effectively.However,there is still no MAB SAR,which will be used to improve the azimuth resolution nowadays.Thus,the study of this model can only be carried out by simulation.But,too many restricts are imposed on the simulations which vended on the open publications and then the results of these simulations are deviated from real MAB systems far away.In order to solve the prior problem,the principle and the echo data model of MAB SAR are introduced firstly.Then,according to the effects of the Earth rotation and yaw steering,the method of echo data simulation is proposed in MAB system.Thirdly,the simulation methods of azimuth phase errors in MAB system are presented.All the simulation methods and results of this paper have important reference value for the further study of MAB.
Ocean color data have been widely used in ocean productivity evaluation,ocean pollution detection,and related fields.But this technology was hindered by the limitation of data coverage,especially in cloud-prone and rainy day.This problem could be overcome by means of data merging.Based on the operational algorithm of weighted averaging,a new method that intended to find a suitable way to merge ocean color data without the support of field data,was studied on the chlorophyll-a(chla) concentration images of the East China sea.Comparisons among different chlorophyll-a concentration data were carried out,which demonstrated that different source data were comparable and consistent on the same day,and can complement each other in spatial coverage,which ensured the feasibility of data merging.Then,the adaptive weighted averaging was put forward and applied to chlorophyll-a(chla) concentration images on the same day.The weight of the individual source was decided by the ratio of the pixels occupied to the sum of the pixels occupied by the respective source.The visual and statistical analysis show that main capabilities of the method,included maintaining the detailed feature of the original data,ensuring the continuity and reducing the noise of the data,improving the spatial data coverage and credibility of the data.
This paper has explored the method of high resolution image classification by combining association rule mining and object-oriented method.Firstly,according to the theory of Classification Based on Association (CBA),and a modified classifier builder was discussed.Secondly,the object-oriented high resolution image classification was achieved by image segmentation,feature extraction,association-rule extracted and classifier building.After that,Class Association Rules (CARs) was mined by the process of CBA-RG.It was proved that these rules correspond with the features of the ground object.According to the order of “confidence → spectrum complexity → support → generation sequence”,a modified classifier was built based on these rules.Finally,we evaluated the precision of the classification result and compared it with the result of K-Nearest Neighbors.The experiment shows that the precision is relatively high and can move away from the dependence on the expert knowledge in a certain degree.
The paper takes Baiyangdian area in Hebei province,Yellow River Mouth area in Shandong province and Xuyi county in Jiangsu province as experimental regions,based on evaluation of aerial image quality,using control points matching algorithm and contrasts modulation method to achieve hundreds of UAV(Unmanned Aircraft Vehicle) images automatic seamless mosaic.Results show that the best quality aerial image is at Hongze Lake area,because flight zone and image distortion is not high;followed by Baiyangdian areas;the flying height difference,flight zone bending degree and maximum rotation degree of the Yellow River Mouth area are relatively high,which indicates the flight zone has a high twist degree,extent offsetting the center line of aerial images is heavy.Taking QuickBird remote sensing images of Hongze Lake in Jiangsu province and field monitoring sites verify the accuracy,which reaches at 81.26%,then certifies that result is reliable and feasible,and provides scientific references for work of UAV remote sensing images automatic seamless mosaic.
The classification of remote sensing image is the basis of studying the land change.Some traditional algorithms of remote sensing image classification have some problems such as low computing rate,low accuracy and hard for convergence.A multi-classification algorithm based on fuzzy twin support vector machine is presented.First,fuzziness is inducted into twin support vector machine by applying the fuzzy membership to every training sample,then fuzzy twin support vector machine is extended to multi-classification,finally,remote sensing image classification is achieved by the proposed method.The study indicate the proposed method is more precise than traditional support vector machine multi-classification algorithm,and has high anti-noise performance and feasible runtime.Fuzzy twin support vector machine is an effective method of remote sensing image classification.
