20 October 2017, Volume 32 Issue 5
    

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  • Jiang Aihui,Liu Guolin,Chen Fulong
    Remote Sensing Technology and Application. 2017, 32(5): 787-793. https://doi.org/10.11873/j.issn.1004-0323.2017.5.0787
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    Due to its data acquisition capacity in all\|weather and all\|time conditions,Synthetic Aperture Radar(SAR) technology has been a valuable tool in change detection,implying the potential in various applications.In this paper,we jointly applied SAR coherent information and intensity information for the change detection (primarily caused by the demolition behavior in 2010~2011)in the study site of Han Hangu Pass (a World Heritage site)using seven PALSAR\|1 remote sensing images acquired in the period from 2007 to 2011.Results show that the two change detection methods,either based on coherent information or intensity information,yielded reliable results,with the detection probability of 0.868 and 0.697,respectively,in conjunction with the false\|alarm probability of 0.385 and 0.197.The comparison related to two methodsindicated that the coherence of PALSAR\|1 was more sensitive to the surface change than the other owing to its higher signal\|to\|noise ratio.Limited by the negative impacts of speckle and low\|median resolution,the application of intensity\|based change detection using PALSAR\|1 still remains problematic.
  • You Jiangbin,Chen Fulong
    Remote Sensing Technology and Application. 2017, 32(5): 794-800. https://doi.org/10.11873/j.issn.1004-0323.2017.5.0794
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    The lost cities of Wulei (the Safeguard City in the Western Region) and Qiemo in Han Dynasty are important ruins described in several historical documents.In this study,a spatial analysis was proposed to digitally locate those two ancient cities by using the distance records from Book of Han.Results indicate that Wulei is located at the northeast of Yangxia Oasis and Qiemo is located at a desert area 100 km north from the modern Qiemo County.Then,a quantitative estimation of Synthetic Aperture Radar (SAR) in subsurface penetration was conducted to evaluate the archaeological performance of ALOS PALSAR-2 (Phased Array Type L-band Synthetic Aperture Radar\|2) data.Simulation results show that the penetration of L\|band SAR is limited in the hot\|spot of Wulei,hampering the direct detection of its ruin.Thereby,the detection of ancient channels,roads and beacon towers adjacent in conjunction with the buffering analysis could be the alternative solution.Nevertheless,the penetration capability of L-band SAR is promising (up to more than 2 m) in the suspected region of Qiemo ruin,implying the potential of PALSAR\|2 data for the lost city prospection.
  • Cao Xiaochen,You Hongjian,Liu Jiayin,Wang Feng
    Remote Sensing Technology and Application. 2017, 32(5): 801-808. https://doi.org/10.11873/j.issn.1004-0323.2017.5.0801
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    DEM has become the kernel data of GIS and remote sensing,and has been applied to various areas.An approach to optimize SRTM1 data using ICESat GLA14 data is proposed in this paper.First,the outliers of GLA14 data is filtered out according to the error distribution of SRTM1 and the coordinate system and height reference of both data is unified.Then,the relationship between the error of SRTM1 and the slope and the aspect of land is analyzed and an error model of SRTM1 is established.Finally,we divide the GLA14 data into control points and check points randomly,use the control points to fit the error model by least square method,use the check points to evaluate the effect of optimization.We repeat the divide several times to prove the effectiveness of our approach.The results of experiment show that our approach has a good effect among different areas and different topography.
