最新刊期
卷 20 ， 期 6 ， 2016
- 摘要：Gap fraction and foliage clumping index play key roles in plant light interception;therefore,they have strong influences on plant growth and canopy radiative transfer processes.Leaves always aggregate in crowns,which are important objects innumerous geometric-optical models of forests.Researchers have mainly focused on the influences of crown shape（e.g.,cylinder,cone,ellipsoid,and cone+cylinder）on gap fraction and foliage clumping index.However,size is also an important characteristic of tree crowns.Crown sizes result from the interactions between plants and environments during the long-term evolution process of the plants.In fact,crown size characteristics have more obvious geographical spatial features than crown shape.Therefore,crown size should be given considerable attention when studying plant light interception and canopy radiative transfer processes in the fields of phytogeography,remote sensing and global change.The main objective of this study is to exhibit how crown size characteristics influence the gap fraction and foliage clumping index of forest canopies.First,a simple and general distance factor,which is defined as the relative allowable shortest distance between centers of any two crowns divided by the mean diameter of tree crowns,is proposed to quantitatively describe the degree of repulsion effect among trees in forest canopies.Second,the Poisson distribution model of trees is completely replaced by the hypergeometric model,which is more suitable for quantitatively describing the repulsion effect and spatial relationship among trees in forest stands.Finally,seven sizes of ellipsoids（from prolate ellipsoids to oblate ellipsoids）are selected for the tree crowns,and the influences of crown size on gap fraction and foliage clumping index are exhibited by means of fixing the radius and the volume of the tree crowns,respectively.Results show that the following:（1） the influences of crown size characteristics are significantly greater than the effects of crown shape on gap fraction and foliage clumping index,regardless if the radius or volume of tree crowns are fixed.（2） From prolate ellipsoids to oblate ellipsoids,crown size characteristics show significant and regular effects on both gap fraction and foliage clumping index.In extreme cases,whenθ=60°,the canopy gap fraction when ellipsoid height（Hb） is 16 m is 92.8%lower than that when Hb=0.25 m,and the foliage clumping index when Hb=16 m is 261.8%greater than that when Hb=0.25 m when the crown radius is fixed.Atθ=0°,the canopy gap fraction when Hb=16 m is 414.1%greater than that when Hb=0.25 m,and the foliage clumping index when Hb=0.25 m is 11397.2%greater than that when Hb=16 m when the volume of crown is fixed.The larger the projected area of the crown in the observed direction,the lower value of the gap fraction and the higher value of the foliage clumping index,indicating that leaves tend to distribute randomly in forest canopies.关键词：crown size;gap fraction;clumping index;hypergeometric model;crown shape
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发布时间：2021-06-10 - 摘要：Owing to the deficiency of conventional observations over oceans,particularly under typhoon conditions,numerical simulation has become the foremost approach to the study of the thermal and dynamic structures of typhoons,and the accuracy of the numerical simulation depends on the initial structure of the typhoon and the accuracy of the model.As the first operational satellite,FY-3C was successfully launched into a morning-configured orbit on September 23,2013.The MWTS onboard the FY-3C is designed with significantly more channels,finer spatial resolution,and better sensor precision than those of FY-3A and FY-3B.Moreover,the observed radiance is insignificantly affected by non-precipitating clouds,making MWTS measurements more suitable to detect the thermal features of typhoons.Radiance in the microwave band is linearly proportional to the entire layer of atmospheric temperature.The weighting functions of all the sounding channels of MWTS are substantially steady,and atmospheric temperature at a given pressure can be expressed as a linear combination of brightness temperatures measured at certain sounding channels.In this study,a stepwise linear regression analysis with a 1%significance level is used.Under a clear-sky condition,the brightness temperatures at channels 3-10 are used to retrieve the temperatures at 21 pressure levels ranging from 100 hPa to 1000 hPa,but channels 3 and 4 are not used for retrieval under precipitation conditions.When the temperature profiles are retrieved,the tangential winds around typhoon "Rammasun" are calculated using hydrostatic equilibrium and gradient balance equations based on the retrieved temperature profiles.Under a clear-sky condition,the root-mean-square error of the retrieved temperature is 1.4 K at the most and even lower than 1.1 K in the upper troposphere and lower stratosphere.These error values are sufficiently low;thus,the thermal structure of typhoons can be monitored.Applied to the super typhoon "Rammasun",the method excellently described the warm core eye and the temperature gradients across the eyewall.The results generated by the proposed method are more accurate than the results generated from the NCEP reanalysis data.The warm core is identified throughout the troposphere,with maximum temperatures ranging from 8℃ to 10℃ near 200 hPa;the warm core extends to the sea surface.This finding on the warm core is seemingly more realistic compared with the typical one.From the anomaly field,the radius of the typhoon eye at the sea surface is approximately 100 km,and the eye tilts outward with height.The maximum wind speed radius is approximately 80 km,and the maximum wind speed can reach up to 51 m/s.Among the most important parameters in monitoring typhoon intensity,studying typhoon inner core dynamics,and constructing the initial vortex for a typhoon simulation,the three-dimensional warm core and tangential wind features derived from FY-3C MWTS measurements are investigated in this study.Evidently,MWTS has considerable potential for improving our knowledge of typhoons and hurricanes.However,only a single typhoon case is analyzed in this study;more cases should be studied to verify the retrieval method.关键词：MWTS;Typhoon;warm core;tangential wind
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发布时间：2021-06-10 - 摘要：A new satellite（code:03） was launched on October 26,2015.It belongs to Mapping Satellite-1（TH-1）,which is a group of HighResolution（HR） optical stereo mapping satellites in China.Three-linear-array/multispectral（TLA/MUX） images are the important data products of TH-1.They are used for stereo mapping,fusion and classification,and remote sensing.Image quality assessment is a critical issue particularly in the regular operation of the HR satellite ground application system,with the assessment results having great significance in the application prospects of the current satellite and subsequent satellite development.In this study,a multi-objective index method is employed to evaluate comprehensive the image quality of the TH-1 03 TLA/MUX sensors.It adopts 10 objective indices,including clarity（CLA）,contrast（CON）,detail energy（DET）,and edge energy（EDG）.Given that the three in-orbit MS-1 satellites have the same sensor and orbit parameters,the images from TH-1 01 and TH-1 02 are used as comparison assessment data.A group of images with the same screening season,terrain type,and natural environment are separately selected from the three in-orbit MS-1 satellites to avoid assessment errors induced by differences