The high precision calculation of radiation is one of the important factors guaranteeing the precision of CO2 retrievals from satellite shortwave infrared measurements.Generally speaking,scattering radiation of earth atmosphere is polarized.So the accuracy of traditional scalar radiative transfer model cannot satisfy our needs about retrieval.Specially,aimed at the instruments tracking principal plane on the American OCO-2(Orbiting Carbon Observatory-2) or Chinese TanSat(Global Carbon Dioxide Monitoring Scientific Satellite),vector radiative transfer model is more necessary.In target mode of special carbon satellite,the viewing angle of satellite changes so large that the plane-parallel hypothesis is not suitable.Thus we need pseudo-spherical approximation to improve the accuracy.In this article,we summarize radiative transfer models using for atmospheric CO2 retrieval from shortwave infrared measurements.Firstly,the development situation of different kinds of radiative transfer models based on different numerical methods of radiative transfer equation is presented.Aimed at the requirement of CO2 retrieval about weighting function,linearized models can provide analytic weighting function,which are more stable and efficient.Then,we summarize some kinds of linearized vector radiative transfer models briefly.Moreover,the differences and similarities of several common radiative transfer models in CO2 retrieval are analyzed and compared.At last,the requirements of high precision CO2 retrievals about the speed of models and the necessity using acceleration to optimize calculation are discussed.The research about methods of accelerating radiative transfer computation is summerized at the end of the paper.
Synthetic Aperture Radar (SAR) remote sensing,with its multi-mode,multi-polarization and high-resolution capabilities,which becomes more and more sought after by archaeologists,for the first time,the mechanisms of SAR remote sensing for applications in archaeology and prospects for future refinements was described.Then the state-of-art of SAR remote sensing for archaeology,particularly with regard to applications of target detection and discovery as well as for monitoring and diagnosi,was reviewed.This was followed by a detail discussion of the key scientific issues and research problems.After that,an archaeology-oriented SAR remote sensing framework was proposed to promote further development of the discipline.Based on practical experiences,much more use of mechanisms,methodologies and models of SAR remote sensing in archaeology were argued,through the launch of scientific projects focused on representative study sites.Such projects if focused on the World Heritage sites of China and elsewhere could significantly contribute towards their conservation and sustainable development.
In Dujiangyan world cultural heritage site,Sichuan province,dealing with 23 ENVISAT ASAR images observing in 2008~2010,the Persistent Scatterer(PS)and Distributed Scatterer(DS)candidates are extracted respectively by means of scattering and coherence mechanisms.Four samples were selected to study the spatial-temporal pattern of PS and DS.The difference between PS and DS is further investigated synergistically using the layover/shadow and land cover images.Experimental results show that PS candidates extracted by means of the amplitude that are high coherent and primarily distributed in urban regions;furthermore,a small amount of PS candidates are distributed in water bodies.According to the recognition criteria applied in this paper,DS candidates are extracted together with the optimal window size(7×35)for the coherence image generation.DS candidates are mainly distributed in bare surface and cultivated lands.Which are affected by human activities and seasonal changes,demonstrating coherence vibration in time series.The study of PS and DS with regard to space-temporal distribution pattern and DS with regard to space-temporal distribution pattern and characteristics provides clues for their discrimination as well as identification.
Visible and Near-Infrared Reflectance Spectroscopy (VNIRS) has extensively been used to estimate soil total nitrogen (TN) concentration,and can provide a rapid,convenient method for quantitatively obtaining soil TN content in a wide range of areas.In this study,we evaluated the prediction ability of Visible and Near-Infrared Reflectance Spectroscopy (VNIRS) for estimating soil TN in the Sanjiang Yuan regions of Qinghai province.Firstly,we collected about 146 surface soil samples (0~30 cm),including four soil types during the period from August 7 to 17 of 2012 in Yushu and Maduo counties;secondly,we respectively measured soil reflectance spectrum by ASD FieldSpec 4 portable spectrometer (Analytical Spectral Devices,Inc.,Boulder Colorado,2012) with the spectral range of 350～2 500 nm,and soil TN by using Vario EL Ⅲ element analyzer of ELEMENTAR Inc.in the laboratory;and then we respectively adopted PLSR and BPNN models to relate soil TN to raw spectral reflectance and its four pre-processing transformations for the overall soil samples and each soil types samples.The results showed that the average coefficients of determination(R2) of calibration and validation for BPNN are respectively 0.87 and 0.81 with the mean RPDval of 2.28,whereas those of PLSR model are 0.75,0.72 and 1.95 respectively,which suggest that BPNN has a better prediction ability than PLSR as a whole;The combination of BPNN and the raw reflectance spectrum (REF) and its all pre-processing transformations performed a good or closer good prediction ability for different and overall soil types;whereas the combination of PLSR model and REF,Log(1/R),BD produced a rough or good prediction ability for estimating TN,however,FDR and SDR with poor prediction ability,especially SDR (R2 cal<0.5,R2val<0.5,RPDval=1.10~1.27) hasnt the ability to predict soil TN;As a whole,TN estimating from the overall soil samples can produce more stability prediction accuracies than single soil types,whereas that from single soil type samples can reflect the difference among soil types;BPNN model accuracies are superior to those of PLSR model,but PLSR has stronger operability,and can show the difference among soil types,and different among transformation indicators as well as.
