Remote sensing monitoring is used by mine remote sensing monitoring and assessment so as to understand the situation of mineral resources development, mining environment and evaluate mineral resource planning implementation, thus providing the basis for mineral resources planning, mining order rectifying and mine environment governing. The authors analyzed the situation of evaluating the implementation effect of the mine remote sensing monitoring, which is characterized by numerous objects and contents, wide applications and considerable difficulties. In view of such a complex situation, this paper puts forward the index evaluation system for the mineral resources monitoring result, benefit and efficiency, which consists of three primary and nine secondary indexes. According to the experimental evaluation conclusion, some constructive suggestions, such as simplification of the index system and emphasis on the dynamic assessment of continuous monitoring area, have been put forward in this paper.

The frequent forest disturbance caused by natural factors and human activities has very important effects on forest resources management, climate change and some other fields. Under the background of global warming, researches on forest disturbance monitoring and its corresponding influence have become one of the hot topics both in China and abroad. Based on a detailed analysis of the previous studies, this paper has reviewed the progress of monitoring methods for the large-area forest disturbance by using Landsat satellite imagery, which mainly include wall-to-wall mapping, sampling mapping and data fusion with the image of low spatial resolution. The advantages and disadvantages of these methods as well as the possible research prospects in the future are also discussed.

Forested wetland is an important type of wetlands. Because of its complex community structure, mapping forested wetland becomes one of challenging issues in wetland research. In this paper, research conclusions published in relevant literatures were analyzed, and then a review of methods for mapping forested wetlands was made from the angles of geographical biological environment, images characteristics and images mapping units. In addition, the basic features of different mapping methods of forested wetlands by remote sensing were presented, and some possible trends of future research were preliminarily predicted. According to understory hydrological inversion, the mapping methods based on geographical biological environment mainly include the hydrogeomorphy- based method and the optical and microwave remote sensing -based classification method, whereas methods based on features of images include decision tree based on radar statistical backscatter characteristics, random forest decision tree and aerial photograph interpretation, which provide data and technical supports for tapping comparing and assessing information, thus conducive to further development of mapping methods. From the angle of imagery interpretation units, the forested wetland mapping methods have undergone the development from per-pixel classification to object-oriented classification.

A comparison with traditional soil moisture monitoring methods shows that the remote sensing method has great superiority. This paper presents a review of the remote sensing methods currently used both in China and abroad for monitoring soil moisture, which include the reflectivity method, the vegetation index method, the surface temperature, temperature-vegetation index method, the crop water stress index method, the thermal inertia method and the microwave method, with a detailed comparative description of the advantages and disadvantages of these methods. Based on summarizing researches on remote sensing monitoring methods for soil water, this paper evaluated the focal points, difficulties and development trend of this research field. It is held that the thermal inertia method and the vegetation temperature index method are relatively mature methods for soil moisture monitoring. With the wide application of geographic information system, the microwave remote sensing will become the key research direction in this field because of its unique advantages.

Taking Beijing as the study area, the authors developed a method of soil moisture inversion by using Radar data and optical remote sensing images in the vegetation-covered area. Firstly, NDWI was extracted by using homochronous optical images, and then water-cloud model was used to eliminate the contribution of backscattering coefficients caused by the vegetation. Secondly, HH and HV backscattering coefficients were employed to construct the soil moisture inversion model in consideration of surface roughness based on backscattering database built by AIEM model and Oh model. Then the simulating data were used to validate the accuracy of this model. The result shows that the RMSE and relative error of HH is 0.044 and 15.5%, and the RMSE and relative error of HV is 0.057 and 20.3% respectively. It is proved that the result of using HH backscattering coefficient is much better than that of using HH backscattering coefficient.

This paper proposes a method for extracting linear object based on Freeman chain code and Hough transform with the purpose of extracting linear object effectively. After the original image is enhanced and filtered, a method based on the gray-level uniformization is used for region segmentation of image. Then the approach of Freeman chain code is carried out. Finally, the parallel linear structure is detected when Hough transform is used for the data of the chain code. The experiment results show that the proposed algorithm can extract the parallel linear structure of the image effectively, as evidenced by the fact that it showed high efficiency and high accuracy when it was applied to network target recognition in the ZY-3 satellite images.

