It has great significance for controlling the influence of oil spill to get pollution oil slick information promptly.Due to its all day-night and all-weather capability,space-borne synthetic aperture radar (SAR) has been proven to be a useful tool for ocean oil spill monitoring.Taking an oil pollution accident occurred in Bohai Sea as study case,this paper puts emphasis on studying radar backscatter feature and the evolution of pollution oil slicks.The potential oil spill source area was deduced by analyzing the change of backscatter values of slicks.The obtained oil spill information was integrated with continuous wind field data to study the evolution of the slicks and its size change during the period.The results show that the corresponding methods should be applied in the different evolution phases for the different SAR image feature of oil slicks.It is possible to monitor and predict the movement of the slicks with consecutive radar imagery combining with additional information.
Fog is an important of environment monitoring.By radiative transfer model RT3,reflectance and brightness temperature of fog,cloud and underlying surface were simulated at different bands of CCD camera and IR camera of HJ-1 B.After analyzed simulation results,the method of monitoring fog by HJ-1B data was given.The method was tested in south China at March 12th,2009.The results show,by HJ-1B data,the fog can be monitored,but for the influence of land surfaces,the thin cloud cant be removed accurate.
In order to estimate the population of small area,a new population estimation method based on residential building attributes is presented.Firstly,buildings are extracted combining LiDAR data with high-resolution remote sensing images,using Dempster-Shafer theory of evidence.Then,all the non-residential buildings are excluded from extraction results according to land use classification map,and the population estimation model is generated by optimization choice of geometric attributes of residential buildings,such as building counts,building areas,and building volumes,based on linear regression method.The results demonstrated that proposed model yielded good small\|area population estimation results,and the proposed method improved the precision and automatic of population estimation.
The normalized distance between the means was firstly calculated,the phenological spectral differences of multi-crop were investigated by the stepwise discriminations analysis method,the decision tree method and the mask technology were employed to monitor the planting areas of spring-sown crops on 28th May and 28th June,using remote sensing images acquired on 26th April,28th May and 28th June 2007,then the areas in counties were computed with the administrate boundaries,last,the accuracy validations were made based on ground truth interpreting points.The results showed that the classification accuracy on 28th May is 84.5% and that on 28th June is 88.0%.The stepwise discriminations analysis was testified to be more suitable to built classification rule and improve the multi\|crop classification accuracy based on multi\|temporal images.
LAI is a key biophysical variable,used in most global models of climate,ecosystem productivity,biogeochemistry,hydrology and ecology.Hyperspectral remote sensing provides an effective method to monitor the physiological and biochemical parameters of vegetation canopy.Two experiments were carried out in South Lake Park and Changchun Park respectively with the major greening tree species in the Northeast as the study object.Totally 240 groups of hyperspectral reflectance and corresponding LAI were obtained.After analyzing the correlations of reflectance and derivative reflectance with LAI,an evaluation of canopy LAI retrieval methods was conducted using 6 vegetation indices,neural network method and spectrum analysis of wavelet energy coefficient and the estimate effects of three methods were compared.The results indicated that all these methods had an ideal effect on the estimation of LAI: ① Compared with RVI and NDVI,Four vegetation indices (DVI,RDVI,MSAVI,TVI) may enhance the precision to estimate LAI; ② The estimations were further improved when neural network method was used (R2 reached 0.850); ③ Spectral wavelet energy coefficients showed a better correlation with canopy LAI.R2 of single variable regression analysis may reach 0.683 and the accuracy of individual wavelet coefficients to estimate LAI was superior to vegetation index method with higher R2 in validated model.When a subset of wavelet coefficients was analyzed,it was found that multi\|variable regression analysis had reduced the error in the retrieved parameter with R2 of measured value and predicted value of the LAI being 0.794.When the LAI was small,vegetation indices were obvious for removing the noise from soil and atmospheric effect and obtained good evaluation result.Neural network method and wavelet analysis could weaken the effect by saturation at high LAI levels and showed better effect for all LAI.
