Wetland ecosystem which has special hydrology,soil,vegetation and biology characters are places where land and water meet.Wetland is the landscape having highest biodiversity and important habitat which has highest productivity and many ecological functions.Tianjin's Binhai New Area(BNA) is located in the northeast of North China Plain,Haihe River downstream and at the center of the Bohai Bay.The Central Government of China and the State Council have made important strategic planning to push forward development of the Binhai New Area and make it become the Comprehensive Reform Experimental Zones.We can forecast that BNA will develop rapidly in future.With the growth in economy,the disturbance to natural ecosystem will enhance.Wetland ecosystem is the main natural ecosystem of BNA,however now the wetlands have been destroyed both in quantity and quality.The wetland ecosystem is facing serious crisis.Combining the integrated technology of landscape quantity analytical method with GIS technology,basing on TM images between 1979 and 2008,the FRAGSTATS software and the investigation data,dynamic changes in wetland landscape pattern of Binhai New Area are analyzed.The results showed that the total area of wetland landscape has not been greatly reduced.The total wetland area is 58.78% of Binhai New Area in 2008 and the landscape dominance is obvious.Much natural wetland converted into urban construction land.Littoral wetland decreased as construction between 2004 and 2008,more than 90% of natural beach was used up to May of 2009.Wetland patch number is increased from 137 to 704 meanwhile the average patch area decreased with the development of economy.Driving factor analyses showed that the main natural factor is the decrease of precipitation meanwhile increase of average air temperature.Main human disturbance factors includes aquiculture,city construction and coastal reclamation.

Net primary productivity(NPP),defined as the net flux of carbon from atmosphere into green plants per unit time,is a fundamental ecological variable to measure the energy input of biosphere and terrestrial carbon dioxide assimilation,to indicate the condition of land surface area and status of a wide range of ecological processes.In recent decades,model simulation becomes a major technique for NPP estimation over large areas,which can be divided into several categories as statistical model,parameter model and process-based model.As one of the essential process-based simulator models,the boreal ecosystem productivity simulator(BEPS) was adopted to simulate the forest ecosystem NPP in this paper.With support of MODIS images,meteorological data and so on,forest ecosystem NPP for Fujian Province of China in 2004 was simulated by BEPS model and validated by forest carbon sink investigation data.The simulated result shows that,in 2004,annual mean NPP of forest ecosystem is about 578.97g C/m²·a,and the sum NPP reaches 46.18×106t C;the order of woodlands NPP is: bamboo ≈ broad-leaved trees>fir>Chinese red pine,and the average NPP is 788.6gC/m²·a,780.0gC/m²·a,519.8gC/m²·a and 437.3gC/m²·a respectively.The time-serials analysis of monthly NPP demonstrates that the forest NPP declined a lot during the period from June to August,which is the highest NPP under usual conditions.The precipitation decrease from June to August in 2004 is most likely to be one of the major factors for NPP decline,after the statistic analysis of meteorological data with monthly NPP.On the other hand,the positive correlation between annual forest NPP and elevation is outstanding,where the determination coefficient reaches 0.99.This special phenomenon implies,to some extent,that with the increase of elevation,the interference from human activity is lightened and the forest ecosystem NPP is improved and sustained.In other words,diminishing human interference to forest ecosystem is one of the key measures to maintain the robust productivity of forest,further to mitigate global climate warming.Finally,the uncertainty of BEPS simulation was discussed in brief with the data and the simulator itself.

Large-scale Man-made Linear Features(LMLFs),resulting from nature remarking,have played an essential role in physical distribution and energy transportation.Though with a wide extension,those linear features primarily concentrate upon infrastructure field,including dams,bridges,highways,railways,metro-lines and pipes.As the development of spaceborne SAR technology,multi-baseline Differential SAR Interferometry(DInSAR) overcomes the spatial/temporal decorrelation and mitigates atmospheric effects in DInSAR procedures,revealing competent for large-area deformation measurement with millimetric-centimetric accuracy.This technology is capable of achieving temporal evolutions,which is significant for the stability management of observed features.In this paper,the LMLF's deformation phenomena,causes and derivative hazards are firstly described.After that,the multi-baseline DInSAR methods are applied for their ground deformation monitoring.Multi-source SAR data,including ENVISAT ASAR(Guangzhou),PALSAR(Lantau Island,Hong Kong),TerraSAR-X(Shenzhen),are used for experiments,the capabilities of the Permanent Scatterers(PS) method and Coherent Targets(CT) method are analyzed.The results demonstrate that,with different interferometric image pair combinations,the PS method is more suitable for the large number of SAR images because of the applied single reference image strategy,and vice versa.Generally,the wave penetrability and critical normal baseline increase with the wavelength.Therefore,when low coherent regions are encountered,the longer wavelength SAR data,such as L-band ALOS PALSAR,are preferred to generate high quality inteferograms,bringing about robust ground deformation results;in contrast,the short wavelength data are selected for high coherent areas,such as TerraSAR-X and ENVISAT-ASAR,to retrieve deformations with high accuracy.Taking the geometric and scattering characteristics of LMLFs into consideration,four viewpoints including thematic layers and GPS observation data,SAR images selection,PS candidates extraction and model improvement are discussed for deformation monitoring using multi-baseline DInSAR methods.Finally,from the experiments and analysis,one can conclude future works should be carried out especially in LMLF-oriented DInSAR model development.