Radarsat-2/SAR and MODIS optical remote sensing data were used to retrieve soil moisture in Dingxi,which was located in the semi-arid region of the Loess Plateau,China.Based on the Normalized Difference Vegetation Index (NDVI) extracted from MODIS data,then estimated crop Vegetation Water Content (VWC) were applied to the Water-Cloud model to separate the contribution of the vegetation scattering and absorption.Using the cross polarization (VV/VH) combination model retrieve the soil moisture of crop covering under the preliminary discussion.The results have shown:Before the effect of vegetation removal,the value of model retrieved soil moisture was significantly under estimation.After separation of vegetation effect,the correlation coefficient R increased from 0.13 to 0.44,and it was through the test of significance of α=0.01;The standard deviation SD reduced from 5.02 to 4.30,which effectively improved the accuracy of model retrieved soil moisture.Most of soil moisture content was between 10％~30％in the study area,which was consistent with the field situation,and can better reflect the regional distribution of soil moisture information.This study showed that optical and microwave remote sensing data has the larger applacation potential in improoing the accuracy of soil moisture readings in agricutural fields.
Based on Landsat8 image on August 15,2014,this study estimated the land surface temperature(LST)of Xi an City by the method of split-window algorithm.Then quantitatively estimated the scope of urban heat island.Besides,Estimated a variety of surface energy component based on the surface energy balance equation.The results show that:① Urban heat island of Xian central areas mainly distributed in densely inhabited district,populated area and business-intensive areas,economic and technological development zones with less vegetation cover;② Thermal and bowen ratio was positively related to surface temperature,anthropogenic heat and temperature were not significantly correlated,net radiation and latent heat were significantly negatively correlated to the surface temperature;③In the structure of the surface energy of the urban heat island,differences in sensible heat and latent heat mainly caused the differences in the urban heat island.
Vegetation Water Content (VWC) is one of the main limiting factors of affecting growth of plants,which is an important parameter to character vegetation physiological status and morphology.Quantitative estimation of VWC by utilizing remote sensing technology has important significances for agricultural drought monitoring,crop yield estimation and scientific research.In this paper,six periods ASTER images and ground\|based measurements of VWC at 11 sampling sites are used to develop the empirical inversion model of VWC,which are obtained during the Heihe Watershed Allied Telemetry Experimental Research (Hi\|WATER) in 2012.The four types of vegetation indexes (NDVI,RVI,SAVI,and MSAVI) are adopted in this study.We analyze the relationship between different vegetation indexes and the measured VWC,then develop and validate these VI\|based empirical models for VWC retrieval.Results show that the correlation is very high between the measured VWC and the selected four vegetation indexes (R2>0.846).It indicates that we can retrieve VWC with high accuracy by using the four types of vegetation indexes.Among these vegetation indexes,the MSAVI\|based retrieval model achieves the highest accuracy and the root mean square error (RMSE) is only 0.794 kg/m2.The study also prove that the developed VWC retrieval model with MSAVI is reliable and an effective way for monitoring spatial variation of regional VWC.
Net primary productivity(NPP) is an important indicator of vegetation’s carbon sequestration capacity,with the rapidly rising of CO2 concentration,NPP became one of the hotspots in the research of global climate change.Using the modified CASA model based on the previous studies,integrating with TM/ETM+ image data and terrestrial weather observation data,estimated Wuhan City’s Winter NPP on the spatial resolution of 30m from 2001 to 2010.The result shows that the average winter NPP in Wuhan is 8.55 gC/m2·m.There was an increasing in winter NPP from 2001 to 2010,and each region has different velocity,Jiangxia is the highest,while shrub has the highest velocity and NPP among all vegetation types.Wuhan winter NPP has an spatial distribution of increasing from city center to around,and the region has the highest NPP which changed from Huangpi to Jiangxia in the past 10 years.
