Multiple cropping index,which monitors the utilization condition of the arable land,is of great significance for agricultural production,climate change and so on.Due to the advantages of objectivity,high efficiency,low cost and better spatial distribution attribute,the remote sensing technology is widely used to monitor multiple cropping index.This paper reviewed the progresses of multiple cropping index using remotely sensed data in the latest decade.At first,it summarized several monitoring methods and pointed out the advantages and disadvantages of each method,and then discussed the development tendency in this field:(1) intensifying the researches on some special cropping systems,like fallow or abandonment;(2) intensifying the researches on multiple cropping index in some heterogeneous regions,like hilly or mountainous areas;(3) intensifying the researches on dynamic changes of multiple cropping index during a long period.
Accurate yield estimation of crop in region scale plays an important role in food safety.In this paper,two methods on regional yield estimation are firstly discussed,that is yield monitoring and yield simulation.Crop growth model can reflect the evolution process of crop growth,and ultimately predicts crop yield.However,due to the great uncertainties of input,parameters and model structure,the final simulation results are fairly uncertainty,especially applied to the regional scale.Yield monitoring,especially the application of multi\|source remote sensing data,can capture the true information of crop growth.But these information is instantaneous.So by using data fusion algorithm,more reliable regional yield estimation results are very useful.So after the discussion on the current major crop growth models,two common data fusion techniques,namely the optimization method and the sequential data assimilation method,were described.Some examples on the use of two methods were discussed.
Forest biomass as the most basic characterization of forest ecological system shows the management and the development level and reflects the complex relationship between material circulation and energy flow.Forest biomass is the basis for the research of the forestry and ecological research.Taking the Great Khingan state ecosysterm research station in Inner Mongolia as the study area,this study used airborne LiDAR point cloud data,combining computer programming to extract structure parameter of LiDAR point cloud data.Including the percentile height and the vegetation density as variables,combining with the field data to generate the regression model.Stepwise was used for variable selection and the maximum coefficient determination (R2) was 0.69,the RMSE was 0.34.Using IDL programming algorithm of LiDAR point cloud data to generate the resolution for raster images of 20×20 m2,and acquired the biomass map of the whole study area,the average estimating accuracy was 83%.
Models were established with the stepwise regression based on Landsat 8 OLI imagery and a survey of 55 plots in Yushan forest in Changshu,Jiangsu province to estimate the forest biomass,and discusses the results of prediction concluding accuracy.As much as 53 independent variables were selected out,including gray value of each band,the linear and nonlinear combinations between different bands of gray value (including 18 vegetation index),texture information,PC(principal component analysis),minimum noise fraction and so on.The Pearson correlation analysis between the 53 independent variables and forest biomass has been calculated to select the better characteristic variables.The results show that for all plots with no partition analysis,correlation coefficients of above\|ground and below\|ground biomass models are all higher than 0.4.The correlation coefficients were much higher when establishing above\|ground and below\|ground biomass models for three different forest types(coniferous,broad\|leaf and mixed) and they are all reached to 0.67 or even higher.For the predictions of above\|ground and below\|ground biomass,the result of mixed forest is better than broad\|leaf forest,and the result of broad\|leaf forest is better than coniferous forest.
For HJ-1 A/B satellite CCD data,The suitable inversion model of Xiamen sea area for chlorophyll a concentration is established based on HJ\|1 A/B satellite CCD data,which provides series time\|data of chlorophyll a concentration for monitoring the red tide in this area continuously.based on the measured spectrum data,the synchronous measured data of Xiamen sea waters and the spectral response function of HJ\|1B satellite CCD2,the correlation between each band’s reflectance and chlorophyll a concentration is compared confirming that the ratio of blue and green band is the highest correlation with chlorophyll concentration.The correlation analysis between the inversion results of five algorithm including OC3 and the measured chlorophyll a concentration is made,the resule shows that each model ‘s correlation coefficient is above 0.7.The chlorophyll a concentration inversion results of HJ-1B satellite CCD2 data on July 30,2013 were validated using the measured data of Xiamen sea during the same period,showing that the localized exponential
Absorption and backscattering coefficient are the important inherent optical properties of water and key inputs for bio\|optical model.They are highly related to the concentration of water constituents.Domestic researches on inversion of inherent optical properties are mainly focused on the ocean,but the retrievals of inherent optical properties of inland waters haven’t been paid enough attentions.In this paper,Lee quasi\|analytical algorithm is proposed based on marine water,and is improved to retrieve the inherent optical properties of Kuncheng Lake,which is an inland lake,based on field data collected on April 23,2010.Results show that coefficients of determination of total absorption coefficients R2 are all higher than 0.984 and the average relative errors ε are less than 14.2% for all validated field samples.To further verify the results,comparison between retrieved total absorption values and measured values at wavelengths of 440 nm、488 nm and 532 nm are made,which show fair retrieval accuracy.Coefficients of determination R2 are 0.655,0.742 and 0.826 and the average relative errors ε are 6.5%,3.6% and 3.4%,separately.As for backscattering properties,the retrieval values of backscattering coefficients have high correlation with reference backscattering coefficients at wavelengths of 440 nm,488 nm and 532 nm.Furthermore,the retrieval values of backscattering coefficient have a good relationship with the concentrations of total suspended matter at 532 nm,which prove that the inversion backscatter coefficients have a high level of creditability.The retrieval model established in this paper can provide an efficient approach for inherent optical properties’ retrieval of Kuncheng Lake.
