92.08%. The results obtained by the authors can provide theoretical support and application reference for urban vegetation classification and mapping.
According to the reflectance spectroscopy of remote sensing (RS) data of field sandy soil and cohesive soil, the shallow sedimentary framework in the middle reach of Chaobai River (MRCR) was interpreted and proved by sediment cores. Considering regional and vertical variation, the authors investigated the macro-characteristics of the sedimentation in the MRCR. The results show that early mid-low spatial resolution Landsat TM data are effective in identifying sandy soil and cohesive soil, and the two kinds of soil have obviously different colors in B7(R), B4(G)and B1(B), and the change of grain size can be reflected by color saturation. The shallow sediment cores are in good agreement with RS interpretation. Finally, the shallow deposits can be divided into five parts: the left floodplain, the recent riverbed, the right floodplain, the paleo-river and the flood lowland. Among them, paleo-rivers are developed in shallow layer as lenses, while the other parts exhibit inherited development at the depth of 20 m and shift with the river swinging.
Based on the data from the rain gauge stations of the Yellow River Basin and using the evaluation index, extreme precipitation index and error analysis method, the authors studied the spatial and temporal variation characteristics of errors and the accuracy of data of two GPM satellite precipitation products (GSMap-gauged and GPM IMERG) obtained from April 2014 to March 2016 and analyzed the extreme precipitation capturing capability of the two products. The results showed that the two products generally underestimated precipitation in the western region of the basin and overestimated precipitation in the eastern part. Compared with GSMap-gauged, IMERG had bigger errors in most areas. In addition, the phenomenon of missing error in IMERG was more obvious due to elevation and precipitation intensity, but IMERG had a more accurate data for micro-precipitation. The daily scale data statistics showed that GSMap-gauged had a better relevance in each sub-basin, and its mean error is smaller. The correlation coefficient value of extreme precipitation index obtained by GSMap-gauged was higher than that of IMERG.
In this paper, according to the land subsidence problem existing in the Beijing-Tianjin inter-city railway (Beijing section), time-series synthetic aperture Radar interferometry was used to obtain the land subsidence information from 2010 to 2015. Combined with the measured data of groundwater, the relationship between the groundwater level changes and the land subsidence at different layers was studied by using the cross wavelet method. Finally, the relationship between land subsidence and compressible clay thickness was analyzed based on the distribution of compressible clay in the study area. The result showed that average annual maximum sedimentation rate in the study area was 121mm/a, that the ground subsidence lagged the pressure level of the pressure level by 910 months, with the lag time of the submersible being 4 months, and that the ground subsidence rate in the control range of the same flushing fan increased with the thickness of compressible clay layer. This research is of great significance for the scientific effective prevention and control of uneven ground settlement on linear ground objects.
Combined with the positioning and orientation system (POS), the airborne LiDAR system acquires the three dimensional coordinate information of ground objects, and has the capability of fast generation of high-precision digital elevation model (DEM). DEM is a basic map for landslide investigation and monitoring. Its precision can reflect the small ground surface changes directly. The DEM can be used to quantitatively analyze landslide characteristics accurately. There are several advantages of airborne LiDAR technology: it is affected little by weather, it can penetrate the vegetation layer to obtain the ground surface information and its data-processing process is relatively simple. In this paper, the LiDAR technology was applied in Zhangjiawan Village, Zigui County, Hubei Province. The results show that, based on LiDAR technology, landslides can be recognized clearly with slide mountain shadow maps made with high precision DEM and, what is more, quantitative analysis can be carried out to measure landslides characteristics.
