20 April 2019, Volume 34 Issue 2
    

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  • Lin Yi, Zhang Mengdan, Zhang Lifu, Jiang Miao
    Remote Sensing Technology and Application. 2019, 34(2): 225-231. https://doi.org/10.11873/j.issn.1004-0323.2019.2.0225
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    Hyperspectral Light Detection And Ranging(LiDAR) is a research direction that is being passionately advanced by both of the communities of LiDAR and hyperspectral remote sensing,because this frontier technology is of high potential for providing a feasible way to realize the beyond-3D RS.Some prototype systems have been developed and principally validated,but,so far,the fundamental technologies aiming at the core circles of its functioning are still in shortage.One of the representative circles is that the backscatting signals of different spectral bands are affected by the incidence angles of lasers,and this angular effect restricts hyperspectral LiDAR from achieving high-performance RS.In order to better grasp this angular effect that is caused by the morphology of object surfaces impacting the spectrum-location-synchronous data collection,this study explored its underlying characteristics,in the case of applying the Finnish Geospatial Research Institute-constructed hyperspectral LiDAR prototype system for measuring the trunk of a Birch tree.The rules of its different spectral bands responding to different laser incidence angles were analyzed and deduced,e.g.,for all of the spectral bands,their angular effects become weakening along with the laser incidence angles increasing.The findings of this study can provide new knowledge about the underlying mechanism of the angular effect,in favor of the following hyperspectral LiDAR researches on system development,data processing,and information derivation.
     

  • Lin Yi , Zhou Guoqing, Tong Qingxi
    Remote Sensing Technology and Application. 2019, 34(2): 232-242. https://doi.org/10.11873/j.issn.1004-0323.2019.2.0232
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    Light Detection and Ranging(LiDAR) Remote Sensing(RS) can map the 3D structures of objects,and polarization RS can implement information retrieval enhancement via strengthening the weak reflectance and weakening the strong reflectance that are inverse scenarios in traditional optical RS.Their combination,theoretically,can open a possible way for developing the next-generation multi-dimensional active optical RS technologies.However,the literature review suggested that the concept of polarization LiDAR was earlier proposed,while its applications mainly occurred in the field of atmospheric monitoring.The focuses were on study of laser backscatter depolarization techniques,e.g.,sensing water content in the air based on the metrics of polarization diversity so as to distinguish the structures and orientations between water vapor and ice crystal clouds in the atmosphere,and further,on investigation of aerosol content in the troposphere and improvement of the related methods.But in other fields,the applications of polarization LiDAR RS have been almost blank.Now,it is time to extend the applications of polarization LiDAR for RS of complex objects.This paper analyzed the mechanisms of the two component modules in terms of their RS principles and listed the fundamental works necessitated for comprising the polarization LiDAR RS technology.Finally,this study proposed the ranges of possibly applying polarization LiDAR RS,such as agronomy,environment,ocean and space exploration.
  • Luo Yubo, Huang Hongyu, Tang Liyu, Chen Chongcheng, Zhang Hao
    Remote Sensing Technology and Application. 2019, 34(2): 243-252. https://doi.org/10.11873/j.issn.1004-0323.2019.2.0243
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    In view of the low accuracy of Tree Height(TH) and Diameter at Breast Height(DBH) estimation,as well as the difficulty of individual tree modeling in dense forest,a method to extract forest structure parameters(TH and DBH) and reconstruct a Three-Dimensional(3D) model of forest in subtropical environment based on TLS point cloud data is proposed.The first step is to apply a multi-scale method to extract the ground points for the generation of Digital Elevation Model(DEM).Secondly,using similarity of principal direction between neighboring points and distribution density of points,trunk and other plant organs are separated.Next the trunk points are processed to automatically estimate the tree position and DBH by iterative least squares cylinder fitting;the tree height is automatically estimated by using the octree segmentation.Finally,by combining with the technology of individual tree modeling,a plot-scale 3D forest scene has been reconstructed by planting individual tree model on the terrain model iteratively.The results showed that the correlation coefficient of DBH is R2=0.996,and the average relative error was 2.09%,RMSE was 0.66 cm;the correlation coefficient of tree height is R2=0.972,and the average relative error was 2.16% with RMSE of 0.92 m.The plot-scale reconstructed 3D model of the forest can express the true shape of forest.
