20 October 2018, Volume 33 Issue 5
    

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  • Integration Framework and Key Technology of Big Earth Datafor SDGs and Beautiful China Evaluation
    Wang Juanle, Cheng Kai, Bian Lingling, Han Xuehua, Wang Mingming
    Remote Sensing Technology and Application. 2018, 33(5): 775-783. https://doi.org/10.11873/j.issn.1004-0323.2018.5.0775
    Abstract ( )   Knowledge map   Save
    Construction of UN Sustainable Development Goals(SDGs) and Beautiful Chinashare the same meaning.Both of them endeavor to achieve national and regional social，environment and economy sustainable development.Accurate，reliable，timely and well classified data is the key for accurate evaluation of sustainable development.In order to address issues such as single data source，poor timeliness，lack of high accuracy and evaluation results unreliable，we puts forward the integration framework and standardization of the bigearth data which includes big network data，big remote sensing data，and big socioeconomic data facing to the evaluation of SDGs and Beautiful China.Then，the key technologies of network data acquisition and analysis，remote sensing data information intelligent extraction and socioeconomic data spatialization are analyzed from different perspectives.Taking the water contamination accident of SDG 6，forest information extraction of SDG 15，population spatialization of common requirements in SDGs as examples，the application of technological routes in supporting sustainable development evaluation based on big earth data are studied consequently.
  • Progresson Sustainable City Assessment Index System for SDGs
    Wang Penglong, Gao Feng, Huang Chunlin, Song Xiaoyu, Wang Bao, Wei Yanqiang, Niu Yibo
    Remote Sensing Technology and Application. 2018, 33(5): 784-792. https://doi.org/10.11873/j.issn.1004-0323.2018.5.0784
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    Urban sustainability has become the world’s most important urban issue.The evaluation index system is an effective tool to objectively diagnosethe current status and problems of sustainable development.The existing urban sustainability evaluation index systems are mostly based on traditional statistical data and they have different emphases.Due to the suitability of index and availability of data，etc，These index systems are hardly used for comparative evaluation amongdifferent cities.With the interpretation of the sustainable city connotation by the Sustainable Development Goals11，and the use of multi-source data such as remote sensing and network big data，it is possible to achieve higher resolution for urban sustainability assessment under a unified standard.Based on this，this work analyzes the evolution of the concept of urban sustainable development and identifies the key areas of the connotation of sustainable city construction.This work also summarizes the research progress of sustainability evaluation indicators and analyzes typical index system.Based on SDG11，this study establish an open city sustainability index system，with combining traditional statistical data and multi-source data，such as remote sensing data and network big data.The framework of the evaluation index system aims to provide reference for the sustainability evaluation of cities in China under the framework of the United Nations.
  • Downscaling of Remotely Sensed Land Surface Temperature with the BP Neural Network
    Wang Zihao, Qin Qiming, Sun Yuanheng
    Remote Sensing Technology and Application. 2018, 33(5): 793-802. https://doi.org/10.11873/j.issn.1004-0323.2018.5.0793
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    Downscaling algorithms based on statistical models have been widely utilized to address the issue of coarse-resolution Land Surface Temperature (LST).However，most methods (e.g.，TsHARP algorithm) could be affected by land environment，including land cover，seasons.In this study，a Back Propagation (BP) neural network was introduced for LST downscaling in a specific area with complex land covers.The method comprises two steps.First，five reprehensive spectral indices were selected to training according to three typical land cover，including vegetation，building，and water.And the structure of network was trained using coarse-resolution spectral indices and LST.Second，high-resolution spectral indices were input to the network to get a high-resolution LST.A stratified linear regression downscaling with land-cover classification was conducted for comparative evaluation.The comparative results showed that in urban，vegetation，and water areas，the Root Mean Square Error (RMSE)，determination coefficient (R2)，and relative accuracy for the proposed approach (BP neural network) were better than those for stratified linear regression.Finally，the verification results show that RMSE and bias of the algorithm are 0.98 ℃ and 0.51 ℃，which is obviously better than the result of stratified linear regression (RMSE is 2.9 ℃ and Bias is 1.7 ℃).It shows that this method has a higher downscaling accuracy.And the approach is potential for producing high-resolution LST for the study on urban thermal environment.