In this paper, the authors studied the coseismic deformation in Ukraine River valley in Tianshan Mountains, and reconstructed digital elevation modle (DEM) graph of 12.5 m spatial resolution for the study area before and after the earthquake using the ALOS / PALSAR data by InSAR remote sensing technique. Then the authors obtained the remote sensing characteristics of seismic collapse of earthquake by difference method of the DEM after the field verification by RTK calibration. The results show that: ①The area of the collapse of the triangle is 104.47 million m 2, the collapse is 1 416.60 million m 3, the starting elevation is 3 225 m, the average slope is 48°, and slope direction is NNW. ②The trumpet-shaped accumulation body area is 78.61 million m 2, the accumulation is 1 424.27 million m 3, and the buried depth is between 35 m and 80 m. ③The river bed was pushed northward by 100 m, and the eastward advance reached 300 m, due to the accumulation body of the south of the river channel. ④Collapse of the body led to the the formation of quake river. The surface area of the lake increased from 0.039 km 2 to 0.059 km 2 within 30 days and the lake area reached the peak of 0.146 km 2 in 2010. ⑤The factors responsible for the collapse included not only seismic activity but also limestone lithology, terrain slope, fault structure and other comprehensive factors.
With Weiku oasis in Xinjiang as the study area, the authors used two polarization methods, i.e., Freeman-Durden and H/α, to decompose and treat 4-polarization data of the Radarsat-2, got the corresponding characteristic parameters, extracted the salinization information of the study area combined with the SVM-Wishart semi-supervised classification method, and finally checked and analyzed the result of the classification with the visual interpretation and the field investigation. Some conclusions have been reached: ① When the impact categories are identified and the parameter feature space is built to get the characteristic parameters, different polarization decompositions yield different resolutions of parameter information, and the distributions of parameters characteristic space are different; after decomposing with H/α, the characteristic space constituted by characteristic parameters are different; ② The effect of using semi-supervised classification method to classify the endings of the Freeman Durden and H/α,Freeman Durden classification is superior to that of H/a; ③SVM-Wishart semi-supervised classification is superior to traditional SVM classification and hence it can be well used to extract the salinization information. SVM-Wishart semi-supervised classification can fully excavate the characteristic parameters after the coherent decomposition of polarization and can improve the classification accuracy, and it has certain advantages in the extraction of salinization information.
Vegetation index is one of the commonly used method for adopting satellite remote sensing image to identify burned areas. Due to the disturbance of fire, vegetation becomes burned area, and its spectral characteristics are easily confused with the spectra of bare land, water body, road, shadow and arable land and some other factors. Therefore, the improvement of the accuracy of remote sensing monitoring for burned area using appropriate vegetation index remains an urgent problem. In this paper, four burned areas in Sichuan Province and Inner Mongolia where fire burning occurred in 2014 and 2017 were selected as the study areas. Based on the spectral characteristics of Gaofen-1 satellite 16 m wide width (GF-1 WFV) data and Landsat8 data, the authors chose normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), global environment monitoring index (GEMI), burned area index (BAI) and normalized burn ration (NBR) and constructed separation index M of different spectral indices to quantitatively evaluate the potential of different spectral indices for burned areas identification. The results show that NBR calculated with near-infrared and short-wave infrared band and BAI based on visible light-near infrared band have a better capability for separating burned areas, the separability of NDVI takes the second, whereas EVI and GEMI have a poor separability. For GF-1 WFV data and Landsat8 data, BAI and NBR which have a good separate capability for burned area identification were used for the burned area in Oroqen Autonomous Banner of Inner Mongolia to separate burned areas (for GF-1 WFV data, only BAI was used to identify burned area), and Gaofen-2 satellite (GF-2) data which have higher spatial resolution combined with confusion matrix method were used to verify the accuracy. The overall accuracy of both methods were higher than 87%, and the Kappa coefficients were all higher than 0.7.