FengYun-3A (FY\|3A),the first in a series of the second generation of polar\|orbiting meteorological satellites of China,has been successfully launched on 27th May,2008.The MicroWave Humidity Sounder(MWHS)is an important payload of FY-3A,which is for atmosphere sounding.FY\|3A/MWHS has been operational application on orbit for more than 5 years.In this paper,we try to make a cross validation for FY-3A/MWHS’ radiance calibration on orbit,to make the guarantee for quantitative application of FY-3A/MWHS.NOAA-17/AMSU\|B is the same kind of microwave radiometer with FY\|3A/MWHS,and we make an extensive analysis between them to acquire the best fitted global SNO samples.Then the radiance basic reference of NOAA\|17/AMSU\|B has been cross transmitted to FY-3A/MWHS.Based on the cross calibration,we achieved the space view bias correction and using of the correction the bias between FY-3A/MWHS and NOAA-17/AMSU-B can be better well.The technique of radiance cross validation and the analysis results will be the basis for two sounders’ remote sensing data application in NWP.
In order to improve the utilization efficiency of spectral and texture information of hyperspectral data,this paper proposed a method which based on the combination of the automatic subspace division method and rough sets theory for spectrum and texture feature selection.Firstly,we have got the primary selective spectral bands through the traditional subspace division method and rough sets method.Secondly,we calculated the texture characteristics of selective bands as above by the gray level co\|occurrence matrix method.Thirdly,we further completed reduction of selected texture information by rough sets method and then obtained the final selective bands of the spectral and texture.Using the CASI hyperspectral data which was obtained during the eco\|hydrological process experiment in the Heihe River region to validate the method.The original spectral bands,primary selected spectrum bands and final bands were used for classification by the SVM (Support Vector Machine).The results showed that compared with the classification overall accuracy of the original spectral data,that of the primary selected bands and final selected bands increased by 0.84% and 2.78% respectively,and KAPPA coefficient rise 0.01 and 0.035 respectively.The experiment results also indicated that the classification accuracy is able to further improve the extracting of the texture information of the spectral bands.
Due to technique and budget limitations,Remotely sensed data,with high spatial and temporal resolutions,can hardly be provided by only sensor.However,the ability to monitor seasonal landscape changes at fine resolution is urgently needed for global change.One approach is to ”blend” the data from coarse\|resolution sensors with frequent coverage (e.g.AVHRR) with data from high\|resolution sensors with less frequent coverage (e.g.Landsat).To combine the high spatial resolution of Landsat and high temporal resolution of AVHRR data,this paper selected a study area in Zoige ,Sichuan province,China.A method for blending NDVI of different spatial and temporal resolution to produce high temporal\|spatial resolution NDVI data set which has been developed based on ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model).The result shows that the new method can combine the temporal information of AVHRR NDVI and spatial information of TM NDVI and realize the reconstruction of high spatial and temporal resolution NDVI data set (the correlation coefficient of three pairs of MODIS NDVI and predicted TM NDVI are 0.89,0.91 and 0.85).This method maintains the temporal trend of high temporal resolution data and the detailed spatial difference information of high spatial resolution data,thereby providing an effective tool to build a relatively high resolution NDVI time series data set.
This paper proposed a method of identifying and eliminating water regions from shadows in remote sensing images based on statistical information of principal component analysis,and a method of shadow achieved compensation by combining the blue\|ray suppression algorithms and the statistical information of the companion regions.This paper first carried out the initial detection of shadow regions by utilizing the Normalized Difference Umbra Index(NDUI),and then identifyied and removed the water regions according to the statistics information difference of the water and shadow regions after principal component transformation,followed the accurate shadow masking by small regions removal and morphological algorithms.Subsequently,H,I and S components are compensated by blue\|ray suppression algorithms and statistical information of the companion regions.Finally,the results are converted back to RGB color space to complete shadow compensation.The methods proposed in this paper are tested by using the high\|resolution images of WorldView2 and UltraCam D.The results show that the proposed methods can effectively distinguish shadow regions from water regions and reduce the impact from the compensation process to the non\|shaded regions.