  • Li Weina,Wei Wei,Zhang Huaiqing,Liu Hua
    Remote Sensing Technology and Application. 2017, 32(5): 809-817. https://doi.org/10.11873/j.issn.1004-0323.2017.5.0809
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    Wetland vegetation biomass is a significant indicator of the health condition of wetland ecosystem,which directly mirrors the growth and productivity of vegetation communities.The estimation of vegetation biomass in alpine wetland contribute to understanding the causal feedback relations between alpine swamp ecosystem and global climate change.Longbaotan nature reserve in Sanjiangyuan area was taken as the research region.Taking advantage of hyper spectral dimension and multi\|angle stereoscopic structure information of CHRIS/PROBA,we analyzed the correlation between wetland vegetation biomass and some remote sensing factors,including spectral reflectivity,narrow band vegetation indices,red edge indices and principal component.The angular sensitivity of biomass was explored at the same time.The results showed that the biomass of vegetation on the alpine wetland was sensitive to satellite observation angle.The forward observation of the +36° degree image was significantly better than 0 and -36° degree images.Besides,the exponential model was the best regression model in the nonlinear models.Among them,REIP index of -36° degree was the fittest variable to biomass,which R2 was 0.599 and F value was 37.404 in dry biomass,and R2 was 0.685 and F value was 54.410 in fresh biomass.The maximum of dry biomass in Longbaotan area is 446.7 g/m2,and fresh biomass is 2 368.1 g/m2.
  • Li Yan,Hou Jinliang,Huang Chunlin
    Remote Sensing Technology and Application. 2017, 32(5): 818-824. https://doi.org/10.11873/j.issn.1004-0323.2017.5.0818
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    A Copula is used to construct a bivariate distribution describing the relation between coarse\|scale and fine\|scale rainfall or soil moisture.This distribution is then used to downscale rainfall or soil moisture.In order to explore the feasibility of spatial downscaling Land Surface Temperature (LST)based on Copula,we implemented LST downscaling based on Copula and ASTER LST and MODIS LST products at Yingke oasis\|desert area in the middle streams of the Heihe River Basin.The downscaled LST was calibrated by the ground observations from HiWATER\|MUSOEXE experiment.The results show that the downscaling method based on Copula is able to achieve the LST downscaling in general,but the method can’t obtain the fine\|scale LST correctly at the interface between oasis and desert.The accuracy of LST obtained from thermal infrared satellite image was improved significantly by the method.The MAE and RMSE in LST are reduced from 2.99 K,and 3.89 K to 1.5 1K,and 2.36 K,respectively.


  • Wu Yi,Deng Ruru,Qin Yan,Liang Yanheng,Xiong Longhai
    Remote Sensing Technology and Application. 2017, 32(5): 825-834. https://doi.org/10.11873/j.issn.1004-0323.2017.5.0825
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    By investigating the law of radiative transfer and the process of scattering and absorption from suspended sediment,chlorophyll and oxygen\|consuming organics,this paper builds the model,it formulate the functional relationship between the reflection and the concentration of this three kinds of water quality parameters,therefore retrieve the concentration.With this model,the chlorophyll concentration in Xinfengjiang Reservoir can be inversed,then using the measured data to verify the inversion result,the correlation coefficient between them equals 0.94,and the average relative error equals 66.67%.By analyzing the characteristics of the chlorophyll spatial structure and its time distribution based on the result,we find out that the lowest concentration of the chlorophyll is always existing in the center area during the whole year,and the area near Xinfengjiang River and Zhongxinshui River has the highest concentration,associated with cage culture.Multiple factors has been considered in the model building in this paper,and hence multi\|spectral remote sensing data were successfully applied to improve the calculation accuracy.
  • Xiang Yiheng,Zhang Mingmin,Zhang Lanhui,He Chanshen,Wang Yibo,Bai Xiao
    Remote Sensing Technology and Application. 2017, 32(5): 835-843. https://doi.org/10.11873/j.issn.1004-0323.2017.5.0835
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    Observation data of 34  in-situ stations located in seven main vegetation types were used to evaluate the performance of SMOS soil moisture products in Qilian Mountain,Northwest China.SMOS data were processed to correspond to the observation data,and three indices:R、Biasand  RMSE were calculated at both annual and seasonal scales for each observation station.Results show that SMOS products were credible in the study area,but underestimated soil moisture in Qilian Mountain,and failed to achieve the intended accuracy target of 0.04 m3/m3.SMOS performed better in estimating vegetation emission than soil emission,leading to its better performance in areas with higher vegetation coverage.Similarly,SMOS performed better in the humid condition than the arid condition,and also better in areas with smaller soil moisture variability than those with large soil moisture variability.At seasonal scale,SMOS products fitted the observations better in the summer and autumn than the spring.