  • Comparison of Land Surface Temperature Retrieval Algorithms in Karst City
    Wang Kaining, Wang Xiuxin, Huang Fengrong, Luo Lianling
    Remote Sensing Technology and Application. 2018, 33(5): 803-810. https://doi.org/10.11873/j.issn.1004-0323.2018.5.0803
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    Urban land covers have changed greatly with the rapid expansion of Guilin karst city in recent two decades.Some agriculture lands，forest and pools converted to buildings and roads，and some karst hills also entered urban district.However，the vegetation on some karst hills was destroyed and parts of limestone hill body were exposed.High air temperature was felt frequently in summer.Spatial distribution of land surface thermal field was affected by land cover changes directly.Land surface thermal field could be quantitatively descripted with Land Surface Temperature(LST).In order to analyze the impact of urban rapid expansion on thermal environment in karst city，LSTs were derived from Landsat 8 images and five retrieval algorithms with the proposal of the emissivity estimation method in the mixed pixels on karst hills.Then the derived LST results were compared with measurements so that the available retrieving algorithm for karst city was got.Finally，sensitive factors on LST were analyzed.Result shows that LSTs from Single-Channel (SC) algorithms are more accurate than those from Split-Window(SW) algorithms with high atmospheric moisture content in karst district.The errors are within1.0 ℃ between LST measurements and retrieval results from Jimenez SC(JSC) and Qin Mono-Window(QMW).LST statistics derived from JSC and QMW are close with average difference of 0.26 ℃ and standard deviation difference of 0.01 ℃.Average LST differences of building and bare rock are 0.43 ℃ and 0.54 ℃ respectively，higher than those of water body and dense vegetation.LST statistics from Jimenez SW(JSW) and Rozenstein SW(RSW) are close with average LST difference of 1.14 ℃ and standard deviation difference of 0.19 ℃.LST statistics from Weng SC arebetween those from SWs and those from JSC，QMW.As five algorithms show high sensitivity to emissivity，LST average will change 0.4~0.7 ℃ with 0.01 increment of emissivity.Five algorithms are relatively less sensitive to air temperature，total water vapor content，atmospheric transmittance in addition to QMW with which 1.0  ℃increment of air temperature will result in nearly 0.5 ℃ error of LST.JSC and QMW with Landsat 8 TIRS band 10 are suitable for LST retrieval with high accuracy in karst city.The research results can provide scientific data for thermal environment monitoring in karst cities.
  • Inversion of Land Surface Temperature and Its Application in Urban Agglomeration Thermal Environment Monitoring based on Tiangong-2 Data
    Shi Man, Chen Jian, Qin bangyong, Li shengyang
    Remote Sensing Technology and Application. 2018, 33(5): 811-819. https://doi.org/10.11873/j.issn.1004-0323.2018.5.0811
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    According to the characteristics of thermal infrared spectrum of wide band imager of Tiangong-2 data，a spilt-window algorithm applied to Tiangong-2 data was proposed.Taking the South of Jiangsu urban agglomeration as the research area，the inversion of the land surface temperature was carried out.On this basis，the spatial distribution characteristics of thermal environment of South of Jiangsu urban agglomeration were analyzed through SUHI index.The results show that the split-window algorithm can be effectively applied to the thermal infrared of Tiangong-2 data.The root mean square error of land surface temperature inversion is within 1 K.The result of land surface temperature are in good agreement with the types of land use in the research area，and the temperature of the building land is the highest and the water is the lowest.A global heat island is formed in Suzhou-Wuxi-Chanzhou，and the intensity and spatial distribution of the heat island in the urban agglomeration are monitored by SUHI index.


  • Land Surface Temperature Retrieval and Its Spatial Heterogeneity in Summer in Chongqing
    Li Jun, Gong Wei, Xin Xiaozhou, Gao Yanghua
    Remote Sensing Technology and Application. 2018, 33(5): 820-829. https://doi.org/10.11873/j.issn.1004-0323.2018.5.0820
    Abstract ( )   Knowledge map   Save
    Land Surface Temperature(LST)is an important parameter in land surface energy budget.In order to improve the accuracy of LST retrieval by remote sensing methods in summer in urban districts of Chongqing with hot and humid atmosphere condition.an improved methodology was presented with the improved atmospheric transmittance estimated on MODTRAN software using the atmospheric profile data of MERRA in urban districts of Chongqing.LST was retrieved from Landsat 8 TIRS band 10 data using single-window algorithm which apply the improved and unimproved atmospheric transmittance respectively.Then the retrieved LST was compared with the 0 cm soil temperatures observedby 4 meteorological stations.Finally，the spatial heterogeneity of LST was analyzed.The result indicated that：(1)The scheme proposed in this paper can improve LST retrieval in summer in urban districts of Chongqing.The Mean Absolute Error(MAE)decrease from 4.89 K to 1.73 K.(2)The retrieved LST has spatial heterogeneity with different terrain factors.Its lapse rate is about 1.17 K/100 m.It decreases with the increase of slope.Moreover，it has obvious differences with aspect.The flat slope>sunny slope>semi-sunny slope>semi-shady slope>shady slope.There also existed highly significant correlation between the LST and hill shade.The LST increases with the decrease of hill shade.(3)Influenced by land cover，the spatial distribution of LST showed significant differences.The average LST inthe built\|up area is highest，while the wet land is lowest.The difference of average LST in other land cover types is little.