  • Xu Fan, Zhang Xuehong, Shi Yuli
    Remote Sensing Technology and Application. 2019, 34(2): 253-262. https://doi.org/10.11873/j.issn.1004-0323.2019.2.0253
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    Aerial images contain abundant spectral information,texture information and spatial information,and airborne LiDAR can provide three-dimensional information of ground objects.An object-oriented classification method was researched by taking advantages of the two types of data.Converting LiDAR point cloud into 2-D raster image by preprocessing,and matched it with aerial image.Then,multi-scale segmentation algorithm was applied to image segmentation based on spectral information and height information.Next,XGBoost algorithm were applied to select features extracted from segmented object respectively.The SVM classifier was used to classify and prove the superiority of XGBoost algorithm by comparing with two traditional feature selection algorithms:Relief and RFE.Finally,objects at shadow regions were distinguished and merged into real objects based on certain rules.Testing the method in three regions,the results showed that the method was feasible and effective,and could be well applied to the classification of urban ground object.
  • Du Yuefei, Liu Zhengjun, Feng Tianwen
    Remote Sensing Technology and Application. 2019, 34(2): 263-268. https://doi.org/10.11873/j.issn.1004-0323.2019.2.0263
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    Through the power line test,this paper presents the feasibility of applying the PPP technology of GPS,GLONASS and BDS multi-source fusion to verify the application of the generated POS data and 3D point cloud data to the high voltage power line inspection.The POS data fusion and calculation of different strategies are used to analyze the POS data accuracy of the POS data,GPS,GLONASS and BDS multi-systems under the base station differential POS data and the single GPS system.The 3D point cloud is generated by the POS data of different strategies,and the deviation distance and distribution of differential point cloud data are statistically analyzed.Finally,it is concluded that the point cloud data generated by the single GPS is about 20-40 cm,and the distribution is not uniform and the deviation distance fluctuates greatly.It can not meet the requirement of the fine inspection of the power line.The point cloud data generated by the PPP technology of multi-source fusion and the distance deviation from the point cloud data under the base station difference are 10 cm in the direction of X,The direction of Y is 6 cm and the direction of Z is 4 cm.The distribution of point cloud data is uniform and stable,which basically meets the requirements of the power line inspection,and the point cloud data can also be used for the fine inspection of the power line.
  • Li Wei, Tang Lingli, Wu Haohao, Tenggeer, Zhou Mei
    Remote Sensing Technology and Application. 2019, 34(2): 269-274. https://doi.org/10.11873/j.issn.1004-0323.2019.2.0269
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    Mini UVA borne LiDAR system is a research hotspot nowadays,It has the characteristics of low cost,easy to carry and flexible to use,and has developed rapidly in recent years.This paper proposes a complete solution of the data acquisition and processing analysis by mini-UAV-borne LiDAR system,which mounted on a multi-rotor unmanned aerial vehicle.Aiming at the time synchronization and structure integration design and settling angle error elimination involved in the development of mini UAV-borne lidar system,a complete solution for data acquisition and processing analysis of light and mini UAV-borne lidar under complex terrain conditions is proposed.The integrated system is used to carry out power line patrol application,and the number of tests are carried out.According to the analysis results,the advantages of the system in power line inspection are fully demonstrated,and its functions and performances are verified.