  • Tian Feng,Chen Donghua,Huang Xinli,Li Hu,Yao Guohui
    Remote Sensing Technology and Application. 2017, 32(5): 844-850. https://doi.org/10.11873/j.issn.1004-0323.2017.5.0844
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    GF-2 is a high resolution earth observing satellite with sub\|meter resolution which is developed by our own technique.To estimate urban building height based on GF\|2 remote sensing image combined with the idea of mathematical morphology and object\|oriented classification.First of all,segment image based on multi\|scale segmentation.Then extract shadow and calculate its length based on object\|oriented classification combined with spectral,shape,Morphological Shadow Index (MSI) and other features.In the end,estimate building height based on the geometrical model of satellite,sun and building and then accuracy evaluation and error analysis are carried out by using the field measurement data.Experimental results showed that 90% of the buildings’ absolute error is less than 1 m.This experiment demonstrate that the method can extract the height of urban building from the GF\|2 image effectively and the immense potential of domestic high resolution remote sensing image in applications on urban building information extraction.

  • Wang Pu,Xing Yanqiu,Wang Cheng,Xi Xiaohuan,Luo Shezhou
    Remote Sensing Technology and Application. 2017, 32(5): 851-857. https://doi.org/10.11873/j.issn.1004-0323.2017.5.0851
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    Based on the spatial distribution and characteristics of LiDAR points cloud of roads in mountainous areas,an effective method for road extraction from airborne LiDAR data is proposed in this research.First,the morphological filtering method is applied to remove above\|ground points cloud (such as buildings,transmission lines and vegetation etc.).Second,a region growing algorithm with multiple rules is used to extract and optimize the road points cloud.Finally,the road boundaries are located and tracked by using Freeman chain code method.Moreover,the mathematical morphology refining processing is used to extract the central line of mountainous road.The experimental results show that the proposed method is effective to extract road information in mountainous areas,and the completeness,accuracy and quality are 93.87%,93.84%,88.43%,respectively.
  • Xiao Hao,Wang Jie
    Remote Sensing Technology and Application. 2017, 32(5): 858-865. https://doi.org/10.11873/j.issn.1004-0323.2017.5.0858
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    Interactive Data Language (IDL) is a language in the development of application based on multiplatform and object\|oriented,which has significant advantages in data analysis and visualization.The MATLAB is a software with powerful features in the image of processing and programming in complex numerical analysis,which based on matrix calculation.Programming in combining the IDL with MATLAB,meanwhile using the Extended Linear Mixed Model for endmember unmixing in Hyperion images.To verify the results of endmember unmixing,adopted the Fully Constrained Least Squares for comparative analysis.The results showed that:The method of programming in combining the IDL with MATLAB not noly possess the advantages of both but also enhance the efficiency in programming,it is conducive to remote sensing image processing.Meanwhile endmember unmixing results show that:the Extended Linear Mixing Model unmixing has a higher accuracy when the proportion of local category in the image is large.On the contrary,the Fully Constrained Least Squares unmixing has a higher accuracy.