Because of sensitivity to soil moisture,the microwave satellite signal at low-frequency is often assimilated into land surface model to improve forecasting of soil moisture and other surface state variables.Assimilation algorithms use mainly statistics,optimization theory and other mathematical knowledge,which do not help to improve description of physical processes of models.This study is to develop a data analysis method to judge the errors of soil moisture predicated by assimilation,which is the prior research when error will be returned to land surface model to correct until they are in line with the satellite observations.
This paper validates the applicability of DisTrad model for thermal sub-pixel mapping over south China.We found the index of NDVI would reach to saturate state in high biomass regions,and its sensitivity would also become decreased.In our study,we proposed to replace NDVI by EVI in DisTrad model for thermal sub-pixel mapping in South China area.The high\|resolution LST image (250 m) was got by the relationships between high-resolution EVI/NDVI and LST images,and the results were verified by co-temporal ASTER LST data with 90 m resolution.The results show that this method can reduce the computation time and remain sensitivity in high\|biomass areas.
Hyper-spectral data offers a powerful tool for predicting soil heavy metal contamination due to its high spectral resolution and many continuous bands.Band selection and spectral resolution,however,are the prerequisite of heavy metal inversion by hyper-spectral data.In this study,soil reflectance spectra and their Cu contents were measured for 181 soil samples in the laboratory.Based on these dataset,band selection was conducted to inverse Cu content using stepwise regression approach,and prediction accuracies of Cu based on partial least-squares regression (PLSR) model with different selected bands were analyzed.In addition,the influences of spectral resolution on prediction results of Cu were discussed by a Gaussian re-sampling function.It demonstrated that the optimal band number was 10 for Cu inversion and the corresponding model prediction accuracy was R2=0.7523 and RMSE of 0.4699.The optimal spectral resolution was 32 nm and the model on this basis had an accuracy of R2=0.7028 and RMSE=0.5147.Results of this paper may provide scientific verification for designing low-cost and practical hyper-spectral space-borne sensors and provide theoretical bases for simulating space-borne sensors to predict soil heavy metals content in the future.
Comparing with traditional remote sensing data,strong band relevant and data redundancy are prevalent in the hyperspectral remote sensing data,so band selection is necessary for the efficient research.This paper taking Dongguan as a study area,using Environment and Disaster Monitoring and Forecasting of small satellite hyperspectral data,comprehensively analyzed the information content and correlation of the bands,then using three kinds of classical band index to choose the best band combinations.Through contrastive analysis of the three models,we improved the Optimum Index Factor by giving thresholds to coefficient of correlation and mean square deviation to select the optimum band combinations on the problems in the classical models.In the end,taking the farmland,woodland and grassland for example,apply Jeffreys-Matusita Distance model to calculate their separability and pointed out that the band combination of 50-80-108、50-79-8 and 50-80-111 are the best combinations for distinguishing grassland-woodland,grassland\|farmland and woodland\|farmland separately.
Taking the Zuli River basin in Loess Plateau of Longxi as the study area,the land classification was carried on using Landasat TM7 data in 1993 and 2007 respectively.The land use types were combined with topographical data from DEM and the spatial distribution data of multi-annual mean precipitation.The distribution spaces of terrain and climate for farmland,forest land and grassland were obtained. The conclusion can be drawn from the approach:① the area about 214.82 km2 of farmland is controlled in 14 years,and about 145.08 km2 of farmland should be converted into grassland and forestland in 2007,because it is located at above the threshold of slope(18.2°); ②forest land is distributed the area where precipitation is between 386 mm and 517 mm,and planted forest is increasing in 14 years; ③coverage of grass has generally been reduced due to the human activities.To mediate the soil erosion and water loss in this primarily hilly loess area,the vegetation’s restoration and reconstruction is very important.The objective of this research will provide the scientific support for returning farmland to forestland or grassland,which will contribute to build the positive cycle of the ecosystem in Zuli River basin.