  • Ding Haining, Chen Yu, Chen Yunzhi
    Remote Sensing Technology and Application. 2019, 34(2): 275-283. https://doi.org/10.11873/j.issn.1004-0323.2019.2.0275
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    The information of soil composition and its spatial distribution could be obtained quickly and efficiently by using spectral technology.In order to accurately estimate the content and distribution characteristics of soil Fe elements in the loess plateau,the typical loess in the eastern part of Yulin was collected in the field.Laboratory physical and chemical analysis,spectral determination and pretreatment,analysis of the correlation between soil iron content and reflection spectrum,screening sensitive bands,using partial least squares modeling to determine the best estimation model.The spectral reflectivity and the selected sensitive bands are mainly distributed at 500 nm,870 nm,1 700 nm and 2 200 nm.The original reflectivity(Ref) modeling results are relatively stable and the prediction effect is the best.The prediction set correlation coefficient R2 is up to 0.73 and the Root Mean Square Error(RMSEP) is the smallest.After derivative transform(FDR and SDR) and continuum removal of CR transform,the prediction set R2 is 0.61 and 0.64,respectively.The optimal estimation model of soil Fe content(Ref) was applied to the Sentinel-2 multi-spectral remote sensing image to obtain the remote sensing inversion map of soil Fe content by band interpolation.It was found that the distribution characteristics of soil Fe content in the studied area were closely related to the strata.The results of this study can provide support for remote sensing analysis of soil Fe element content and realize rapid spectral mapping of soil iron in the Loess Plateau.
  • Li Chunjiang, Shen Guozhuang, Zhang Jichao
    Remote Sensing Technology and Application. 2019, 34(2): 284-292. https://doi.org/10.11873/j.issn.1004-0323.2019.2.0284
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    based on the Grey System Theory,combing the three periods(2013,2015,and 2016) of the Radarsat-2 Polarimetric SAR(PolSAR) data and the vegetation physical parameters data collected from Poyang Lake wetland,we established the relationship model for the vegetation physical parameters with vegetation biomass and the polarization decomposition components,respectively.We analyzed the contribution of different vegetation physical parameters to biomass accumulation and their influence on polarization decomposition components.The results show that from the vegetation growing faster to slower stage,the plant parameters and underlying surface parameters are the main factors that contribute to the vegetation biomass accumulation.The main effective factors for the polarization decomposition components are the land surface parameters and the stem parameters.The parameters of field sampling are analyzed and determined based on the larger correlation degree data at each stage.
  • Song Xiaoxia, Wang Jing, Chu Xiaoqing
    Remote Sensing Technology and Application. 2019, 34(2): 293-302. https://doi.org/10.11873/j.issn.1004-0323.2019.2.0293
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    The sea surface velocities field plays an important role in seawater exchange and substance transportation.In this paper,the Wide Swath Mode(WSM) data derived from the ENVISAT Advanced Synthetic Aperture Radar(ASAR) are used to retrieve the high-resolution sea surface velocities field.based on the theoretical model of SAR Doppler shift,the errors caused by the relative motion between Earth and satellite are removed.Then we use the C Band Doppler Frequency Model(CDOP) and the Bragg scattering model to remove the errors caused by the sea surface wind and Bragg scattering,respectively.The data used to verify the accuracy of the retrieval results are AVISO(Archiving Validation and Interpretation of Satellite Oceanographic) velocities and the GLD(Global Drifting Buoy) velocities.Our method is applied both in the coastal area with land cover(Agulhas) and the open sea(Kuroshio) without any land.Results show that in Agulhas,the velocity ranges from -1.8 m/s to 1.8 m/s,and their directions agree very well.In the Kuroshio,the ASAR current can clearly reveal the flow path and direction of the Kuroshio,and it matches well with the AVISO current.The comprehensive results shows,the Root Mean Square Error(RMSE) of ASAR and AVISO is 0.17 m/s,and the RMSE of ASAR and GLD is 0.11 m/s.This implies that the methods used here not only simplified the processes but also has high accuracy to retrieve sea surface velocities both in the coastal area and in the open sea.