in seasons,terrain types,and natural environments.Assessment results show that the objective indices of the TH-1 03 TLA/MUX images,including CLA,CON,DET,EDG,power spectral density,and information content are significantly higher than those of the images of TH-1 01 and TH-1 02.The higher values of these indices demonstrate that the TLA/MUX images of TH-1 03 are better than TH-1 01 and TH-1 02 satellites in terms of describing the detail texture and edge texture features of ground objects.The TLA/MUX images of TH-1 03 have higher information content as well.The signal-tonoise ratio reveals that the capability of the TLA/MUX sensors of MS-1-03 to suppress noise is superior to those of the sensors of TH-1 01 and TH-1 02.Furthermore,RadEdge and GainAdj indicate that the radiometric uniform level of TH-1 03 is in between those of the TH-1 01 and TH-1 02 satellites.The results of the image quality assessment for TH-1 03 TLA/MUX sensors are analyzed and reported for the first time,and the preliminary conclusion is discussed in this paper.According to the assessment results,the image quality of TLA/MUX sensors of TH-1 03 is improved compared with those of the TLA/MUX sensors of the TH-1 01 and TH-1 02 satellites.Owing to the development in the image quality,the use of TLA/MUX data of the TH-1 03 satellite can enhance target recognition,classification,and stereo mapping accuracy.In summary,this paper has presented the first study on the analysis of the image quality for TH-1 03 TLA/MUX sensors and has made an exemplary contribution.关键词：Mapping Satellite-1 03;panchromatic image;multispectral image;radiometric quality assessment;objective indices
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发布时间：2021-06-10 - 摘要：The geolocation accuracy of meteorological satellites is a key factor in remote-sensing applications.The microwave radiation imager（MWRI） onboard the FengYun（FY）-3B satellite provides measurements of the Earth’s atmosphere and surface at 10.65 GHz,18.7 GHz,23.8 GHz,36.5 GHz,and 89.0 GHz with dual polarization.Although FY-3 MWRI data have been widely distributed to the user community,their geolocation accuracy must be corrected to render the MWRI data useful for quantitative remote sensing.To improve the geolocation accuracy of FY-3B MWRI measurements,a method based on the brightness temperature difference between the ascending and descending orbits is used in the conical scanning MWRI at the 89 GHz channel and called Node Differential Method（NDM）.The core principle of the geolocation error correction algorithm is minimizing the number of pixels along the coastlines,where the node difference in brightness temperature is greater than the threshold（20 K） and the distance from the coastlines near the Mediterranean Sea is less than 100 km.In this study,a satellite attitude model（satellite coordinate system） is established based on the vectors of the satellite position and velocity to estimate the satellite attitude angles,and a nonlinear optimization algorithm is used to minimize the objective function.This algorithm can avoid the effect of the tuning order of pitch,roll,and yaw in the traditional method.The satellite attitude offset（i.e.,pitch,roll,and yaw） can be derived and further utilized to adjust the satellite attitude.After the satellite attitude angle correction,the geolocation errors in the MWRI at the89 GHz channel are corrected.Results show that the FY-3B MWRI at the 89 GHz channel has the mean offset of the satellite attitude,with the pitch,roll,and yaw angles from January to September of 2015 being-0.220°,0.068°,and 0.062°,respectively.The mean geolocation error in the along-track direction is approximately 3—4 km,and that in the cross-track direction is less than 1 km at 89 GHz.The geolocation errors are stable during this period.They exist in the Mediterranean Sea,Australia,Red Sea,and Southeast of South America in the MWRI LI data.The geolocation errors are detected from the high absolute values of brightness temperature difference between the ascending and descending orbits near the coastal regions and from the maps of brightness temperature compared with the coastlines.After geolocation error correction,the MWRI geolocation accuracy is improved at the 89 GHz channel.In this study,the geolocation error correction algorithm is applied to the selected coastal regions from the middle and high latitudes of the north and south hemispheres of Earth.This algorithm can be extended to global measurement correction.The attitudes normally explain the status and stability of the satellite.Although the geolocation error can be corrected by adjusting satellite attitude offset,other factors,such as sensor mounting,uncertainty in the ephemeris data,or timing error,must be considered in future studies to improve geolocation accuracy.关键词：microwave radiation imager;geolocation error;node differential method;conical scanning;satellite attitude;nonlinear optimization
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发布时间：2021-06-10 - 摘要：With the development of digital mapping cameras and image-matching techniques,highly dense point clouds can be automatically obtained by stereo image matching.These point clouds can be applied to 3D building reconstruction and DEM/DSM generation.However,owing to certain factors,such as textural differences,geometric distortions,and shadows,point clouds sustain gross errors.To address this problem,a semivariogram-based weight moving least square（SWMLS） method is proposed for gross error detection in point clouds obtained by stereo image matching.The SWMLS method is an iterative process.For each iteration,a semivariogram is used to estimate in advance the covariance between points in different distances and aspects.The weight can be calculated based on the estimated covariance and applied to approximate the least square and terrain surface separately.The distance between each point and the surface is calculated with the approximated local parametric surface,and the distance-constrained histogram method is used to detect the gross errors.Ingross error detection,the least square approximation move through the entire region of the point cloud by the same steps in the x-axis and y-axis.In this paper,we focus on discussing the adaptability of semivariogram to the digital elevation model and its variation across different terrains.The A3 system-generated point clouds of two datasets of urban and mountainous area ares used in the experiments.The point cloud for the urban area contains a 4.00%gross error,whereas the point cloud for the mountainous area contains 0.19%gross error.To test the efficiency and advantages of our method,the results of our method and those of the equal weight based MLS（EWMLS） method are compared manually to the detected results.Experimental results show that both SWMLS and EWMLS have removed all the gross errors in the point cloud.However,the results of a further quantitative analysis suggest that SWMLS is more accurate than EWMLS.For the urban area,the misclassification by S WMLS is 2.07%,whereas that by EWMLS is 7.72%.For the mountainous area,EWMLS has committed 2.56%misclassification,whereas our proposed method generated a reduced misclassification（0.76%）.We may draw the conclusion as below.（1） As a regionalized method,semivariogram is efficient in weight estimation in MLS.（2）Both SWMLS and EWMLS are effective in detecting gross errors in point clouds generated from stereo images.However,both methods may commit few misclassifications for urban and mountainous areas.（3）Compared with EWMLS,SWMLS generated less misclassification in both datasets,suggesting that SWMLS demonstrates a better performance in the gross error detection in stereo-matched point clouds.关键词：stereo-images-matched point cloud;gross error detection;moving least square;weight;semi-variogram