  • Bao Gang,ao Yulong,Alateng Tuya,Bao Yuhai,Qin Zhihao,Wang Mulan,Zhou Yi
    Remote Sensing Technology and Application. 2017, 32(5): 866-874. https://doi.org/10.11873/j.issn.1004-0323.2017.5.0866
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    Based on the third generation GIMMS NDVI time\|series datasets during 1982~2011,we extracted the start of growing season (SOS),end of growing season (EOS) and length of growing season (LOS) in the Mongolian Plateau using cumulative NDVI based logistic regression curves,change rate of curvature in NDVI logistic regression curves and change rate method of NDVI and further analyzed the spatio\|temporal changes of phenology.The results showed that the cumulative NDVI based logistic regression curves and change rate method of NDVI performed better predictions in SOS and EOS modeling,and the mean value of these two methods improved the extraction accuracy of phenology in the Mongolian Plateau.SOS in the Mongolian Plateau mostly started from the middle of April to the end of May and ended from the end of the September to the middle of the October.Most LOS ranged from 125 to 175 days.Spatially,the earlier SOS,later SOS and longer LOS occurred in the humid and sub\|humid area of the plateau,and later SOS,earlier EOS and shorter LOS occurred in arid and semi\|arid regions of the plateau.Temporally,during the 30\|year observation period,approximately,51.6% and 33.9% of the plateau experienced advanced and delayed SOS,respectively,and 21.2% and 12.4% of which are statistically significant;Approximately,35.6% and 49.8% of the study area experienced delayed EOS,respectively,and 8.2% and 12.0% of which are statistically significant;Accordingly,40.3% (17.8% are significant) and 44.8% (18.9% are significant) of the plateau showed shortening and lengthening of the LOS.

  • Bu Fan,Shi Yuli
    Remote Sensing Technology and Application. 2017, 32(5): 875-882. https://doi.org/10.11873/j.issn.1004-0323.2017.5.0875
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    With the support of airborne Light Detection and Ranging (LiDAR) data and high spatial resolution aerial imagery,this paper presents an individual tree extraction method that takes the region of urban as the study area.The elevation difference model originated from LiDAR data was used to extract regions of interest including trees. Then,masking was applied to the high spatial resolution aerial imagery to get the same regions. Besides,image segmentations,based on the marked watershed algorithm,were processed on the high spatial resolution aerial imagery and the elevation difference model separately to extract individual tree crowns. Finally,we took a visual interpretation to delineate tree crowns manually and this result was regarded as the reference crowns map. The extraction accuracies were assessed by comparing the spatial relationships of the reference crowns and the automated delineated tree crowns based on the elevation difference model and the high resolution imagery. The results show that the LiDAR data is developed to improve the efficiency of obtaining forest region that the canopy height model include 85.25% forest information. In addition,the tree crowns extraction accuracy based on the high resolution aerial imagery is 57.14%,while another extraction accuracy based on the elevation difference model is 42.47%,which indicated that the marked watershed algorithm proposed in this paper is effective and the high resolution imagery is better than the elevation difference model to extract tree crowns.
  • Hao Jiansheng,Zhang Feiyun,Zhao Xin,Liu Yunxiao,Li Lanhai
    Remote Sensing Technology and Application. 2017, 32(5): 883-892. https://doi.org/10.11873/j.issn.1004-0323.2017.5.0883
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    Climate change and human activities significantly influence regional distribution of water resources and socio\|economic and environmental sustainability.The separation of water formation and dissipation in the arid region leads to a challenge for regional water resources management.A clear insight view on regional water storage variation lacks due to the scarcity of ground observation data in the region.This study applied the satellite data to investigate regional water storage variation in the Ili\|Balkhash Basin.The water storage variation data derived from UTCSR\|RL05 L\|2 data with 1°×1° was compared with the data from GLDAS within a period from January 2003 to May 2014,then applied to investigate the temporal and spatial characteristics of water storage variation as well as their influencing factors in the Basin.The results indicated that water storage  increased from every November to April of the next year,and decreased from May to October.Among the factors influencing  water storage change in the basin,bias between precipitation and evaporation(BPE) is a most important factor to influence regional water storage change,followed by evaporation.Spatially,the factors influencing water storage change differ in water formation and dissipation region.Water storage change is most influenced by precipitation in the water formation region,but by BPE in the water dissipation region,and the impact of evaporation on water storage change in the water dissipation region is greater than that in the water formation region.
