Disastrous weather is the main factor which threatens the transportation security. It is difficult for traditional ground observation stations to monitor and forecast weather condition along roads to meet the transportation needs. Remote sensing (RS) technology can overcome this shortage, and shows great potentiality in transport weather research. Disastrous weather conditions will cause significant changes of geometrical or spectral features in RS images. Based on the research results obtained by domestic and foreign scientists, this paper mainly introduced the application of RS to transportation networks, weather disasters monitoring and forecasting, traffic flow, secondary disasters and loss assessment. Researches on application of RS to several typical weather conditions, such as heavy fog, rain storm, dust storm, low temperature, ice and snow coverage, were emphasized. There are good theoretical basis and practical background for RS to be applied to transportation meteorological disasters. Notable effects have been primarily manifested. Such researches will become the trend and the hot spot in transportation meteorology. With the development of RS quantification level, this application will acquire more progress and will provide the basis for disaster mechanism analysis, roads dynamical monitoring and forecasting.

Smart city is the inevitable choice for the development of China’s new urbanization. As a product of informatization and urban integration, smart city is gradually realized as an efficient and fine tool for managing people, money, material and things intelligently. The study of influence of UAV remote sensing technology in the construction of intelligent city plays an important role in accelerating the construction of smart cities. In this paper, the authors first reviewed the definition and development status of smart city, and then introduced the applications of unmanned aerial vehicle (UAV) from urban planning, illegal construction supervision, engineering environmental management, waste management, intelligent transportation, and other aspects.Finally, the development tendency was discussed.

On the basis of CNKI and with the papers published in Remote Sensing for Land and Resources from 1989 to 2018 as the statistc resources, the characteristics of highly cited papers were researched by the method of bibliometrics. The results show that the journal published a total of 30 volumes until April 2018. The number of papers published annually is increasing, but the average time delay is relatively long. For the first 100 highly cited papers, the papers were mainly published from 1999 to 2005 and funded from the national natural science foundation. There are 9 authors who published two or more papers as the first authors and there existed certain core authors in the journal. The organizations of the authors are mainly universities and research institutions. In the research field, it covers hot topics such as urban heat island, geological disaster, dynamic monitoring of ecological environment and coastline. Besides, it can be seen that the impact factor of the journal is on the rise and the influence is developing rapidly in the past ten years.

Different types and planting methods can result in the discrepancy of rice growth and yield. It is of great importance to provide accurate growth vigor information for rice growth monitoring and estimation using fine distinction of different rice varieties and planting methods. As a new type of SAR sensor, compact polarimetry synthetic aperture Radar (CP-SAR) provides the possibility for the fine mapping of paddy land with abundant polarimetric information and large width. In this study, the authors firstly used RADARSAT-2 fully polarimetric SAR data to simulate CP-SAR data and extracted 22 types of feature parameters. In addition, on the basis of the multi-dimensional feature information CP - SAR data, the support vector machine and sequential forward selection (SVM + SFS) strategy were performed for feature selection, and the optimal feature subset of paddy land fine classification was obtained. Moreover, the decision tree and SVM method were used for paddy land fine classification based on feature subset. The results show that paddy land fine classification method based on decision tree can achieve better classification results. The overall classification precision and Kappa coefficient of optimal feature subset respectively are 92.57% and 0.896, which are higher than those of the set of all feature parameters by improving 1.2% in overall classification precision and 0.016 in Kappa coefficient.

Hyperspectral remote sensing can distinguish small spectrum differences between ground objects, and is expected to solve the classification problem of tree species. In this paper, by using Hyperion hyperspectral image, combined with the ground measured samples, classification of Pinus massoniana and Cunninghamia lanceolata in Wucheng of Huangshan City was conducted. With the 1st and 2nd differential transformation of the image, spectral band combination of 487~559 nm and 681~742 nm differs significantly, and hence was chosen to conduct supervised classification using support vector machine. Classification accuracy of raw, 1st and 2nd differential transformation image reaches 76.50%, 81.42% and 88.52% with Kappa coefficient being 0.528 4, 0.625 7 and 0.769 1 respectively. The results show that 2nd differential transformation and band selection of hyperspectral data can improve the classification accuracy of Pinus massoniana and Cunninghamia lanceolata, thus providing a foundation for further study of classification of coniferous forest with hyperspectral remote sensing.