The general road system is composed of the actual surface layer of asphalt concrete,cement,gravel link layer,cement stabilized crushed stone base and road grass-roots form of multi-media,which can be regarded as a cement concrete pavement system with characteristics of one-dimensional band-limited Weierstrass fractal.The electromagnetic scattering from this practical system of cement concrete pavement is studied by Finite-Difference Time-Domain method (FDTD),the bistatic scattering coefficient from three-layer pavement system is calculated concretely,the curves of scattering coefficient with varying angle and the frequency of incident wave are obtained.The varying regularity of scattering coefficient with the dimension of fractal of pavement,the frequency of incident wave,angle of incidence,the dielectric constant of pavement materials,the thickness of surface and base layer is analyzed,the influence of the packing of the cavity with a cylindrical shape inside surface layer on the scattering coefficient is discussed,the characteristics of electromagnetic scattering from the actual pavement system with band limited Weierstrass fractal is obtained.The numerical results show that the influence of fractal dimension of pavement,the incident wave frequency,angle of incidence,the dielectric constant of pavement materials,the thickness of surface layer,the thickness of the grass-root,the existence of empty surface on the scattering coefficient is very complex.
The high spatial resolution remote sensing image SPOT5 for study data,using object-oriented classification method,this paper compared the classification accuracy of pixel-based and object-based.The results show that pixel-based classification has certain limitation and object-oriented classification method takes advantage of the spectral information,geometry information,spatial information,even the context information in processing high resolution remote sensing image.Therefore,object-oriented classification method has more advantages and clear application prospect.
Hyperspectral reflectance features can be used as quick and nondestructive assessing method for soil properties,like soil total nitrogen content (TN),the ratio of carbon to nitrogen (C∶N),and so on.However,the prediction models are affected by many factors,such as particle size level of soil samples,spectral pretreatment termed spectral indices.In this paper,three-leveled particle size of 2,0.25 and 0.15 mm soil samples were prepared and Multiple Scattering Correction (MSC),First Derivative (FD) and Continuum Removal (CR) were taken as spectral pretreatment and compared to raw spectra (RAW).Correlation between TN and C∶N spectral reflectance show that the response of spectral reflectance to TN content can be improved by soil grinding,and that difference between TN and content,C∶N spectral reflectance of various particle sizes can be obviously reduced by FD,CR and MSC pretreatment.This study indicates that maximal correlation between TN content and C∶N spectral reflectance is located at 2250 nm and 2280 nm of FD-pretreated spectra of 0.25 mm soil samples.However,assessment accuracy of linear predicting models based on these single wavebands is less than that of Partial Least Square Regression (PLSR) derived from full wavebands.The highest RPD (3.49) and R2 (0.92),and least RMSEP (0.1 g/kg) were yielded by PLSR model for predicting TN content based on RAW spectra of 0.25 mm soil samples,however,which is not satisfying that the accuracy of predicted C∶N conducted by PLSR models derived from FD-pretreated spectra of 0.25 mm soil samples.The worse prediction of C∶N is perhaps related to less range of C∶N of collected soil samples,therefore,much more samples or types of soil should be collected so as to get larger range of C∶N for improved assessing capability.
Multi-source remote sensing data was used for mineral resource prediction study in little or non geological data. Western Ethiopia was the study area,ASTER and ALOS PALSAR single polar data were adopted as the remote sensing data source;magma tic liquation iron deposit was chosen as the typical mineral deposit.Firstly,remote sensing model was built for magma tic liquation iron deposit prediction,which included alteration,structure and lithology information related to deposits.Then,alteration information was extracted from ASTER data using band ratio and principal component analysis methods,and structure and lithology study was conducted by the fusion of ASTER and ALOS PALSAR single polar data.Finally,based on ArcGIS software,through the synthetical study of the remote sensing model,three promising mineral areas were predicted. For testing the accuracy of the model result was compared with the geochemical data in 1∶250 000 scale of the study area,and showed that were consistent. This study illustrated that remote sensing model provided a quick and accurate way for mineral resource prediction.
Using AMSR-E brightness temperatures and in-situ temperatures,this paper establish an air temperature inversion model (error=2.5 ℃) during the 2008 chilling injury.The minimum air temperatures are all smaller than 9 ℃.The temperature cooling duration in most areas is larger than 10 day.We further establish a chilling injury index based on three important chilling injury factors (minimum air temperature, air temperature difference and temperature cooling duration).The chilling injury degree gradually decreases from northern mountain areas to southern coastal plain areas.Severe chilling injury mainly occurs in northern mountain areas.Less severe chilling injury occurs mainly in central regions,paralleling to the coastline.General chilling injury and no chilling injury mainly distribute in coastal regions.The severe and less severe chilling injury equals to 40.1% of the total area of Guangdong.Orchards suffering severe and less severe chilling injury reach 49.61% of the total orchard area.