All the energy problems in different countries or regions are facing great challenges. The timely and accurate grasp of the spatial dynamic change of energy consumption can make reasonable layout to occupy the initiative, make the optimal allocation of energy structure and put forward the feasible solution. In this paper, the authors put forward the combined DMSP/OLS night light data from 1992 to 2013 and Xinjiang statistical yearbook and the application of mathematical statistics and analysis method, selected the average light intensity, DN value and light area as independent variables by using multiple regression analysis. Considering the downscaling and modifying the model, the authors made the simulation of the Xinjiang state municipal energy consumption data, and made grading of the spatial distribution difference of annual simulation data. It is found that Changji, Urumqi, Tacheng and Kashihave relatively high energy consumption level. This paper puts forward a new method for the study of dynamic energy consumption in Xinjiang.
In order to monitor production state of iron and steel enterprises with auxiliary, the authors took Tangshan iron and steel enterprises as study cases to obtain the land surface temperature in tenth band of TIRS inversion derived from Landsat8 data on February 7, March 10, March 26, May 13 and May 29, 2016,in combination with the spatial structure of iron and steel enterprise information provided by GF-2 data from September 26, 2015 and September 10, 2016. The land surface temperature was finally divided into low temperature region (mainly non-production area) and high temperature region (mainly production area) by using threshold. On such a basis, the authors established production thermal radiation model to determine the production status of iron and steel enterprises in this period. Finally, the results obtained by the authors were preliminarily validated by the spatial structure change information provided by GF-2 satellite data and monthly output data of iron and steel enterprises. The results show that it is feasible to evaluate the production status of iron and steel enterprises by using thermal radiation model of production based on thermal infrared remote sensing.
The Beishan rift is an important metallic zone in China. The principal analysis component (PCA) technique and band ratio were used to extract alteration information related to gold deposits in the study areas in Hongshijing region and south of Cihai region lying in the west of Beishan rift. As a result of regional metamorphism, the alteration zones extracted by PCA and band ratios contain a great deal of interference information. With the method proposed in this study, most useless alteration information was eliminated and the gold deposits were located at or near the selected alteration areas in Hongshijing region. To prove the capability of this method, the authors validated the selected alteration areas south of Cihai region by fieldwork and found two gold ore spots. It is concluded that this method is effective in eliminating useless alteration information and thus is recommended for application in similar geological settings in Beishan rift.
In this paper, the authors systematically summarized the remote sensing interpretation signs of active faults and, by using Landsat ETM+, ASTER GDEM and Google Earth image data in combination with previous research results, obtained the spatial distribution of active faults in the study area and analyzed in detail fault properties and activities. It is shown that Pingwu — Qingchuan fault, the northeastern segment of Beichuan — Yingxiu fault, Chaba — Linansi fault and Guangyuan — Jiangyou fault are roughly parallel in spatial distribution. On the basis of the measurement of offset streams dislocation, the contrast of exaggerated 3D landforms, the historical earthquakes analysis and the analysis of topographic relief extent of land surface, the authors have reached the conclusion that Pingwu — Qingchuan fault, the northeastern segment of Beichuan — Yingxiu fault, Chaba — Linansi fault and Guangyuan — Jiangyou fault are main active faults in the zone, which are all right-lateral strike-slip and thrust faults. Among them, the activity of the Pingwu — Qingchuan fault is strongest, the activity of the the northeastern segment of Beichuan — Yingxiu fault and Chaba — Linansi fault is weaker than that of Pingwu — Qingchuan fault, and the activity of the Guangyuan — Jiangyou fault is the weakest.
Studies suggest that Lianhuashan fault has extended to marine space. Based on remote sensing image interpretation using terrestrial ETM+ data and structural characteristics of aeromagnetic deduced faults in marine space, the authors analyzed sedimentary control of the Lianhuashan fault on the Pearl River Mouth Basin. On the one hand, the result of remote sensing interpretation shows that Lianhuashan fault zone presents a NE-trending fascicular linear feature with two branches extending into the sea in SW direction. The main fault belt is a mountain with low hills and plains on both sides characterized by steepness in the east and gentleness in the west. The authors found that the NE-trending straight line river is developed along the fault zone, and the eastern side of mountains develops deep "V" ravines, vertical cliffs or fault triangles. On the other hand, the magnetic field feature reveals that the branch has extended to the north of Pearl River Mouth Basin and the south branch has passed through Pearl River Mouth Basin to the south of Hainan Island. Magmatic activity is developed obviously along the fault belt. The Lianhuashan fault zone is the dividing line of different magnetic fields, which dominate the NEE-trending basement structure of the Pearl River Mouth Basin with deep source magma activity. It is the first stage deep fault in the Pearl River Mouth Basin.