China South-western karst rocky desertification has been more and more concerned due to its increasingly far-ranging influence on economical society and ecological environment.By remote sensing,a wide range of rocky desertification information can be practicably obtained.However,information extraction of rocky desertification based on remote sensing is not just an isolated technology,but a process of comprehensive analysis and feedback.To further explore the technology on information extraction of rocky desertification,this paper is mainly related to illustrating current developing of information extraction technology on rocky desertification,and on the basis of this,a new strategy for information extraction of rocky desertification has been raised.Specifically,we firstly analyze current researches of rocky desertification classification system,and then make a comparison between karst area and rocky desertification region.Furthermore,we illustrate the relationship between land use and rocky desertification,and summarize the current key technology of extracting rocky desertification information.Finally,we have drawn four aspects of conclusions as following: Firstly,due to huge differences in the aspects of classification amount,classification index and classification regions in current classification system of rocky desertification,it is useful to introduce the scale division method into the system,which can unify different classification types of rocky desertification levels.Secondly,the content-based characteristic discerned algorithm is a helpful research taste for karst area extracting,which can raise the precision of extracting rocky desertification information at last.Thirdly,it is an important way to transfer land use vector data into rocky desertification information by rationally using the corresponding relationship between different types of land use data and different rocky desertification levels.Fourthly,traditional research methods that building rocky desertification symbol,improving vegetation index model or establishing spectrum relationship model to extract rocky desertification information cannot well compromise the comprehensive characteristics of rocky desertification,so an extraction method based on rocky desertification comprehensive index has been put forward as a result.

Baiyangdian is the largest natural lake and the typical inland wetlands in the North China Plain.In recent 40 years,Baiyangdian wetland is facing serious environmental problems,such as water shrinking,ecological function degradation,biodiversity reduction,and water pollution.In this paper,by using the CORONA spy satellite image in 1964,Landsat MSS images in 1974 and 1983,ETM+ image in 2002,the land cover change and driving forces of Baiyangdian was analyzed.The eCognition 5.0 was used to classify the remote sensing images of the study area,and the spatial analysis superimposed tools in ArcGIS 9.0 was used to obtain the land cover conversion data in different years.The result indicated that the area of the Baiyangdian wetland is decreasing in the study period.The area in 1964 is 407.3km²,and 274.63km² in 2002.Furthermore,great change happened in the water area.In 1964,the water area is larger,with the value of 346.75km².In 1974,the water area decreased to 94.65km2.In 1983 and 2002,the water area is smaller,with the value of 67.27 and 46.86km² respectively.The main factor that causes the land cover change is the variation of water supply.There are two main reasons to impact the water supply of the study area.One is climate change,including the decreased precipitation and the increased evaporation.The other one is human activities,such as water consumption for urban development,land use change,the construction of reservoirs and water transfer projects.This paper applied the early CORONA spy satellite images to Baiyangdian wetland land-cover change analysis,solved the problem of lacking in data of early time period in the region.It made the research time back up to 1960s.Furthermore,the multi-temporal satellite remote sensing data used in the study improved the temporal resolution of land cover monitoring,and provided basic data for performance of regional land-cover change analysis.

During past five decades,land use and climate changed substantially in upstream basin of the Naoli River,Sanjiang Plain,resulting in huge hydrological changes accordingly.However,quantitatively distinguishing the hydrological responses to each factor-land use change and climate change,is not an easy issue.In this paper,the impacts of land use and climate change to hydrology in this region were assessed.First,based on the hypothesis of one factor changes slightly,we built the functions that hydrological processes responses to land use and climate change separately and forecasted the hydrological change resulting from cropland change and climate change respectively.Second,the impact of each factor to hydrologic change was quantitatively distinguished and then the hydrological processes driving force model was built based on precipitation and cropland area through least square arithmetic.Last,we gave a forecast model of hydrological processes with both natural and anthropogenic factors.The results showed that between 1956 and 1975,climate had a strong effect on hydrological processes.However,after 1975 the influence decreased gradually.Taking one with another,climate model behaved better to simulate annual average discharge than to simulate the peak discharge.Furthermore,cropland expansion that has being done since 1954 did not bring on significant change to hydrological processes.But comparing with annual average discharge,land use change impacted peak discharge more.Hydrological processes driving force model in this study was fairly ideal and it gave the modeling errors of 0.5 and 1.04,indicating the precision improved highly,which,was in contrast to a single factor model.In addition,the dynamic model performed more suitable for annual average discharge whose precision improved more with the R2 of 0.933.As a whole,hydrologic processes in the basin were mainly affected by climate change,while the effect of land use change was weaker relatively but unneglectable,especially,effect to peak discharge is increasing.