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发布时间：2021-06-10 - 摘要：Synthetic Aperture Radar（SAR） is an all-day,all-weather high-resolution senor that has been widely used in civilian mapping,military investigation,and strategic reconnaissance.Ground Moving Target Indication（GMTI） can detect and track moving targets,and obtain their moving information,which has been an important application of SAR for ground moving target detection and imagery.SAR,which has GMTI capability,can obtain focused stationary target images and moving target information at the same time,and form a more detailed battlefield map.Thus,it has increasingly become an important part of battlefield surveillance and strategic reconnaissance systems.Jamming technology research against SAR-GMTI has been an issue in the area of electronic countermeasures because of the growing threat of SAR-GMTI systems to important military moving targets.SAR-GMTI systems typically use multiple channels to suppress cluttering and jamming.Thus,traditional jamming methods for SAR are usually invalid for SAR-GMTI systems.In this paper,a new jamming method for multi-channel SAR-GMTI is proposed;this method is cosinusoidal phase-modulated repeater jamming based on a moving jamming station.First,the imaging feature of repeater jamming of a moving jamming station is analyzed by the principle of stationary phase and is used to realize azimuth jamming extension.At the same time,the cosinusoidal phase-modulated signal model is proposed and used to realize range jamming extension,thereby enabling their combination to realize two dimension jamming extension in range and azimuth.Second,the jamming performance of the proposed method for SAR is analyzed thoroughly,and the relationship between jamming parameters and performance is obtained.Then,the countering performance against SAR-GMTI is analyzed by using the cancelling technique of tri-channel interference,and the cancelling output expression of the final interference is derived.Finally,the simulation experiment and application example are presented.Computer simulation and theoretical analysis show that the method can produce flexible and controlled two dimension banded or planar-covering jamming performance for SAR and SAR-GMTI.Moreover,because of interference suppression and cancellation of multichannel GMTI,the amplitude of false targets is affected by sinusoidal modulation coefficient,thereby revealing enhanced and weakened areas.The proposed method is simple,flexible,and controllable.Compared with the deceptive jamming method,its dependence on reconnaissance is lower;and compared with the traditional barrage jamming method,its jamming signal power requirement is lower because of the high two dimension coherent processing gain.The method can protect stationary and moving ground targets at the same time,thereby effectively strengthening the countering performance for multi-channel SAR-GMTI systems.Moreover,the method has important military application value.关键词：synthetic aperture radar（SAR）;Ground Moving Target Indication（GMTI）;moving jamming station;cosinusoidal phasemodulated;interference cancelling
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发布时间：2021-06-10 - 摘要：Clouds cover approximately 70% of the Earth’s surface.They can balance energy and water cycles,so they are considered one of the most important parameters in the Earth’s surface system.Remote sensing provides a rapid yet efficient cloud detection approach,especially through moderate-resolution imaging spectro radiometer（MODIS） imagery that has been scanning the Earth’s surface at a large scale,with a reasonable 0.25 km to 1km spatial resolution,more than once a day since 1999.The use of remote sensing for cloud detection has long been considered a simple issue because of the significant difference among cloud spectrums and other surfaces or atmospheric conditions.However,its instability is due to the complexity of cloud types,seasonal surface changes,and varied atmospheric conditions.Thus,this research aims（1） to compare typical cloud detection methods,and（2） propose and validate improved methods based on the previous ones.We selected an area in East China with complex surface and atmospheric conditions（e.g.,aerosol pollution,Asian dust,and snow cover）as the study area,and obtained nine MODIS L1 B products on typical seasons in the study areas for a cloud detection experiment.Typical spectral signatures of cloud,high reflectance in visible bands,and low-bright temperatures in infrared bands are commonly used for cloud detection;however,their results remain uncertain because of varied surfaces and atmospheric conditions.Thus,we compared three commonly used cloud detection methods,abandoned unstable infrared bands,and considered snow detection,from which we proposed two improved methods.The proposed methods improved the previous ones as results showed high and stable overall accuracy.One of our proposed methods obtained the best overall accuracy of 92.6±7%,and the average mapping accuracy of cloud area and no-cloud area was at 95.8%and 88.2%,respectively;the other method had a low overall accuracy of 82.9±13%,which was similar to the rest of the methods,but could detect almost all cloudy pixels.The proposed methods also widened the applicability of MODIS imagery under complex Earth surface and atmospheric conditions as results found that snow,air pollution,and Asian dust could be better distinguished from clouds by using the two methods based on MODIS data,except under some extremely heavy dust conditions.The two proposed cloud detection methods have different applicability in research.One obtained high and stable detection accuracy（92.6±7%）;while the other achieved a relative low-detection accuracy,but detects most cloud cover information,which is suitable to remote sensing research with high sensitivity to cloud errors.Our proposed methods improved cloud detecting ability under complex ground and atmospheric conditions.关键词：cloud detection;MODIS image data;detection uncertainty;earth surface system
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发布时间：2021-06-10 - 摘要：Image segmentation has been a hot topic in image processing.It involves two tasks:determining the number of homogeneous regions and segmenting them.Most image segmentation algorithms mainly focus on the latter and determine a priori the number.Artificially determining the number of classes is difficult for certain reasons.Consequently,the number should be automatically determined.A statistical and region-based segmentation approach for color remote-sensing images is introduced in this paper.First,the image domain is partitioned into groups of regular sub-regions（or blocks） by regular tessellation.Second,the image is modeled on the assumption that the intensities of its pixels in each homogeneous region follow an identical and independent multivariate Gaussian distribution.The Bayesian paradigm is applied to establish the image segmentation model.Third,a reversible-jump Markov chain Monte Carlo（RJMCMC）scheme is designed to simulate the segmentation model,which determines the number of classes and roughly segments the image.A refined operation is performed to improve the accuracy of the image segmentation results further.Real and synthetic color remote-sensing images from Worldview-Ⅱ and multispectral IKONOS images are tested.Qualitative and quantitative evaluation results are obtained to verify the feasibility and effectiveness of the proposed method.The proposed method exhibits advantages over two other segmentation methods,namely,the ISODATA method and the segmentation method combining Voronoi tessellation with EM/MPM algorithm.An image segmentation approach based on regular tessellation and RJMCMC algorithm is proposed in this study.The proposed approach can not only automatically determine the number of classes but also segment homogenous regions better.Furthermore,the test results also show that the approach demonstrates high performance and high efficiency.In future studies,other tessellation methods can be used to partition the image domain.关键词：segmentation of unknown number of classes;multispectral remote sensing image;regular tessellation;RJMCMC algorithm;Bayesian paradigm