This paper introduces the design of remote sensing model integration platform and the key technologies.The purpose of model integration is to integrate the models into a universal platform,and the remote sensing models can be applied to study on this platform.With the help of the redevelop language built\|in this integration platform,we can achieve the model variable assignment,circulation,judgment,logic operations and so on.By this way,we can get the automatic correlation of multiple models between inputs and outputs,and realize complex simulation which single model cannot achieve,to assist to carry out the quantitative remote sensing research.In the end,a set of simulation test is carried out verifing the correctness of the integration platform.
Effective band selection algorithms can greatly improve the hyperspectral image processing speed and effect simultaneously.In order to automatically determine low signal\|to\|noise bands,a new image signal\|to\|noise ratio estimation (SNRE) method is proposed based on wavelet transform.Performing wavelet transform on each band image which is assumed to be only corrupted by additive Gaussian noise,and the mid\|value of high frequency component of the wavelet transform is used to estimate the noise variance,then further to calculate the SNR.This method is then integrated with three band selection methods based on information such as optimal index factor defined by variance and correlation coefficient (V_COR),maximal information (MI) and high order moments (kurtosis or skewness) combined with the divergence (K3_KL) to select bands respectively.These improved methods are evaluated by experiments of hyperspectral anomaly detection.Experimental results demonstrate that SNRE combined with MI or K3_KL which can further improve the results of anomaly detection.
Through the classification method study of high spatial resolution RS image based on multi\|scale image segmentation,the wetland information can be obtained in more detailed type and rapid way,and that is important for wetland protection.Taking the coastal wetland,located in Qingkou River estuary in Lianyungan,as study area,and the WV\|Ⅱhigh spatial resolution RS image and Arial image as test data,the images were divided into different level objects by multi\|scale segmentation,and then the wetland classification method was studied combing with spectral,shape,texture characteristics using the object as basic unit in different segmentation levels.The results showed that each wetland type in the study area had better classification precision by the method.Based on multi\|scale segmentation,various image characteristics could be fully used for the wetland classification.So,this method can obviously reduce the disturbance of salt\|and\|pepper noise in the classification results to get more accurate results.It is found that appropriate segmentation scale and parameter play an essential role in the process of classification based on multi\|scale segmentation.
Research on crop identification has an important significance for temporal,spectral and the time\|series of Normalized Difference Vegetation Index(NDVI) profiles features.In the paper,a series of NDVI dataset based on the Savitaky\|Golay filtering smoothing method was generated and the classification method based on object\|oriented was used in the study area of Hongxing Farm by HJ images.Then this paper discussed the effects on the multi\|temporal data classification accuracy by time\|series NDVI database.The results indicated that the object\|oriented classification method is suitable and feasible for the northeast area in our country,and compared to only using 3 temporal HJ images by the NDVI time\|series curves in classification which can improve crop classification accuracy,the overall accuracy improved by 5.45%,and the Kappa increased by 0.09.NDVI profiles features associating with multi\|temporal data is used in crop identification to enlarge the application of remote sensing data and have the potential of spread in the domain of agriculture.
To solve the limitation of the existing models for cloud removal in practical application,in this paper,a new method was proposed based on spatial and temporal data fusion models.First,the data,like TM image at target time was composed by enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) based on temporal change of MODIS data and spatial information of auxiliary TM data;Then,the pixels in target TM image where were contaminated by clouds and shades which were replaced by the compose data.The result show that the color of the replaced area is consistent with the color of area uncontaminated by clouds and shade.Ultimately,the precision of the replaced data is verified indirectly based on the data of target TM image and composed image without cloud and its shade cover.Compared to actual image,the result showed that the relative difference of individual band of composed data is less than 1%;The mean relative error of each band are 16.29%,12.92%,13.47%,12.87%,9.71%,11.84%,respectively;All correlation coefficients are greater than 0.7;The accuracy of non\|cloud and non\|shade area fusion data indicates indirectly that the accuracy of each band of the data to fill the area,contaminated by cloud and shade,is better than 83%.Therefore,the method proposed in this paper which can repair the data contaminated by clouds and shades from TM image and improve MODIS and TM data utilization level.