Unmanned aerial vehicle (UAV) is an effective tool and an appreciate alternative mean to supply,even partially replace the traditional ground investigation,especially at large scale investigation,due to its greater availability and flexibility.However,there is little study on area estimation using UAV in domestic,and this investigation approach is not yet adopted operationally.Therefore,it is necessary to explore the applicability and flexibility,and point out the issues that should be noted at the area estimation processes with UAVs in practical cases.In this study,the area estimation of rice,main crop in south China was implemented in Yangchun,Guangdong Province.Satellite imageries were acquired at key growth period of late rice.Support Vector Machine (SVM) was used to generate the classification of arable land.This study constructed 300 m×300 m square grids covering the whole study area.Then,grids that contain the arable land in classification were reserved to form the population.After that,we proposed a sampling method to reduce the cost and made full use of UAVs’ intensive investigation ability.This method included four steps:firstly,stratified random sampling method was used to select investigation samples from 300 m×300 m square grids and the variance of those samples (V1) was calculated.Secondly,big rectangle grids were built up and covered the whole study area,and each big rectangle grid contained 5×6small square grids which may be from different strata.Thirdly,the variance of 5×6small square grids (V2) in each big rectangle grid was calculated and compared with the variance of selected samples in step1.If the V2 of a given rectangle grid is equal to or near to V1,the big rectangle grid can be used to replace the samples selected from step1.Fourthly,according to the required sample size and the comparison results in step 3,we selected three big rectangle grids as final samples for UAVs investigation.Ratio estimator and difference regression estimator were adopted to estimate the sowing area of late rice in Yangchun of Guangdong Province in 2013.Their area estimations were 22 501.1 hm2 and 22 781.1 hm2,respectively,and their coefficient of variation were 8.84% and 1.03%,respectively.Above the results suggest that the UAVs investigation provide reliable ground truth.The proposed sample selection method in this study enhances the feasibility and operability of UAVs in crop area estimation.
Urban heat island (UHI) phenomenon has attracteds more and more attention.Radiative transfer and exchange between urban canopy is blocked by the surrounding buildings,which is one of the important reasons for urban heat island phenomenon.Sky view factor is a morphological parameter that describes the degree to which the sky is obscured by building block,and has been commonly used to analyse UHI.In this study,SVFs was estimated by 3D building data with a high spatial resolution for Australia Adelaide central city,and compared with SVFfish-eye calculated from fish\|eye photos.On the basis,the paper discussed the relationship between SVFs and urban heat island intensity(UHII) in different times of various seasons.The results show that SVFs is very consistent with SVF fish-eye,and the correlation coefficient is 0.97;and there exist a high negative linear relationship between SVFs and UHII at night time for all seasons under clear weather conditions.The correlations are significant positive in the noon of the summer,autumn and winter;while the positive linear correlation is not obvious in Spring.Under clear,cloudy and rainy weather conditions,there are little difference between SVF and UHII at night time,however,the positive correlation under clear sky is greater than that under cloudy and rainy weather conditions in daytime.
Land surface albedo,defined as the fraction of incident solar radiation reflected in all directions by the land surface,is one of the most important parameters controlling the climate change.Albedo directly determines the amount of solar energy absorbed by the ground,and hence,the amount of energy is available for heating the ground and lower atmosphere and evaporating water.The study on albedo changes through space and time is crucial to understand the global radiation balance and its influence on climate and vegetation dynamics.In this study,the intra\|daily,daily,monthly and seasonal variation of land surface albedo in the North Tibetan Plateau is analyzed using the ground observation data and then compared with MODIS derived albedo using narrowband to broadband conversion algorithms of land surface albedo developed by Liang.The results show that the intra\|daily changes of albedo in the North Tibetan Plateau is obvious,which indicated that it is high at mooring and afternoon with a larger range of changes,and low at noontime with a smaller range of changes ,which is characterized by “U” type.There is a statistically significant negative relationship between the observing albedo and the Solar Elevation Angle (SEA) with a correlation coefficient of -0.91,which implys SEA is main factor affecting seasonal changes of albedo.The albedo is deceasing with increase in SEA from morning to noontime,and the lowest value of albedo occurs in 14:00 to 15:00,then,albedo is increasing with decrease in SEA from noontime to afternoon.The highest albedo occurs at 8:00 of the morning in summer and at 18:00 of afternoon in other seasons.At seasonally,the highest albedo is in winter with 0.28,followed by spring and fall with 0.23,and the lowest averaged albedo is 0.19 in summer.The albeo derived from MODIS is very consistent with from ground observations at around 12:00 MODIS crossing time.However,at about 13:00 MODIS crossing time,MODIS derived albedo is systematically lower than observation data with lower than 14.29% on average.Compared to the ground observation data,the inter\|daily albedo from MODIS is more fluctuant and less smooth.