  • Chen Yangbo,Zhang Tao,Dou Peng,Dong Liming,Chen Hua
    Remote Sensing Technology and Application. 2017, 32(5): 893-903. https://doi.org/10.11873/j.issn.1004-0323.2017.5.0893
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    Urbanization is the world developing trend in the past century,which significantly changed the land use/cover of the urbanized area,and caused a series negative impacts,such as water shortage,flood increase,environment pollution,ecosystem degradation.How to estimate the land use/cover change more accurately has the prerequisite of studying the urbanization processes and its impacts,and is the research hot and challenge of the remote sensing and application communities.Dongguan city expressed the rapidest urbanization in China since China’s reform and opening door,and transferred from an agriculture county to a modern international metropolitan in less than 30 years,which has made a miracle in the world urbanization process.To prepare a high accuracy land use/cover change dataset for studying Dongguan’s urbanization process and its impacts,this paper first estimated the land use/cover change dataset by employing Support Vector Machine auto\|classification algorithm based on 12 Landsat remote sensing imageries from 1987 to 2015 at an average interval of 3 year.Then the error sources is analyzed by comparing the results estimated by using auto\|classification algorithm and visual interpretation,and a post data processing algorithm is proposed for refining the auto\|classification results.The final dataset of land use/cover change of Dongguan City is produced with the above method with an average accuracy of 86.87% and a Kappa coefficient of 0.83,which implies this product has a very good accuracy for analyzing the urbanization process of Dongguan city and its impacts.

  • Lu Meiqi,Wei Ming
    Remote Sensing Technology and Application. 2017, 32(5): 904-912. https://doi.org/10.11873/j.issn.1004-0323.2017.5.0904
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    The Global Precipitation Measurement(GPM)mission is designed to provide the next generation of global satellite precipitation products.The GPM Microwave Imager (GMI) carried on the Core Observatory adds high\|frequency channels which can detect light rain and snow.The Dual\|Frequency Precipitation Radar (DPR) combining the data of Ku\|band and Ka\|band helps to obtain more information of cloud and precipitation particles.To detect the cloud and precipitation structure in the typhoon,lots of information from both bands such as precipitation distribution,vertical structure,rain top height and structure of stratus cloud and convective cloud of typhoon ‘Mujigae’ occurring in early October 2015 are analyzed,using 1C\|GMI and 2A\|DPR products of GPM.The results show that the Ku\|band of DPR is better at detecting precipitation and bright band.However,Ka\|band has better effects on detecting small particles of the cloud top.The near\|surface precipitation rate centers upon values below 20mm/hr and partly focuses on 20\|60mm/h.The maximum value of near\|surface precipitation rate is 88.68mm/hr.The rain top height centers on values between 5km and 10km,and the maximum value exceeds 15km.The proportion of stratus cloud precipitation in the typhoon reaches 63.4%,but its average rainfall rate per unit area is lower than convective cloud precipitation by 37%.The detecting results of DPR and ground\|based SA\|band radar are very close which proves the reliability of DPR data.

  • Li Ying,Chen Huailiang,Li Yaohui
    Remote Sensing Technology and Application. 2017, 32(5): 913-920. https://doi.org/10.11873/j.issn.1004-0323.2017.5.0913
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    Multispectral satellite remote sensing data of low or moderate spatial resolution are widely used in large range crop planting area extraction.For those areas with complex structure,when the low or moderate spatial resolution remote sensing data sources is used to extract the planting area of target crop,mixed pixel is the main obstacle factor to restrict the area extracting precision.Extracting it on sub\|pixel scale could overcome the restriction of low or moderate spatial resolution and develop the extraction precision.However,the extraction method of target crop planting area on sub\|pixel scale now usually directly use the end\|member abundance to instead the percentage of planting area.Therefore it may cause some errors.On the basis of previous researches,taking Hebi City,Henan Province as the study area,which located in Huang\|Huai\|Hai plain,has the largest summer maize planting area and the complex planting structure.Taking FY3/MERSI data as the main information source.Using the method of spectral matched adaptive best end\|member combination of pixel unmixing to extract the summer maize end\|member abundances.Making regression modeling in various equation forms between summer maize end\|member abundances in pixel and the percentage of planting area.Then select the optimal regression equation form to build regression model,and estimate the actual summer maize ground planting area.Summing up the correlation coefficient when the model was building,significance test and the RMS errors condition of sample point verification.Then choose the cubic model to estimate the planting area of summer maize in the study area.It is proved by remote sensing estimation that the area precision of summer maize planting area is 97.1%,the position precision is 82.5%.