Lunar secondary crater, a kind of geological feature that is easily confused with the primary craters on the Moon, can introduce significant errors in lunar dating. However, it can be used to determine the impact direction of the primary crater, so it is important to identify secondary craters. In this paper, based on remote sensing data and topography data, comprehensive consideration of the spatial location and diameter of the lunar primary crater, the authors selected five typical Copernican primary craters to study the quantitative morphological indices so as to characterize their secondary craters, including depth-diameter ratio, rim height-diameter ratio, irregularity, and ellipticity. On such a basis, the intelligent identification, extraction and spatial distribution of secondary craters were studied. As a result, a total of 17 811 secondary craters were detected, from which a geodatabase was established that included five categories according to location, size, morphological indices, distance, and impact direction of secondary craters. The scale and distribution characteristics of secondary craters were studied based on the distance range from primary crater edge. A new method based on secondary crater major axis was developed. Some conclusions have been reached: ① As for craters size, the lunar mare secondary crater diameter is (2.7±0.11)% of its primary crater diameter, the lunar highland secondary crater diameter is (3±0.3)% of its primary crater diameter. The spatial distribution law is consistent between lunar highland and lunar mare. The secondary distribution distance is (57±7)% of the maximum distribution distance. ②The impact direction of the Tycho crater is W-E. The impact directions of the Copernicus crater and the Kepler crater are SE-NW. The impact directions of the Aristarchus crater and the Jackson crater are NW-SE. This study will be helpful for more accurate study of crater impact direction.
Using three-dimensional landscape modeling to do traditional village digitization has the advantages of high efficiency and reduction degree. By launching the reconstruction of 13 villages in southern Anhui, with multi-rotor aircraft which carries five cameras to complete the villages’ image data acquisition and preprocessing and, in combination with ContextCapture software to complete aerial triangulation and reconstruction, the model of village model is finally acquired and the model window is transferred and used in the website of the digital museum of the traditional village of China. The results show that this method can be applied to village of model building, and automatically produce dioramas with high resolution, good topological relations and rich details, thus playing an important role in the construction of digital museum of Chinese traditional village. Through the analysis of the data processing time of multiple villages, the authors have obtained the intrinsic relationship between aerial triangulation calculating time and data acquisition time, and give some suggestions on the implementation of the aerial task under the context of traditional villages.
This paper analyzes the current situation and existing problems of geological data processing services,and proposes a distributed geological service system based on message scheduling mechanism. The architecture is a new attempt of the geological service system,and it preliminarily solves the reuse problem of geological data services and processing services. Firstly,the concept of message mechanism and application model is introduced; secondly, geological processing service application architecture of message scheduling mechanism is designed and the geological algorithm library service package as well as geological data sharing service is realized,and the geological message processing process is described. Lastly,an online processing services flow is demonstrated. Practice has proved that the scheduling mechanism can greatly reuse geological data services and processing services so as to meet the rapid development of Internet application system to save algorithm re-encoding and service production, so it can be widely used in geological data processing services.
In order to analyze the influence of traffic network on urban emergency evacuation, the authors studied the vulnerabilities of traffic network and the model of emergency evacuation by GIS spatial analysis. Searching for vulnerable points in road network was conducted by minimal cost maximal flow (MCMF) algorithm. Then the algorithm was evaluated using the road network data in Beijing. Compared with existing software, the proposed method based on GIS platform extracts the road network vulnerabilities more accurately, and it also increases the utilization of the road network in the emergency evacuation and speed up the evacuation.