  • Algorithms Comparison of Land Surface Temperature Retrieval from Landsat  Series Data：A Case Study in Qiqihar，China
    Jin Diandian, Gong Zhaoning
    Remote Sensing Technology and Application. 2018, 33(5): 830-841. https://doi.org/10.11873/j.issn.1004-0323.2018.5.0830
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    Land Surface Temperature（LST)is considered to be one of the significant indicators of urban environment analysis.Landsat  thermal infrared series data is an important data source for retrieving surface temperature.In this paper，the thermal infrared band of the Landsat  data in 2002，2008 and 2016 were used to retrieve LST by three different algorithms in municipal area of Qiqihar，China.These algorithms were the Mono-Window algorithm（MW algorithm)，the Single Channel algorithm（SC algorithm) and the Radiation Transport Equation method（RTE algorithm).And the results of the retrieval were compared to each other and verified by MODIS surface temperature products.The LST distribution maps were accomplished according to the retrieval results.The results showed that：(1)The spatial distribution of the LST obtained by the retrieval of the Landsat  series by the three algorithms is consistent，and the LSTof the urban center is higher and thetemperature of water is the lowest；(2)Based on ETM+ data，the consistency between SC and RTE algorithm results is good，among which the SC algorithm has the highest precision，and the MW algorithm has large errors in different land cover areas；(3)The retrieval results by MW algorithm based on the TM data has the highest accuracy，RTE algorithm results is second，and the LST form SC algorithm is less consistent with the corresponding MODIS temperature products；(4)Based on the Landsat  8 TIRS data，the SC algorithm has the highest accuracy and the RTE algorithm has a large error.
  • Information Measure of Polarimetric SAR Coherence Matrix based on A Shannon Entropy
    Li Jiaojiao, Liu Yu, Chen Kunshan
    Remote Sensing Technology and Application. 2018, 33(5): 842-849. https://doi.org/10.11873/j.issn.1004-0323.2018.5.0842〖
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    The incoherent target decomposition of fully polarimetric SAR based on an average coherence matrix is a powerful means to characterize the targets.The objective of this study is to measure the information content contained in the average coherence matrix，which is usually not quantitatively estimated.In so doing，the Shannon entropy is used to assess the information content during the course of estimating the coherence matrix by applying the proper window size so that information loss may be minimized.Experimental results show that the average coherence matrix of different targets has different information content.For example，the information content for the Buildings is relatively low，under the same window size，and the increases almost linearly as the window size increases，while for ocean and vegetative area are comparatively high and the amount of information content increase rapidly at first，then become stable with the increasing of window size.The dependence of the information content on the window size remains quite similar for different time and frequency.It demonstrates that the Shannon entropy based information measure is robust and yet effective to quantitatively evaluate the target information content in average coherence matrix estimated，and thus it offers a fair criteria for determining the optimum window size to preserve maximum information content when estimating the average coherence matrix in SAR.
  • The Data Fusion of Multi-source Downward Surface Solar Radiation and Evaluation
    Liu Junjian, Shi Chunxiang, Han Shuai, Jiang Zhiwei, Zhang Tao
    Remote Sensing Technology and Application. 2018, 33(5): 850-856. https://doi.org/10.11873/j.issn.1004-0323.2018.5.0850
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    In order to improve theprecision of downward surface solar radiation product based on the FY-2G visible channel data of geostationary satellite，it is fused by the method of STMAS withthe downward surface solar radiationcalculated by Hybrid，examining and evaluating SWDN and SWDN-MERGE using ground observation data (OBS) from 91 ground observation stations in China (2015)，and compared with CERES satellite-derived application and ERA-Interim reanalysis data.The results show that：(1)CERES has the highest precision，FY-2G follows，ERA-Interim is the worst.(2)SWDN-MERGE has a greater improvement than SWDN when comparing the two sets of downward surface solar radiation data derived from FY-2G satellite.(3) There is good consistency between the four sets of data from the analysis of 2015 year average value，however the surface downward shortwave radiation data derived from FY-2G satellite is higher in spatial resolution and sharper in details.The above results could be used as scientific evidences for revising the four sets of data and the study of climate change in China.