Hyperspectral remote sensing can simultaneously acquire spatial images of space and fine spectral information so as to describe the features more accurately. However, when the phenomena of different spectra in the same objects or the same spectra in different objects occur, the classification of hyperspectral images will face a daunting challenge. Light detection and ranging (LiDAR) can obtain the terrain topology information and can be used to construct the surface 3D model. However, features cannot be accurately identified by using LiDAR data only. Based on the above two points, the authors carried out a study to fuse hyperspectral images and LiDAR data. Morphological attribute profile was used to extract features, and sparse multinomial logistic regression (SMLR) was used to do classification. The fusion and classification effect in different combinations of characteristics were also investigated. The CASI/SASI aerial hyperspectral image and LiDAR DSM data were used to validate this method based on the Zhangye Oasis agricultural area in the middle reaches of the Heihe River which is a good target for the classification of crop. The results show that the method using hyperspectral and LiDAR data can obtain better classification results with higher accuracy and stability, and the best classification accuracy is 94.50% by fusion features based on the extended morphological attribute profile.

To tackle the low classification accuracy problem of hyperspectral image classification caused by similar samples under the condition of small-sample-size, this paper proposes a hyperspectral image classification algorithm based on Fisher criterion and TrAdboost (H_TrAdaboost). Firstly, an auxiliary sample is determined with the spectral angle mapping (SAM) method and spectral information divergence (SID) so as to improve the total number of training samples. Secondly, the samples are studied separability based on the improved Fisher criterion to obtain relatively strong samples set. Finally, the weight of positive and negative sample distribution is adjusted dynamically by using TrAdaboost algorithm so as to achieve hyperspectral similarity class classification in the small sample size problem. In the comparative experiments with other compared algorithms, the highest classification accuracy is achieved, which fully shows that H_TrAdaboost algorithm can well solve the similar hyperspectral image classifications.

Complex scene classification has great significance for mining the value information in remote sensing images. The proposed convolutional neural networks (CNN) can improve the complex scene classification of remote sensing images. The CNN method extracts features automatically, avoiding problems in the image pretreatment and the feature extraction by manual labor. An eight-layer CNN model is constructed in this paper, and the pre-treatment module has enhanced the adaptability of this method. Given the problem in choosing classifiers, this paper provides the Softmax and support vector machine (SVM) in the presented CNN. The experiment results in two datasets, the UC Merced Land Use and the Google of SIRI-WHU indicate that the presented CNN method can increase the accuracy of classification by more than 2% compared with the CNN with Overfeat feature method and the SRSCNN method, and the total classification accuracy of the two classifiers is over 95%.

In order to reduce the influence of mixed pixel on dictionary, the author has constituted principal component analysis (PCA)united sparse dictionary from the first principal component extracted with sparse dictionary of panchromatic image and unmiximg image by the online dictionary learning algorithm and PCA. The sparse dictionary can include multi-spectral image and high-spatial resolution image features, while considering the mixed pixel problem. The sparse coefficients of panchromatic and multi-spectral images are calculated using PCA united sparse dictionary and orthogonal matching pursuit(OMP) algorithm, then the sparse coefficients of fusion image are calculated using nonnegative matrix factor(NMF) fusion algorithm, thus reconstructing fusion image. In consideration of the root mean square error of the reconstructed image and the limitation of computing, research on the dictionary matrix size shows that the final matrix size of sparse dictionary is 64×480. An analysis of five quantitative assessment indexes demonstrates that more texture details and multi-spectral information can be obtained by the proposed fusion than by united sparse dictionary NMF fusion, wavelet fusion and PCA fusion. The proposed method can obtain better fusion result.

In this paper, a land and water segmentation algorithm based on hybrid fuzzy is proposed for the segmentation of SAR images. The algorithm is based on the gray features of SAR images, and dynamic multi threshold maximum between-class variance (OTSU) method is used for SAR image rough segmentation. The gray mean value of each homogeneous region is used as the initial clustering center of the fuzzy C mean algorithm so as to get the classification result by clustering iteration. Finally, the connected region method is used to remove the small pixels in the neighborhood, thus achieving fine segmentation of land and water. The experiments on GRDH data of Sentinel-1A show that the method has a relatively good segmentation effect for the SAR images of land objects and inland water areas with more tributaries.