  • Sun Xiao,Wu Mengquan,He Fuhong,Zhang Anding,Zhao Deheng,Li Bo
    Remote Sensing Technology and Application. 2017, 32(5): 921-930. https://doi.org/10.11873/j.issn.1004-0323.2017.5.0921
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    A recurrent floating green algae bloom was detected in the Yellow Sea since 2007.The Ulva.prolifera is non\|toxic,but the massive accumulations can result in significant environmental damage and cause economic loss to marine industries.In this study,the spatial and temporal patterns of Ulva.prolifera green tides were investigated in the Yellow Sea during 2015 using HJ\|1A/1B and MODIS satellite images by means of NDVI (normalized difference vegetation index)and artificial interpretation.The results showed:(1)A little Ulva.prolifera was discovered firstly in adjacent sea of Yancheng,Jiangsu province in early May with distribution area 0.831 km2.Under the action of the southeast monsoon,Ulva.prolifera was gradually drifted to Shandong peninsula waters from south to north.The influential area and range reached a peak value with 1 752.756 km2 in late June,and gradually subsided from July to August.And Ulva.prolifera about 38.791 km2 was monitored in the South Bay of North Korea.In conclusion,Ulva.prolifera in the Yellow Sea in 2015 has experienced five major processes including “Occur\|Development\|Outbreak\|Recession\|Disappeared”.(2)Typhoon "CHAN\|HOM" certainly influenced the northward pathway of Ulva.prolifera and shifted towards the southwest,resulting in most of Ulva.prolifera moved to the east coast of Lianyungang,and speculated that minority Ulva.prolifera drifted to the South Bay of North Korea.(3)From the monitoring data,the spatial resolution between MODIS and ENVISAT (HJ\|1A / 1B)is difference significantly,250 m and 30 m respectively.A functional relation of the two data with monitoring area difference about 2.26 times was established to make up for the shortage of the environmental satellite (HJ\|1A/1B)images.


  • Lin Qigen,Zou Zhenhua,Zhu Yingqi,Wang Ying
    Remote Sensing Technology and Application. 2017, 32(5): 931-937. https://doi.org/10.11873/j.issn.1004-0323.2017.5.0931
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    Earthquakes in mountain area often induce hundreds of thousands of landslides resulting in destructive casualties and economic damage.It is urgent needed to rapidly detect the extent areas of the landslides.With the advent of very high resolution satellite remote sensing,the application of the object\|oriented classification method in this area have significant advantage comparing to those of visual interpretation and pixel\|based methods.However,the study of object\|oriented landslide detection is relatively few,and the study usually has a small study area.The method of object\|oriented rapid identification of landslides based on the spectral,spatial and morphometric properties of landslides and a 2.5m SPOT5 multi\|spectral image is proposed in this paper and is applied in a relatively large study area.The normalized difference vegetation index (NDVI) threshold was set to remove vegetation objects and obtain landslide candidates.Then,the spectral characteristics,texture,terrain features and context of the image were used to build indicators to gradually separate the landslide from false positives.The small scale chessboard segmentation was conducted to further eliminate vegetation objects and get the landslide objects.The object\|oriented detection results show that the adopted method can recognize about 95% of the landslides in the study area.When considering the landslide excessive detection and omissions,the landslide detection quality percentage of the proposed method is 74.04%.Hence,the method proposed in the article can help to rapid assess landslide disasters caused by earthquakes or heavy rainfalls,providing a reference for post\|disaster emergency relief and reconstruction work.