  • Improved HMRF for Remote Sensing Image Segmentation
    Yang Jun, Pei Jianjie
    Remote Sensing Technology and Application. 2018, 33(5): 857-865. https://doi.org/10.11873/j.issn.1004-0323.2018.5.0857
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    The traditional Markov random field algorithm used for image segmentation is often associated with some known problems，such as unsmooth edges of the segmented regions due toimage noise and abnormal pixels values，thus，subsequently inaccuracy segmentation results.On account of this phenomenon，an algorithm that follows the hidden Markov random field which is based on finite Gaussian mixture model is put forward.First，the initial segmentation results are obtained by replacing traditional K-means method with the Expectation Maximization (EM) algorithm，and they are smoothedby using the bilateral filter.Next，the finite Gaussian mixture model and the Potts modelare used to model the feature field and the mark field，and the EM algorithm is used for its parameter estimationto obtain the feature field energy and the mark field energy.Finally，the energy function is minimized by using the Iterative Condition Model (ICM) algorithm in order to achievean optimal segmentation result.Experimental results show that our approach achieved a more efficient result by comparingto the classical MRF method and the traditional HMRF method，and the probabilistic rand index and global consistency error indicators are better than that of existing
  • Improve the Height Measurement Accuracy in Three-dimensional Imaging Altimeter Using Nadir Interferometric Phase
    Miao Xiangying, Miao Hongli, Zhang Xudong, Huang Xiafeng, Wang Guizhong
    Remote Sensing Technology and Application. 2018, 33(5): 866-872. https://doi.org/10.11873/j.issn.1004-0323.2018.5.0866
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    Roll angle is an important parameter for three-dimensional imaging altimeter to measure Sea Surface Height.The height error due to the roll angle error grows linearly across the swath and a slight error of roll angle will results in a large height error within observation range.So a very accurate knowledge of the roll angle error is required.Nadir interferometric phase has a strong correlation with the roll angle，so using the nadir interferometric phase from the interference image can get roll angle more accurately than instrument measurements.It’s possible to meet the centimeter-level accuracy after correcting the roll angle error.Simulation measurement and elevation reconstruction based on the geometric relationship were performed，and the research of correction the roll angle error by nadir interferometric phase was carried out.Both theoretical calculation and numerical simulation show that the measurement accuracy of roll angle can be as high as 0.03 arcsecond when the accuracy of nadir interferometric phase is 10-3rad.In this case，the height measurement accuracy have been averaged across a swath between 10 and 60 km of nadir will decrease to 0.48 cm which is 6.02 cm when the roll accuracy is 0.36 arcsecond.It means that the nadir interferometric phase method can effectively correct the roll error of three-dimensional imaging altimeter.
  • Data Fusion and Visualization of Multi-time OceanColor Remote Sensing Data
    Zhang Xiaofeng, Lü Xiaoqi, Zhang Xinxue, Zhang Jikai, Wang Yueming, Gu Yu, Fan Yu
    Remote Sensing Technology and Application. 2018, 33(5): 873-880. https://doi.org/10.11873/j.issn.1004-0323.2018.5.0873
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    In order to solve the problem that the ocean sensor data such as MODIS and SeaWiFs are easy to emptied，an algorithm combining multi\|time data with multiple fitting methods is proposed.The vacancy value is estimated by the least squares fitting method.To set a threshold named A according to the date of different time in corresponding position.When the effective value is larger than A that the vacancy value is estimated by the method of least squares fitting，when the effective value is smaller than A that the linear interpolation method is used to estimate the vacancy value.There is no valid value the bilinear interpolation method is used for the second repair with the valid data of the adjacent position.The experimental results show that the algorithm has a smooth visual effect when the threshold selection is appropriate，and the error of the data analysis results is relatively small，then the global data can be well restored and the effectiveness of data coverage will be improved.