Precisely get the acurate information of soil salinization is very significant for both soil salinization monitoring and study of the dynamic changes of soil salinization.In this paper,saline soil information is extracted by using object-oriented method based on Landsat TM image data and field survey data.Firstly pre-processing on the remote sensing images has been carried out,including geometric correction and radiometric correction.Secondly the image segmentation has been processed on the remote sensing image using the method of multi-scale segmentation method,feature selection,object-oriented image classification and accuracy assessment of classification .
The classification results of object\|oriented methods and the traditional pixel-based classification such as maximum likelihood and minimum distance method were analized comparatively.The results showed that: the use of object-oriented method on the classification of TM image,not only reduce the “salt and pepper phenomenon” effectively,but also it has the higher classification accuracy than traditional classification methods,and will provide a broader prospect to the automatic extraction of saline soil information .
his study is to classify land use/cover of Australia in 2000 based on data MOD13Q1,for it's sensitive to climate change.In order to reconstruct high quality NDVI time series data,Savizky-golay filtering method was applied for the NDVI data set.In this study,a combination of unsupervised classification of ISODATA and hierarchical decision tree was applied for the land use/cover classification over the whole country of Australia,and NDVI values of specific season,transformed NDVI values,and principal component analysis were also performed on the data set for decision tree threshold value choosing.Based on area of contract and spatial location matching to assess the accuracy of the classification,comparison the classification results of MOD12Q1,GLC_2000 with the results was done for this study.Random selected samples and generate confusion matrix.The overall classification accuracy of 63.65% and the Kappa coefficient of 0.56 were achieved in this study.This result is better than the result achieved in MOD12Q1 and GLC_2000.
As a comprehensive quantitative indicator of surface conditions covered by plant communities,vegetation fraction and its changes become an important instruction of regional environmental changes,which has great significance for the regional hydrology and ecological conditions and even the regional response to global changes.In this paper,MODIS NDVI was selected as the data source to finish the extraction of vegetation fraction in Jilin from 2000 to 2007 and vegetation fraction maps from different periods using the two sub-pixel model.It also makes further analysis on the reason for the changes of vegetation fraction.It shows from the results that the vegetation fraction,which is best in Baishan,decreased gradually from east to west in Jilin.Over the past eight years,the overall trend of vegetation coverage in Jilin was on the rise,and the highest had reached 83.04% in 2007.During the period,the vegetation coverage in both central and western regions had increased 9 797.52 km2,accounting for 74.79% of the total area changes.The main factors affecting the changes of vegetation cover are those,the ecological restoration engineering,the precipitation,the temperature and so on.
This paper describes the platform/plug-in model in software designing,and uses plug technology to develop the multi-source remote sensing image fusion system based on OpenRS.The experiment shows that using platform/plug\|in software structure in multi\|source remote sensing image fusion system designing not only reduces the redundancy of system,but also can make the system more extensible and more flexibility.
Based on the basic principles of InSAR,we can draw the following conclusions:an inverse proportion relation exists between errors of baseline,phase and atmosphere delay and length of effective baseline in terms of height measurement; atmosphere delay error impact on two\|pass deformation measurement changes little with incidence angle difference,and its impact on three-pass deformation measurement changes little with baseline ratio.When incidence angle is 23°and baseline ration 1/2,if we let its impact on deformation error less than 1 cm,atmosphere delay error must be less than 6.5 mm for two\|pass DInSAR and 7.5 mm for three-pass DInSAR.
This paper proposes an automatic and high-precision registration method based on point features for multi-source remote sensing images.The proposed method is a two-step process including pre-registration and fine-tuning registration.Firstly,the method detects the matching points by the Scale Invariant Feature Transform(SIFT) algorithm and the input image is pre-registered by using linear polynomial model.As a result,the input image is transformed to the same spatial pixel size and the reference coordinate system as the reference image.After a large number of feature are detected based on the Harris corner detector in the image pre-registered,tie point pairs are found in a small search scope which is identified in the reference image by correlation coefficient.Tie point pairs with large errors are pruned by Baarda data snooping method.Finally,the images are divided into a number of triangular regions by constructing the Triangulated Irregular Network(TIN),and the high precise rectification and registration can be fulfilled on each triangular region one by one.Experiments demonstrate that the method achieve precise registration of multi-source remote sensing images.