The article presented a Nearest Neighbor Index Fuzzy Clustering (NNI-FC) algorithm for ecological regionalization based on multi-layer grid model with consideration of spatial distribution of geographical factors such as climate, vegetation and topography. It's a "bottom-up" regionalization approach and solved the problem of how to determine the ecological regionalizations type and its boundary by calculating the Nearest Neighbor Index (NNI) and the similarity between grids. Numeric and non-numeric features were considered simultaneously in the NNI and similarity, so the algorithm integrated both qualitative and quantitative regionalization methods. The algorithm consisted of three consequence steps: data preprocessing, core region generation and fragmented region elimination. In the data preprocess, some numeric property values were transformed to non-numeric ones through classification of the combination of geographical factors. Next, core regions and fragmented regions were generated by ROCK algorithm, which clustering the adjacent discrete grids with the same property values. Then, on the basis of analyzing the fragmented regions area coverage and its spatial distribution by using NNI, the algorithm divided the fragmented regions into small pieces and merged them into the core region which has the biggest similarity. Finally, an eco-regionalization scheme for the given natural section is formed. The experiment of ecological regionalization for North of Xinjiang shows that the algorithm achieves over 80% classification accuracy and can be very good at expressing the diversity of regional characteristics. Besides, different levels of eco-regionalization scheme can be obtained by adjusting the thresholds of the algorithm and its time complexity is between linear and quadratic ones depending on the thresholds.

Sanjiangyuan Region, as the headwaters of the Yellow River, Yangtze River and Lancang River, is known as China's Water Tower. In recent several decades, continuously grassland ecosystem degradation and soil erosion in the region were increasingly serious due to natural and human activities such as global warming, overgrazing, mining etc. Currently, the degradation of grassland ecosystem has attracted attention worldwide. Therefore, the spatial and temporal patterns of soil erosion in the region were beneficial to provide scientific foundation for ecological protection and construction. This study aims to analysis the relation between grassland degradation and soil erosion, and spatial variations and dynamic of grassland soil erosion in the region since 1970s, according to remote sensing interpretation of soil erosion, land use and grassland degradation information. This paper analyzed spatial difference of soil erosion and its dynamic status over the past 30 years in grassland ecosystem in Sanjiangyuan Region, Qinghai Province. The results showed that the proportion of soil erosion in this region account for 46.74%, and 56.04% in the grassland. The primary types of soil erosion in Sanjiangyuan Region are composition of freeze-thaw and wind or freeze-thaw and water erosion, about 41.93% and 20.48% of total erosion area respectively, and most of the erosion degree is slight. The distribution altitude range were 3200~4600m, 2800~3600m and more than 4400m, and the slope range were 5~25°, less than 3° and 5~15° for water erosion, wind erosion and composition freeze-thaw erosion respectively. The erosion degree of wind erosion and composition freeze-thaw erosion increased with elevation rising, and decreased for water erosion. Soil erosion from the beginning of 1990s to 2004 showed more enormous degradation than the former period, and the warming climate and grassland degradation resulted overloading and overgrazing were the main driving forces.

Digital Elevation Model (DEM) is the main data source for digital drainage network extraction. But the extracted result would have great subjectivity because of the scale influences by the parameters such as the drainage area threshold and DEM resolution. Therefore, it is of great significance that how to optimize these factors for extracting the drainage networks better. The existing researches are mainly concerned on any two factors qualitatively or quantitatively. It is yet to be answered about how to define the drainage area threshold and the spatial resolution in a given research area. Taking Xiamen City of Fujian Province as the study area and using the Digital Line Graphic (DLG) on the scale of 1:10 000 as the original data to construct the DEM with grid cell size of 2.5m, we re-sampled the DEM into a series of DEMs with nested grid size as 5m, 10m, 20m, 40m, 80m, 160m and 320m using the Nearest Re-sampling method. And then the D8 algorithm and the Hydrology module in ArcGIS software was employed to extract the drainage networks from each DEM mentioned above with 7 drainage area thresholds as 0.9km², 2.7km², 4.5km², 7.2km², 9km², 13.5km² and 18km². Finally, the length of the drainage networks, as a key factor, was calculated to analyse quantitatively the relationship between the three parameters mentioned above and to further discuss the optimization of these parameters. The research results revealed that:(1)The length of the drainage networks obeys one composite power function;(2)When thresholds keeps constant, drainage network features decreased with DEM grid cell size continuously;(3)According to the mathematical analysis of the composite function, the optimal grid cell size and drainage area threshold are 40m and 7.2km² respectively. The extracted drainage networks with the optimal parameters met with those extracted from the SPOT5 remote sensing image very well.