  • Simulation of Solar Radiation based on BP Neural Network andIts Spatio-temporal Change Analysis in East China
    Feng Jiaojiao, Wang Weizhen, Li Jing, Liuwenwen
    Remote Sensing Technology and Application. 2018, 33(5): 881-889. https://doi.org/10.11873/j.issn.1004-0323.2018.5.0881
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    Solar radiation data are important parameters of crop model，hydrological model and climate change model，however，the distribution of solar radiation sites is scarce and uneven throughout the country，and it is difficult to obtain spatial continuous solar radiation by using only rare radia.Therefore，the lack of solar radiation data restricts the development of the relevant model，and the neural network on the solar radiation has a good predictability，many Artificial Neural Network ensemble models were developed to estimate solar radiation using routinely measured meteorolological variables，but it did not consider cloud，aerosol，and precipitable water vapor influence on solar radiation.In this paper，we used cloud，aerosols，atmospheric precipitable water vapor from MODIS atmosphere remote sensing products and conventional meteorological data including air pressure，temperature，sunshine duration and latitude and elevation，based on the LM-BP neural network model to simulate the 90 conventional weather stations in Eastern China from 2001 to 2014.The results show that the model has a good fit of 0.95，and the root mean square error is controlled within 2 MJ·m-2.The average deviation error is between -1 MJ·m-2 and 1 MJ·m-2.Finally，using the simulated values of the model and the measured values of 13 radiation sites，the spatial distribution of the annual solar radiation in the East China region from 2001 to 2014 is obtained by spatial interpolation and the spatial variation trend is analyzed. 
  • Study on Differences between Sentinel-2A and Landsat-8 Images in Rape Identification
    Han Tao, Pan Jianjun, Zhang Peiyu, Cao Luodan
    Remote Sensing Technology and Application. 2018, 33(5): 890-899. https://doi.org/10.11873/j.issn.1004-0323.2018.5.0890
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    Rape is one of the most important crops for many countries，so it is important to obtain accurate rape area.Compared with Landsat-8 data，Sentinel-2A has many advantages，but whether the results of Sentinel-2A data in crop identification are better than Landsat-8 is still an unknown question.The study site is located in a typical agricultural region：Gaochun District in Nanjing，the capital of Jiangsu Province，China，with central coordinates of 118°52′E and 31°19′N.One Sentinel-2A and one Landsat-8 image were obtained during the flowering stage of rape，and then rape area was extracted by using different classification methods based on spectral characteristics and vegetation indices.By comparing the identification accuracy of two images under different classification conditions and methods，the results show that：(1) The difference of spectral characteristics and separability of vegetation indices of different objects in Sentinel-2A were higher than those of Landsat-8 images；(2) Under the classifier of support vector machine，the Producer’s and User’s accuracy of rape of Sentinel-2A based on spectral characteristics were 89.7% and 91.3% respectively，which were 7.0% and 6.2% higher than the identification accuracy of Landsat-8 data；(3) After adding texture information，the overall accuracy and kappa coefficient of two kinds of data were significantly improved，but there was no increase in the producer’s and user’s accuracy of rape.The result presented in this paper show that compared with Landsat-8 data，Sentinel-2A data is more suitable for extracting crop distribution information in small areas with complex planting structure，which can lay a theoretical foundation for crop identification and application of Sentinel-2A data.
  • Retrieval of Total Suspended Matter in the Lower of Minjiang River based on PSO-RBF
    Xie Xu, Chen Yunzhi
    Remote Sensing Technology and Application. 2018, 33(5): 900-907. https://doi.org/10.11873/j.issn.1004-0323.2018.5.0900
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    Total suspended matter (TSM) is one of the important parameters of water environment.As the spectral characteristics of the Case-II water are complicated，so it is not suitable to represent the relationship between spectral characteristics andTSM by simple linear models.In this paper，the test data，which is acquired by water quality sampling and spectral measurement of 40 points from July 12 -13，2017，together with GF-1 WFV1 bands reflectance data are used to analysis the correlation between remote sensing factors and TSM.Taking advantage of high correlation coefficients between bands，such as b3，b3/b2 and b3/b1，we construct PSO-RBF and RBF neural network model to inverse TSM.At the same time，a empirical b3/b2 ratio model is also proposed.The result shows that PSO-RBF neural network model’s performance is better than traditional RBF neural network and the empirical model，whose R2=0.890，RMSE=3.01 mg/L.On this basis，the GF-1 WFV1 remote sensing image is used to inverse TSM of Minjiang River，which is calculated by the well-trained PSO-RBF model.Furthermore，the spatial distribution characteristics of TSM is also studied.The result of TSM inversion comes to RMSE=3.65 mg·L-1，MRE=14.11% respectively，and remote sensing image retrieval results accuracy was significantly higher than that of Kriging interpolation results，and there is
  • Extraction and Analysis of Hyperspectral Data and Characteristics from Pedicularis on Bayanbulak Grassland in Xinjiang
    Gao Sha, Lin Jun, Ma Tao, Wu Jianguo, Zheng Jianghua