A new approach to the fusion of multifocus images based on wavelet transform is proposed to solve the problem that some parts of the images are blurred because of the different focus points. The images are firstly decomposed by using wavelet transform, and then the low and high frequency coefficients are fused by using different fusion strategies: the low frequency coefficient is fused with a rule weighted average of energy, while the high frequency coefficient is processed with the regional grads. After that the fused image is obtained by inverse wavelet transform. Experiments prove that the fused image obtained by the method has a better subjective visual effect and objective evaluation criteria, thus attaining a better result than other traditional fusion methods.

Taking into consideration the needs of the remote sensing monitoring and application with Chinese resource satellite in west highland lakes, the authors carried out research on water extraction method by using ZY-1 02C satellite images. The two traditional spectral indexes i.e., the normalized difference vegetation index(NDVI)and the normalized differential water index(NDWI), were used to extract the water bodies in Longyangxia reservoir and, by combining these two kinds of water extraction method, a decision tree water extraction method was presented in this paper. Taking the manual interpretation of the water region as the reference,the authors used the overall area of the water extraction results, the detail extraction and the rate of error extraction results to make statistic and comparative analysis. The experimental results show that the NDVI method is susceptible to the influence of thin cloud, but is less affected by snow and terrain. Under the imaging conditions of snow,thin cloud and mountain shadows,the NDWI method is subject to different degrees of impact. In spite of the fact that it is susceptible to mountain terrain effects such as shadows,the method of the decision tree can effectively eliminate the interference of climate conditions such as snow and thin cloud.

Impervious surface plays an important role in monitoring urban sprawl and understanding human activities. Linear spectral mixture analysis (LSMA) is commonly used to estimate impervious surface due to its simple structure and clear physical meaning. However, previous researches found that LSMA seemed to overestimate slightly impervious surface fraction in less developed areas (0-20%) but underestimate it in the central business district (CBD) (over 80%). To tackle this problem, the authors developed impervious surface of Fujin Town in Heilongjiang Province from the Landsat Thematic Mapper (TM) image by using LSMA model under different constrained conditions and end-members. The results indicated that three end-members (high albedo, soil, and vegetation) semi-constrained LSMA provided a fine performance with a RMSE of 16.71%. Moreover, the paddy field in impervious surface fraction image was removed by using land surface temperature and vegetation coverage data.

A method for extracting building boundaries using airborne LiDAR point cloud data and imageries is proposed in this paper. Firstly, an α-shape algorithm is used to extract the rough outline of buildings from point clouds. Then building edge line segments are extracted from the registered images by an straight line segments extraction algorithm based on line region support. By using voting mechanism and point-to-line distance, the true boundaries of the buildings are obtained. Finally, a new method for refinement of a building outline is put forward, in which the extracted edge information is utilized to correct the rough outline extracted by the point cloud image, and the revised outline is processed by Douglas - Peucker algorithm to remove redundant nodes. the force intersect method is employed to restore the corner of the building, and finally the accurate outside contour polygons of the building is obtained. The effectiveness of the proposed method has been verified by experiments.

Traditional geological mapping methods usually cannot conduct mapping for the whole study area and takes little account to the situation that a variety of features has symbiotic combination in one pixel, which makes it difficult to reflect the complex geological distribution characteristics. Since the unmixing accuracy of the linear model cannot meet actual application need, the secondary scattering model was used to the unmixing of hyperspectral data. On such a basis, this paper proposed k (k ≥ 2) class mapping rules based on the unmixing result. The Nevada Cuprite AVIRIS data were used in the experiment, and actual mapping results obtained by Clark et al. were taken as the reference. The comparison results have shown that mapping results based on the secondary scattering mixture model are closer to actual ground feature distribution than those based on the linear model and, in comparison with the results from one class mapping rule, the results using k (k ≥ 2) class mapping rules have richer details and are closer to the results obtained by Clark et al.

In optical image registration, the polynomial regression model generally supposes that the reference control points (RCPs) used as the coefficient matrix is error-free. However, the actual RCPs often inevitably contain errors and RCPs residual errors between different images are not the same. The general least squares method (LS) only considers the error in the observation vector whereas the total least squares method (TLS) takes the errors of both the observation vector and the coefficient matrix into account and assumes that they have the same residual error. In view of this situation, this paper introduces a more reasonable weighted total least squares method (WTLS) for polynomial regression coefficients estimation. Experiments show that the WTLS can estimate the parameters better and significantly improve the image registration accuracy.