As an important kind of land resources, cultivated land is related to the country’s food security. So it is very significant to have a fast and accurate method for obtaining information of cultivated land. The traditional supervised classification methods of remote sensing image are based on the consistency of the spectral features or texture features between the training samples and the pixels/patches to be classified. These methods have strong dependence on training samples. This paper proposes an automatic classification algorithm of cultivated land based on the image window subarea. By using the machine learning algorithm, the automatic classification of cultivated land or non-cultivated land in the sub region of the image window can be realized by extracting the multi-spectral and multi-level features. Using this method, the unsupervised automatic classification of the type of the image window subarea is realized by establishing the feature database of the remote sensing image of the cultivated land in a certain area. With the high spatial resolution remote sensing image of Northeast China as an example, the experimental results show that the accuracy of the automatic classification algorithm is 90.8%. Being able to automatically acquire the cultivated land information, this method can also be used to extract any pure ground object from remote sensing images.

The elm sparse forest is an important component in the Otindag sandy land ecosystem, which is of great significance for windbreak and sand fixation. In order to obtain the spatial distribution information of elm trees quickly and accurately, this paper proposes a method of automatic sand elm identification based on remote sensing technology. With the data of domestic high spatial resolution satellite GF-2, the research was implemented on Zhenglan Banner, Xilin Gol League, Inner Mongolia. Combined with the characteristics of elm sparse distribution in the sand, normalizd difference vegetation index (NDVI) threshold was firstly used to quickly extract the coarse distribution of elm. Then, a method based on geographic object based image analysis (GEOBIA) was used to extract the distribution of elm accurately. To compensate for the uncertainty of GEOBIA method in feature selection and rule set construction, this study used SEaTH algorithm to optimize features and automatically calculate the feature threshold. The results show that the proposed methods reached the overall accuracy of 88.17% and Kappa coefficient of 0.76 in identifying the sparse elm. Among them, elm mapping accuracy could reach 99.14%. Therefore, it is effective to identify elms by using GF-2 and the method proposed in this study. This method can provide technical support for the further research and production practices of elm sparse forest in the Otindag sandy land.

Mapping and protecting the key ecological service regions has been considered as the basis of the development of the society and economy. With the development of remote sensing (RS) and geographic information system (GIS) technology, this technique has been applied to monitoring the ecological environment and evaluating the ecological services. This paper proposes an approach to calculating the efficiency index of protection and mapping the key ecological service regions in the situation where the index is maximal. As a application, Wanyuan, the typical mountain region, was examined. The results show that the key ecological service regions cover a total area of 2 651.99 km ², accounting for 65.4% of the study area. And it could be concluded that the key ecological service regions are distributed from steep slopes [15°,35°) where the elevation is more than 800 meters. The results suggest management to increase the efforts on protection. This paper can provide technical support for mapping the key ecological service regions of mountains.

Radio-frequency interference (RFI) over European land was detected and analyzed using convergence metric of one dimensional variational retrieval (1D-VAR) method and then its influence on the retrieval of land surface temperature (LST) was studied based on FY-3B microwave radiation imager (MWRI) Level 1 measurements conducted. Next, two linear regression equations were proposed to correct RFI-contaminated MWRI data. By comparing the retrieved LST products through 1D-VAR method from MWRI measurements before and after RFI correction, it was found that the convergence metric of 1D-VAR analyzing RFI identification method was effective for the observations over the land. Moreover, retrieved LST which were interfered by RFI were abnormally high with large deviations. And the RFI correction algorithm was used effec tively to improve the inversion precision and the utilization ratio of microwave data. Therefore, it is necessary to effectively identify and correct RFI prior to low-frequency observations with spaceborne microwave imagers to retrieve LST.

With the rapid development of research fields such as smart city, intelligent navigation and automatic drive, the problem as to how to quickly achieve three dimensional space information of city buildings and build a high-precision 3D detailed model become a key problem. Based on the 2.5D features of airborne and vehicle-mounted LiDAR data, the authors established a technical scheme to generate 3D detailed model based on data integration with the using of 2.5D dual-contour method. The research shows that the method can express the details of the facade, such as the window and balcony, and has the advantages of simpleness, high efficiency and full automation.

