In recent years,airborne laser bathymetry technology has got a rapid development.It plays an important role in monitoring of offshore area,measuring water depth and surveying underwater topographic and geomorphic features.This paper analyzed the development history of airborne laser bathymetry in the world and discussed the research progress of data processing and applications.This paper studied the problems of airborne laser bathymetry technology in the development and application,and pointed out future development trend and direction in the end.This research will provide detailed reference information for researchers and users of airborne laser bathymetry technology.
Microwave remote sensing could interact with forest internal scatterers,because of its penetrability.So it could provide the parameters indicating forest vertical direction and be considered to have the potential to estimate forest vertical structure parameters.With the rapid development of the InSAR,PolInSAR,multi\|baseline InSAR and multi\|baseline PolInSAR technologies,the application in forestry for microwave remote sensing has been extended,provides a feasible solution to forest vertical structure parameters estimation.Firstly,the methods of extracting forest vertical structure profile based on the technology of tomography are summarized.Then,the methods for estimating topography,forest height and forest above\|ground biomass are described.Finally,the existential problems and research trends for forest vertical structure parameters estimation are analyzed.
Lunar photometric model concerns how the brightness of the moon surface〖HT5H〗〖STHZ〗〖JY〗(下转第652页)〖HT〗〖ST〗depends on the illumination and observing geometry.It’s mainly used for removing the effect of the local scene viewing and illumination geometry to Lunar spectrum data.And it’s important in the application of image mosaicking,identification and inversion of lunar mineral,calculation of lunar physical parameters and research of abnormal photometric area.LRO’s multi\|angle and CE\|3’s in\|situ spectral data are promoting research of Lunar photometric model.At first,this paper summerized mainly photometric models and their characteristics.Then discussed the development tendency in this field:① Using different photometric model and correction method to different data to have a research on fitness and comparision;② Researching on the issue of partion and normalized angles to improve the correction accuracy with limmited lunar spectrum data.③ Trying to acquire more Luanr multiangle spectrum data to promote the research on the Moon by later Lunar exploration.
Glaciers are considered to be a sensitive indicator of climate variation,the best way to decide the glacier response was the mass balance measurement,it is difficult to implement the systematic and continuous observation of mass balance,and snowline altitude can approximate representative equilibrium line altitude.In this paper,the research background and significance of snowline were introduced,we stated related concepts of snowline,analyzed the existing research methods (such as optical method,statistical method,the direct measurement,and topographic map),summarized methods to verify the snowline altitude,then discussed the influence of the temperature and precipitation on the snowline altitude.Finally,we summarized the various applications of glacier snowline altitude.
Building extraction is one of the most challenging research topics in remote sensing image understanding.It is of great significance in practice to exploit automatic,intelligent,accurate building extraction approaches.This paper firstly outlines the history and recent development of building extraction from remote sensing imagery,and then provides a comprehensive survey of state-of-the-art approaches,to divide them into the bottom\|up (data-driven) methods and top-down (model-driven) methods.Finally,the remaining problems and future development trends are provided for building extraction from high resolution remote sensing imagery.
Remote sensing driven light use efficiency models have been widely utilized to calculate the productivity of terrestrial ecosystems.The outputs of these models are very sensitive to maximum light use efficiency (ε max.In this study,the province\|level yield census data,MODIS reflectance data,locally observed meteorological data,and the Vegetation Photosynthesis Model (VPM) were employed to derive annual mean province\|level cropland ε max in the mainland of China from 2001 to 2011.Then,the spatial,temporal variations of ε max and possible driving factors were analyzed.The results show that,during the study period,the province\|level means of cropland ε max in 31 provinces varied between 0.57~2.20 g C·MJ-1,which was higher in the east and central parts and lower in the northwest and southwest parts.Annual mean cropland ε max increased in most provinces but showed interannual fluctuations during the period from 2001 to 2007.It relatively steadily increased since 2008.The interannual fluctuations of province\|level cropland ε max were normally higher in the north than in the south,and higher in the east than in the west.The annual means of cropland ε max had strong positi e correlation with the amount of fertilizer used in per unit area of cultivated cropland in most provinces,and it reached significant level (P<0.05),so the increase of the consumption of chemical fertilizer in these regions was one of the main causes of the increase of cropland ε max.Since the interannual fluctuations of ε max were also related to the yield fraction of C4 crops (corn),the increase of the yield fraction of C4 crops could also induce the increase of cropland ε max.This study proves that it is of importance to develop a parameterization scheme accounting for the temporal and spatial variations of max for improving the calculation of productivity in croplands by using light use efficiency models and remote sensing data.
