Integrating CASI and SASI airborne hyperspectral data could help acquire both continuous narrow-band spectra covered the visible\|near infrared\|shortwave infrared range and high spatial resolution information.It provides a new method for tree species identification in high coverage rate forest.However,due to the different spectral response of the two sensors,there were differences between the received radiances.How to integrate the two dataset effectively for tree species classification is still a problem.CASI and SASI data have both different coverage of spectrum and atmosphere impact.According to the characteristic of reflectance and absorption spectra of vegetation,experience linear method based on statistical model and MODTRAN model based on radiative transfer theory were used for atmospheric correction of CASI and SASI data respectively to retrieve the real spectral reflectance of ground object.Then CASI and SASI data were integrated (CASI + SASI) by Savitzky-Golay filter function smoothed continuue removal spectra to reduce noise and abnormal points.Compared the integration of CASI + SASI spectrum with the field measured spectrum showed that the two curves matched well each other.In addition,the integrated spectra have richer spectral information for tree species distinction than any single one.Tree species mapping was realized base on the integration of CASI and SASI data using SVM classifier.An overall accuracy of 86.21% and Kappa coefficient of 0.8297 were obtained.And the result indicated that the proposed method to integrate CASI and SASI data is feasible for a more accuracy in forest tree species classification,and it would be a reference for the later research.
Mass observation and mass prevention is the main technology of geological disaster mitigation in china.Utilizing network technology,communication technology and WebGIS technology to establish Lanzhou city geological disaster mass observation and mass prevention informationization system.This system is a significant method of scientific forecasting and early warning of geological disasters and mitigating the hazard of geological disaster.It consists of uploading data module,analyzing disaster information module,publishing disaster information module and browsing disaster information module.Adopting three\|tier distributed architecture,namely,application tier,business tier and data tier as the system framework.System model library include the spatial prediction module of geological hazards and time prediction model of geological hazards.The relational database,SQL Server2005,is the backend database.Complete the development of the system by.NET platform.Running results show that the system is stable and can effectively service for all levels of users of the geological disaster mass observation and mass prevention system.
MODIS 16 days composited NDVI time\|series of 2007\|2009 in northern Tibet are taken as study case to compare the characters of three principal fitting methods,that is the double Logistic function fitting (D-L),asymmetric Guassian function fitting (AG),and Savitzky-Golay filtering (S\|G) methods.To begin first,the basic principles and implementation process of the three algorithms are introducted based the TIMESAT 2.3 program.Then,the results of NDVI time\|series fitting based on the three methods are emphatically compared and analyzed,from the aspects of fitting effect for the upper envelope curve of original NDVI series and the ability of preserving the high-quality NDVI fidelity.The results show that the three fitting methods would raise the mean value of NDVI samples to some degree,and that the AG and D\|L fitting algorithms generate more consistently reconstructed NDVI time\|series to the original NDVI temporal curve than the S\|G filtering method.Secondly,the fitting NDVI values of AG and D-L methods are higher than points of the upper envelope curve,the S\|G filtering method is opposite.Among the three algorithms,AG fitting produce the most approximative results.Besides,the AG and D\|L methods perform extremely similarity to keep the fidelity of high\|quality NDVI samples,and their fitting NDVI series are better than that of S-G filtering except the peak period of growing season.
Based on TM data,the land surface temperature (LST) and normalized difference vegetation index (NDVI) on 5th October 2007 were retrieved quantificationally in this paper.Besides,along with the theory of fractal dimension of urban form and GIS technology,the spatial pattern of thermal environment and the relationship between land use type change and their corresponding thermal environment classes in Hefei region were analyzed.The results suggested that:the intensity of urban heat island (UHI) in Hefei was relative stronger (weaker) in southeast (northwest) area,and the core of UHI did not arise in central Hefei city.The LST has a significant linear\|negative correlation with NDVI (the correlation coefficient is -0.734),the LST would decreased 0.93℃ correspondingly if NDVI increased 0.1.The weighted heat effect index (WHI) of urban area is 1.14,which made the strongest positive effects on UHI of Hefei; while the WHI of farmland is 0.51,which made the strongest negative effects on UHI of Hefei.These results indicated that the farmland may play a critical role in all kinds of thermal environment classes.This work provided some scientific basis for reduction of UHI effect in Hefei.