Lenticular stereoscopic images have been widely used in the three-dimensional expression of characters, landscapes and geomorphic features, but they have not yet been found in the visualization of depression shape of regional subsidence. Based on the research and introduction of the lenticular stereoscopic principle, the authors produced a lenticular stereoscopic subsidence map. The J50 thematic map of land subsidence at the scale of 1:1 000 000 was used as the data source. After the three-dimensional simulation using the subsidence isoline, the images series with different viewing angles were generated based on the simulated scene. Then, the images series were sampled and arranged to compose the base map. Finally, the base map was printed on the lenticular plate and properly processed. The research shows that the subsidence field can be innovatively and intuitively visualized using 3D GIS, digital image processing and lenticular stereoscopic technology.

Forest pests constitute one of the important threats to the healthy growth of forests, and the monitoring of its damage is of great significance to forest protection. In this paper, a method of monitoring the degree of forest pests by using interferometric synthetic aperture Radar (InSAR) is proposed. Xiangyun County of Yunnan Province was selected as the study area and the multi-temporal C-band Sentinel-1 images were applied. Based on the information of Radar backscattering intensity, interference phase and coherence coefficient, the time-varying characteristics of coherence coefficient and backscattering coefficient were analyzed by combining the phenological phase of Yunnan pine and relative humidity in the height of 2 meters. Fusion of multi-temporal data was applied to the classification of health forest and different degrees of damaged forest. Some conclusions have been reached: ① The temporal variation of the backscattering coefficient and the coherence coefficient are related to the phenological phenology of Yunnan pine. ② The correlation between the relative humidity and backscattering coefficient is higher than coherence coefficient, which reaches 0.78 in the mildly damaged forest. ③ Field data validation shows that classification accuracy of the multi-temporal coherence coefficient is higher than the backscattering coefficient, and the descending image has the highest precision which reaches 83.15%. The result shows that the coherence coefficient of C-band SAR time series can effectively identify the problem as to whether the forest is healthy or suffers different degrees of damage. ④ The method has certain advantages in monitoring and classification of forest pests in cloudy areas as well as in further enhancing the capability of remote sensing on monitoring pests.

The growth of grassland in Sanjiang Source Area has an important influence on the development of livestock husbandry and the ecological balance of Sanjiang ecosystem. It is of great importance to estimate grass yield reasonably and accurately. In view of the larger area and the complicated surface structure, this study is aimed at predicting the grass yield by using MODIS product data. The authors built a prediction model of grass yield in Sanjiang by using six kinds of MODIS products (LAI, FPAR, NDVI, EVI, GPP and LST) from April 2009 to October 2009 and, in combination with partial least squares regression (PLS) and multiple linear regression method, accomplished estimation of grass yield by remote sensing. Based on the built model, the authors used the 140 scene data from April to October 2011 for application testing, and then compared the predicting results with standard values which were measured from June to August 2011 in 16 grassland ecological monitoring stations in Sanjiang. The results show that there is a good correlation between grass yield estimated based on the six MODIS products and the measured actual grass yield. A comparison with the result of multiple linear regression shows that the result of PLS has a higher coefficient (R ²≈0.829~0.878) and lower root mean squared error (RMSE≈42.457~93.674 kg·hm ^-2).

Remote sensing has been widely used in the study of natural geographical environment as an effective method to monitor the large-scale of surface. Among them, as a regular and periodic event in nature, vegetation phenology plays an important role in the natural environment, especially climate change. With Shaanxi Province as the study area and on the basis of the time series reconstruction of the MODIS NDVI data by using the Savitzky-Golay filtering method, the authors extracted and analyzed the vegetation phenology parameters of Shaanxi Province for the spatio-temporal changes from 2001 to 2016. The conclusions are as follows: ①The spatial distribution characteristics of vegetation phenology are in good agreement with the spatial distribution of different landforms in Shaanxi Province. ②The average start of season(SOS) was on the 120th day, the average end of season(EOS) was on the 280th day, and the average length of season(LOS) was 160 days; ③ From 2001 to 2016, the change of SOS was -0.79 d/a (R ²=0.4, P<0.01) , the EOS was 0.50 d/a (R ²=0.25, P<0.05), and the LOS was 1.29 d/a(R ²=0.37,P<0.05); ④ In the period of different phenological stages, the spatial distribution of phenological change trend of vegetation in Shaanxi Province is relatively large.