  • Zhang Helin, Peng Dailiang, Zhang Xiao, Fan Haisheng, Xu Fubao, Ye Huichun
    Remote Sensing Technology and Application. 2019, 34(2): 303-312. https://doi.org/10.11873/j.issn.1004-0323.2019.2.0303
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    For the estimation of annual Gross Primary Productivity(GPP),it is proposed an estimation method with simple parameters and small errors.By taking each type of vegetation in the area of Three-North Shelterbelt Program(TNSP) as the research subject,the MODIS vegetation indices were obtained,and the seasonal variation curve of vegetation indices were built.Then,the fitting relation between the integral of time series vegetation indices(ΣVIs) and GPP products of MODIS was established,so as to realize a simple GPP estimation method and study the applicable ΣVIs for estimating the GPP of all vegetation types.The results show that:(1) ΣVIs is suitable for estimating the annual total GPP in research area and significantly correlated with MODIS GPP at the confidence level of p<0.01;(2) ΣEVI2 is applicable to estimate the GPP of evergreen needleleaf forest,decidious needleleaf forest,decidious broadleaf forest,mixed forest,woody savannas,savannas,permanent wetlands,croplands,croplands/natural vegetation mosaic,while the effect of ΣNDVI for estimating the GPP of closed shrublands,open shrublands,grasslands,croplands,and barren or sparsely vegetated is superior to ΣEVI andΣEVI2;(3) Since the NDVI itself is saturated in the area of high Leaf Area Index(LAI),the error of estimating the GPP of high LAI vegetation type by ΣNDVI is larger,while using ΣEVI and ΣEVI2 to estimate them has better accuracy,and the limitation from blue band of EVI2 reduces compared with EVI,which can be applied to the GPP research of long time series better.


  • Zhang Mingyue, Zhang Qili, Wang Lu, Tian Weixia, Wang Maozhi
    Remote Sensing Technology and Application. 2019, 34(2): 313-322. https://doi.org/10.11873/j.issn.1004-0323.2019.2.0313
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    According to the definition of spectral integral,a new spectral characteristic parameter,with the name Reversed Spectral Absorption Integral(RSAI),is proposed and used to retrieve the chromium content based on the Partial Least Squares Regression(PLSR) model.The contrastive study with other traditional spectral characteristic parameters,including differential transformation,inverse transformation,absorption area,etc.indicates that(1) the first derivation of square root transformed model can predict the chromium content quantitatively in terms of spectral transformations.(2) the stability of the absorption area model is slightly poor,and the chromium content of samples can only be estimated roughly.(3) However,as to the inversed spectral absorption integral model,the adjustment determination coefficient(Ad-R2) of the modeling and verification is 0.73 and 0.77,while the Root Mean Squared Error(RMSE) is 2.63 mg/kg and 2.36 mg/kg respectively with Relative Percent Deviation(RPD) being 3.21,which shows that the RSAI model has excellent prediction ability.So,the inversed spectral absorption integral new model can improve the accuracy and stability used to retrieve the chromium content,which provides a new idea for monitoring the chromium contamination in soil.
  • Remote Sensing Technology and Application. 2019, 34(2): 323-330. https://doi.org/10.11873/j.issn.1004-0323.2019
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    The Leaf Area Index(LAI) is an important parameter of the canopy structure of the vegetation.There are problems such as missing data and low quality in the MODIS LAI data product because of the influence of atmospheric conditions and other factors,which seriously affects its the application.As a case study of Jiangxi Province,MODIS LAI time series products data set in 2009-2013 were reconstructed by integrating pixel quality analysis,S-G filter and annual sequence abnormal value detection and filtering technology.The results show that the high quality of the broad-leaved forest was the lowest,only 51.76%,and proportion of low quality and retrieval failed pixels reached to 20% to 30%.The integrated filtering method proposed for low quality data set reconstruction.Compared with S-G filtering,the mean value of the reconstructed LAI by the integrated filtering method is more consistent with the original mean for high quality pixel.And its correlation coefficient between the high quality pixel and the original data reaches 0.97.So it has better fidelity.The outliers of the low- and medium-quality pixel reconstruction are filtered,and the area of null value is filled.Meanwhile,the standard deviation is reduced,and the low-value area or abnormal point is better identified and repaired.The overall stability becomes better and it can fit the time series change curve well.