Forest structure parameters are very important inputs for ecological and hydrological models.The spatial distribution of these parameters is urgently needed by distributed eco-hydrological models.It is a great success in obtaining vegetation parameters using airborne LiDAR(Light Detection and Ranging),which has the powerful detection ability of forest spatial structure.This paper selected Tianlaochi catchment of the upper reaches of Heihe River as the study area and forests as study object in the study area.First,we derived a vegetation classification map by high resolution images data(Geoeye-1)in the study area.And then,forest structural parameters(i.e.,canopy height,crown width,diameter at breast height(DBH),leaf area index(LAI))were retrieved based on airborne LiDAR data.Finally,the retrieved results were validated by field investigation.The results show that it is a good way to estimate the forest structural parameters using LiDAR data.There were good correlations between the measured value and estimated value(i.e.,canopy height,crown width,and LAI).Correlation coefficients(R2)were 0.98,0.84,and 0.73,respectively.In addition,we analyzed the variation of average canopy height and LAI with the increase of elevation.This study could draw the conclusion that airborne LiDAR technology is better way to retrieve forest structural parameters than passive remote sensing,but it still needs to be improved.The retrieved parameters will provide inputs for the distributed eco\|hydrological model built in the subsequent research.
The data of the Medium Resolution Imaging Spectrometer (MERSI) on board FY\|3A were made available since the end of 2008.It provides the data with five bands at 250 meter resolution,which captures abundant vegetation information and is a unique data source of the similar sensors in the world.However,few literatures reported the utilization of these data.In this paper,we used the spectral data during the growing season of winter wheat in Gucheng experiment site,Hebei province in 2013,to calculate the vegetation index,and combined with the NDVI data which calculated from the 250 meter MERSI data,to establish a linear transformation model Y=1.1458X+0.1916 between the two NDVIs.At the same time,we used the spectral NDVI and the measured leaf area index to establish a NDVI\|LAI conversion model Y=0.0899e4.459X.Then,we used the 250 meter MERSI data to retrieve the leaf area index for Taihang piedmont area of winter wheat.Finally we compared the results with the observed LAI in the field and the MODIS\|LAI product in the same period.The result shows that,there is a good exponent relationship between the leaf area index retrieved from MERSI\|NDVI data and the observed LAI in the field,and then its spatial distribution was similar with MODIS-LAI,but MODIS\|LAI was significantly smaller.
Surface albedo is the key parameter to quantify solar radiation budget on land surface,which deeply feedback to local or regional climate system.Usually,vegetation has altered climate change by modifying energy budget in different physical(albedo) and biological processes(evaportransporation).And physical process has become one of important factors in altering land surface process and climate change.Combined with plant functional type data with surface albedo datasets from MODIS land surface products,this study tried to examine the pattern of plant functional types and their influence on the surface albedo variation from 2008 to 2013 in Beijing.The variation of Plant function type has influenced surface albedo change with their mean surface albedo from high to low in Beijing as follow:cropland,urban and built\|up,shrub,deciduous broadleaf trees,evergreen needleleaf trees,water,which indicated that forest has lower albedo than crop and urban due to its high absorption for solar radiation.Surface albedo in Beijing has heterogeneous patter with high albedo in east and south,and with low albedo in west and north.Meanwhile,seasonal fluctuation of surface albedo was detected with slowly decreasing trend by -0.79×10-3per year from 2008~2013 in spring and summer.