China is experiencing rapid urbanization process,timely and accurate quantification of the urbanization process is pivotal for the currently social and economic development in China.This study used Multiple Endmember Spectral Mixture Analysis(MESMA)model to extract impervious surface information from a time series of Landsat TM and ETM+ images data under the framework of Ridd’s Vegetation\|Impervious Surface\|soil(V\|I\|S)model.For MESMA implementation,minimum noise fraction transform(MNF)was applied to transform the TM or ETM six spectral bands into the MNF space and four endmembers representing vegetation,high\|albedo surface,low\|albedo surface and soil were determined for images acquired over the Beijing City.The results show that MESMA yielded relative accurate estimate vegetation,soil and impervious surface for the Beijing city.Accuracy assessment indicates that MESMA resulted in the lowest RMSEs for impervious surface,vegetation and soil are 14.6%,17.3% and 11.9%,respectively.Further,the MESMA model generated the low Mean Absolute Error(MAE)value.This work demonstrates that applied MESMA to a time series of the moderate\|resolution multispectral remote sensing image can be an effective way to monitor the dynamics of urban environment variables dynamics and urban expansion,which has great potential for urbanization monitoring with MESMA modeling under the V\|I\|S framework.
MODIS surface reflectance product (MOD09A1) is an important and basic product among series data products of MODIS.However ,it is found that the data still carry on the problem of stripe missing from original data in practical applications.With projection transformation ,the missing data shows new patterns expect the stripe in original data,and the commonly used methodologies is no longer applicable to eliminate the stripe.This paper used QC data that describes the MODIS data acquisition and processing quality within MOD09A1 datasets to locate missing strip,and use smoothing algorithm with the correct pixels in eight neighborhood for interpolation.The data randomly selected to acquire at different times which is processed with the mehod presented in this paper,and all results are good that demonstrates the universality of the method. Different methods used previously and presented in this paper where are used with the same data,and the reswts demonstrate the latter is better.The de\|strping data generated from simulated stripe missing data with the method in this paper is campared with the ture data,and the difference between the two is very small.The result shows that the method for removal of strip missing is effective,and information of non\|stripe missing pixels is not effected.
In recent years,as various high\|quality SAR imaging devices are widely used,mass SAR image data provides us with abundant evidence for analyses.Given the SAR images,this paper proposes a novel river extraction method which fuses the graph model and the river prior model.First,taking the complexity of the river contour into consideration,a regionalized model establishment method is proposed and a series of minimum bounding rectangles are combined to simulate the complicated river contour.Further,the image is segmented by a new regulation which is adaptive to the task of river extraction.Then,the river contour prior is utilized for river region recognition,removing the background noise.Experimental results demonstrate that in contrast to the traditional gray threshold based image segmentation method,the proposed method not only has the ability to accurately extract the river region and the extracted region cover the real river region with more than 90 percent,but also be superior to the counterpart in the performance of background noise removal and only less than 2 percent of the background are left in the extracted region.
Based on the integrated methods of ISODATA classification,map generalization,discrete surface features removal and shoreline tracking,this paper implemented the shoreline extraction by ENVI and ArcGIS.With four Landsat images acquired in 1979,1987,2000 and 2005,the shorelines along Hangzhou Bay were extracted.In addition,the shorelines were resampled to 10 levels at different spatial resolution from 30 to 960 m,and the fractal dimensions of shorelines and its variation were computed.Six selected coastal areas witnessed drastic shorelines changes were analyzed.The results demonstrated that the shoreline length of Hangzhou Bay has increased by 37.5 km from 1979 to 2005.Amongst the shoreline changes along Hangzhou Bay(includes both North and South Shores),the increases along North Shore(24.9 km)were larger than that of South Shore(12.5 km).The goodness\|of\|fits shows that there is a strong feature for shoreline of Hangzhou Bay,and the fractal dimension increased both from 1979 to 1987 and from 2000 to 2005,but it decreased from 1987 to 2000.An analysis of the six selected coastal areas,i.e.Ningbo,Shaoxing,Jiaxing,Jinshan,Fengxian and Luchaogang,illustrated that the shorelines of Jinshan,Fengxian and Luchaogang were mainly affected by the sediment deposition,harbor construction,coastal industrial constructions and land reclamation,whereas the shorelines of Ningbo,Shaoxing and Jiaxing were mainly affected by natural conditions,offshore aquaculture and intertidal zone reclamation.