As we all know,GPS has brought great convenience to surveying and mapping work. Its horizontal accuracy has reached sub-millimeter. However, due to the influences of such factors as the effects of ionosphere,multipath effect,GDOP(geometric dilution of precision) and height of antenna,the vertical deformation measurements from GPS are seldomly used by precision survey workers. Based on the reasonable layout of deformation monitoring network of Shuping landslide in the Three Gorges Reservoir Area,the authors obtained a large number of leveling and GPS data from long-term monitoring,and used leveling data as the reference to analyze the height precision of single-frequency static GPS in landslide vertical deformation monitoring. The results show that the vertical deformation monitoring precision of single-frequency static GPS is ± 2cm, that the deformation monitoring accuracy should be greater than 1/5 of deformation in the measurement period according to the monitoring code of landslide, and that single frequency static GPS is suitable for the landslide whose vertical deformation is above 10 cm in its monitoring period. For the Shuping landslide,GPS measurement can provide good monitoring data in the second to third period whose vertical deformation is up to 30 cm,but it is unsuitable for other monitoring periods in slow state.

How to obtain suitable threshold to distinguish radiation fog,clear sky surface and clouds is the focus of the study of fog detection. The Santa Barbara DISORT atmospheric radiative transfer(SBDART)model can simulate the fog top brightness temperature. In this paper,the authors obtained the brightness temperature difference(BTD)between MODIS B20 and MODIS B31 bands based on the model and applied it to the detection of radiation fog at night. The data used for feasibility test were from EOS MODIS satellite in the North China Plain on November 25, 2007,and ground validation data were from the National Satellite Meteorological Center. The varification results show that the accuracy of using the model to monitor the night time radiation fog (POD)is 78.3%,the false alarm rate (FAR)is 21.7%, the reliability index(CSI)is 0.643,and the Kappa factor is 0.730. To further validate the stability of the method,the authors selected the sequence of eight satellite images in northern China for the time series analysis. The results show that the mean value of reliability index is 0.744,suggesting that the proposed method can serve as the foundation of night time fog forecasting and parameter inversion.

The spatial resolution of hyperspectral data is generally very low,the mixed pixels are extensively distributed, and hence fuzzy classification is commonly used in the mixed pixel analysis. As the accuracy of fuzzy classification is often limited by the feature dimensions and fuzzy samples selection,the random forest (RF) algorithm is put forward in this paper to select features and obtain fuzzy samples; in the low-dimensional feature space, fuzzy samples are used to make fuzzy classification. Fuzzy classification and RF are merged by using two-step classification,following the principle of unanimity assumption. Using different samples,different experimental areas and different partition optimization situations,the authors conducted three comparative experiments, and the results show that the method proposed in this paper solves the limitation of fuzzy classification and improves its accuracy. It is also proved that the classification accuracy of the method is robust for the original sample.

In consideration of the features of remarkable difference in the gray-scale of the remote sensing image with multi-scales,this paper presents an image registration method with improved Hausdorff distance based on scale-invariant to solve the registration of multi-source remote sensing images. According to the method,the scale-invariant features of multi-scale images were firstly extracted by using the feature extraction method based on scale-invariant feature transform(SIFT),and then the Hausdorff distance was used as the fitness function to seek for geometric image transformation parameters with the help of genetic algorithm(GA). At last,the image to be registered was re-sampled by using the transformation parameters and matched with the references image. The experimental results show that,compared with the traditional method of Hausdorff distance,the new method has higher registration accuracy and stability, and is more suitable for image registration.