  • Zheng Fei,Zhang Dianfa,Sun Weiwei,Yang Gang
    Remote Sensing Technology and Application. 2017, 32(5): 938-947. https://doi.org/10.11873/j.issn.1004-0323.2017.5.0938
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    Rapid urbanization has significant contributions to the Surface Urban Heat Island (SUHI).Analyzing the SUHI distribution and its impact factors using remote sensing data has received increasing attentions in the past decades,whereas few study has investigated that of the surface Urban Heat Sink Island (SUHI).The paper selects Hangzhou metropolis as a case study to explore SUHI/SUHS spatial patterns and its causes.We first retrieve the Land Surface Temperature (LST) using ASTER thermal infrared remote sensing imagery and extract the region of SUHI/SUHS using the Mean\|Standard deviation method.Landsat8 OLI data is used to classify land use and extract both impervious surface and vegetation information.After that,different landscape patterns within SUHI/SUHS area are analyzed and quantified by using several selected landscape index.The largest impact factors in SUHI/SUHS areas are identified.Finally,we analyze the spatial characteristics of LST using the spatial gradient analysis method,and reveal its relationship with vegetation and impervious surface.The results show that:(1) a large landscape pattern difference exists within SUHI/SUHS area;the impervious surface has the greatest impact on LST of the SUHI area,whereas the vegetation has more obviously cooling effect on LST of the SUHS area than the water body;(2) with the increasing distance from the city center,the same trend was found between the mean LST values and the impervious surface density (positive correlations),whereas the opposite trend between the mean LST values and the vegetation density (negative correlations).And the warming effect of impervious surface is greater than the cooling effect of vegetation in Hangzhou.


  • Qin Jun,Leng Hanbing,Zhao Guangqi,Jing Jun,Zhou Jianhua
    Remote Sensing Technology and Application. 2017, 32(5): 948-957. https://doi.org/10.11873/j.issn.1004-0323.2017.5.0948
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    Classification from remote sensing imagery has been proved to be a quick and effective method for automatic mapping of vegetation population.However,the interference from background noises and the weak spectral separability between vegetation populations have a negative impact on the identification of urban vegetation populations from remote sensing images.This make the classification accuracy unsatisfied as using conventional classification methods.In order to solve this problem,a new method of classification from remote sensing imagery,named as SLPA,is proposed.SLPA consists of two parts:Spectrum\|Location joint analysis (abbreviated as S\|L analysis or SL) and Phenology remote sensing Analysis (PA).By adding density descriptors to a feature space and by combining the descriptors derived from winter and summer images of a scene into the same feature space,these two kinds of analyses can be embedded into the classification process.The embedding increases the number of available independent descriptors therefore making the feature space rich enough to adapt to such a complex classification;meanwhile,uncertainty in the classification can be reduced and the classification accuracy can be improved significantly.The data from accuracy assessments show that with the S\|L analysis and the phenology analysis,the overall accuracies of the classification for urban vegetation population will increase by 15% and 29.3% respectively.In addition,the S\|L analysis almost does not increase the computational complexity because a frequently used computation for the density of gray elements is replaced with a calculation of binary mean value,a much more time saving operation.Experiments indicate that SLPA is good at robustness and universality in the classification of urban vegetation population.