    Remote Sensing Technology and Application. 2018, 33(5): 908-914. https://doi.org/10.11873/j.issn.1004-0323.2018.5.0908
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    Through the field acquisition of three vegetation spectral datas，flowering Pedicularis，non flowering Pedicularis and common vegetation on Bayanbulak grassland，the first derivative，the two derivative and the reciprocal logarithm transformation were used to the smoothed and denoised data to analyze the difference sensitive bands of vegetation.The results showed that in visible light，non flowering Pedicularis and common vegetation showed the overall consistency，but the spectral curve of flowering Pedicularis showed a significant difference.In the red band and near infrared band at 750nm，non flowering Pedicularis reflectance increased significantly，and the three kinds of spectral reflectance showed significant differences.The reciprocal logarithmic transformation in the visible 580~680 nm band could be used to distinguish the Pedicularis as sensitive area.The spectral reflectance difference between the three at 655 nm was the most obvious.That solved the non flowering Pedicularis and common vegetation confusable problems.The improved normalized difference vegetation index by calculation，to further validate and showed the reciprocal logarithmic transformed values NDVI RLR could be distinguished the difference of flowering Pedicularis，non flowering Pedicularis and common vegetation.The extraction and analysis of hyperspectral data and characteristics from Pedicularis provided a theoretical basis for remote sensing monitoring of Pedicularis，and Remote sensing technology has great significance in Pedicularis resource survey and monitoring application.
  • Parameter Inversion of Internal Waves Using GF-3 Images based on Nonlinear Schrodinger Equation
    Mei Yuan, Wang Jing, Sun Lina, Zhang Xudong, Meng Junmin
    Remote Sensing Technology and Application. 2018, 33(5): 915-922. https://doi.org/10.11873/j.issn.1004-0323.2018.5.0915
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    GF-3 microwave remote sensing satellite provides abundant image data for deep research of internal waves.With the advantages of high spatial resolution and wide range imaging，high precision inversion of amplitude and velocity of internal waves near Dongsha Atoll in the South China Sea was conducted.The high order fully nonlinear Schrodinger equation was used to describe internal waves，and an inversion model of amplitude and wave velocity was established.GF-3 remote sensing images of internal wave from August 2016 to August 2017 near Dongsha Atoll were collected，which aims to explore the revolution of amplitude and propagation velocity at the continental slope of the South China Sea.The calculated results of theoretical model were compared with the measured data of the study area in 2013.The inversion amplitude was similar to the measured value.During the propagation process of internal waves from east to west，the water depth becomes shallower，the nonlinear effect is enhanced，the dispersion effect is weakened，both amplitude and propagation velocity decrease.This offers the references for quantitative study of the energy transportation，dissipation and future prediction of internal waves.

  • Monitoring Spatial-temporal Change of Cotton Phenology in Xinjiang and Its Response to Climate Change
    Rupiya Xilaer, Yang Liao
    Remote Sensing Technology and Application. 2018, 33(5): 923-931. https://doi.org/10.11873/j.issn.1004-0323.2018.5.0923
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    Observations of vegetation phenology provide valuable information regarding ecosystem response to environmental conditions，especially to climate change.Cotton is one of the most important economic crops in Xinjiang，and its phenological change can directly reflect the change of climate in Xinjiang.This research was an attempt to extract cotton phenological parameters in Xinjiang by using 16 years’(2001 to 2016) time series MODIS Normalized Difference Vegetation Index(NDVI)：firstly，filtering noise from the time-series data using Savitzky-Golay filtered method；then detecting cotton phenology parameters (Start of Growth Season(SOS)，End of Growth Season(EOS)，Long of Growth Season(LOS)) using Dynamic Threshold method；finally，the spatial patterns and temporal trends of observed cotton phenological characteristics were analyzed over the past 16 years and the relationship between cotton phenology and temperature changes was also discussed.The result of this study showed that the spatial patterns of cotton phenology were significantly different in study region：SOS delayed gradually from Nanjiang to Beijiang，and mainly occurred before 151st and after 151st days respectively；EOS gradually advanced，most areas of northern Xinjiang ended up 292nd days ago，while the southern Xinjiang happened 298th days later；LOS shortened，Nanjiang is generally longer than 150 days while Beijiang is usually shorter than 150 days.The trend of cotton phenology(2001~2016) under climate change in northern and southern Xinjiang were not completely similar：SOS and EOS in southern Xinjiang showed a delay-advancing-delay-advancing trend，and LOS was unsignificantly delayed；While SOS in northern Xinjiang were slightly advanced and EOS exhibited a delay trend followed by an advancing，LOS showed a shorten-lengthen-shorten trend.In addition，cotton phenology showed a strong correlation with the temperature：SOS and EOS were positively correlated with the beginning date of 15℃ and the end date of 10℃ respectively；SOS was negatively correlated with the spring temperature，while EOS had a positive correlation with autumn temperature.