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发布时间：2021-06-10 - 摘要：The intensities of pixels in images of multi-look Synthetic Aperture Radar（SAR） are usually modeled by gamma distribution.To obtain a segmentation result,the parameters that indicate the distributions should be estimated.However,traditional expectation maximization（EM） algorithm cannot approach the shape parameter through taking partial derivation of the likelihood probability.Thus,to solve this problem,the shape parameter should be made equal to the number of looks,and only the scale parameter should be estimated.Nevertheless,the conclusion is obtained under the assumption that a pixel is constructed by infinite scattering units,which is impossible especially in highresolution SAR images.Apparently,that assumption is unreasonable and will lead to inaccurate estimation of the corresponding parameters.Moreover,gamma distribution with fixed-shape parameter cannot completely describe the characteristics of intensity distribution in multilook SAR images.Thus,this paper proposes a scheme for solving the parameter based on Expectation/Conditional Maximization（ECM） algorithm and develops a new segmentation algorithm for multi-look SAR images.First,the neighborhood system on the label field is considered,and Markov random field model is employed to define prior distribution.Then,intensities in each homogeneous region of a multilook SAR image are modeled by gamma distribution,in which both shape and scale parameters are considered as variables.Finally,the gamma distribution along with the prior distribution constructs the posterior distribution,on which purpose it should be maximized.In this paper,Metropolis-Hastings algorithm is used as a random sampling method in estimating the marginal posterior probability model and changing the labels of each pixel.However,the estimation of the shape parameter remains a problem because it cannot be solved through partial derivation.ECM algorithm introduces the Newton iterative method to approach the real value of parameters,which cannot be directly solved from an equation.Therefore,shape parameter is evaluated by ECM algorithm,and all variables are obtained ultimately.The proposed algorithm is applied to simulated and real multi-look SAR images.Experimental results show that the proposed method can accurately estimate shape and scale parameters of gamma distribution.The proposed algorithm’s user,product,and total accuracies,and kappa coefficient are all higher than those of the EM algorithm’s,and the estimated values of the algorithm are much closer to the real values.The proposed algorithm can estimate the parameters of gamma distribution and the corresponding reality of the label field under the circumstance of maximized posteriori probability.Experiments on simulated and real multi-look SAR image verify the effectiveness and feasibility of the proposed algorithm,and the shape and scale parameters of gamma distribution can quickly converge to their stable state in a considerably short time.The ECM algorithm introduced in this paper can estimate the value of the shape parameter in gamma distribution,which is treated as the number of looks in the traditional EM algorithm.Thus,the connections between different looks are included in the distribution,and the corresponding segmentation results are improved.关键词：multi-look SAR images;ECM algorithm;image segmentation;histogram fitting
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发布时间：2021-06-10 - 摘要：Sand invasion is intensified by the serious degradation and disappearance of Nitraria bushes,which has a serious effect on the oasis ecological security of deserts.Quantitative analysis of different multiple scattering factors in mixed spectral contribution for the ecological environment on deserts is particularly important.Timely monitoring of spatial and temporal variations in photosynthetic/non-photosynthetic vegetation（PV/NPV） fraction cover provides essential information for guiding management practices on land desertification and research on vegetation recession mechanism.In this paper,taking the typical vegetation of Nitraria bushes in Minqin County of Gansu Province as an example,mixed and endmember spectra,and fraction information were acquired by ground-controlling spectroscopy experiment.Then,the fractional cover of PV(fpv) and that of NPV(fnpv) were estimated by linear and nonlinear spectral mixture models（NSMM）（including Kernel NSMM（KNSMM） and bilinear spectral mixture model（BSMM））,respectively.Fully constrained least square method was adopted to mix the models,and the fraction of every endmember and the accuracy information of all the samples were calculated.The performances of the models were compared based on root mean square error（RMSE） of the unmixing model and accuracy of field validation,and the endmember fraction of field validation is based on the abundance of digital image classification by the neural network classification algorithm.Results show that（1） compared with the traditional three-endmember model（PV,NPV,and bare soil（BS））,the four-endmember model,which incorporates an additional shadow endmember,can effectively improve both the accuracy of spectral mixture model（RMSE decreased from 0.0429 to 0.0052 and improved 16%in accuracy） and the estimation precision of fpv and fnpv（increased by 44%and 83%,respectively）.（2）Moreover,the precision of the unmixing of model could be improved by BSMM considering the multiple scattering between NPV and BS endmembers.However,the improved precision was insignificant.Also,considering the nonlinear parameters,the performance of KNSMM was slightly lower than that of the LSMM model.（3） The validation RMSE of fpv was 0.1177（R2=0.7049）,and that of fnpv was0.0835（R<sup>2=0.4896） with LSMM based on PV/NPV,BS,and shadow endmembers.Process monitoring describes the multiple photon-scattering effect among PV/NPV,BS,and shadows in Nitrariabushes.The selection and application of the types of NSMMs should be confirmed according to specific research object and the required precision.Shadows cannot be ignored in estimating vegetation fractional cover,especially in improving fnpv accuracy.This finding illustrates that the types and number of endmembers chosen are significant in improving the accuracy of fraction estimation.The conclusion also shows that LSMM is suitable to estimate fpv and fnpv of Nitraria bushes accurately based on PV/NPV,BS,and shadow endmembers.关键词：hyperspectral data;photosynthetic/non-photosynthetic vegetation;linear/non-linear mixture model;arid and semi-arid area;multiple photon scattering