  • Xie Shuntao,Ju Tianzhen,Li Bing,Lu Xiujuan,Zhao Xinxin,Wang Jing,Wang Shuang
    Remote Sensing Technology and Application. 2017, 32(5): 958-965. https://doi.org/10.11873/j.issn.1004-0323.2017.5.0958
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    In recent years,the atmospheric environmental issues become increasingly significant.Formaldehyde (HCHO) as a kind of carcinogen,its global testing to understand the spatial and temporal distribution and content in the atmosphere,has important significance for the detection of air quality and public safety.The study on the use of satellite AURE mounted OMI (Ozone Monitoring Instrument) a new generation of atmospheric detection sensors,data for the 2005~2014 January,April,July,October Tianshui vertical columns of tropospheric HCHO concentrations of trace data for each year.By VISON,GIS and other software combined with the product handling,explores the spatial distribution of Tianshui area HCHO,the time variation and their influencing factors.The results show that: the study area,the vertical columns of tropospheric HCHO concentration in 2005 showing sustained growth trend in 2012,2012~2014 chronology exhibit significantly decreased;winter and summer HCHO vertical column concentrations significantly higher than the spring and autumn,which highest in summer and winter followed; the eastern part of the study area and adjacent areas in Shanxi\|parts of Gangu County,Wushan County,exhibits a significantly higher value and lower central region of HCHO Tianshui vertical column density,and in 2014 the performance of HCHO concentration in the study area values are generally higher.Studies have shown that remote sensing is important for large\|scale atmospheric environmental monitoring.

  • Zhou Jinlin,Ma Mingguo,Xiao Qing,Wen Jianguang
    Remote Sensing Technology and Application. 2017, 32(5): 966-972. https://doi.org/10.11873/j.issn.1004-0323.2017.5.0966
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    The response of the vegetation dynamics to climate variability in the southwestern China was analyzed based on the dataset from remotely sensed data and ground observation data,i.e.,MODIS NDVI data with the spatial resolution of 250 meters and temporal resolution of 16\|day,and meteorological data composed of monthly temperature and precipitation data collected from 121 weather stations spanning periods of 2001\|2013,respectively.Seasonally Integrated Normalized Difference Vegetation Index (SINDVI) utilized linear regression to characterize the trends in vegetation shifts.Anomaly analysis was applied to characterize the yearly average fluctuation.Furthermore,the monthly maximum Normalized Difference Vegetation Index (MNDVI) and meteorological data were employed to calculate the correlation coefficient at different time scales.The results indicate that vegetation coverage has extensive decreased trends in the west of Sichuan Basin,the Dayao Mountain in Guangxi and part of the Yunnan\|Guizhou Plateau.Conversely,it shows an increased trend in the south of Sichuan Basin,the Daba Mountains,the west of Guizhou and the coastal areas in Guangxi.In more than half part of southwestern China,vegetation conditions are positively correlated with accumulated temperatures.But the partial correlation coefficient between vegetation condition and total precipitation has evident differences in regions.
  • Jin Meng,Deng Shunqiang,Yang Chengshu,Yu Bailang,Wu Jianping
    Remote Sensing Technology and Application. 2017, 32(5): 973-982. https://doi.org/10.11873/j.issn.1004-0323.2017.5.0973
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    Understanding spatial distribution of urban clusters at regional and national scales is increasingly important for many fields especially urban planning.Previous Studies have demonstrated urban built\|up areas can be derived from stable nighttime light satellite (DMSP\|OLS) images.Population and economic variables (i.e.GDP) have been proved significant positive correlations with nocturnal light brightness.However,less studies focused on the spatial distribution of extracted urban built\|up area.an improved DBSCAN algorithm is proposed to cluster the urban objects extracted from nighttime light image in different scales based on density,of which our urban spatial clusters are proved corresponding with urban agglomerations identified by statistical data.The traditional DBSCAN method is based on points which is not the same case with urban objects.The inclusion relation is refined,assuming that only if all the vertexes of each polygon are within the given distance,it is included in the area.Moreover,the parameters for the DBSCAN clustering model are determined by valleys of distances of every objects to classify urban spatial clusters.Besides,in a larger scale,the clustering results imply the different patterns of urban agglomerations on both sides of the Huhuanyong Line.