Model coupling has been a prevalent research method in geoscience recently. Modeling framework is of the same category among various modeling environments by coupling, which gains many applications at present time. But the requirement of the frameworks decreases their usability, and also throws a heavy burden to users, which is researchers have to perform the coding and compiling tasks for coupling models. Taking ESMF as the foundation that is a representative modeling framework, this paper gives a detailed discussion on the problems presented in the research of building an icon-based modeling environment by coupling and their solutions. A methodology of generating codes of coupled models automatically is first proposed according to the coupling modes refined, as well as the relationships implicated by them which are the orders of calling models and the dependency of the input and output variables of coupled models. Then an approach of Open Concept Framework is developed with the purpose of expressing and confirming the semantic similarity between model variables. Based on the OCF and its building foundations, model metadata, a method is designed to verify the spatial and temporal consistency between the coupling models, and the matching between the input and output variables. Lastly, a prototype of icon-based modeling environment by coupling is developed, which implements the methodologies given above. Furthermore, two testing scenes are devised to examine the characters of the prototype. The results indicate that the entire error coupling operations existing in the scenes are refused by the system, and oppositely the correct couplings are accepted completely. Also, valid codes for the accepted coupling models are generated by the system and run in ESMF successfully.

The remotely sensed imagery classification is an important preparation work for geo-science analysis with remotely sensed imagery.The accuracy of classification will influence the quality of the result of geo-science analysis.We can hardly achieve satisfactory classification result using traditional classification method in high-resolution remotely sensed imagery classification.The research shows that the texture is a very significant feature for improving the accuracy of classification of high-resolution remotely sensed imagery.The time-frequency method of wavelet transform has the spatial advantage in texture analysis of remotely sensed imagery.In this paper,the texture feature of remotely sensed imagery is extracted from QuickBird imagery via wavelet decomposing.The feature space is composed of texture feature and spectrum response value of every band of the image.Then the image is classified by fuzzy C mean-value clustering(FCM clustering) approach.The experimental result indicates that the accuracy of classification can be increased when the texture feature is added into feature space.Meanwhile,the inner-homogeneity of a single category is improved.We can also see that the wavelet analysis can extract subtle texture better than coarse texture.In this paper,it is from the gradient image that the texture feature is extracted,so there are some misclassified pixels at the edge of the image,like the sides of roads.Some tiny objects can be amplified.The change of parameters used in this method can impact on the result of classification,such as the window size of the wavelet mode variance,the window size of mean filter,the coefficients of spectral responses,and so on.How to find out the influence mechanism of these parameters and how to choose proper parameters are what need to be resolved in next researches.

Urban heat island has close relationship with urban environment and global climate change.Using meteorological observation data to study urban heat island is usually to calculate the difference of the mean temperature between rural meteorological stations and urban ones.Therefore,it is important to distinguish the urban stations from the rural ones correctly,which directly affects the accuracy of urban heat island research.Previous classification methods,usually based on population census data of administrative regions,did not consider the spatial distribution of population.The city's large population cannot reflect the situation around the station,which may lead to wrong results.In addition,it is subjective to designate rural and urban stations by manual interpretation based on remote sensing and land use,which cannot reflect the combined action of land use to meteorological stations.In this paper,BP artificial neural network was employed to build a nonlinear model between the land-use types within 5 km around the meteorological stations by remote sensing and their rural/urban property.And the model was tested by population grid data obtained from remote sensing and statistical data.The simulated result fitted the stations' rural/urban property well.This model avoids the limitation from administrative regions' population census data and reflects the combined action of land use to meteorological stations.Furthermore,this model can combine the strengths both of the density of population and land-use types by a further analysis.In order to test the temperature difference between city and rural stations,we make a temperature comparison between some city stations and the surrounding rural stations.The difference is obvious.At the same time,we build the background temperature of Anhui Province in 2000 by rural stations classified by the model.Compared with the background temperature,city temperature is averagely approximately 0.4℃ higher.This model can be adopted in lager areas and even for the whole country,where has the characters of larger coverage and shorter time of remote sensing images.