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发布时间：2021-06-10 - 摘要：Characterizing spatial and temporal variations in an aerosol is critical for a thorough understanding of its formation,transport,and accumulation in the atmosphere.The Pearl River Delta（PRD） region is one of the most densely urbanized regions in the world and one of the main hubs of China’s economic growth.In this study,long-term（2000—2013） Moderate Resolution Imaging Spectroradiometer（MODIS） level 2 aerosol products were used to study the spatial and temporal distributions of both aerosol optical depth（AOD） and fine aerosol optical depth（FAOD） over the PRD region.To investigate the variation characteristics of particulate pollution in the PRD region for a long time,we used long-term（2000—2013）MODIS AOD and FAOD data with 10 km resolution to calculate the monthly,seasonal,annual,and 14-year means.In this process,we screened the cloud pixels.Results indicate that the spatial distribution of AOD of higher values are located in the central region of the PRD region（e.g.,Foshan,north of Zhongshan,and southwest of Dongguan）,whereas that of lower values are located in the western and eastern parts（e.g.,Guangzhou,Huizhou,Zhaoqing,and Jiangmen）.The spatial variability of FAOD is less significant than that of AOD,and the highest value of FAOD is located in Zhaoqing.Both annual mean AOD and number of areas with high AOD increase from 2000 to 2006.Between 2006 and2013,although the annual AOD level over the PRD region fluctuated,the condition of particulate matter pollution has generally improved since 2006 because of the strict air pollution control implemented in the PRD region.The FAOD data set shows an increasing trend from2000 to 2012,and the spatial variation in FAOD over the PRD region becomes increasingly significant.Therefore,a further improvement in the air quality in the PRD region requires a stricter regulation of fine particulate matter concentrations.A comparison of the annual MODIS FAOD values and the annual AERONET FAOD values at the PolyU site（114.18°E,22.30°N） shows that,from 2006 to 2013 both variables have a good agreement and the absolute differences between them are all lower than 0.08.The lowest AOD（0.39±0.10） and FAOD（0.25±0.05）occur in winter.The maximum AOD（0.72±0.13） in the PRD region occurs in spring,whereas the FAOD（0.45±0.10） reaches the maximum in autumn.From 2000 to 2013,the maximum fluctuation in AOD appears in summer,with the AOD standard deviation（std）reaching 0.28 in July.The season with the minimum fluctuation in AOD occurs is winter,with the AOD std being only 0.086 in January.Similar to AOD,the maximum（std=0.21） and minimum（std=0.038） fluctuations in FAOD occur in June and January,respectively.A long-term MODIS AOD and FAOD series can reflect the spatio-temporal variation trend of particulate matter pollution over the PRD region.Although aerosol extinction has decreased significantly since 2006,the fine aerosol extinction has still increased to a certain extent,indicating that further control on fine particulate matter pollution is necessary in the PRD region.关键词：AOD;FAOD;MODIS;Pearl River Delta region
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发布时间：2021-06-10 - 摘要：The temporal and spatial coupling of population and economy is an important embodiment of the spatial equilibrium of regional social and economic development in China.It can effectively guide the development and implementation of policies and measures of economic spatial pattern,civil affairs,transportation development,and environmental management in China.The paper achieves to reveal the temporal-spatial trend and coupling of population and social economy.Under the support of geographic information system,several methods are applied in the study,such as index of population structure distribution,Gini coefficient of population density,migration of population gravity center,and spatial autocorrelation analysis.County-level census data in the years of 1935,1953,1964,1982,2000,and 2010 were interpolated into a grid format,and population density line was further extracted to analyze the changes and trends of population distribution in China.Population and social economic statistics in different provincial administrative units from 1952 to 2010 were combined.The study revealed a spatial-tempoTal difference of social economy and population distribution,and further analyzed the distribution and its trend of population and social economy from two angles:population and economic center of gravity space coupling,and spatial consistency.Spatial-temporal evolution of population spatial distribution varies in China because of the effect of social history,natural environment,and economic conditions.The difference between east and west regions is large,and the imbalance of the situation is becoming increasingly obvious.The line of Hu Huanyong can still be a good generalization of population distribution in China.The characteristic of spatial distribution pattern is still narrow and thick in the southeastern region,and sparse and wide in the northwest region.In addition,according to the change of population density line,population distribution densities in Gansu,Ningxia,and Inner Mongolia Autonomous have increased obviously.Population distribution has broken the line limitation of Hu Huanyong in partial area in China and has transferred to the west region at some extent.Different regional characteristics of spatial distribution and economic development of population are remarkably obvious in China.However,the consistency of the provinces is shrinking,and the population has a strong economic orientation.Population and economic consistency,and their change trends in the country and the four major regions are different during 1952—2010.The degree of national demographic and economic space consistency is relatively low.Population distribution and social economy in the northeast area have a high consistency degree and continues to improve.The eastern and central regions are in a stable condition.The situation in the western region is poor and still weak.关键词：Hu Huanyong line;population distribution;spatial analyze;the consistency of population and economy
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发布时间：2021-06-10 - 摘要：Differences in cloud height are obvious in high-resolution data,especially because cloud edge heights have become an important factor in cloud shadow identification and estimation of surface solar radiation.However,the resolution of cloud heights calculated by thermal infrared data is low and lacks detailed characteristics.Cloud edges of visible and thermal infrared bands differ considerably both in shape and geometric features.The edge of high-resolution image has rich characteristics,whereas that of thermal infrared cloud height data is single and fuzzy in geometric characteristics,so they cannot match exactly.Although some feature points of clouds can be obtained by some feature point matching methods such as Scale-Invariant Feature Transform（SIFT） and Harris,the difference between two data on geometric features made available by feature points was less.This result cannot satisfy the need to match the information of thermal infrared cloud heights and high-resolution cloud edge data,and obtain a result with abundant diversity.To solve this problem,an algorithm was presented in this study.First,SIFT algorithm was utilized in this method to extract feature points for further image registration and correction.Then,cloud edge heights were calculated by thermal infrared data and re-sampled to a high resolution.Next,Euclidean distance transform was performed for each cloud edge pixel of high-resolution data to all thermal infrared cloud edge pixels,which could obtain spatial relationships between the two types of data.As the two types of data differed considerably in edge characteristics,directly