GDP is a key indicator of socioeconomic development,urban planning,and environmental protection,accurate estimates of the magnitude and spatial distribution of economic activity have many useful applications in resources and environmental sciences.Developing alternative methods may prove to be useful for making estimates of gross domestic product when other measures are of suspect accuracy or unavailable.Based on the summary and analysis of existing economic activity spatialization approaches,this paper explored the potential for spatializing GDP through China using night-time satellite imagery(DMSP/OLS) and land-use data.In creating the GDP linear regression model of secondary industry and tertiary industry,night-time light intensity and lit areas,under different types of land use,were employed as predictor variables,and the GDP statistical data was as dependent variable,meanwhile,model of primary industry based on the landuse data.To improve model performance,31 zones were created according to provincial administrative boundary.The model of primary industry is observed to have a correlation(R2) ranging from 0.7 to 0.95 in majority zones and R2 of secondary industry and tertiary industry modle is ranging from 0.8 to 0.98 in majority zones.A comparison of the results of this research with other researches shows that spatialized GDP density map,prepared on night-time imagery and land-use data,which reflects the GDP distribution characteristics more explicitly and greater detail.Meantime,the density map is significant sustainable economic development policies and basically explores the relationship between socioeconomic and regional ecological environment interaction.
Synthetic Aperture Radar (SAR) data has the advantage in detecting the crop types in South China where has the frequent cloudy and rainy days.Based on object-based method,this study used the multi-polarization satellite SAR data,including the dual-polarization ALOS PALSAR and polarimetric Radarsat-2 data,to discriminate the rice and dry-land crop in Haian county,Jiangsu Province.For dual-polarization SAR data,the intensity information was used.For polarimetric SAR data,besides the intensity-information-based classification,the statistica-properties-based method was also applied.The result shows that the L-band ALOS PALSAR outperforms the C-band Radatasat-2 data in discriminating the dry-land mulberry.Moreover,the classification accuracy from ALOS PALSAR based method (87.5%) is also higher than the other two results from Radarsat-2 data based methods (75% and 85% respectively).
Based on MODIS/Terra Snow Cover Daily Data MOD10A1,the snow spatial-temporal information of Jilin province including snow cover duration (SCD),snow cover onset dates (SCOD) and snow cover melting date (SCMD),are extracted and the variation characteristics are also analyzed.Then,the relationship between snow cover and climate are analyzed.And the climate data covers the temperature and precipitation data of 23 base stations in recent 10 years.The result shows that snow cover duration spans from 30 to 90 days in most regions of Jilin;it is in the eastern area that snow cover lasts for a longer time,SCOD begins earlier and SCMD ends later.There is a negative relationship between SCD and temperature and a positive relationship between SCD and precipitation,with coefficients of -0.7407 and 0.6875 of temperature in snow season and precipitation in summer respectively;there is a negative relationship between SCMD and temperature and a positive relationship between SCMD and precipitation,with coefficients of -0.5414 and 0.4647;on the contrary,with coefficients of 0.7430 and 0.5479 respectively.In all,snow remote sensing information were more sensitive to temperature than to precipitation,as well as to snow season than to summer;The lower the temperature was and the more the precipitation was,the longer the duration of snow cover.Temperature is more essential for maintaining snow cover.
Satellite multi-channel synthetic can reflect some features on the meteorological satellite imagery directly.This paper adopts MODIS multi-channel data after pretreatment,and uses “Day natural colors”,“Day microphysical”,“Day solar”,“Air mass” multi-channel RGB synthetic methods.This study combines with the actual rainfall distribution and the ground meteorological observation station data,taking once heavy rainstorm process in jianghuai river on July 13,2010 for example to qualitative analysis the microcosmic physical properties of severe convective process cloud systems on the satellite cloud picture and deduce cloud particles size and phasestate et al,also contrasts high temporal and spatial resolutions “Local Analysis Prediction System(LAPS)” mesoscale physical quantity fields and multi-channel combining analysis.The results show that satellite multi-channel RGB synthetic images can make pointed references extrude convection systems,cloud particles microcosmic physical properties etc in the form of color,and have definite accuracy and applicability,in favor of mesoscale severe convective weather monitoring such as heavy rainstorm.
The information of NDVI and Land Surface Temperature (LST) were extracted based on TM images in 2006 taking Guilin city as an example.Through the contrastive analysis of the two indices statistical characteristics in different landcover types,the paper discusses the linear relationship between NDVI and LST,regional altitude.The results show that the forest have the largest average value of NDVI,water is the minimum;the construction have the highest average value of LST,forest is the minimum.From the whole of the regional perspective,NDVI and LST have a significant negative correlation.The correlation between NDVI and altitude is segmented characteristics,whichis not apparent in low altitude area(160 m),when the altitude of more than 160 m,the correlation of both presents a significantly positive correlation.