This paper evaluated the accuracy of five large\|scale land cover datasets based on sub\|fractional error matrix,by taking Landsat TM/ETM+ image classification results of seven typical areas in China as the reference data,which provides the scientific basis for the use of datasets.The sub\|fractional error matrix can avoid errors caused by the scale difference between reference data and datasets,and evaluate the accuracy on sub\|pixel scale and reflect the classification accuracy and classification method error with different dominant fraction.The results show that:the overall accuracy of GLC2000 is the highest in all typical areas,is at 65.64%;and UMD is the lowest in all typical areas.GLC2000 has a higher classification accuracy in the areas covered by forest,cropland and grass;the classification accuracy of UMD is the lowest or the lower one in each typical area.The five land cover datasets have a lower classification accuracy in urban and other;while with a higher classification accuracy of grass and water in each typical area of arid region of northwest China and Tibet Plateau region.
Vegetation coverage is an important index to evaluate urban ecological environment.For the subtropical urban/suburb heterogeneous vegetation cover characteristics,this study selected NDVI Transform Model on the pixel scale,Vegetation\|Soil components Model (V-S Model) and Vegetation\|High albedo-Low albedo components Model (V-H-L Model) on sub\|pixel scale to estimate urban vegetation coverage of Guangzhou,and then compared the estimate precision of three models with the field survey data.The results show that scale of the model and brightness of background have different influence on vegetation estimation.NDVI Transform Model overestimates vegetation coverage is 27%,while V-S Model and V-H-L Model underestimate vegetation coverage are 23% and 5% on the overall estimation respectively.However,NDVI Transform Model has the best performance for high density(＞60%) vegetation areas with underestimating vegetation coverage is 4%,while V\|H\|L Model has the optimal estimation for medium (40%~60%) and low(＜40%) density vegetation areas,only underestimating is 2%.And the influence of background brightness is the smallest.Thus,NDVI Transform Model fits to estimate the vegetation coverage on high density vegetation areas,while V\|S Model and V-H-L model are suitable for low and medium density Vegetation coverage estimation in which V-H-L Model is the most optimal one.
The Yingcheng mining linear surface deformation is obtained in Jiutai City,Jilin Province by using of small baseline InSAR technology to link discrete DInSAR observations.Then the nonlinear deformation is retrieved by removing atmospheric delay phase ,and temporally continuous subsidence field is obtained.Verified by field survey,InSAR results have well consistent with the scope of mining and the destruction of surface buildings.The distribution of the mining subsidence and the changes of surface deformation over time in 2012 is emphatically analyzed,which revealed the distribution of mining subsidence funnel,development and evolution.
Rare earths are important resources which are required by modern technology.The high economic benefits from rare earths mining that lead to seriously over exploitation,which causes the serious environmental problems and needs to be monitored for the purpose of protecting environment.Remote sensing as a macro observing method is good at land cover change detection.There are many different kinds of remote sensing data,has become a important change monitoring data base.In this study,spectral information is used to calculate the normalized difference vegetation index (NDVI),and the NDVI of estimation of vegetation coverage.the Landsat\|TM/ETM+ and HJ\|1/CCD data are used together to monitor the rare earths mining circumstances,including mining numbers,mining starting date,mining close date,mining type,mining areas,and the change of vegetation cover from 1988~2012 at Dingnan,Jiangxi province.Through the use of HJ\|1/CCD satellite data,shortening the period of field monitoring,reduce costs,and vegetation coverage index can direct response,mining area of variations provides the reference for the protection work of the study area.
Bingcaowan area in Gulang County,Gansu Province,China,is at the south marginal part of Tengger Desert.Based on three Landsat TM images in 1991,2000 and 2009,land use/land cover distribution at the corresponding period was extracted by an object\|based image classification method.On the basis of the classification,the pattern of land use and land cover and the conversion among types were analyzed.The result indicated that,(1) the overall precision of object\|oriented classification was better than that of traditional classification method;(2) the area of farmland and residential land in Bingcaowan area continued to increase and expand towards the desert,saline,and alkaline land during 1991 and 2009;(3) mutual conversion among land use and land cover types which happened during 2000 and 2009 is obviously slower than that during 1991 and 2000;(4) the main driving forces of land use and land cover change are the construction of the second Jingtai Electric\|irrigating Project and ecological migration programs.
With the increasingly serious conditions of marine environment and resources,scientific and reasonable management of fishery resources is more important to sustainable development of marine resource.A fishery analyzed model by fishery spatial data can be built through GIS,which provides basic evidence for reasonable developing of marine resource.In this study,the design and development of fishery service system of the Bohai Sea is based on the Flex environment,B/S architecture and the fishery analyzed model which published on the ArcGIS Server.The system was realized the functions of online fishery data editing and fishery analyzed model calling through feature and geoprocessing service.Meanwhile,the data communicating between the client and server was realized through the open source library of BlazeDS and JavaScript language under the driving of Tomcat server.The objective of system is to achieve efficient query,visualization,analyzing and simulating the spatial distribution of fishery resources.