A CEOS Virtual Constellation is a set of individual space and ground segment capabilities operating together in a coordinated manner to implement the Earth Observation under uniform standards.The CEOS Virtual Constellation concept will provide a new approach to facilitate the rational and efficient operation of space resources.Based on the review of Virtual Constellation concept，this paper introduced the CEOS Virtual Constellation and the development of its six prototype constellations,which include Land Surface Imaging,Ocean Surface Topography,Atmospheric Composition,Precipitation,Ocean Color Radiometry,and Ocean Surface Vector Wind.Finally,the major problems of Virtual Constellation development are analyzed,and the application potential of the Virtual Constellation concept in our countrys space\|based Earth Observation field is pointed out.
High-resolution Synthetic Aperture Radar (SAR) satellites will provide valuable data for urban area monitoring and promote the identification of urban targets greatly,because targets take on multi-dimension characteristics in high\|resolution SAR images.Nevertheless,urban targets become very complicated in high-resolution SAR images due to the spatial complexity of urban targets,multi-bounce scattering between adjacent targets,as well as the inherent geometric distortions and speckle noises of SAR imaging.In this paper,literature was reviewed surrounding electromagnetic scattering and SAR imaging mechanisms,fine structure extraction and 3D reconstruction of urban targets,and future research focus of this domain was analyzed.
The high resolution and all\|weather observation abilities have made the synthetic aperture radar (SAR) one of the most important ways of ocean wind retrieval in large area.This helps us understand the physics process of all kinds of different oceanic phenomena especially in coastal area where SAR wind becomes more valuable.According to the method of wind direction determination,this paper reviews the progress in ocean wind retrieval form SAR data in detail,which includes linear feature based wind retrieval,external wind direction based wind retrieval and wind retrieval from other information contained in the SAR images such as the difference of incidence angle and multi-polarimetric backscattering.Lastly,a viewpoint of the development trend of SAR ocean wind retrieval is formulated.
K-nearest neighbour method,as a non\|parametric method,is so suitable to the classification and the parameter estimation of the remote sensing data which is non-normal distribution and has unknown density function.KNN has been widely used in the multi-source surveys and the estimation and retrieval of forest volume in boreal and sub-boreal.This paper introduced the basic principle of KNN method,firstly.Then,by comparing the traditional method of estimating forest volume with KNN method,this paper described in detail the characteristics of the KNN method and the difference between the KNN method and the K\|mean method.At the same time,the paper summed up the evaluation's model and the metric of forest volume estimation error.Then,a detailed description of estimation method of forest volume with remote sensing based on KNN method at home and abroad was developed.It was showd that multi-source information,which was used in the estimation of forest volume with KNN,was very important.The estimation of forest volume with KNN included two methods:plot-level and stand-level.The paper also included the detailed description of some factors which influenced the estimation of forest volume with KNN.Finally,It suggested that systemic research with KNN should develop in low-latitude areas.
Uncertainty of GIS is a study hotspot.The main study is mainly concentrated on traditional methods as probability theory.We use concept of lower approximation and upper approximation,take the attribute data of GIS as attribute sets for information system,then induct attribute data for equivalence class by different attribute assemble,and then use roughness,roughentropy,rough precision to measure the uncertainty of GIS attribute data.Then we find rough precision and roughertropy is descending along with thining in different knowledge granularities for having same attribute.It is corresponding with people's cognitive habit,and would give a new direction for uncertainty and congnitive study for GIS.
As the important tool of remote sensing analysis,remote sensors have been the research focus of this field.The number of the related literatures is also increasing.This present paper selected and analyzed the research papers of the past 10 years by word frequency,co-words,factor analysis and cluster analysis.The research focus and development trends of the remote sensor were obtained,and the author hopes that the result can bring some enlightenment to related domestic scholars.