The cloud had a significant influence to the quality of optical remote sensing images and the retrieval accuracy of land surface parameters.As one of the variable spatial-temporal factors,cloud would limit the application of optical remote sensed images.For the MODIS data with its swaths cover about 2330 km,the current metadata standards can only describe cloud quantity of the whole image,which limited regional studies and the application of MODIS images.Based on studies of current remote sensing metadata standards,we introduced a new metadata item named "regional cloud-cover" in order to describe the images' cloud spatial distribution,and extract the cloud cover information of specific areas from the MODIS level two cloud mask production(MOD35).This algorithm can extract provincial cloud quantity fast and accurately,and users can upload the vector boundary data or use tools provided by the system to draw their study area on the map,in order to specify the region that they study in the remotely sensed images,which would bring convenience to the regional research using MODIS data.In this paper,we realized the algorithm in the Visual C++ environment.First,we overlaid the girded vector boundary onto the raster data which has no projection.Then extract the cloud-cover percentage from sub-image that tripped from the original image.Finally we made a comparison between the results retrieved from the remote sensing images before and after geometric correction.It showed that,the geometric correction can improve the accuracy of the result little,but need to take much more time.We also made a comparison of the cloud cover percentage between the whole image and the regional one(Anhui Province),the result shows that the regional cloud-cover can represent the cloud-cover status of the specify region much more accurately than before.

As an important indicator of vegetation,vegetation coverage,which is generally estimated by visual measurement,is always used as an important index in vegetation evaluation and a key parameter for remote sensing inversion.But this method strongly depends on individual variables,and without the reproducibility of the results.That is to say,different observers almost certainly record different measurements with the same quantity.The advent of digital photography and automated image processing promises a revolution in the way vegetation coverage is measured.Recently,an automatically extracting algorithm for vegetation coverage was studied based on color features and some threshold values,and have a high accuracy when it is used to calculate the winter vegetation coverage with vertical digital photographs.However,this automatic method isn't suitable for Tibetan Plateau due to its various types of vegetation and background.Though analyzing on the color characters of vertical photographs,we found a named excess green method,which is sensitive to green vegetation and enhances the contrast between plant and soil background.A k-means clustering algorithm was designed to divide photographs into plant and background,and calculate vegetation coverage automatically.Compared with the result from the manually supervised classification method,the root mean square error was less than 5%,but it spend less time than supervised classification and had a higher accuracy than that of visual methods.Moreover,some approaches to improve classification accuracy were discussed by analyzing the error source of automatic classification.

In order to integrate distributed and heterogeneous earth system scientific data resources and provide one-stop data sharing services for different users,a distributed data sharing platform is needed urgently in China. In this study,we analyzed interoperability requirements of the distributed earth system scientific data sharing plat- form(ESSDSP)in the first place.After compared with several architectures and technologies of distributed inter- operability,we chose SOA(service-oriented architecture)as the architecture of ESSDSP(hereafter called SOA4GS,SOA for Geo-data sharing).The basic principle of SOA4GS was to abstract a series of web services from the whole data sharing activities which included information synchronization service of data centers,distributed metadata synchronization management service and multi-resource data access service.Through deploying these web services in different data sharing nodes,the software can easily implement the interoperability among them.Finally we introduced the design,implementation and deployment of these web services in detail.SOA4GS has the following characteristics:(1)Distributed nodes with uniform logic ESSDSP are comprised of many centers which are deployed in different organizations at different locations.They have the uniform business logic to ensure they can communicate each other by web services.(2)With open and extensible architecture based on SOA,ESSDSP is an incompact architecture.So other systems based on web can be easily integrated into the platform after little function model revision for compliance with the interface of web services,while,on the other hand,systems such as e-government and e-commerce can use data sharing functions by calling web services to share the data provided by the platform.(3)Easy to use Web service platform is a web software module.Its client code can be created automatically from the web service' address by using some development tools such as Eclipse,JBuilder.So users can easily call the platform's web services or revise their system's functions.In practice,ESSDSP has been used in Earth System Scientific Data Sharing Network,one of National Infrastructure and Facility Development Environment Building for Science and Technology Industries Program of China.It comprises one main center,one authentication center and sub-centers.So far,it has integrated 10.23 T Byte data resources and Byte data resources have been downloaded by 34,597 users.