Rapid and accurate access to the oil spill information is of great significance for dynamic monitoring, conservation and sustainable use of the oceans. HJ-1 is a new satellite platform designed for ecological environmental pollutions and disasters. However, the multispectral image obtained from HJ-CCD has insufficient spectral bands, and the accuracy of acquiring the oil spill coverage only by spectral information is low. In this paper, the oil spill that occurred in the Gulf of Mexico was selected as the research object. Based on the spectral analysis of different features, the authors chose the right texture structure factors and extracted the texture characteristics which affect oil spill identification by gray co-occurrence matrix. A decision tree model combining spectral characteristics with texture characteristics was established to extract the oil spill on the sea surface. A comparative analysis by using the result of maximum likelihood supervision classification method was performed, and the results show that, in comparison with the maximum likelihood classification method, the decision tree method could improve the user's accuracy and the producer's accuracy of oil spill extraction by 11.85% and 4.28% respectively.

For the purpose of better predicting the soil organic matter content in the study area, the soil near the tailings dam of the Dexing copper mine was chosen as the study object. Using the ASD device in the laboratory, the authors measured 68 groups of soil samples and, by studying soil reflectance spectral characteristics, took the logarithmic differential transformation of the selected reflectance spectra as the dependent variable of the soil organic matter prediction model. The correlation analysis of soil organic matter and soil spectra showed that the first derivative of logarithm of 402 nm and 2 312 nm wavelength reflectance was the best. Finally, from the multiple regression analysis and fuzzy mathematics, two models of organic matter content prediction were established. The results demonstrate that the research method based on fuzzy mathematics is better than multiple linear regressions, with the correlation coefficient up to 89.3% and the error relatively smaller. Studies have shown that using ground measured spectra to predict soil organic matter content has such advantages as short cycle and low cost.

With the support of remote sensing and geographical information system, the land use/land cover maps of the coastal wetland in Jiangsu were obtained by classifying three-period Landsat TM/ETM⁺ images, and the land surface temperature (LST) was retrieved by mono-window algorithm. Based on the classification maps and LST of 1992, 2002 and 2009, the authors investigated the land use/cover change and corresponding LST responses. The results show that the major land use/cover changes are dominated by human activities, and the most obvious change trend is from natural land cover types to manmade land use types, demonstrating that human activities are the main driving forces of wetland change. Corresponding to the land use/cover change, the land surface temperature patterns are also affected obviously, specifically, a certain degree of temperature increase trend was observed.

The application of the Radar remote sensing data to landslide investigation is of great importance, especially in cloudy and rainy areas. The high-performance airborne synthetic aperture Radar system(HASARS) developed by Institute of Electronics,Chinese Academy of Sciences,is the first home-made system characterized by multi-band and multi-mode,which has the capability of interferometric survey of X band and double antennas as well as polarimetric observation of P band. In this paper, the accuracies of landslide information extraction from polarimetric SAR data using different polarization combinations were investigated to evaluate the technology, methodology and implementation ideas of the landslide applications with the HASARS, and the focuses included two aspects: the methods of information extraction and the ways to select the feature. The results show that, based on Bayes decision theory and using the samples of landslide and non-landslide in the image to analyze and make decision, the method of feature selection could make classification of polarimetric SAR image satisfactorily. Based on the results of feature selection, the authors extracted the landslide regions from SAR images with supervised classification methods, with their accuracies higher than 90%. The airborne SAR system, with high spatial resolution, high precision DEM production and P band polarimetric observations, can obtain the thematic information of landslide surface more flexibly and precisely, and hence it has a broad prospect in the landslide disaster relief applications.

Based on five Landsat TM/ETM⁺ remote sensing images of Xiamen Island acquired from 1987 to 2007,the authors carried out the quantitative analysis and evaluation of urban landscape pattern effects on urban forest ecosystem services by using such methods as remote sensing interpretation of the landscape elements,landscape index,ecosystem services evaluation and gray relational analysis. Some conclusions have been reached: 1 The area of the forest in Xiamen Island had been reduced from 1987,and reached its lowest value in 2002; correspondingly,the value of ecosystem services reached its lowest value of 1.281 billion yuan per year in 2002. The value of ecosystem services increased to 1.363 billion yuan per year in 2007,of which the water conservation service value was the largest share,followed by carbon oxygen release service. 2 The results of gray correlation analysis show that the urban forest ecosystem service values in Xiamen Island have a remarkable correlation with the patch density,patch number and Shannon diversity index. The lower correlation coefficient with the built-up area indicates that the land used to built-up in Xiamen Island increased at the expense of the other landscape. The urban forest in the island was under better protection.