  • Sun Xinyi, Zhang Yunhua, Dong Xiao, Zhai Wenshuai
    Remote Sensing Technology and Application. 2019, 34(2): 331-336. https://doi.org/10.11873/j.issn.1004-0323.2019.2.0331
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    Tiangong-2 Interferometric Imaging Radar Altimeter(InIRA) is the first wide-swath radar altimeter adopting small incidences angles with short baseline.The knowledge of the roll angle of the baseline is crucial for measuring the wide-swath Sea Surface Height(SSH) with centimeter-level accuracy.In this work,we aim to validate the technique of baseline angle determination from spaceborne nadir interferometric echoes which has been tested by airborne experiment.According to the observation geometry,the interferometric phases of nadir echoes acquired by Tiangong-2 Imaging Radar Altimeter(InIRA) are related to the roll angle of the baseline,so it should be possible for us to retrieve the incline angle from the interferometric phases under some ocean conditions not so high.In order to do so,the Tiangong-2 was tilted about 5°  so as to realize a 0° baseline and in this way,a lot of data was collected.In this paper,we present the retrieved roll angles and compare them with the measured angles by the platform.Due to the Earth is an ellipsoid,and the control of the Tiangong-2 is referring to the Earth center,while the retrieved roll angle is referring to the nadir point on the Earth surface,there is a systematic error related to the orbit,and after calibration of which,the expected results are obtained:two measurements agree with each other very well not only for the measured trend but also for the standard bias between them.
  • Zhang Xiaoke, Du Xindong, Lu Xuyang, Wang Xiaodan
    Remote Sensing Technology and Application. 2019, 34(2): 337-344. https://doi.org/10.11873/j.issn.1004-0323.2019.2.0337
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    Vegetation phenology is the most intuitive and sensitive indicator of seasonal and inter-annual variability under environment conditions.The phenology monitoring methods mainly include field observation,phenology model,digital camera and remote sensing technology.On the Tibetan Plateau,the start of growing season for alpine grassland had four viewpoints:experiencing an advancing and advancing in fluctuating trend,a postponing trend and no change,respectively.Phenology phases experience interactions with climatic factors.These factors,such as temperature,precipitation,CO2 concentration,snow cover and extreme climate,play an important role in alpine grasslandgrowth.Meanwhile,phenology changes at different scales and the driving factors are uncertainty.Finally,the existing problems and the future research directions were discussed.
  • Zhou Yuke
    Remote Sensing Technology and Application. 2019, 34(2): 345-354. https://doi.org/10.11873/j.issn.1004-0323.2019.2.0345
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    Vegetation phenology is an important ecological indicator for global climate change.Plant greenup phenology in the spring time has been well studied,whereas autumn phenology and its asymmetry with spring phenology still remain unclear.Here,the GIMMS NDVI3g dataset for Northeast China was applied to extract the key phenological parameters during plant growth process,then three phenological asymmetry indices were defined according to the difference between greenup rate and senescence rate(AsyR),growth length in spring and autumn(AsyL),mean vegetation greenness index in spring and autumn(AsyV).First,plant growing curve was fitted with double logistic function and the phenological parameters was calculated.Second,the spatiotemporal pattern of asymmetry indices was explored.The results indicate that the three phenological asymmetry indices show a significant interannual variability and a time cycle of around ten years.The direction of amplitude for AsyV and AsyL was opposite with that of AsyR.Three indices could depict the phenological asymmetries from various perspectives and have a degree of uncertainty.The landscape pattern for AsyV and Asy R is similar.AsyV and AsyR show a capability of distinguishing cropland and natural vegetation cover.AsyL reflects a complex spatial distribution.Phenological asymmetries reveal that coniferous forest and broad-leaved forest present a dominant control of senescence vegetation activities.These natural vegetation commonly show a growth feature of rapid growth in spring and slow decrease in autumn.Cropland exhibits a slowly growing rate in spring and a rapid decrease in autumn.Phenological asymmetry is not significant in grassland area.Phenological asymmetry could enhance our knowledge on ecosystem carbon sink.In a practical way,phenological asymmetry could serve as a useful tools in vegetation type classification,agricultural investigation and plant ecosystem management.