Inversion of atmospheric parameters and integrated radiativecorrectionbased on hyperspectral remote sensing image data have important research significance and application value.First of all,this papersimulated the EO-1 Hyperion remote sensing water vapor content through the 6S radiative transfer model,and analyzed the spectrum absorption characteristics near the water vapor absorption regions near the 940 nm and 1 130 nm.Secondly,using the two\|band ratio method and three-band ratio method,both inversion accuracy and sensitivity analysis were carried out by using different bands combined of hyperspectral remote sensing imagery,the results show that both the correlation coefficient R2 and the root mean square error (RMSE) of three-channel ratio algorithm were better than the two-channel algorithm.Finally,we used three EO-1 Hyperion hyperspectral remote sensing images to retrieve the water vapor content,the results demonstrated that the RMSE of using 1 124 nm by two-channel and three-channel ratio methods were 36.9% and 12.8% respectively,which proved that the specific quantitative values of the latter method was much closer to the ground\|level observation data which was provided by ground-based CE-318 sun-photometer measurements,Simulation and verification.The Hyperion inversion results using three-channel ratio method was verified owing better consistency with ground\|based measurement data.
It has a great significance in monitoring the suspended matter concentration of dam rivers to evaluate the impact of water conservancy and hydropower project on the water quality.The image of HJ-CCD has a high time and space resolution that apply to monitor the water quality of river near the Manwan Dam which is a kind of small and medium scale reservoir has greater practical significance.The atmospheric correction method is used the dark-object subtraction,FLAASH and QUick Atmospheric Correction (QUAC) which based on the satellite image for the atmospheric correction .Through comparing the atmospheric correction effect,the dark-object subtraction method has achieved a good result that the average relative error of band 2 and band 3 are 16.1% and 17.9% respectively,then we used the bands which has a good quality in atmospheric correction to construct a suitable inversion model for suspended matter concentration and the result shows that the decision coefficient of this model is 0.92,the root mean square error (RMSE) is 4.83 mg/L and the average relative error is 33.1%,then we used this model to apply to the images which have a good image quality and covers of the full year of 2014 and got the space distribution of suspended matter concentration and it reflects the change of suspended matter concentration near the Manwan Dam.
As a result of different kinds of RS data containing varied information about green plants,to avoid the problem of low precision,the joint inversion model that constructed by the least squares method combined optical and radar remote sensing data such as Landsat8/OLI and Radarsat2 data was put forward to estimate LAI.And this research area was based on Remote Sensing Synthetic Experiment Station of Chinese Academy of Sciences in Huailai,Hebei Province and the research objects were maize.First of all,conventional method was used for remote sensing image preprocessing and then measured LAI was considered to build the empirical expressions between the extracted information from multi\|spectral data and radar data.Secondly,the least squares method that combined with Regression Model from different data was used to build the joint inversion model.At last,the joint inversion model was used to estimate the LAI based on iteration method and assess the result by the verification data.For comparison,the empirical model using vegetation index or backscattering coefficient as predicted variable,the weighted averaging model using multi\|source data and the Look\|up table method from physical model were also considered for LAI estimation.The result shows the better fit result was found between the predicted LAI from Partial Least Squares method and measured LAI (R2=0.5442,RMSE=0.81).Moreover,partial least squares method also couldimprove the overestimated and underestimated phenomenon from empirical method or weight fusion model due to the data quality,system error or saturation of remote sensing data.