In this paper,a method of ecological security evaluation in Xinfengjiang River Valley was studied,and a theoretic framework was set up with RS and GIS technology.Based on ALOS image,10m×10m grid was taken as basic unit,while township as comprehensive unit.Then Pressure-State-Response(PSR) framework was improved and GIS was used to acquire 18 evaluation factors information,such as settlement pressure,road pressure,mining pressure,water quality,vegetative cover index,rivers density index,land deterioration index and so on.Ecological security index was calculated next by different models in every grid and every town in order to make ecological security classification maps.According to the study,ecological security index is divided into six levels.levelⅠmeans very safe,and level Ⅵ means very unsafe.From levelⅠto level Ⅵ,the ecosystem services become less and less perfect,the ecosystem structure become less and less complete,and the capacity of ecological restoration and reconstruction reduce gradually,while the destruction degree of ecological environment and the possibility of ecological disaster increase gradually.The results show that(1) Ecological security in Xinfengjiang River Valley is good from global aspect.It accurately reflects the status of ecological security in Xinfengjiang River Valley.(2) Security level distribution in was not balanced,which has evident regional characteristics.The worried status of ecological security were in residential、industrial and mining area,while states of Xinfengjiang reservoir area were better.(3) 82.43% of this area is in safe,while about 17.57% of the area is in warning.Despite the overall relatively good situation,insecurity has also reached an area of about 217.600 8km2,which accounted for 3.8% of the watershed area.Of the 41 towns in the river valley,there are 35 towns are safe,5 towns are in warning,with only one town unsafe.The study also shows that RS and GIS technology has positive significance for protecting and improving the ecological security in Xinfengjiang River Valley.

Soil is the most basic and most important resource for the living and development of human beings.At present,there is an urgent need to obtain accurate soil spatial information for environmental modeling and agricultural applications.The theory of soil-landscape relationships relates difficult-to-measure soil information,which includes soil type and properties,with some easy-to-obtain soil-forming environmental factors.This makes it possible to infer soil spatial variations from the easy-to-obtain environmental factors.Satellite remote sensing techniques generally serve as auxiliary tools for researches on soil spatial information extraction.The techniques can be used to acquire landform and vegetation data.Then,based on their relationships with soil conditions,soil spatial variation can be derived.In areas with median to strong landform gradient,easy-to-measure landform and vegetation factors have played an important role in soil mapping.However,for the plains and topographically gently undulating areas,where spatial variation of soil properties such as soil texture usually cannot be effectively indicated by the landform and vegetation,a scientific problem is how to reveal spatial variation of soil properties.This paper presents an approach to using land surface dynamic feedback captured by high temporal resolution remote sensing over a short period immediately after a major rain to distinguish soil texture over such areas.The results of this study showed that land surface dynamic feedbacks captured by high temporal resolution remote sensing can be used to identify spatial variation of soil texture.Locations experiencing different soil texture have obviously different dynamic feedbacks,and the more similar the soil texture between locations the more similar their dynamic feedbacks.The findings are very encouraging.The work reported in this paper provides an effective solution to the difficulty of identifying soil spatial variation over low relief areas.It exhibits the potential of high temporal remote sensing on the exaction of soil information.

LUCC informatics system,LUCCIS,is a newly developed research field on the basis of integrating the classical methodology of physical geography and the new progresses in GIS technologies.As a research field focused on studies of temporal and spatial processes of LUCC,LUCCIS has developed rapidly in past decades in which some technical components have been improved greatly.LUCCIS focuses on material cycles and macro life processes of multiple scales on the land surface with supports from remote sensing,GIS,and data fusion technologies.In this paper,the progresses of those kernel methodologies,techniques as well as compositional research achievements in China are described in detail,which make it possible for the establishment of LUCCIS.Specifically,the development in the technologies of extraction and analyses of remote sensing information,the exploration of driving mechanism of LUCC over space and time dimensions,the simulation of the spatial-temporal process and pattern of LUCC,the evaluation of the macro-ecological effects of LUCC and the establishment of research information platform of LUCCIS are all promoting the development of LUCC studies in China.The domestic research trends indicates that the development of extraction and analytical technologies of remote sensing information,the case study of exploring driving mechanism of LUCC,the methodological innovation of simulating the temporal and spatial process of LUCC,and the more accurate description of macro-ecological effects of LUCC play important roles in the methodology framework of LUCCIS.Also,the construction of multi-source and temporal-spatial data platform and the realization of massive data availability and integration are all kernel components of the methodology framework of LUCCIS.We can expect that LUCCIS would play a more and more important role in promoting LUCC research activities at regional and global scales.

2009年11月25日是我国著名科学家陈述彭院士逝世一周年的纪念日。一年来,陈述彭院士在各种场景的身影,同我亲切交谈时的面容,特别是在医院的最后几次见面交谈时的难忘情景,时常出现在我的脑海,悲痛、回忆与思念经常交织一起。我自1961年7月从苏联莫斯科大学地理系地图学专业毕业回国,被分配到科学院地理研究所地图研究室工作,时任地图研究室主任的陈述彭先生对我非常信任和器重,当即委任我担任专门地图学科组副组长,并负责国家自然地图集的统一协调与土壤图组的编辑,1962年还安排我在全国地图学术会议上作大会学术报告。