Tropical forest plays important role in keeping carbon balance by its biomass,which occupies 40% of the world.In order to provide basic data for tropical forest change monitoring,object-oriented classification methods was applied to study tropical forest mapping technology using Ortho-rectified PALSAR images.The study includes multi-scales segmentation,feature extraction and rules establishment and so on.The study results show that the ratio of polarization features (HV/HH) is sensitive for forest and the object-oriented classification methods can identify forest primely,meanwhile the different classification results reflect the changes of forest between 2007 and 2009 clearly.
Using Landsat-5 TM image,this paper tries to find another way to determine the threshold when extract water body through image binarization based on spectral analysis and water indices model,and improves the defects of single threshold by studying the spectrum value of the typical objects and the principle of some different water indices model.Analyzing the spectrum value find that the spectrum value of shadow stay within a stable range,while the spectrum value of water change in a big range for different depth and the content of the sediment and contaminant.Due to the high overlap of the gray value in remote sensing data,it is difficult to define shadow and water with the formula:(TM2+TM3)-(TM4+TM5)>T,in which T is difficult to be determined.To solve this problem,the paper presents the weighted gray threshold method,which give each band a weight and then sum the product of gray value and its weight,to find the threshold.The shadow and water can be separated effectively through this method.The study conclude that the water area can be obtained more accurately with the method of both water indices model and weighted gray threshold method combined.
The capabilities such as weather-and-sunlight independence make synthetic aperture radar (SAR) become a powerful tool to earthquake emergency response.This paper show a summary of the parameters of the SAR systems that have been used to extract earthquake-induced building damage information,and the earthquake cases in which the SAR images have been used to monitor disaster,also gives a comprehensive introduce to all kinds of methods of extracting earthquake-induced building damage from SAR images.These methods are classified into visual interpretation and automatically information extraction by computer.The latter includes the single-temporal method with textuure and polarization features,and multi-temporal method with intensity and coherence features.All of these methods were compared to find their disadvantanges and this paper discussed the development of the methods of earthquake-induced building damage information extraction.
The application of remote sensing technology in the studies of landslides in China has a history of more than 30 years.As the chief means of landslide research,remote sensing technology has played an important role in the damage investigation and risk assessment of landslides in large-scale engineer construction.With the rapid development of remote sensing technology,especially the utilization of high-resolution satellite data,which had become an indispensable mean for landslide identification and inventory,monitoring and assessment.This paper reviewed remote sensing applications for landslide research in China,including four aspects:① application of remote sensing in landslide inventory;② application of remote sensing in large-scale landslide study;③ application of remote sensing technology in landslide monitoring;④ application of remote-sensing in hazard risk assessment.With the gradual perfection of remote sensing technology theory,and the gradual improving of spatial resolution,time resolution and spectrum resolution of remote sensing imagery,the remote sensing technology has become one of the most important means to carry out landslide inventory,dynamic monitoring,early warning,and damage evaluation.
Road network extraction from high resolution remote sensing images is an important problem in ground object auto-extraction.In this paper,the theory and the procedure of road network extraction from high resolution remote sensing images were firstly introduced.Then the representative methods of road network extraction were analyzed and classified from the view of elements level.Finally,the difficulties of the road network extraction are pointed out and the prospect of this field is forecasted.
Using ArcEngine GIS component technology,Doppler weather radar information was inversed in GIS map after being gridded,real-time information of automatic station was displayed and analyzed in GIS map,and then the short-term early-warning platform had been established in VB6.0 development platform.This platform had achieved dynamic monitoring to radar data,automatic station live data,and warning signals under the GIS,and can release early-warning information automated through mobile phone,so that it has strong decision of early-warning ability,which can help to improve the now-casting warning level on sudden disastrous weather of meteorological station.
The mass appraisal of real estate is different from the traditional single-case appraisal,should be based on a powerful and detailed database.Thus,how to build a appraisal database with high-reliability,high scalability and high level of integration,which has become a key issue.Based on the analysis of real estate data,this paper firstly comes up with a technical route how to build a mass appraisal database based on GIS technology,constructs the overall structure of the database,then studys the methods of integration of thest data.Finally,the illustration of Shenzhen city proves the effectiveness of the database construction.