  • Chen Siyu, Liang Tiangang
    Remote Sensing Technology and Application. 2019, 34(2): 355-366. https://doi.org/10.11873/j.issn.1004-0323.2019
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    Based on grassland vegetation phenology extracting from EVI2 data sets,distribution and dynamic variation characteristic of SOG,EOG and LOG were analyzed in recent 30 years.The results showed that grassland phenology had shown an obvious regional difference from southeast to northwest in TP.The grassland vegetation in eastern and northwestern part of plateau turned green earlier and brown late,with a relatively longer growth season than other regions.The changes of grassland vegetation phenology from 1981 to 2010 in the east and west regions were also remarkable in TP.The Start of Growth Season(SOG) was in advance in eastern region,with the advanced rate of 0.49 d/a(R2=0.54).There were remarkable difference in phenology distribution and changes in different elevations and aspects.When the altitude had risen 1000 m,the SOG delayed 4 days,EOG advanced 5 days,and LOG shorten 9 days.With the increase of altitude,the SOG rate of grassland increased gradually,and LOG change rate showed a decreasing trend.In addition,SOG in south aspect was later than that in north aspect.LOG in south aspect was shorten than that in others.Average delay rate of SOG in north aspect was lower than that in south aspect.

  • Remote Sensing Technology and Application. 2019, 34(2): 367-376. https://doi.org/10.11873/j.issn.1004-0323.2019.2.0367
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    Global climate change characterized by temperature increase has been a hot topic of widespread concern,and environmental protection and governance has become a significant issue affecting sustainable development.As the major component of terrestrial ecosystems,vegetation plays an important role in the aspects of ecologicalenvironment assessment and carbon cycling.based on the logistic function method,the long-term Normalized Difference Vegetation Index(NDVI)from GIMMS3g and meteorological data over 1982~2015 were used to calculate the start(SOS)and the end(EOS)of the season of Jiangsu province.The spatial and temporal characteristics of vegetation phenological changes were also investigated.The effects of main meteorological factors(temperature and precipitation)on phenological changes were explored by correlation analysis.The results showed that:①spatially,SOS showed an increased trend while EOS decreased from south to north,②temporally,SOS for most regions(83.1%)featured advanced trends with a rate of around 1~2 days per year,while 69.2% of pixels showed a delayed EOS by 0~1 day per year,and ③vegetation phenology responded well to air temperature and precipitation,as 70.5% and 55.5% pixels of SOS had significantly negative correlations with air temperature and precipitation.However,for EOS,more than half areas,i.e.55.2% of pixels and 71.2% of pixels respectively demonstrated positive correlation with air temperature and negative correlation with precipitation.Overall,a higher temperature trigged an earlier SOS but increased precipitation did not necessarily advance SOS.Furthermore,climate changes in autumn showed complicate effects on EOS that neither changes in temperature nor precipitation can lead to one directional changes of EOS.These results will deepen the understanding of the interaction between climate change and vegetation ecosystem,and provide a reference for future vegetation and climate change analysis.
  • Wang Yingying, Yuan Jinguo, Zhang Ying, Wu Chaoyang
    Remote Sensing Technology and Application. 2019, 34(2): 377-388. https://doi.org/10.11873/j.issn.1004-0323.2019.2.0377
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    Land surface phenology is defined as the seasonal timing of life cycle events of vegetated land surface on local or global scale.Most studies of vegetation phenology in China’s temperate zone are focused on single vegetation type in certain area,the studies about long-time vegetation phenology on large scale is rare.The influence of vegetation phenology on GPP(gross primary productivity) remains to be determined.Using Moderate Resolution Imaging Spectroradiometer(MODIS) MCD12Q2 data from 2001 to 2014,start of growing season(SOS),end of growing season(EOS) and length of growing season(LOS) in temperate China(>30°N) are obtained.GPP from MODIS MOD17A3 data for the same period is also obtained.Using regression analysis and correlation analysis methods,spatial and temporal patterns of SOS,EOS and LOS are analyzed.The impacts of SOS,EOS and LOS on interannual variability of GPP are also analyzed.Results show that the average and standard deviation of SOS,EOS and LOS from 2001 to 2014 are 121±10,270±12 and 153±12 days,respectively.The trend of earlier SOS,delayed EOS and increased LOS are not significant(p>0.05),but LOS shows positively correlated to GPP.The spatial distribution of annual average LOS and GPP from 2001 to 2014 presents an increase trend from northwest to southeast.Regions with significant interannual variation(p<0.05) of SOS,EOS and LOS are 13%,21% and 13.2%,respectively.Regions of significant correlation(p<0.05) of SOS,EOS and LOS to GPP account for 8.31%,9.33% and 8.72% of the study area.GPP has mainly medium correlations(p<0.05,0.5<|r|<0.8) to SOS,EOS and LOS.