  • Error Analysis of Change Detection based on Land Cover in High Resolution Remote Sensing Images in Urban Areas
    Ji Xinying, Wei Yuchun, Wang Wenyao, Fang Hong
    Remote Sensing Technology and Application. 2018, 33(5): 932-941. https://doi.org/10.11873/j.issn.1004-0323.2018.5.0932
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    Land use/land cover change detection using high spatial resolution remote sensing image is an important content in land monitoring.However，the problems of shadow，image registration，threshold selection，detection method selection and image post-processing are more prominent in high-resolution images compared with that in medium and low resolution images，which result in more difficulties and uncertainties.Change detection of land cover was carried out base on aerial color images between 2009 and 2012 in Xianlin District of Nanjing，and the errors were analyzed in terms of intra-class and inter-class.The results show that the inter-class error accounted for 97.6% in the omission error，and the intra-class error accounted for 87.1% in the commission error.According to the error sources，72.6% of the false negative pixels are derived from the detection method，43.6% of the false positive pixels are come from detection method while 39.7% from radiation inconsistent.The analysis results in the paper provided reference for the development of new change detection algorithm.
  • Distribution and Variation of Glacier Velocity in Himalayas based on ALOS PALSAR
    Wang Shizhe, Ke Changqing
    Remote Sensing Technology and Application. 2018, 33(5): 942-955. https://doi.org/10.11873/j.issn.1004-0323.2018.5.0942
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    Based on the 12 scenes ALOS PALSAR data from December 2007 to February 2010，combined with SRTM，we estimated the glacier velocityof the Himalayasin three periods by feature tracking method.The results show that glacier velocity of the winter of 2007，the summer of 2009 and the winter of 2009 were between 0~300 m·a-1，and accumulation area of the glacier have obvious movement only in summer，butthe movement of the glacier tongue is obviousin any season.Long tongue glacier velocity decreasesslowlyalong the mainstream line，while the short tongue glacier velocity fluctuatesalong the mainstream line，and even increases.The glacier in east aspecthas the largest velocity.The glaciervelocity in southeast aspect and the southwest aspect are second，and glacier velocity is minimum in the north aspect.In addition to climatic factors，it also closely relates to terrain factors.In terms of the slope of the four aspects，the north aspecthas the smallest slope，andit is one of the main causes of the smallest glacier velocity in the north aspect.There is an inter-annual variation of glaciers，that is，mean velocity of glaciers increases in winter，and the increased velocities are between -5~18 m·(2a).The glacier with small areavaries greatly，and the glacier with large area varies little.Meanwhile，the glacier velocity also has seasonal variation.The overall velocity in summer is larger than that in winter.The velocity in mainstream line fluctuates strongly in summer，and there are many peaks，but velocity in winteris gentle.However，the seasonal variation of the mean velocity in mainstream line is not obvious.The inter-annual variation and seasonal variation of the four typical glacier velocities are similar to those of the glaciers in the study area.There is a close relationship between velocity and climate and the shape of the glacier.The characteristics of glacier advance and retreat are not obvious，and the glaciers in the study area are in equilibrium.
  • Model Building and Application for Monitoring and Assessing the Aging Conditions of Asphalt Road Pavementsbased on Remote Sensing Data
    Cheng Junyi, Zhang Xianfeng, Sun Quan, Zheng Xiaolan
    Remote Sensing Technology and Application. 2018, 33(5): 956-964. https://doi.org/10.11873/j.issn.1004-0323.2018.5.0956
    Abstract ( )   Knowledge map   Save
    To meet the demands in monitoring the health conditions of road pavements over a relatively large area，by means of derivative and continuum removal approaches this study analyzes the spectral features of asphalt road pavements aging degrees based on the field measurements of pavement spectra.Distinct spectral features of new and aged asphalt road pavements were observed in the wavelength regions of 400~680 nm and 860~970 nm.After that，a WorldView-2 image in Liangxiang area，Fangshan district，Beijing City were captured and the corresponding bands were used to create a Multiplication Aging Index (MAI) to reflect the aging conditions of asphalt road pavements.Comparison between the MAI and in-situ measurements of the spectra and aging conditions of the road pavements in the study area was performed，and statistical analysis was also conducted based on the Munsell brightness values collected in the field investigation.Through the contrast，the aging condition from MAI has good relevance to the in-situ measurements.Results indicate that the proposed MAI index can reflect the aging conditions well and is further used to monitor the pavement quality of the 14 road pavements in the study area.According to the evaluation，six roads in the study area need road maintenance.The research can offer a new technology for road management departments to make their road maintenance plans.