Rice planting in southern hills exhibits many characteristics,such as scattered,small land,diverse shape and so on.Using low\|resolution remote sensing data for the extraction of rice planting area can not meet the required precision.Object\|oriented classification of SPOT5 image has been carried out to extract the rice planting area of Yulin city in Guangxi province.As different characteristics of the rice planting area,appropriate scale and parameters were selected to multi\|scale image segmentation,a hierarchy of images objects is established.Then we calculated the object's spectral,geometric and topological characteristics and form the classification rules for different rice areas to extract rice information.Field survey data was used in area and category of classification consistency test,the overall accuracy of 96.31%,Kappa coefficient of 0.9226,an area of consistent accuracy of 99.92%.
Night\|light data from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) has been widely utilized to derive the urban built\|up areas.Four major methods,including Empirical Thresholding,Sudden Change Detection,Statistics\|assisted Thresholding,and TM\|assisted Thresholding,have been proposed to determine the threshold value for separating the illuminated urban areas from the dark background of rural areas in DMSP/OLS night\|light images.This paper makes a comprehensive assessment of those methods through a case study of Shanghai,China.The methods are implemented to extract the urban built\|up area for Shanghai using DMSP/OLS stable light data acquired in 2003.Then,the same threshold values obtained from 2003 DMSP/OLS night\|light data are applied respectively to the 2000 and 2006 DMSP/OLS data,resulting in a significant error in urban built\|up area detection.This analysis result suggests that the threshold value determined for a specific year cannot be extended and transferred to other years.The failure in temporal extensibility of threshold value means that an appropriate threshold value has to be determined for every year when a time series of DMSP/OLS nigh\|light data need to be processed.Therefore,the method that determines threshold value independent of reference data is more suitable for processing time series DMSP/OLS data.The Sudden Change Detection method does not require ancillary reference data and is the best choice of those methods considering the convenience,accuracy,and automated data processing,and it is then adopted to derive the urban built\|up areas of Shanghai from 2000 to 2006.
Soil texture is a key input parameter for the land surface process models,hydrological models and atmospheric models.Many digital soil mapping methods based on the soil\|landscape model concept have been widely studied and applied.The general methods include the decision tree algorithm,the support vector machine approach and the fuzzy logic theory.In this paper,a study was conducted to compare the above three different soil mapping methods in an area of Zhangye of Heihe River Basin by integrated 200 ground measured soil samples and 13 types of environmental factors.Meanwhile,different soil texture maps based on the three methods were predicted in the study area respectively.Results show that:the support vector machine model gets relatively low accuracies both for test soil samples and training samples,which are 90% and 55% respectively.The decision tree model gets the highest accuracy of 98% for training datasets among the three methods,but its accuracy for testing data decreases into 57%.The fuzzy logic model gets the highest accuracy of 64% for testing data and a compromise accuracy of 74% for training data.As for the structure characteristics of the texture soil maps,the study finds that the support vector machine model predicts a much simplified soil texture map as it may weak the prediction ability of thematic and continuous environmental factors.The decision tree model often gets unstable predictions of unexpected combinations of environmental factors,resulting in a relatively fragile structure of the soil texture map.The fuzzy logic model predicts the most reasonable soil texture map among them,because it can not only keep the soil texture structure much holistic,but also can illustrate an appropriate relationship between different soil types and different environmental factors.It is suggested that combining the decision tree algorithm with the fuzzy logic theory might be an appropriate method to map the soil texture distribution of Heihe River Basin.