  • Li Xiaohui, Wang Hong, Li Xiaobing, Chi Dengkai, Tang Zengwei, Han Chongyuan
    Remote Sensing Technology and Application. 2019, 34(2): 389-397. https://doi.org/10.11873/j.issn.1004-0323.2019.2.0389
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    It is crucial for agricultural production to know crop planting situation.Temporal remote sensing images and subtle spectral characteristics of ground features play an important role in extracting crops distribution.At this point,multi-temporal Landsat 8 OLI images were used to extracting the distribution of main crops in the east of Xinrong district of Datong city by using Spectral Angle Mapper(SAM) combined with the decision tree classification,and the extracting result was compared with the result that maximum likelihood extracted.The results show that:① The planting area of spring corn,grain,soybean and potato is decreased and mosaic distribution in order.② The overall accuracy obtained by SAM combined with the decision tree classification is 85.34% and the Kappa coefficient is 0.76,which is outperformed the results of maximum likelihood with the increase of 22.51% and 0.31,respectively,the classification results was more consistent with the actual distribution of main crops.③ The classification accuracy of main crops used the multi-temporal remote sensing images was obviously higher than that of single-temporal image,and the difference between ground features and spectra in middle or high resolution images can effectively weaken by analyzing multi-temporal data from the perspective of difference of spectral angle.The results not only confirmed the positive effect of multi-temporal remote sensing images on crops classification,but also developed the SAM combined with decision tree classification in crops classification of medium-high resolution remote sensing images,which has a certain application prospect.
  • Luo Qingzhou, Zhu Chuanwu, Wang Peifa
    Remote Sensing Technology and Application. 2019, 34(2): 398-403. https://doi.org/10.11873/j.issn.1004-0323.2019.2.0398
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    In this paper,we present a method of obtaining instantaneous surface vapor pressure in complex terrain region.The proposed algorithm estimates surface vapor pressure over cloud-free land area using MODIS near-infrared data directly,instead of using MODIS total precipitable water data.Guizhou province is selected as study region.There are four direct models,exponential model,polynomial model,channels linear model and integrated model,are developed.In each model,we can choose one from three absorption channels(17,18 and 19),and choose one from two remote sensing images(reflectance and radiance).So twenty-four equations are built to calculate surface vapor pressure.According to the regression calculation of ground stations,we select the best fitting equation.We compare the errors between the direct method and the indirect method.The result shows the integrated model(equation 27) is the best fitting equation,which gives R2 0.741 and residual standard error(RSE) 1.098 hpa.This integrated model calculates surface vapor pressure using reflectance ratio of channel 17 to channel 2 and elevation information.The mean absolute error and mean relative error of this proposed direct method are 1.234hpa and 8.2% respectively,which are lower than 1.806 hpa and 12% of indirect method.

  • Ma Pengfei, Li Qing, Chen Hui, Zhang Lijuan, Zhang Yuhuan, Wang Qiao, Zhou Chunyan, Mao Huiqin, Chen Cuihong, Wang Zhongting
    Remote Sensing Technology and Application. 2019, 34(2): 404-411. https://doi.org/10.11873/j.issn.1004-0323.2019.2.0404
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    There are serious pollution of enterprises in the BTH and its surrounding area,especially in the rural-urban continuum areas.Non-point source pollution is difficult to control.Rapid and accurate determination of targets for air pollution monitoring and informationization of on-site environmental law enforcement are the keys to the monitoring of atmospheric environment.This paper retrieves haze,PM2.5,NO2 and SO2 from the MODIS and OMI data.based on the comprehensive analysis of the satellite data of twelve typical heavy pollution episodes since September 2016,we have calculated the Heavy Pollution High Incidence Index(HPHII),and then combined with the high resolution images to find out the suspected gathering area of the polluting enterprise as the key areas of environmental supervision.This work will provide technical support for environmental law enforcement and improve the accuracy of law enforcement and law enforcement efficiency effectively,so as to solve the problem of human shortage in environmental monitoring.