  • Study on Dynamic Remote Sensing Monitoring of LUCC and Soilwind Erosion Intensity in the Beijing-Tianjin Sandstorm Source Control Project Region
    Chi Wenfeng, Kuang Wenhui, Jia Jing, Liu Zhengjia
    Remote Sensing Technology and Application. 2018, 33(5): 965-974. https://doi.org/10.11873/j.issn.1004-0323.2018.5.0965
    Abstract ( )   Knowledge map   Save
    The Land Use/Cover Change(LUCC) and soil wind erosion intensity of the Beijing\|Tianjin sandstorm source control project region were monitored by remote sensing.The spatial and temporal patterns of LUCC and soil wind erosion in the project region were analyzed.The results showed that there was significant difference in LUCC and soil erosion intensity before and after the project was implemented.In the recent 30 years，the LUCC process mainly manifested the change from cultivated land reclamation to ecological conversion of farmland to forest and grass，with the ecological restoration and desertification effectively inhibited.The overall arable land showed an increase and then decreased.The area of  arable land increased from 2000 to 2015，the area of cultivated land converted to forest and grassland was 446.10 km2 and 1 129.32 km2，with the most obvious in the west；the area of land for construction expanded obviously；the trend of unutilized land decreased significantly The type of conversion is dominated by grassland conversion to grassland with an area of 493.12 km2.The erosion-mitigating modulus of soil erosion in the project region with wind-blown sand control decreased overall，especially after the implementation of ecological engineering (p<0.001).The eastern and southern areas are covered with high-coverage grassland and soil wind erosion in the area with the main type is small；Soil wind erosion in the Hunshandake sandy land is larger，but the overall trend is decreasing.Different land use/cover types have a greater impact on soil wind erosion intensity.The order of soil wind erosion modulus is Sandy land> Sparse grass> Moderate grass>dryland> Shrub>Paddy>Dense grass> Other woods> Sparse woods> Forest；The conversion of low coverage to high coverage grassland types effectively inhibited soil erosion (-66.12%)，and the increase of vegetation coverage effectively reduced soil erosion.The soil wind erosion increased (58.26%) in the surrounding area of sandy area，the soil wind erosion increasedduring the conversion process of low coverage grassland type，and the grassland was converted into sand，and the wind erosion in the dry land increased.
  • Sea Ice Classification of Polarimetric SAR Imagery based on Decision Tree Algorithm of Attributes’ Subtraction
    Wang Changying, Tian Dezheng, Han Yuanfeng, Sui Yi, Chu Jialan
    Remote Sensing Technology and Application. 2018, 33(5): 975-982. https://doi.org/10.11873/j.issn.1004-0323.2018.5.0975
    Abstract ( )   Knowledge map   Save
    Polarimetric SAR image usually has multiple polarization attributes，and the imaging brightness of different sea ice types in different polarization modes is obviously different.A decision tree (SDDT) analysis method on attributes’ subtractions suited for sea ice classification of polarimetric SAR imagery is proposed in this paper.The subtractions between any two attributes based on a given n attributes are calculated.Then their classification ability and optimal divided threshold are calculated.The most effective attribute is discovered and used to construct classification tree.According to this strategy，it equal to find the optimal subtraction attributefrom n+C 2 n features for classification，which include original n attributes and C2n  subtraction attributes.In addition，we use GainRatio to compare the classification ability between different attributes firstly.When there are several attributes with a same GainRatio，we consider the width of the split point (ΔWidth) and the total width of the attribute (TotalWidth) and define a classification ability index ClassifyAbility=GainRatio* ΔWidth/TotalWidth.By calculating and comparing ClassifyAbility index，an optimal attribute with the largest attribute classification abilitycan be selected.Experiments show that the accuracy of SDDT algorithmhas ten percent higher than that of C4.5 algorithm using a same training samples.
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