This paper aimed at exploring the relationship between the impervious surfaces area (ISA) and the land surface temperature (LST), and estimating ISA and LST in Haidian District, Beijing by using TM remote sensing data based on the vegetation-impervious surface-soil (V-I-S) model and the mixed pixel decomposition method of fully constrained least squares (FCLS). Furthermore, qualitative analysis and quantitative evaluation were made based on the estimated results. It can be found that there exists an obvious consistency between the spatial distribution and the variation trend of urban ISA and LST, with the correlation coefficient being 0.752 5. The ISA information could be a better indicator to reflect the spatial distribution of heat flux in urban environment.

Taking Landsat7 ETM⁺ as the data source and using "de-interfered anomalous principal component thresholding technique,i.e., DAPCTT",the authors extracted weak mineralization and alteration remote sensing information (i.e. hydroxyl and iron-stain remote sensing anomaly information) in Guizhou Province. As this kind of information contains large quantities of discrete data and uses the grid file form,the authors used the density weighted method to process the surface interpolation of the discrete outliers and drew the contour maps. The contour maps show that the remote sensing anomaly distribution has certain regularity and is in good agreement with the formation, structure and distribution of mineral resources. In combination with geochemical element anomaly distribution map,high resolution remote sensing images and field measured alteration zone data,the comparison and verification were made for the remote sensing anomalies, and the results show that the weak mineralization and alteration remote sensing information extracted by these methods is reliable and can reflect the basic relationships between geological structures,rocks and mining fields. In addition, it can supply new clues to the evaluation of mineral resources potential in Guizhou Province.

Xiaoginling region has densely-distributed magama hydrothermal gold deposits. The structural framework in this region is the Neoarchean gneiss dome,where intense magmatic activities occurred in Yanshanian period. The gold deposits are closely related to the distribution of quartz veins and altered mylonite. In the ETM⁺ remote sensing image,the deposits often find expression in linear, parallel linear intensive belts and flexible-brittle shear zones. Consequently,the distribution of remote sensing linear structures and the intensive belts of linear structures are very significant for the gold ore prediction in this region. On such a basis and with the ETM⁺ remote sensing image as the basic map,the authors made interpretation of the linear structure intensive belts and circular structure of Yanshanian granite bodies in Xiaoginling region. The gold deposit distribution and metallogenic regularity were analyzed based on remote sensing interpretation,the maps showing the favorableness of the metallogenic remote sensing characteristic conditions for gold deposits in this region were compiled, and the remote sensing prospecting prediction of gold resources in this region was made. These results will provide some reference for the prospecting of gold deposits in Xiaoqinling region and the assessment of mineral resource potential in Henan Province.

The North Sanjiang metallogenic belt in Qinghai Province has excellent metallogenic conditions and geological background to form large or superlarge metal deposits, and Narigongma is the most typical porphyry Cu-Mo deposit in this region. Based on the typical characteristics of alteration zoning of the porphyry Cu-Mo deposit and using the ASTER data,the authors extracted the remote sensing alteration anomaly information of kaolinization,propylitization,pyritization and silicification by using Crosta technology and band ratio method. The distribution map of granite porphyry and faults was obtained on the basis of remote sensing interpretation of the ASTER and QuickBird images. According to the metallogenic regularity and ore-controlling factors of the Narigongma deposit,eight mineral prediction areas were delineated based on the mineral predictive factors derived from remote sensing data. The remote sensing characteristics of alteration zoning of Narigongma can provide important reference information for the exploration of porphyry Cu-Mo deposits in the North Sanjiang metallogenic belt.

Huashan granite body and Guposhan granite body are two neighboring granite bodies in Guidong area of Guangxi. They are closely related to the polymetallic mineralization of the area. The study of their formation is of practical significance for expanding prospecting for the non-ferrous metal mineral resources in Guidong area. As the fault structure was genetically closely related to the igneous geological action of the granite body,the authors investigated their spatial contact characteristics so as to help the discussion of the genetic correlation between the granite bodies. Using the ETM⁺ remote sensing image as the source of information and based on extracting the linear structure information in four directions,i.e., SN,EW,NE and NW, from the remote sensing images of Huashan and Guposhan granite bodies,the authors carried out the comparative statistical analysis,comparative rose diagram analysis and comparative equi-density map analysis as well as comparative structural stress field analysis of the fractures through trend surface simulation of the remote sensing linear structure in the two rock bodies. Finally,the genetic relevance and difference between the two rock bodies were detected through the comprehensive analysis.