The process of spatial data mining is closely related to the process of spatial concept formation and analysis. From the view of recognition,centered in the thought to extract the concepts in different hierarchies and granularities, based on the formal theories of concept formation and analysis,i.e.concept lattice and cloud model, this study analyzes and summarizes the methods and their application of spatial data mining based on concept analysis, including the methods and their application of spatial association rule mining based on concept lattice,those of conceptual clustering based on concept lattice,those of spatial association rules mining based on cloud model, those of spatial clustering/classification based on cloud model,etc.Concept lattice provides a kind of theory which can express and analyze spatial concepts and their concept structural relationships by mathematical and formal methods,which is good for the understanding of human,and is convenient to process by computers;Cloud model is a kind of uncertainty theory which can deal with fuzziness,randomness and their association,it is a more powerful and effective uncertainty theory than fuzzy sets and rough sets.The further research directions of spatial data mining based on concept analysis are as follows:(1)the formal expression of spatial concepts;(2)the formal analysis of the process of spatial concepts formation;(3)the formal expression and analysis of the structural relationships of spatial concepts;(4)the construction of the spatial concepts hierarchy and their formal expression and analysis;(5)the expression and analysis of spatial concepts and the concept structural relationships with uncertainty .

Geo-spatial relations reflect the complex association between geographical phenomena and environments,so,are very important in dealing with geographical issues.They can be generated according to geometric attributes of spatial phenomena,such as distance,direction,connectivity,similarity etc.Also they can be generated based on combinations of geometric attributes and non-geometric attributes such as statistical correlations,spatial autocorrelations,space interactions,spatial dependencies etc.There are still other geo-spatial relations arising from non-geometric attributes of spatial phenomena.Extracting geo-spatial relation rules effectively helps to improve the accuracy and efficiency of solutions of geographical issues.This paper discusses both the expression of geo-spatial relations and the extraction process of geo-spatial relationship rules based on rough set theory.To illustrate the extraction process of the rules,we take the land use vector data of the Pearl River Delta in 2000 as an example to extract the geo-spatial relationship rules of urban areas and rural residential areas in Shenzhen and Hong Kong.

The principal advantage of hyperspectral remote sensing is to identify composition and components in objects and inverse the physical and chemical parameters according to the acquired and rebuilt pixel spectral signatures.This paper reviewed the composition and components of rocks,soils,atmosphere,vegetation,water body and manmade structures that can be recognized with hyperspectral remote sensing,described spectral identification methods and then,discussed some key characteristic techniques comparing with multispectral remote sensing,such as spectral matching,spectral unmixing,endmember selection,and quantitative inversion as well.

As the largest nature reserve of China,"Three River Sources" Nature Reserve plays an important role in protecting ecological security.The change of NPP,which is a vital indicator for ecosystems,is still concerned by us.We utilize NPP,which is simulated by GLOPEM model,to calculate the difference of NPP between both inside and outside of the protected areas.We define the buffer region of a protected area as the outside.The outsides we defined are used to calculate mean value of NPP to subtract from the mean NPP of insides.Also we calculate the difference in three periods,i.e.1988-2008,1988-2004 and 2004-2008.If the differences of insides and outsides in 1988-2004 are lower than the value in 2004-2008,we define it as turning better,especially when the value in 1988-2004 is negative but is positive in 2004-2008,we call it notable better.After that,we find that there are 13 protected areas better and 2 notable better than before,and only 5 protected areas become worse than before.In the 5 regions there is a snow-mountain protected area,although the result in this region is worse than before,it does not mean the ecosystem is worse,in contrary it means the environment is turning better because of the spread of main ecosystem decrease the NPP.We conclude that in this region,since the implementation of ecosystem engineering,NPP rise obviously with the rate of 0.47 gC m^-2 yr^-1,and most protected areas are turning better.

Fuzhou was chosen as our study area.Using Temperature/Emissivity separation algorithm,land surface temperatures(LST) were retrieved from ASTER(Advanced Spaceborne Thermal Emission and Reflectance Radiometer) data set which was collected on April 6th,2004.Two methods named equal interval method and mean-standard deviation method were employed to divide land surface temperatures into four,five and six classes respectively.Synchronously,the domain of urban heat island(UHI) was determined according to its different definitions.Then multi-angle comparisons covering the structure,spatial distribution and the tiny undulation of the UHI delimitated by different methods were applied to choose the better methods.The result indicated that the percentage of the UHI area delimitated by both methods was fluctuant with the class number,while for the intensity of the UHI,mean-standard deviation method was less sensitive to the number of thermal field than that of equal interval method.Furthermore,the former was better than the latter in describing UHI spatial distribution and details.Based on standard deviation of land surface temperature against its mean value,the mean-standard deviation method,to some extent,likely minimized the problems that resulted from the inconsistent time periods of collecting remote sensing data in studying the multi-temporal urban heat island.However,our research was only a preliminary case study,more details and uncertainties about these methods and results needed to be further studied.