  • Wang Lei, Jiang Zongli, Liu Shiyin, Shangguan Donghui, Zhang Yong
    Remote Sensing Technology and Application. 2019, 34(2): 412-423. https://doi.org/10.11873/j.issn.1004-0323.2019.2.0412
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    As an international highway,the Karakoram Highway(KKH) is of great importance to China,Pakistan,South-Asian and West-Asian Countries.It crosses the Karakoram Mountains,one of the most glaciers concentrated regions,and is being threatened from glaciers along KKH all the time.Currently,Synthetic Aperture Radar(SAR) have been proved reliably profitable on analyst glaciers’ movement.In this study,we analyzed the characteristics of glaciers’ movement along KKH by using feature-tracking method based on 81 scenes of Sentinel-1A data obtained between Jan.2016 to Jun.2017.And ERS-1 data in 1993 and ALOS PALSAR data in 2006 are taken as comparison for analyzing the trend of glacial surface flow rate.Our results indicated that the glacier flow streamline is basically consistent with the geometric center line of the ice tongue and the velocities increase away from both margins;summer motion was higher than winter motion.The glaciers along the KKH can be divided into three parts:north,central and south,based the boundaries of Tajik Autonomous County in Sinkiang and Khuda Abad,the northern city of Pakistan.The glacier surface velocities of the trunk is lower than 0.15md-1and relatively stable in the northern.The scale of glaciers in the central region is generally small and far from the KKH.The glaciers in north and central regions may not affect the operation of highways.While parts of the large scale glaciers in the south region had higher velocities,particularly along the section of the highway near Batura Sar.Here,4 glaciers were selected to analyze in detail which are closest to the KKH include Batura,Pasu,Ghulkin and Gulmit in the south region.Seen from all results,the Pasu glacier has rapid motion during the study period,the normal velocity of its trunk is between 1 and 1.5 md-1,and can reach 3 md-1 in special place.All these 4 glaciers were flowing faster than other glaciers which reflect the high quantitative mass balance.Accordingly,the safety operation and residents’ life along the Batura Sar may be threatened by these four glaciers advance.
  • Xu Minduan, Deng Ruru, Qin Yan, Liang Yeheng, Tang Yuming
    Remote Sensing Technology and Application. 2019, 34(2): 424-434. https://doi.org/10.11873/j.issn.1004-0323.2019.2.0424
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    The basic optical properties of clear-sky aerosol of Guangzhou in 2015 were retrieved using CE318 sun photometer data,and their temporal variation characteristics were analyzed and local pollution sources were explored.based on the HYSPLIT backward-trajectory model,the characteristics of external transport sources of air pollution in Guangzhou were studied.The results show that:(1) in 2015,the annual average AOD in Guangzhou is 0.625,which is at a relatively high level.Among them,AOD is the largest in spring,followed by autumn,winter,and summer,with obvious seasonal variations;(2) the daily variation of aerosol basic optical properties are closely related to human activities,and road traffic pollution is the main source of atmospheric aerosol;(3)Air pollution affected by external transport has obvious seasonal variation characteristics,which is superimposed with local emissions,causing air pollution in Guangzhou to be worse,and marine particles transport occurs all year round;(4) the wavelength exponent of all seasons is greater than 1,and is mainly distributed within the interval of 0.8 to 1.4.The aerosol components are stable throughout the year,mainly with smaller particle size aerosols.In general,the aerosol type in the Guangzhou area is mainly a mix of urban-industrial and marine aerosols.
  • Yin Yuwei, Tang Danling, Liu Yupeng
    Remote Sensing Technology and Application. 2019, 34(2): 435-444. https://doi.org/10.11873/j.issn.1004-0323.2019.2.0435
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