At present,the study on spectral characteristics of vegetation at home and abroad is more concentrated in summer or autumn,while this study on winter is relatively rare.In this paper,the Fieldspec4 portable spectrometer and ASD integrating sphere were adopted to collect spectrum data of the 6 typical vegetation in July and December,2014.Then canopy and defoliation reflectance of these vegetation were acquired.The spectral characteristics and variations of canopy and defoliation were analyzed,and the effects slope factor and measurement methods having on vegetation spectral reflectance also were investigated.The results indicated that evergreen vegetation had a difference in different seasons and the seasonal changes of different vegetation canopy spectrum were different.Winter evergreen vegetation with similar spectral features,whereas different vegetation had obvious differences.In winter,vegetation canopy spectrum first decreased and then stabilized.Because of being affected by several factors such as leaf pigment content,water content and soil background,vegetation defoliation spectrum didn’t show apparent regular changes during its senescence period.Within a certain slope range,vegetation spectral reflectance increased with increasing slope.The reflectance of vegetation spectrum by different measurement methods differed,while their variations kept the same.
As Chinese new satellite of high resolution for earth observation,the application of air pollution monitoring for GF-1 data is a key problem which needs to be solved.Based on Deep Blue algorithm,by taking count of the characteristic of GF-1 16 m camera,the contribution of land surface was removed by MODIS surface reflectance product,and aerosol optical depth (AOD) was retrieved from apparent reflectance in blue band.Therefore,Deep blue algorithm was applied to GF-1 16 m camera successfully.Then,we collected GF-1 16 m camera data over Beijing area between August and November,2014,and the experiment of AOD was processed.It is obviously that the retrieved image showed the distribution of AOD well.At last,the AOD was validated by ground-based AOD data of AERONET/PHOTONS Beijing site.It is showed that there are good agreement between GF-1 AOD and AERONET/PHOTONS AOD(R is about 0.9).But GF-1 AOD is obvious larger than ground\|based AOD which may be brought by the difference of fliter response function between MODIS and GF-1 camera.
Synthetic Aperture Radar(SAR),with all-weather and all-time imaging capabilities,is able to carry out continuous and effective earth observation,which provides a stable data resource for environmental monitoring.This paper presents a high precision Object-oriented water extraction scheme based on polarimetric SAR(PolSAR) data.First,a watershed segmentation is used for preserving waterlines.Meanwhile,the Gary-Level Co-occurrence Matrix(GLCM) and a decomposition method are applied to extract texture and scattering features,respectively.Then a majority voting is utilized for water segment recognition.Both Radarsat-2 data and TerraSAR-X data are used to verify the efficiency of the proposed method,and a pixel-based scheme,texture feature based method,polarimetric decomposition based method and a proposed method without the majority voting step are applied for comparison.The experimental results indicate that the proposed method has the highest classification accuracy while the segmentation is able to maintain accurate waterline,and the combination of texture feature and decomposition components is able to distinguish grass,barren and shadow while the voting strategy is capable of detecting small water area.
An albedo downscaling algorithm based on image fusion with consideration of physical meaning was proposed to enhance the spatial resolution of the Global LAnd Surface Satellite (GLASS) albedo product and produce high\|resolution albedo rapidly.Firstly,data processings including image reprojection,image cropping,snow pixels masking were conducted for 1km GLASS albedo and 30m multiband reflectance acquired by the CCD sensor onboard the Chinese satellites HJ1A/1B (short for HJ/CCD).Secondly,the preliminary high\|resolution albedo was estimated by HJ/CCD reflectance through the method of narrowband to broadband conversion.Thirdly,considering the spatial response of GLASS albedo,the coarse\|resolution GLASS albedo and the preliminary high\|resolution albedo were fused to yield the final high\|resolution albedo.In the end,the fusion albedo with high spatial resolution was shown,analyzed and validated with In Situ data of middle Heihe River Basin.The results show that the high\|resolution albedo obtained by downscaling algorithm based on image fusion of physical meaning is with high precision (RMSE was 0.02 or so),and the algorithm is fast and efficient,indicating its potential to become an operational algorithm for high\|resolution albedo product.