Monitoring the effectiveness of natural forest protection project (NFPP) is of great significance. In order to know about the changes of natural forest in western Sichuan before and after the implementation of the NFPP, based on the comparative analysis of land cover classification results of Landsat series remote sensing images in 1989, 2000 and 2017, the characteristics of vegetation cover change in western Sichuan natural forest area in recent 29 years were compared and analyzed. The results showed that the vegetation coverage of the natural forest area in western Sichuan was declining as a whole before the NFPP from 1989 to 2000, and it showed a slow rising trend after the projection from 2000 to 2017. The vegetation coverage area which were middle grade and above decreased 6 291.56 hm ² before the projection and 4 384.01 hm ² increased after that. The land cover types in the study area in the last 29 years were dominated by arbor forest and shrub forest, and the area transfer occurred mainly between them. In the past 29 years, the change of farmland area was obvious, which increased before the NFPP and decreased after that. In the last 29 years, the forest coverage rate in the study area was rapidly decreasing at first and then slowly increasing. The rate dropped to 61.77% from 1989 to 2000, a decrease of 2.57%, then slowly rose to 65.61% from 2000 to 2017, and the increase was 3.84%. The results of the study show that the implementation of the NFPP has been effectively protecting the natural forest resources in western Sichuan.

Based on the large quantities of remote sensing data in 2015 and topographic data, the authors studied the interpretation keys and extraction technique of different types of natural resources. In general, the results show the current situation of the natural resources in Guangdong-Hong Kong-Macao Greater Bay Area, such as the shoreline, the mangrove wetlands, the wetlands, the arable land, the garden plot,the forest land, the grassland,the surface water and the desertified lands. The length of the artificial shoreline accounts for 60.34% of the total length of the mainland shoreline, The area of the arable land is 7 820.59 km ², whereas the area of the desertified lands is 396.80 km ². Statistics of ecological footprint and eco-capacity in 2015 of Guangdong-Hong Kong-Macao Greater Bay Area show Haizhu District, Tianhe District, Huangpu District, Panyu District and northwestern Baiyun District of Guangzhou, Nanhai District, Chancheng District and Shunde District of Foshan, northern Dongguan, Baoan District, Nanshan District, Luohu District, Futian District and northwestern Longgang District of Shenzhen, Huiyang District of Huizhou, west Huidong County, Zhongshan, Jianghai District of Jiangmen and Xiangzhou District of Zhuhai are inadequate in ecological footprint in terms of eco-capacity and are weak in eco-function.

Annual land use change survey is very important for keeping the land use data of China up-to-date. Currently, Land use change information acquisition is mainly based on artificial visual interpretation, which is low in efficiency. A new method of land use change detection based on multi-source data is presented in this paper. Classification samples for current phase image are acquired form previous phase land use vector data through a processing of sample refining. So automatic classification for current phase image can be implemented, which makes automatic change detected by comparing classification result with previous phase land use. The multivariate alteration detection (MAD) transformation method for the two phase image is used to eliminate pseudo change. The changed polygon objects with accuracy boundary are extracted based on change detect. Experiment shows that the proposed method is more effective with working time less than half that of the traditional method, and can better find real land use change without omission.

In view of the situation of the natural condition, social environment, economic development, and land use status in Savan water economic zone in Laos, a land use suitability evaluation was carried out in this paper. The suitability of the agricultural land, forest land, and construction land in the study area was evaluated by integratedly using the analytic hierarchy process method, Delphi method, and ArcGIS spatial analysis technology. Through the evaluation of single-factor suitability and comprehensive suitability, the spatial optimal allocation direction of land use was proposed. The results show that it is suitable to allocate agricultural land in fertile regions along the Mekong River which is located in the west of the study area. The forest land is suitable to be allocated in the northern and southeastern mountainous regions of the study area. The construction land is suitable to be allocated in the flat regions with well-developed infrastructures, which are located in the southwest of Savan water economic zone. This study can provide data support for the land use allocation in Savan water economic zone, Laos. Furthermore, the research findings also provide experience and references for land use allocation in similar regions in Southeast Asia.