In order to analyze the sensitivity of Atmospheric Model and Ground Elevation to FLAASH model,the effects of Ground Elevation and atmospheric Model in FLAASH Model were systematically analyzed with two landsat8 OLI images which located in atmospheric model transition area of Mid-Latitude Summer and Sub-Arctic Summer.Preliminary results showed that:① The average difference of reflectance image was less than 0.21% ,which revealed that FLAASH model isn’t sensitive to atmospheric Model and Ground Elevation in the case of a single image;② In overlapping region of the two neighboring images,the average difference of reflectance image increased from 0.5% to 1.29% in visible bands,which show that sensitivity of Atmospheric Model and Ground Elevation to FLAASH model significantly increased;③ The FLAASH model can not only eliminate the effect of atmospheric scattering and atmospheric absorption upon Landsat8 images,but also can enhance image information.The effect of atmospheric correction was obvious especially in shortwave bands.
:In this paper,based on MODIS-NDVI 16 d synthetic datasets of growing period of rape cultivation,we extracted the three growing seasons of 2008~2009,2009~2010 and 2010~2011 of winter rape cultivated area in Qidong city,Jiangsu province by remote sensing method.According to the phase change to the value of NDVI of rape growing period,the NDVI timing curve was established;after excluded non-cultivated land data by utilizing threshold,and using Minimum Noise Fraction Transform (MNF) method to compress the data,finally we used Spectral Angle Mapper to determine rape spatial distribution of rape growing region and comput the rape planting area.Compared the results with the actual statistical area,the accuracy was more than 90%,which indicated that this method serves as a rapid method for monitoring rape planting area,has its good accuracy for monitoring.
Fractal is locally similar to the whole in some way.Different types of land as a feature of goesciences is reflected by the remote sensing images,brightness—area of the pixel mode is also in line with the fractal theory.The paper via the sum method of fractal theory distinguish different land use types,and this method can make good of classification results for ground samples quantity selected in supervised classification in a certain extent select.Taking land use classification in 2003 and 2009 of Panyu Guangzhou as an example,the paper separated from the background of forest vegetation in the pond,and the water be separated from the background,the unused land type was isolated from the whole background using ETM/TM data based on fractal method.Finally,respectively total classification accuracy of different types of land are 94.6685% (2003),91.1384% (2009),and probes into some problems related to land use change in this area.
Landsat satellite data is widely used in monitoring land cover change and land cover classification by its medium resolution and long time\|series records.In this paper,twenty Landsat TM/ETM+ images in Qitaihe district in Sanjiang Plain were collected,and quantitatively processed for time\|series ground surface reflectance stacks from 1989 to 2012.Then,the forest index and wetland index were designed,and the spectral characteristics and their time\|series variation features of different land covers were extracted from these time\|series reflectance stacks.Thirdly,a decision tree\|algorithm was designed to classify different land covers and detect the temporal change of vegetation land\|types from 1989 to 2012.Finally,the classification result was validated by the ground survey data,with an overall precision of 90.04%,and a Kappa coefficient of 0.88.The result proved the potential of time\|series Landsat images for land\|cover and land\|use change.
In order to systematic evaluate the precision and reliability of the monitoring result of time-series InSAR and further exploit the geographic information hidden in the monitoring result,two different time-series InSAR have been carried out to monitor the Tianjin city and its surrounding ground subsidence.Based on the leveling data,the monitoring results of different time-serise InSAR and the night light data,a systematic algorithm has been proposed to evaluate the precision and reliability of the InSAR’s results.Also the ArcGIS interpolation and gradient calculating method has been used to analyse two seriously subsidence regions’ gradient of the study area.The results show that:①Monitoring precision and reliability of the time-series InSAR is very good;②Most of the seriously subsidence regions locates in the surrounding areas of the Tianjin city,while the Tianjin city has not subsided nearly.③Large subsidence gradient mostly distributed in the edge of the “sinking funnel”,there is no direct link between the subsidence rate and gradient.
The improve fusion algorithms of Pansharp,HPF,Gram\|Schmid and SFIM were studied in this paper based on the pan and multi\|spectral images of GF\|1 in Beijing Capital International Airport and the area around.We evaluated the gains of spatial information and the fidelity of spectral information using the indicators of the standard deviation,the entropy and joint entropy,the mean grads and the relative deviation.The results showed that all of the four evaluation indicators can improve the spatial information and keep the spectral information of images as well for GF\|1 images.The utilization of the images was improved.The comprehensive effect of Pansharp was the best.The method of HPF obtained the clearest boundaries.The result had the highest fidelity of spectral information which was calculated using the method of SFIM.Gram\|Schmidt represented the best in the near\|infrared band fusion.The study results indicates that GF-1 images will serve the production and science research better if we use different fusion algorithms and parameters according to different study purposes.