The traditional pixel\|wised classification methods is lack of hyperspectral image (HIS) ignore spatial information and generalization ability,resulting in a big limit of classification performance.Morphological attribute profiles is an effective method to express spatial information of remote sensing image.In the meanwhile,ensemble learning machines possess high ability of power generalization so that to improve classification stability.An ensemble method based on morphological attribute profiles is proposed for hyperspectral image in order to make full advantage of spatial information to improve stability.Firstly,principal component analysis and minimum noise fraction are utilized for feature extraction,and then morphological attribute profiles operations are carried out on the first a few features which have most information of the image;Secondly,supporting vector machines and extreme learning machines are used as base classifiers for their good classification performance.Finally,the results of each base classifier are combined by 〖JP3〗the way of majority voting.Compared with other ensemble method,different feature extraction algorithm and different base classifiers are both combined to form the joint integrated model.In addition,spatial information deriving from morphological attribute profiles is introduced to improve classification accuracy. Experiment on AVIRIS data set and ROSIS data set respectively illustrate that the method can obtain better classification performance in terms of precision and stability,and the overall accuracy of them reach to 83.41% and 95.14%,respectively.
Through the Image De-noising method study of remote sensing via sparse representation based on non-local self-similarity,the following image analysis,recognition and higher levels of processing can be provided assurance,which is important.Due to non-local self-similarity and sparsity of remote sensing images,on the basis of traditional sparse representation de-noising model,the group is composed of non-local patches with similar structures,which is exploited as the unit of sparse representation,and group-based sparse representation is used for image de-noising.In addition,because learning a dictionary of the whole image has high computational complexity,the characteristics of group is analyzed,and self-adaptive dictionary of each group is learned.Finally,in order to obtain an effective de\|noising result,the iterative shrinkage thresholding algorithm is developed to solve the L0 minimization problem.The results of the "Resource III" remote sensing images showed that the algorithm can better remove noise of remote sensing images,improve the peak signal to noise ratio and keep the structural information.Based on non\|local self-similarity,the information of patches can be fully used for the image de-noising,so this method can improve the image quality.
It is of great significance for parametric and non\|parametric classifiers to assess their classification accuracy and performance influenced from the training sample size.The theoretical training sample size (10~30 p,p denotes the bands number of remote sensing image) is widely used as a criteria for training sample selection.The principals of classifiers,such as parameter and non\|parameter classifiers,are different,and the theoretical training may be not universal and suitable for all the parameters.This paper carried out a study focusing on the analysis of classification accuracy with different training sample size,and the maximum likelihood classification (MLC) as parametric classifier and support vector machines (SVM) as non\|parametric classifier are the typical and popular classifiers were introduced.The results demonstrated that the accuracies of MLC and SVM are improved and tend to be stable accompanying with the sample amount increment.It was interesting that the increasing speed of MLC is higher than that of SVM because there are more informative training samples which can describe the land cover information for MLC,while the edge pixels of land cover feature space is the informative training sample for SVM.For MLC,the accuracy fluctuation with 5 training samples is obvious,while stable results with more than 30 training samples can be achieved,which represents the MLC classifier is sensitive to the training sample amount.For SVM as non\|parameters classifier,the higher stable accuracy compared to MLC could be also obtained with little sample,even with 5 samples,representing small training sample is suitable for SVM and break the limitation of theoretical training sample size.MLC could achieve higher accuracy than that of SVM when theoretical training samples as more than 30 were used.Under such condition,the training sample set can describe the normal spectral feature space for MLC,while the sampled selected randomly from the training sample collection has not enough informative pixels to construct the support vectors which is the basis for SVM.Analysis on the principle of different classifier,the classification accuracy for land cover mapping is different influenced from the different training sample size,and the theory of theoretical training sample is not the sole criteria for training sample size determination.The different optimized training sample selection according to classifier’s principle is further explored based on above research results.
In view of the problem of the response of the altered mineral extraction technology in different mixed background,based on the image simulation,this paper used the ratio method,principal component analysis and spectral angle mapper the three commonly used alteration mineral extraction technology for two common alteration minerals (hydroxy,iron staining) extraction experiment in the simulated mixed background,and carried out the statistical analysis of the degree response.The result showed that there were differences in longitudinal (among different algorithms) or horizontal (the same algorithm for different kinds of mineral alteration information) algorithm response,but this study can establish a link between them through the same degree of response range,and the corresponding abundance values of the different degrees of algorithm response can reflect the general mixed background of different algorithms for the two kinds of altered mineral information.The research results provide practical reference value for the reasonable selection of the algorithm and the comparison of the results of the algorithm in different mixed background.