The extraction of drainage system is necessary in many geoscience research fields. For instance, drainage system is an important indicator for structure and lithologic interpretation, sample sites in stream sediment geochemical exploration are designed according to drainage system, and drainage system needs to be recognized and masked in mineral alteration extraction. The drainage system in remote sensing image is generally extracted according to spectral features of water body. However, in the dry drainage systems, such as gullies and seasonal rivers in dry season and under prolonged dry condition, the method based on water body is not applicable. To tackle this problem, the authors propose a method based on independent component analysis (ICA). ICA is a signal decomposition technique that converts multispectral data to independent components which represent independent signal sources, thereby enhancing and separating the specific target in the image. The streambed system extracted by ICA may be still accompanied by noisy data. A series of methods are used to enhance image and remove noise, which include background suppression, morphological filtering and de-noising based on morphological features. The proposed method was tested with ASTER data from Huogeqi area of Urad Rear Banner in Inner Mongolia Autonomous Region, and the result was compared with that derived from supervised classification. The results indicate that the method proposed in this paper can be used to identify dry drainage system, and the recognition result is better than the traditional supervised classification method. The method put forward by the authors performs better in interference information reduction and de-noising, and training data are not needed in this method. In conclusion, the method proposed in this paper is ideal and practical in the extraction of dry drainage system.

In this study, field data such as the concentration of total suspended matter (TSM) in Hangzhou Bay and its adjacent areas in Hangzhou’s coastal waters were observed, meanwhile, hyperspectral remote snesing data were measured with SVC GER1500 spectrometer during four cruises carried out on 20th, 22nd, 23rd and 24th July 2010. The coastal water-leaving refectance of HICO was simulated from in situ hyperspectral remote sensing spectra. The normalized peak area of remote sensing reflectance in the near-infrared region was applied to retrieving TSM after the spectra of simulated HICO were analyzed, as well as the application of single band model and band ratio model. The result indicated that the band ratio algorithm of Rrs(724.84)/Rrs(461.36) of HICO could be used to retrieve TSM in Hangzhou Bay. This study is helpful to retrieving TSM in coastal waters using HICO.

With the Ebinur Lake as the research area and the ZY-3 multi-spectral image as the data source, the authors preprocessed the data by such means as ortho-rectification, radiometric calibration and atmospheric correction. The authors analyzed the spectral characteristics of different water bodies, found interpretation signs for artemia information extraction, and built the oriented-object artemia information extraction model by the spectral, texture and shape information. The classification results were validated using the confusion matrix, with the overall classification accuracy being 91.74% and Kappa coefficient being 0.89. In addition, classification accuracy between object-oriented method and pixel based method was analyzed and compared for the artemia water of different densities. The classification accuracies of object-oriented method for high density, medium density and potential regions were 95.08%, 92.30% and 91.26%, respectively, whereas those of pixel based method were 90.16%, 87.18% and 86.40%, respectively. The results show that the object-oriented method is more effective than the pixel based method. The object-oriented method greatly avoids the phenomenon of “salt and pepper” and can distinguish the artemia densities. The study can provide the effective method for monitoring the distribution and intensity of artemia and has great significance for scientific and reasonable artemia fishing.

Existing monitoring approaches are not effective in dealing with routine thermal discharge monitoring requirements of nuclear power plants. This paper describes a monitoring methodology using an aerial remote sensing monitoring system based on an unmanned aerial vehicle (UAV) platform by taking the monitoring of the thermal discharge of the Hongyanhe Nuclear Power Plant, Liaoning Province as an example, and conducts a study of remote sensing extraction of thermal diffusion information of the thermal discharge. In this study, quartic polynomial fitting and real data correction are used to correct the wide-angle distortion and acquire the water body surface temperature information, respectively. Synchronized measured data validation of independent samples indicates that the system can acquire diffusion information of the thermal discharge accurately, and the retrieval error of the surface water temperature is within 0.4 °C. Results analysis shows that this efficient and convenient aerial remote sensing monitoring system based on a UAV platform can effectively make up inadequacies of existing monitoring technical measures and offers high precision. This system is expected to be further adopted and applied for post-assessment of the environmental impact of nuclear power plant thermal discharge and service monitoring.