determining the optimal matching point was difficult.Thus,a hierarchical searching method was used here.While the searched objects had different significance to matching points,the weight was given by distance to determine the final matching height.Finally,real cloud heights were determined according to the matching method of cloud shadow similarity.We used five images of HJ-1B CCD and IRS data in the Heihe area on June 8,2012.From all the matched results,the corresponding regions of the matched results had high cloud heights where thermal infrared data also had high cloud heights.At the same time,resolution and details were improved.To evaluate the accuracy of the calculated heights,we selected 10 highly recognizable feature shadow points in each image and marked their coordinates in the image.We set the actual shadow points as reference and calculated the offset of the same feature shadow point,in which we could obtain the error of each cloud height.We found that the errors between 0.1 km to 0.3 km were 70%among all 50 points,12%were less than 0.1 km,and 0.25 km was the average error of all points.Compared with other studies,cloud height accuracy was higher in our study.Also,we chose SIFT algorithm to match cloud heights by using two types of data and compared the matched results of some feature points with our method.The height accuracy obtained by SIFT was lower than that of our algorithm.In addition,unlike some feature point matching methods,our method can complete full-information matching.A cloud edge height matching method based on Euclidean distance transform by hierarchical searching is proposed in our paper.The method can match the cloud height information of low-resolution thermal infrared to corresponding cloud edge of high-resolution image.Experimental results showed that the matched results followed the distribution and variation law of thermal infrared cloud heights,as well as the cloud edge heights with high accuracy and detailed characteristics.To a certain extent,our study solved the resolution problem in obtaining cloud height by thermal infrared data.In addition,compared with some matching methods of feature points,our method could complete full-information matching and had a higher accuracy.However,the accuracy of matched results would be influenced by many factors,such as accuracy of cloud detection,surface in homogeneity,and image registration.The method in our paper is only for cloud edge height.Thus,matching for other parts of cloud still needs further research.关键词：cloud edge heights;Euclidean distance transform（EDT）;contour scanning;cloud shadows
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发布时间：2021-06-10 - 摘要：An accurate estimation of forest biomass is important for global carbon balance and climate change studies.The integration of hyperspectral and high-resolution data can provide abundant spectral and spatial detail information,and accurate biomass estimates.However,acquiring hyperspectral and high-resolution data at the same time is difficult,and related applications of integrated data are limited in the range of subtropical forests.This study used hyperspectral and high-resolution data acquired simultaneously in a subtropical natural secondary forest with field-measured data to estimate forest biomass.It provided a new approach in estimating the biomass of subtropical forests accurately.In the study,LiDAR-CCD-hyperspectral-integrated sensor was used to obtain hyperspectral and high-resolution data at the same time in a subtropical natural secondary forest.A total of 30 square plots（30×0） were established across the study site.First,tree crown features were extracted from high-resolution images by multi-scale segmentation based on edge detection.Second,five sets of spectral metrics were extracted from hyperspectral images.Third,the metrics extracted from hyperspectral and high-resolution images were integrated to build forest biomass models by using stepwise regression.Finally,cross-validations were used to assess the estimation accuracy of the models.The accuracy of integrated models（R2=0.54-0.62） is higher than that of hyperspectral models（R2 =0.48-0.57）;the accuracy of aboveground biomass in hyperspectral model（R2 =0.57） is higher than that of belowground biomass（R2 =0.48）;and the accuracy of aboveground biomass in the integrated model is（R2=0.62） higher than that of belowground biomass（R2=0.54）.Cross-validation results indicate that the integrated model is better than the hyperspectral one,and that hyperspectral and high-resolution data obtained at the same time through integrated sensor can be used in estimating biomass in subtropical forests effectively.This study provided the effective approaches in estimating the biomass of subtropical forests by using remotely sensed data,and it proved that the integration of hyperspectral and high-resolution data can also help in estimating forest biomass accurately.This approach may provide an important basis for research on ecosystem and carbon cycle in subtropical forests.关键词：subtropical forest;biomass estimation;remote sensing reversion;high resolution data;hyperspectral data
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发布时间：2021-06-10 - 摘要：Landsat 8 OLI images have become important data sources;however,they are usually covered by clouds and cloud shadows,which reduce data availability.Therefore,a rapid method for detecting clouds and cloud shadows in a single image is necessary for followup data recovery applications.Threshold setting has been a commonly used method;however,it is difficult to use because the same threshold value usually indicates different objectives and varies across different data sources,as well as across different period images.Thus,the relative position relationship between a cloud shadow and a cloud is the key to detect a cloud shadow.First,a cloud index（CI） with a cirrus band and coastal/aerosol band was established to distinguish the thin and thick cloud pixels by setting thresholds.Second,a normalized dark pixel index（NDPI） with coastal/aerosol band and short-wave infrared 2 band was established.Furthermore,a ratio of shadow index（RSI） was established based on the NDPI and normalized differential vegetation index.RSI was used to distinguish the potential cloud shadow pixels in thin-cloud and non-cloud areas also by using thresholds.Third,an azimuth search method based on the solar azimuth angle and an appropriate searching distance were adopted to detect the cloud shadow from the potential cloud shadow pixels in the images.Two Landsat 8 OLI images were selected for the case study;the image captured on April 22,2014 was used for the test,whereas the image captured on July 12,2015 was used for validation.For each type（e.g.,thick cloud,water,cloud shadow,or other kinds of shadow）,200 random sampling points were used to assess the detection accuracy.Results showed that CI could quickly distinguish cloud from on-cloud pixels by statistics according to the cloud coverage ratio in the header files,with thresholds of CI ≥ 0.0011 and CI ≥ 0.0048 in the test and validation OLI images for the thick cloud type.Both user accuracy rates of detection for the thick cloud samples were over 99%.RSI could enhance the difference among water,cloud shadow,and other kinds of shadow,thereby facilitating the differentiation among the different types.The pixels with 0.45 ≤ RSI < 0.76 in the test image and 0.36 ≤ RSI < 0.76 in the validation image belong to potential cloud shadows.Using the solar azimuth angle at the time of imaging as the searching azimuth angle and a reasonable searching distance（500-2200 m in the test image and270-800 m in the validation image）,the azimuth searching method simplified the models of the relative position relationship of a cloud shadow to a cloud and accurately distinguished cloud shadow from water and other shadows.The detection precision levels for both test and validation reached more than 93%,which compensated for the limitation of the threshold method.The proposed shadow detection method combines threshold with simplified relative relationship model and leverages band difference.The method is feasible and rapid when applied to a single image,further improving the utilization accuracy of OLI images.关键词：ratio of shadow index;cloud index;azimuth search;cloud shadow detection;OLI image