As a key parameter to measure the water\|heat balance of earth surface,land surface temperature has two spatio\|temporal distribution characteristics:One is spatial distribution stability,that is,the correlation between the land surface temperature and the land surface bright temperature among those pixels whose properties are similar and relatively stable;the other is time series periodicity,and for one pixel,the time is closer,the temperatures are more similar.based on these characteristics,combined space statistical model with time series filtering,a spatio\|temporal domain algorithm was used for the reconstruction of land surface temperature,which was proposed.In the paper,the temperatures were reconstructed in 9 provinces (Xinjiang,Qinghai,Sichuan,Yunnan,Henan,Anhui,Hubei,Hunan,Jiangxi) of China with MODIS temperature products (MOD11A2),Landsat TM data and AMSR_E brightness temperature data (AMSR_EL2A) in 2008.Then,the inversion precisions in 9 provinces of the proposed algorithm were calculated based on arithmetic average method,and compared with the reconstruction results of multi\|channel statistical model based on the surface classification products from MODIS (MOD12).The results show that the proposed algorithm is practical,and that can be applied in any kind of LST images even there are lots of null values;and the average inversion error of this method for China with MOD11A2 products is about 1.2 K,decreased by 76% compared with multi\|channel statistical model,therefore the reconstruction accuracy is significantly improved.
Variations and trends in extreme climate events are more sensitive to climate change than the mean values,which have received much attention.In this study,the features for 8 in\|dices of precipitation extremes over Northern China are examined,broadly based on daily precipitation data from 90 meteorological stations during the period 1951~2013.The methods of 9\|year smoothing average,linear regression,Mann\|Kendall test and continuous wavelet transform were employed to delineate the rate of change,abrupt change points,statistical significance of the trends,and periodicity of extreme precipitation indices.The results show that Consecutive Dry Days(CDD) exhibited significant increasing trend during the recent 63 years,the maximum 5\|day precipitation(RX5day),Consecutive Wet Days and Annual Total Wet\|day Precipitation(PRCPTOT) decreased by 0.05 significance levels.Other 4 extreme precipitation indices showed a decreasing trend,and not notable.There is an obvious linear variation trend in all extreme precipitation and Annual Total Wet\|day Precipitation.Consecutive Dry Days and Annual Total Wet\|day Precipitation have a weak correlation,other indicators showed a strong positive correlation.With regard to the period of variation,almost all extreme precipitation indices vary at three or four years scale,and most indices have such periods as 8a,16a and 32a.In terms of the change point,Consecutive Dry Days occurs in 1964,which much earlier than other indices.From the spatial distribution characteristics of extreme precipitation indices,the variation trend in central North China is more distinct.Consecutive Dry Days increases,and other indicators show a decreasing trend.
An automated extraction method was proposed for rural resident in plain area in this study based on GF\|1 multi\|spectral Remote Sensing image.The study makes full use of multi-spectral information to calculate feature image which can highlight certain classes,and then establish new similarity feature space.Accordingly,we use the idea of traditional classification and Thresholding to extract primary selected resident area,but avoid the shortcoming of traditional classification in the process of fast automatic extraction and avoid lost image details caused by Thresholding.By studying how to select apposite structural elements,we could carry out purposeful and controllable morphological processing\|filling the internal holes of primary selected resident area,and then expend the whole area by using these structural elements\|to get the rural resident extraction mask.At last,using mask extraction and NDVI Thresholding method get the final result.Experimental results show that the method has high extraction accuracy,the overall accuracy of 86.9%,and high extraction speed,which is very useful to rural resident extraction and town planning and construction.