In this paper, by utilizing 4 phases of multi-resource remote sensing images in 1987, 2000, 2010 and 2015, the authors carried out the information extraction of coastline in Ningyuan Estuary in 4 phases from 1987 to 2015,and analyzed spatial and temporal characteristics,influencing factors and development trend of coastline transition by using the density segmentation and binarization processing based on object-oriented information extraction technology. Some conclusions have been reached: The coastline of Ningyuan Estuary overall showed a trend of growth from 1987 to 2015, with the total length increasing up to 8.14 km. The ecological environment of the coastal zone in Ningyuan Estuary has changed greatly with the disappearance of mangrove resources near the estuary largely, the expansion of artificial breeding zones and the continuous deterioration of water quality. The overall feature in the delta of Ningyuan Estuary is siltation, and that in the west of the estuary is erosion. The major influencing factors include coastal erosion and siltation, artificial breeding in the tideland and the construction of the artificial island as well as the building of a dam for sand control. The situation of Ningyuan Estuary is not optimistic in recent years. The erosion of the artificial island’s west coastline tends to become worse and the coastal siltation will be further intensified in the east of the sand-protecting dam.

With the deepening of urbanization, the problem of urban expansion has become more and more prominent, and the problem of land resource waste and eco-environment pollution has also occurred with urban expansion. Based on the support vector machine (SVM) classification method and the way of man-computer interactive interpretation, the authors classified the land of Kaifeng and extracted the city proper, analyzed the urban construction land expansion and spatial distribution in the past 25 years by using the indexes such as expansion speed index (ESI), expansion intensity index (EII), mean center (MC) and standard deviational ellipse (SDE), and analyzed the drive factor of urban expansion based on the social statistics. The results reveal that, firstly, the urban construction land of Kaifeng expands from 38.08 km ² in 1990 to 125.86 km ² in 2015, with the expansion speed reaching 3.51 km ²/a. Secondly, MC and SDE show that the average central position of Kaifeng City has changed a little in the past 25 years, it has moved northwest entirety, and the spatial distribution of construction land tended to expand evenly from 1990 to 2009, the construction area continued to expand and the spatial distribution was directional obviously from 2009 to 2015. Finally, it is concluded that the quantity of urban population and the economic development are the main driving forces of urban expansion in Kaifeng City.

This paper presents the subsidence results of the Huainan coal mine from Sentinel-1 TOPS images during the period between November 3, 2015 and March 14, 2016 using Stacking technique. The high accuracy coregistration comprising three steps was firstly used to get differential interferograms without phase jump. Then the trend phase was removed by polynomial fitting, and the subsidence rate was retrieved based on the least squares linear regression method. The subsidence velocity map shows that there are several subsidence centers mainly distributed in the west and the north of the research region. The maximum subsidence rate is 80~90 cm/a, and the subsidence is inhomogeneous spatially. The mining subsidence of the study area has the characteristics of high gradients varying from 10 to 80 cm/a, with small subsidence coverage for only 3.13% of the total area. From the differential interferograms the authors found that the deformation magnitude is variable in different observation spans, which implies the nonlinear characteristics of the mine.

The spectral data in existing object spectrum library have some disadvantages, such as single data acquisition sensor; unreasonable ground objects classification and no efficient integration of the latest spectral analysis models. In order to solve these problems, this paper proposes a typical target ground spectral information system. The ground spectral data acquisition, analysis, processing and integrated information extraction are designed and integrated, and the information expression of the system is constructed based on the client layer and the server layer (C/S) structure. Taking the extraction of rocks, mines, and heavy metals from soils as an example, the authors conducted a demonstration study. The research shows that the system has achieved the goal of efficient, fast and accurate excavation of the rich ground information contained in the spectra, thus providing technical support for land identification, resource exploration and ecological environment evaluation.

In the process of mine remote sensing monitoring, technicians need to use ArcMap to extract valuable information from geographic data and make the interpretation record table aimed at monitoring mineral exploitation. Manual interpretation record table has many problems, such as high cost, more professional skill requirements, heavy work load, format inconsistency and low accuracy of data. The method of automatically produced mine interpretation record table can automatically extract spatial and attribute information from geographic data based on ArcGIS Engine(AE) plug-in, then Word documents are edited based on document object model(DOM),and finally the production of interpretation record table is realized automatically.

Remote sensing interpretation map has characteristics of complex sources and various formats, while the traditional image database system cannot display map symbols and manage spatial data simultaneously, which makes a large number of maps unused after field confirmation. Taking advantage of the MapGIS and ArcGIS platform in management of the map data and spatial data and considering the needs of users for map service, the method puts forward integrated storage and management of map data and element layers and develops the remote sensing interpretation map database system. According to the characteristics of maps, the flexible tool of importing data is designed. This system realizes the information and integration management of the multiple format maps, which greatly improves the service capability and research value of the maps.