In North China region,the autumn is a prevalent season of heavy fog,which has become a major severe weather in recent years.Real\|time monitoring of fog is essential for transport industry and public activities.Two heavy fog events occurring in North China Plain at 11∶00 LST on 19 November 2006 and at 05∶00 LST on 20 November 2006 were detected by employing principal component image transformation of MTSAT\|1R Imagery.The results show that the principle component analysis technique can enhance the difference between fog area and other regions and detect heavy fog.Based on the principle component analysis,extract fog information by using threshold detection,the sensitivity analysis of fog detection threshold was done and selected the appropriate detection threshold.MTSAT\|1R data from 16 October to 30 November 2008 were processed by using threshold detection method with batch process,and the results were compared with conventional ground\|based meteorological observation data.The results show that the improved remote sensing method based on PCA can detect areas of fog affected,the detection threshold is of good stability,accuracy rate of detection fog is high and the method is objective.Both methods can effectively and consecutively detect development and dissipation of fog events and the real time detection of heavy fog by using these two techniques have much superiority over traditional surface detection in temporal\|resolution,objectivity and veracity.
In forestry applications,Airborne LiDAR has its unique superiorities compared to passive remote sensing.In this paper,a new individual tree recognition method was proposed to isolate individual tree and extract tree height,also the area of the tree crown,this method is the fusion of marker\|controlled watershed segmentation and valley\|following segmentation.Finally,a real LiDAR dataset was test,experiment results show that this method can perform edge detection effectively compared to methods before because detection constraint was added into it.The detection constraint can avoid over\|segmentation to some extend,and reduce the detection objects,which can avoid unnecessary detection noise.
Glaciers can sensitively reflect the changes of the regional environment that they has become one of the important factors to study the global changes.The glaciers of the Kunlun mountains are concentrated.This area is the ideal one to study glacier dynamic changes.According to the Landsat TM and ETM+ remote sensing images of the year from 1991 to 2009 about the Kunlun moutains,we studied the changes of the glaciers in the recent 20 years.As a result,we found these glaciers changed obviously and concluded that:① The glaciers to the north of the Guozhacuo lake owned the fluctuation pattern from 1991 to 2009;② There existed the difference between the east and west that the changed amount and rate of the east were larger and faster than the west;③ About the phenomenon that the Zhongfeng glacier increased largely between the year 2001~2004,we inferred it may correspond to the Ms 8.1 strong earthquake occurred to the west of the Kunlun mountain pass on November 14th,2001;④ Combined with meteorological data analysis,we inferred that the changes of the glaciers in this area were synthetically caused by the temperature and precipitation.
Taking Bohai Bay as a case constructed dynamic oil spill model and resulted in DLL in terms of marine environment dynamics in order to prevent the spread of spill oil pollution.We developed component GIS embedded with DLL based on ArcEngine controllers under Visual Studio.net program language circumstance to obtain GIS spill oil system.First,input parameters on spill oil accidents,then DLL automatically processed this data to generate SHP file of spill oil trajectory,and the system could convert the former SHP file into KML file,following which the system would start Google Earth and also load the KML file obtained in the above step.The movement trajectory of marine spill oil was revealed through Google Earth,which provided decision support to urgently manage spill oil incidents for government department.The results demonstrated that the combination development of GIS and Google Earth could enhance the run efficiency and also save cost as well as accurately predict the spill oil change on the sea,therefore,which had a potential meaning for further promotion.
It was analyzed that correlation between the transformed spectrum value and leaf nitrogen content,further more it was researched that retrieval precision for the nitrogen content with different form spectrum variables in 550nm and 680nm wave band by carrying on 6 kind of transformations to the corn leaf spectrum data.The results showed that differential form(D(R),D(Log(R))and D(N(R))) could significantly change the correlation between the nitrogen content and the spectrum value,and the normalized form(N(R))was next and the last was logarithm form(R and Log(R),N(R)and log(N(R))).In addition,determined coefficient of regression model was larger and the model precision was higher correspondingly with better correlation between different form spectrum variables and nitrogen content.In this study,the normalized logarithm form(log(N(R))) parameter of spectrum data could significantly improve the precision of regression model for retrieval nitrogen content in 550nm and 680nm wave band.