In order to analyze the spatial-temporal change characteristics of the cropland in typical areas of Middle- Lower Yangtze Plains from 1984 to 2010, the authors extracted the land cover information of the study area in 1984, 1990, 1995, 2000, 2005 and 2010 from Landsat TM/ETM⁺ images. The results show that the amount of cropland decreased over the past 30 years. There were three stages of the cropland change: Fast reduction, slow reduction, and sharp reduction. The total area of cropland loss was 374.10 km² from 1984 to 1990. The decrease of cropland was 66.09 km² from 1990 to 2000, and the loss speed was relatively slow. The cropland in the study area was reduced rapidly to 1 724.33 km² from 2000 to 2010. Most of the lost cropland was transformed into the construction land, and the cropland in cities along the Yangtze River, such as Nanjing, Maanshan, and Wuhu, was decreased more seriously than that in counties. The speed of urbanization was accelerated. Moreover, plenty of cropland was turned into ponds to breed fish and crabs around the Shijiu Lake and the Gucheng Lake, particularly in western regions where the conversion was prominently frequent from 1990. On the other hand, cropland was increased along the boundary of cropland and mountain from 1984 to 2000. After that, the cropland decreased because of the implementation of returning cropland to forest and creating economic forest.

In this paper, ground surface parameters such as normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), normalized difference build-up index (NDBI) and normalized difference impervious surface index (NDISI) were extracted from TM (1989) and ETM⁺ (2001,2010) images of Nanjing, and land surface temperature was retrieved from thermal infrared image so as to make a comparative analysis of factors responsible for the variation of each parameter at the temporal and spatial level. The authors also analyzed the correlations between ground surface parameters and land surface temperatures. The results of the experiments show that land surface temperature is positively correlated with NDISI and NDBI but negatively correlated with NDVI. Further analysis shows that NDISI, NDBI of Nanjing City have been significantly increased, vegetation coverage decreased and the urban heat island effect exacerbated in the past 20 years, and these trends are in accordance with the trend of urban sprawl. The research result of this experiment has certain reference value for revealing the urban heat island effect, optimizing land use allocation and boosting eco-city construction of Nanjing.

Based on the classified result of Landsat ETM⁺ remote sensing images of 2006 and 2009, the paper simulated and forecasted land cover types of Qinhuai River Basin in the future by using the CA-Markov model. In the model-building process, the transition probability matrix and the transition area matrix were obtained through the Markov model, which determined the conversion rules and iterative times of the CA model. The land cover pattern of the study area in 2012 and 2018 was simulated and forecasted with the CA-Markov model. Then the forecast result was compared with the actual classified data of 2012 to verify the forecast accuracy. The raster number forecast error of each land cover type is not higher than 6.5%, and the spatial location accuracy is 76.5%. The forecast results show that the paddy field decreased to 33.3% and the impervious surface reached 31.1% of Qinhuai River Basin in 2018. Most of the paddy field converted into impervious surface. The impervious surface of urban areas expands obviously in such urban areas as Nanjing, Lukou, Jurong and Lishui. The methods can provide a basis for dynamic monitoring as well as sustainable development of Qinhuai River Basin.

The development,activation,operating mechanism and data policy of international Charter were described in this paper. The activation and application cases aimed at investigating the disaster in China were analyzed. Then the main problems existent in Charter were summarized in the situation of concurrent multi-disaster and frequent severe disaster, which include the lack of activation condition and uniform operation system, the insufficient thematic data service, and the imperfect intellectual space databases system. These problems have led to the low efficiency in data acquisition, simple data type, and lower degree of automation. In order to solve these problems, this paper presented a dynamic and automatic data acquisition program aimed at the specific target, which can release time window constrains during the different disaster events and disaster processes. The results indicated that this program can pay more attention to the satellite scheduling and users' needs so as to ensure the timeliness and accuracy of multi-source data acquisition and avoid the data redundancy.