The physically\|explicit hydrological model,Distributed Hydrology\|Soil\|Vegetation Model (DHSVM),has been widely used for modelling hydrological processes in the meso\| and micro\|scale catchments.The model is driven by various forcing data with specific formats,needing complicated works to prepare those inputs.In particular,the stream network data are generated through the outdated Arc/Info AML script language,which is commercially licensed,making the data preparation very inconvenient for users.This study designed and implemented an automatic work by using an open source GIS library and C++ flow for generating stream network data files required by DHSVM.The representation and organization of stream network data in DHSVM were fully examined before the implementation.A strategy of separating executables and configuration files was adopted in order to improve its usability.This work lays a solid foundtion for future automation of the whole DHSVM running covering input preparation,model running and results analyzing,which is necessary when DHSVM is integrated to any operational forecasting system.
Scientific data publishing is a new and effective way to promote data sharing.In this paper,a novel collaborative publishing model for scientific data was proposed by incorporating data center and academic journal.In this model,data are published on a traditional academic journal in a form of data paper which has been rigorously peer reviewed,and preserved in a selected data center which is committed to data stewardship,management and sharing.This model takes advantage of the mature framework of peer review in the academic journal system to ensure the quality of published data.The form of data paper makes it more acceptable by the existing academic evaluation system in China, which usually neglects any contributions in form of data.As a result,it helps foster positive atmosphere in data authors of sharing data.The model can therefore effectively address two key issues mostly concerned:controlling of data quality and protection of author rights.The proposed model is easy to operate and can also be learned by disciplines other than geoscience we showcased in this study.
Earth surface microwave emissivity,representing the capacity of the earth's surface emitting microwave radiation outwards,is one of the key physical quantities of microwave radiometric characteristics for earthsurface.Satellite-borne passive microwave emissivity shows an overall and macroscopic expression of the surface microwave radiation on a large scale.It is important basic data for empirical parameterization acquisition in the geophysical parameters quantitative inversion from passive microwave observations,and also an approach of understanding the land surface microwave radiation on a large scale.Considering the synchronous observation characteristic of the Advanced Microwave Scanning Radiometer (AMSR-E) and Moderate Resolution Imaging Spectroradiometer (MODIS) mounted on the Aqua satellite,taking surface temperature and atmospheric water vapor data from MODIS as input data,this data set produced multi-channel microwave instantaneous emissivity through the emissivity estimation model during the operating cycle (June 2002~October 2011) of AMSR-E sensor under the global clear-sky condition.The results obtained from inter\|comparison,statistical analysis and the validation analysis from the frequency dependence and the correlation under different land covers indicate that,the dynamic range of instantaneous emissivity is larger,the monthly diurnal standard deviationare within 0.02,and the Radio Frequency Interference analysis,spatial and temporal variation,frequency dependence and correlation are in consistence with the understanding of natural physical geography process and microwave emission model understanding.The dataset,which includes daily,five-day,ten\|day,semi\|monthly and monthly global land surface products within the AMSR\|E full life span,may be used in the inversion of satellite borne passive microwave remote sensing simulation,land surface model,land surface temperature,snow parameters,precipitation,water vapor and perceptible water content etc.
The transitions of land surface freeze/thaw status indicate the dormancy and activeness of land surface processes,like a switch controlling hydrological cycle and ecosystem activity.This paper introduced two datasets of long-term land surface soil freeze/thaw states of China based on dual-indices algorithm and decision tree algorithm Covering the period from 1978 to 2015 and from 1987~2009 respectively.Both datasets can be used to derive the distribution and trend of onset of surface freeze/thaw status,frozen period,the number of frozen days and frozen extent over China,the information is useful to support cryosphere and hydrology related research,and climate change analysis.Both datasets can be accessed forfree in Environmental and Ecological Science Data in West China (http://westdc.westgis.ac.cn).
Plant functional type map is the basis of modeling global vegetation dynamic.This paper provided a new and more accuracy plant functional type map that developed by translate the MICLCover (A Multi-source Integrated Chinese Land Cover map) according to climate rules proposed by Bonan et al.The spatial resolution of the map is 1 km.This map has been published via Environmental and Ecological Science Data in West China,and can be free downloaded.The plant functional type map is useful to support vegetation dynamic modeling and other related research in ecological field.