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发布时间：2021-06-10 - 摘要：In the period of "Twentieth five-year" Currently,although the earth observation technology has rapidly developed rapidly in our country;however,it still has the major issues,such as the lack insufficiency of products with high accuracy in the fields of earth resource and environment monitoring,incomplete ability incompetence in the dynamic monitoring,and deficiency in the timeliness of service and portability.This paper comprehensively reviews the status of related technologies comprehensively,and points out identifies the development direction of the development of dynamic monitoring technology of earth resource and environmental dynamic monitoring technology in the future.Comparisons and analysis were done conducted on the main research progress of the dynamic monitoring technology of earth resource and environmental dynamic monitoring technology at home locally and abroad internationally.Then Thereafter,the major research contents were proposed in the future to meet the urgent needs in developing the ability and technology system of the continuous dynamic observation in the globe world and in the key regions for of our country.Four major aspects should be paid attention considered in the future for the dynamic monitoring technology of earth resource and environmental dynamic monitoring technology:（1） Mmulti-source remote sensing stereo observation of remote sensing and universal data processing technology;（2） the remote sensing dynamic monitoring and simulation technology of remote sensing for the factors of earth resource and environment;（3） formulating formulation of the system and platform of the dynamic monitoring technology of earth resource and environment dynamic monitoring technology;（4） the integrated demonstration application of demonstration on dynamic monitoring of earth resource and environment dynamic monitoring.It is urgent to promote the ability of the continuous dynamic observation over the whole entire world and the key regions,to is urgent to accomplish the following:（1） establish the global product and application system on the resource and environment dynamic monitoring;to break through the（2） develop theories and key technologies related with the to global resource and environment research;（3） establish the system of remote sensing monitoring factors and technologies of remote sensing monitoring on the global resource and environment;（4）form an independent technological system,including the stereoscopic global cooperation observations;the optimization of（5） optimize resource aggregation,intelligent information processing,and cloud platform business applications;（6） improve the ability of online remote sensing information service of remote sensing,such as the task task-driven data gathering,the model scheduling,and the product generating generation;and to（7） deliver achievements,such as national and global,intercontinental and country’s remote sensing information products with high quality and high spatial resolution,thematic application system and,technical reports,etc.Finally,we will provide data and service for the knowledge discovery in the field of global resource and environment,and We will also support the services in the global resource and environment monitoring and evaluation of global resource and environment,major disasters monitoring and early warning of major disasters,and responses to national security and global changes in our country.关键词：globalization;resource;environment;high precision monitoring;dynamic monitoring;cloud platform
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发布时间：2021-06-10 - 摘要：China has substantial data,but less information and insufficient knowledge,which results in poor international influence in global change research;thus,changing this situation is urgent.In 2016,a new project of the National Key Research and Development Program on Global Changes and Adaptation entitled, "Big data on global changes:Data sharing platform and recognition",is launched.The project attempts to establish a global change-big data（GloBiD） sharing platform to change the current situation.This paper introduces this project,which comprises the establishment of three fast processing systems of global data（i.e.,multi-source data aggregation and processing system,fast production system on global satellite products with 30 m spatial resolution,and fast production system on FY satellite products）,uncertainty analysis of multi-source data impact on global changes,and the recognition of sensitive factors of global change driven by big data.Cloud technology,cluster computing,Apache Spark engine,Apache HBase,and HDFS data storage technology are adopted to build the GloBiD sharing platform.This project will provide high-quality data for the global change research of China.It will also provide valuable information on the main problems of global change for policy making based on the recognition of sensitive factors.Hopefully,the establishment of scientific recognition and data sharing platform of global change data will promote the development of the global change research of China in the future.关键词：global change big data platform;fast algorithm;uncertainty;sensitive factors recognition
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发布时间：2021-06-10 - 摘要：In recent years,extreme global weathers have gradually become more frequent.Natural disasters have also become more serious,and violent terrorist activities dramatically surged.These occurrences cause considerable harm to people’s lives,property,and social stability.Emergency responses for earthquakes over magnitude six and violent terrorist attacks,as well as prompt supports for important events,such as Winter Olympics,and electricity network transmission,have become especially urgent in the background of global climate change and the rapid development of China.Achieving precise emergency services and decision-making through the integration of various modern information technologies and the comprehensive analysis of multi-source and multi-dimensional heterogeneous data obtained from spaceborne,airborne,and ground measurements are vital supports for implementing the "One Belt,One Road" national strategy.Emergencies are usually complex and diverse,and conventional emergency information services are too extensive.Therefore,establishing a precise emergency service system based on cooperative remote sensing monitoring is urgent.In response to this significant demand of diverse emergencies,the "Precise emergency service system construction and demonstration through synergy observation of spaceborne,airborne,and ground remote sensing" program（No.2016YFB0502500） is initiated in July 2016 with the support of the National Key Research and Development Plan.The project will be conducted by the Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,together with 13 other research