Precious studies have approveds the advantages in remotely sensed land cover to stimulate spatial distribution of population.However,these studies also showed the shortcomings in revealing the details of population distribution within a single land-use type.This study presents a method which can improve the spatial distributing of census data:re-classification of urban residential areas.By using DMSP/OLS night\|light imagery data as an information source indicated the urbanization level,the urban residential land-use data in the middle reaches of the Yangtze River which were reclassified under the support of GIS technology.based on the population regionalization,a linear regression models were established with integrating the reclassified urban residential land\|use data with the rural residential land\|use data.Then the models were employed to estimate the population distribution in the middle reaches of the Yangtze River in 2010.The results showed that the urban residential land\|use data reclassified by the night\|time imagery offered urban population distribution with a better accuracy,with R2 of partition models raised 0.8 and the overall average relative error reduced by 12.32%.Compared with the conventional statistical regression method,the modified model can improve the spatial accuracy of population data at county scale.
Vegetation coverage is not only the basic data to describe the regional ecosystem,but the important surface parameter in many global and regional land surfaces process,ecological and hydrological models.For Ejina Oasis,the desert riparian forest vegetation coverage can not be accurately described by remote sensing images mainly based on LandSat 30 m resolution.However,the characteristics of the high resolution imagery are the clear outlines of target objects,the abundant spatial detail information and so on,which contribute to classify the fragmental and strongly heterogeneous vegetation coverage information based on arid background.This study mainly extracts primary vegetation cover types,including farmland,Populus euphratica,Tamarix chinensis,grassland,barren\|land and so on,from QuickBird imagery in Ejina Oasis through using object\|oriented classification method.The overall accuracy and Kappa coefficient of object\|oriented classification result are 84.71% and 0.7986 respectively.This study shows,the classification results are better and able to meet the accuracy requirements through the use of object\|oriented classification method to discriminate vegetation cover information from the high resolution images.
To solve the problem of geometric distortion caused by oblique aerial image,we presented an oblique image correction method based on the spatial transformation.First,the size of the new image and the new image coordinate system of the original image could be obtained according of the mathematical model of coordinate transformation and the sampling frequency of the new image,and then,collections of points were built in the new image coordinate system.Eventually,the inversion transformation model was derived and linear equations about the image coordinate of the original image were established.After solving the equations and transforming the solution into pixels,the digital values of the points were obtained with the nearest neighbour interpolation.Matlab was used in the study,it showed that the results of oblique image correction was good,and processing time was shorter than the traditional method.The method could be used to deal with the oblique image registering,mosaic and modeling.
A Truck-mounted Multi\|frequency Microwave Radiometer (TMMR) was set up by State Key Laboratory of Remote Sensing Science in 2007.Up till now,brightness temperatures of several typical land surfaces have been obtained using TMMR through ground\|based experiment.The experimental data substantially supported researches in algorithm development and model validation of passive microwave remote sensing.In this paper,an overview of TMMR characteristics,calibration and operation was introduced,followed by the applications of the TMMR observing data over different land surfaces,i.e.soil,vegetation and snow.This work provides a valuable reference for designing and performing an experiment using TMMR.
Sparse representation using the trained dictionary can reflect the inherent characteristics and structure of signals.A novel fusion method based on the à trous wavelet transform and joint sparse representation for multi\|spectral image and panchromatic image is proposed,aiming to solve the spectral distortion.Firstly,the IHS transform is applied to multi\|spectral image.Then,the panchromatic image and the intensity components of multi\|spectral image are decomposed by à trous wavelet transform.The trained dictionary is learned from their low components.By exploiting joint sparse representation model on their low frequency components,common component and innovation component can be obtained.The finally result is obtained by fusing the sparse coefficients.Experimental results on urban area and mountainous area from IKONOS satellite indicate that the fused image has higher spatial resolution and great spectral fidelity.And the proposed method outperforms traditional methods by visual analysis and quantitative evaluation.
Data automatic assembling system for a wireless sensor network in Heihe Watershed Allied Telemetry Experimental Research was released officially in July 2012.With the surge of the number of data applicants,accessing control mode and the download process adopted before which has been unable to meet users’ demand.In response to the users’ need in sharing application of Automatic Data in Heihe River Basin,this paper combines downloading system in Heihe plan data management center with downloading system in Automatic Assembling System for a wireless sensor network in Heihe Watershed Allied Telemetry Experimental Research to improve access granularity,authorization,data generation and acquisition mode,which greatly improves the efficiency of data sharing which is significant for automatic observation data for online sharing.