With the development of remote sensing,there are a few new types of sensors from which can acquire abundant new types of remote sensing data.These data have been applied successfully in many different fields of national economy.In this paper,applications of four new types of remote sensing data in uranium deposit prospecting,i.e.multi-spectral,high-spatial-resolution,hyperspectral and radar data,have been summarized.

The paper took Chifeng City of Inner Mongolia,a typical agro-pastoral transitional zone,as an example to investigate land use change process from 1988 to 2000 based on three periods of land-use data(1988,1995,and 2000) which were acquired from interpretation of TM images.The Land Exploitation Degree,the Land Conversion Degree,the information entropy,and spatial conversion matrix were adopted for finding characteristics of land use change especially land cultivation from grassland and cropland abandonment for grassland in two stages.The results showed that:(1)Land reclamation and grassland decreasing were main land use change processes in the whole stage.The area of cultivated land increased by 2424.12km²,and grassland reduced by 1959.65km²;(2)Comparing the two periods,in the first stage from 1988 to 1995,the changes between cultivated land and grassland were acute,and reached 2040.50km² and concentrated in the north of Aohan County,634.75km² in the opposite direct from cropland to grassland.Unused land also was conversed into grassland with a large area in Kerqin County;in the other stage from 1995 to 2000,the land use change speed slowed down.And main changes were the conversion of grassland and forest into cultivated land.There was an area of 828.50km² of grassland turning to be cultivated in Aohan County and the north of Linxi County;(3)The information entropy for land use structure in three periods were 1.290,1.301,and 1.304,respectively,showed that the order manner for land use system was in reduction.The study revealed the transitions of grassland cultivation and cropland abandonment from 1980 to 2000,and provided supports for accurate portrait of regional ecological security and human-environmental coupling system.

地球信息科学是面向21世纪形成的新兴学科。它是融合地球信息,遥感信息科学,全数字制图系统,地理信息系统、全球定位系统、虚拟现实多维可视化技术和信息网络等多学科的系统论、信息论和控制论于一体的科学体系。它以信息流为主要研究对象,探索地球信息机理,地球信息认知方法和地球信息时空图谱等,新学科生长点的科学研究。陈述彭先生从地球科学与环境科学和信息科学的高度,运用系统工程学原理,勤奋治学,长期潜心研究,将地理信息制图,遥感信息技术与地理信息系统等融汇贯通,拓展了地球信息科学研究的新途径。

A method to extract marine hydrographic characteristics based on line process is introduced in this paper.The program written in Visual C++ has already integrated in MaXplorer,which is a marine geographic information system platform developed by China.By this method,time series MODIS-SST data were used to extract sea surface warm front of Kuroshio in East China Sea.The seasonal change of the warm front location was analyzed based on the extracted results above.It showed that the seasonal change of the warm front location of the top layer of Kuroshio is not very evident in East China Sea in general,except in the area of about 30°N and the area near the northeast of Taiwan Island.Because the change of sea surface temperature(SST) in East China Sea in summer is relative small at an average temperature of 28℃,it is difficult to determine the locations of the warm front.The warm front locations in other seasons were occurred along the shelf break near the isobath of 200 meters.In north-eastern of Taiwan Island there is a clear curve which showed a cyclone pattern then an anticyclone pattern.The locations of warm front in winter were occurred in the relatively east part while in autumn,showed a relatively complicated route.

Geospatial data is a kind of strategic information resource and is widely used in economic,social and environmental applications.GIS vector data is closely related to the overall situation of socio-economic development and national security due to its high production cost and high precisions.In recent years,researches about copyright protection of GIS vector data mainly focus on copyright marking techniques.To meet the requirements of copyright protection for spatial data files,a real-time copyright protection scheme for spatial data files is proposed in this study,based on the Copyright Marking technology,especially on the Digital Watermarking technology which was only used for the authentication,file system filter driver technology and encryption technology,which can be used for dynamic encryption and decryption of data files and real-time detection of copyright marking.This scheme achieves the purposes to keep confidential,detect copyright marking in a real-time way and control access.In addition, it lowers the limitations on the capacity of copyright marking information and the robustness of embedding algorithms.

A path finding heuristic algorithm of road network hierarchy is discussed in this paper,which adopting a road-cost evaluating function and the road hierarchy is an important factor considered here.The path searching process is conducted on as higher hierarchies as possible,so as to restrict the searching scope.Hence,the algorithm greatly speedups the path searching process,gives results under controllable accuracy levels,and makes the results consist with human cognition.Compared with conventional hierarchical spatial reasoning algorithm,the algorithm presented in this paper can be implemented more easily,with similar efficiency and accuracy results.The theoretical analysis and case-study validate the algorithm.