institutes,universities,and enterprises.This project aims to do the following:（1） focus on the four categories of the most representative emergency services in natural disaster,social events,major social events,and lifeline engineering;（2） study the standards and specifications of emergency monitoring service of remote sensing;（3） overcome difficulties in cooperative monitoring,analyzing comprehensive data,and extracting precise information;（4）build emergency services and dispatching platform;and（5） conduct demonstration of emergency response service along the route of "One Belt,One Road" and around the sensitive areas.This project will change the past extensive flooding pattern of information services,establish a new precise emergency service system,and address the long-standing problem,in which multiple sources of emergency information are usually isolated and separated from each other after three years of technical research and demonstration.This project mainly includes six parts:（1） study the evolution process and mechanism of emergency,and establish an overall program for emergency response;（2） study the institutional mechanisms of remote sensing emergency services for cooperative networking observation,and establish emergency services standards;（3） research cooperative observation methods for multi-platform collaborations and create transmission links for emergency data;（4） discover the key techniques for fast spatial data processing,emergency information extraction,and customization services;（5） secure extendable precision emergency services and dispatching platform that integrates remote sensing,communication,and navigation technologies;and（6） conduct combat-type demonstrations on precise customization and command of emergency services.The results of the project will be used in safe guarding UHV transmission lines along the China-Pakistan economic corridor,as well as the China-Kazakhstan and China-Russia routes,thereby producing significant economic benefits.Simultaneously,precise emergency services will reduce the loss of life and property significantly,enhance the accurate detection of terrorism,and assist in responding to abnormal weather effectively.In summary,the project will push the deployment of "One Belt,One Road" strategy effectively by demonstrating regionally,promoting economic prosperity and social stability,and enhancing the influence of China globally.关键词：national key research and development plan;remote sensing;synergy observing;precise emergence service;demonstration
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发布时间：2021-06-10 - 摘要：The research project entitled, "Generation of global climate data records and their use for monitoring the key variables and processes of climate change," was recently funded by the Chinese Ministry of Science and Technology under the Global Changes and Responses Program.This project focuses on the essential climate variables proposed by the global climate observing system.It aims to improve surface-air-space observing systems;produce long-term,highly accurate,and highly spatiotemporal consistent satellite products（i.e.,climate data records,CDRs） of the atmosphere,ocean,and land surfaces;and monitor the key variables and processes of climate change dynamically.This project will produce the first CDR suite in China.This research project is divided into four.The first three sub-projects focus on the satellite product generation of the atmosphere,ocean,and land surfaces.Each of these three sub-project includes ground observation,inversion and fusion methods of remote sensing data,and production and application demonstration of climate dataset.Ground observation is mainly used for algorithm development,product validation,and application demonstration.Sub-project 4 will comprehensively assess these satellite products and use them for climate change studies.Sub-project 1 on the atmosphere will mainly focus on the variables that are essential for climate change studies,such as aerosol optical thickness,cloudiness,precipitation,CO2,ozone,solar incident radiation,reflected solar radiation,outgoing long wave radiation,and energy imbalance.Nine CDRs will be generated at the end.The application demonstration will be based on the long-term atmospheric climate dataset;it will be combined with foreign satellite-related products to study the global climate effects of aerosol,dynamic monitoring of polar ozone concentrations,energy balance of the Earth,and other applications.Sub-project 2 on the ocean will mainly focus on methods and techniques for producing a total of 21 products,including the balance components of ocean energy（i.e.,shortwave incident solar radiation,shortwave broadband albedo,longwave downward radiation,emissivity,and net radiation）,dynamic environmental parameters and processes of the ocean（i.e.,sea surface wind,ocean wave,surface flow,sea surface temperature,sea surface salinity,sea surface temperature,and oceanic ice color（reflectance,chlorophyll concentration,particulate organic carbon,and primary productivity））,and sea ice（i.e.,concentration,thickness,and drift）.At the end,17 of these will be generated as ocean CDRs.Their applications to the global ocean matter and energy transport will be demonstrated based on the global ocean climate data set for the major estuarine water changes of the world in response to global climate change.Sub-project 3 for land surfaces will mainly focus on 20 variables that characterize the key processes of climate change,including the global energy balance of land surfaces（i.e.,shortwave incident radiation,shortwave broadband albedo,longwave downward radiation,land surface emissivity,land surface temperature,and net radiation）,water cycle（i.e.,evapotranspiration,water surface dynamics,and wetland）,carbon cycle（i.e.,leaf area index,fractional photo synthetically active radiation absorbed by green vegetation,vegetation coverage,forest biomass,gross primary productivity,net primary productivity,residential area,land cover,and fire burned area）,and polar and cryosphere（i.e.,elevation and area of ice surfaces,snow cover,snow water equivalent,and freezing and thawing of permafrost）.At the end,15 of these products will be generated as land CDRs.The application demonstration component will include assimilating these data products into land surface process models;analyzing the effect of these products on the diagnostic ability of temporal and spatial characteristics of climate change;and quantifying the scientific values of these products in characterizing regional carbon cycle,water cycle,and energy balance.The atmospheric,oceanic,and terrestrial CDRs will be analyzed in Sub-project 4 by using the key process of global change as the constraint.They will be tested based on their spatiotemporal and logical consistency.Basic global change indicators will be proposed based on the entire process of acquisition and analysis of information data of global change.We will also determine the states and trends of the key processes,evaluate their causes and effects comprehensively,and investigate direct evidence of the roles played by key processes and elements in global climate change.关键词：quantitative remote sensing;climate data records;climate change
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发布时间：2021-06-10 - 摘要：<正>封面图片为上海市卫星气象遥感应用中心利用中国风云二号（FY-2F）卫星红外通道（03·113μm）监测的第22号强台风"海马"的遥感图像,获取时间为201 6年10月20日14:00时（北京时间）,台风的中心气压955 hPa,西北向移动,移速26 km/h。FY-2F是中国自主研制的第4颗业务静止气象卫星,除了常规观测以外,该卫星能够根据各种气象灾害监测及重大气象保障服务的要求,对特定区域提供快速区域扫描观测,观测频次为每6 min
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发布时间：2021-06-10
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