Prior to multiple\|sources remote sensing data analysis and applications,a quality assessment of geometric correction or registration is needed.Traditional position accuracy assessment is root\|mean\|square error (RMSE),which is easy to calculate.However,it can not describe the spatial distribution characteristics of GCP residuals after geometric correction or registration.In this paper,cross validation is employed here to overcome traditional RMSE limitations.Furthermore,to assess the spatial characteristics of GCP residuals,Moran's I is adopted to test the spatial independent of GCP residuals,and standard deviational ellipse is introduced to measure the distribution trend and investigated the spatial isotropy or anisotropy for a set of GCP residuals points.Simulation experiments show that with the aid of those measurements,it can be insight into the principle of transformation function and help users to select an appropriate correction model to improve both accuracy of remote sensing image geometric correction.
The image segmentation is the basis of object\|based image analysis method,which is usually carried out based on the hypothesis of spectral homogeneity.But this hypothesis is not quite applied to the saline lake in arid region which composed of two surface features with high contrast (crystalline salt and brine) in spectrum.Aiming at the shortcoming of segmentation only based heterogeneity of spectrum and shape in object based image analysis methods,the segmentation algorithms based on texture are proposed.In this paper,we firstly extract texture features using window Fourier power spectrum method.Next,multi\|resolution segmentation are carried out on texture image and multi\|spectral image.Finally,we classify polygons produced by segmentation in multi\|level.The paper takes SPOT 5 image acquired in November 2008 of Jilantai Saline Lake and the surrounding areas for example,and extracts saline lake from SPOT 5 image using the method mentioned above.The result shows that the proposed method is capable of effectively extracting features in saline area.
Image definition is an important indicator of digital image quality.Oriented to the application of No\|reference image quality assessment,some representative definition evaluation algorithms are introduced in this paper.In order to accurately and objectively assess each algorithm and to effectively evaluate image definition in the practical application,comparisons and analysis on performance of each algorithm are also done from the following aspects:single peak,no bias,sensitivity; the independence of image size and content; the consistency with subjective perception.Results show the ReBlur and NRSS algorithm have better evaluation performance than other algorithms for the No\|reference image quality assessment.
This paper summarize recent progresses in the technologies and methods of satellite\|based instruments for atmospheric CO2 remote sensing, discuss the basic theory and retrieval method, and analyze the factors debasing the precision of CO2 measurement. Three kinds of instruments are discussed in detail:① Currently operating satellite instruments, which observe atmospheric CO2 and other components, such as Atmospheric Infrared Souner (AIRS), Scanning Imaging Absorption Spectrometer (SCIAMACHY) and Infrared Atmospheric Sounding Interferometer (IASI);② Specific instruments for detecting CO2 content in the lower troposphere, such as Orbiting Carbon Observatory (OCO) and Greenhouse gas Observing Satellite (GOSAT);③ Satellite projects focus on active instrument\|laser lidar,such as Active Sensing of CO2 Emissions over Nights,Days,and Seasons (ASCENDS) and Advanced Space Carbon and Climate Observation of Planet Earth (A\|SCOPE).In addition,we introduce technical basic in hyper spectral instrument in China.Validation,data assimilation and potential development of CO2 satellite remote sensing are discussed at the end of paper.
The applications of microwave remote sensing in agriculture require the crop parameters (eg,biomass,water content,crop type,etc.) estimation using active and/or passive microwave remote sensing data.Several agricultural microwave remote sensing models have been developed to understand the interaction between the microwave scattering(or emission) and the agricultural field parameters.This paper states the advances of microwave remote sensing models including the empirical,semi\|empirical and theoretical models in agriculture field.A number of suggestions were made for developments of microwave remote sensing models of agriculture field in the future researches.
