20 February 2019, Volume 34 Issue 1
    

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  • A Survey of Depth Semantic Feature Extraction of High-Resolution Remote Sensing Images based on CNN
    Dong Yunya, Zhang Qian
    Remote Sensing Technology and Application. 2019, 34(1): 1-11.
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    In recent years，deep learning has been developed and applied in many aspects as a research hotspot of computer vision.Feature extraction is the key basis for understanding and analyzing high-resolution remote sensing images.In order to promote the development of high-resolution remote sensing image feature extraction technology，the research and development of deep learning model in high-resolution remote sensing image feature extraction technology，such as：AlexNet，VGG-net，and GoogleNet convolutional network models，have been summarized in depth semantic features.In addition，the application of extraction is also focused on the application and innovation of various deep learning models based on convolutional neural network models in high-resolution remote sensing image feature extraction，such as：application of migration learning；The combination of the CNN model and other model structures enhances the ability to extract deep semantic features.Finally，the problems of the convolutional neural network model in the extraction of deep semantic features of high-resolution remote sensing images and the possible research trends are analyzed.
  • Progress in Haze Monitoring by Remote Sensing Technology
    Xiang Jiamin, Zhu Shanyou, Zhang Guixin, Liu Yi, Zhou Yang
    Remote Sensing Technology and Application. 2019, 34(1): 12-20. https://doi.org/10.11873/j.issn.1004-0323.2019.1.0012
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    The haze weather is one of the serious disasters affecting the human health and social economic development.Quantitatively monitoring the haze spatio-temporal distribution with a higher precision by remote sensing technology is the basis to predict the haze spreading and then warn its influence early，which has been a hot issue in the research field of atmospheric environment.The corresponding progress in haze monitoring by remote sensing technology at home and abroad were summarized in this paper.The main methods of haze monitoring can be classified intothree categories：the image transformation from multi-channels and construction of haze indices based on the spectral differences，monitoring directly by the aerosol optical depth and indirectly by estimating the content of atmospheric particulates，and monitoring vertical and horizontal distribution features from multi-sources remotely sensed data combined the passive optical sensors with the active laser radars.Then the existing problems and difficulties were also discussed.In the future，on the basis of developing three-dimensional haze monitoring technology by multi-sources remote sensing methods，research on haze simulation and prediction with high spatio-temporal resolution as well as its practical application need to be further strengthened.
  • Research Progress in Monitoring and Forecasting of Crop Diseases and Pests by Remote Sensing
    Lu Junjing, Sun Leigang, Huang Wenjiang
    Remote Sensing Technology and Application. 2019, 34(1): 21-32. https://doi.org/10.11873/j.issn.1004-0323.2019.1.0021
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    Crop diseases and pests are the first natural biological hazards that threaten food production and quality.The investigation and sampling in field of plant protection department can’t meet demand of the accurate，non-destructive and efficient monitoring and warning.Currently，remote sensing which can monitor dynamically in real time provides the possibility for the rapid acquisition of continuous surface information，and is also the main development direction monitoring and prediction of crop diseases and pests in the future.Research status of three main directions，including classification of different stresses，severity estimation and stress forecasting，are summarized，and the methods of feature extraction，feature selection，and algorithms are expounded.Then，the application of diseases and pests of three major foodsby remote sensing was analyzed by means of domestic retrieval platforms.On this basis，the existing problems and future development trend of monitoring and forecasting of crop diseases and pests by remote sensing are discussed to promotethe long-term mechanism of agricultural sustainable development.
  • Spatio-temporal Characteristics of Urban Land Cover Structures in Prefecture-level Cities of Inner Mongolia Autonomous Region based on Remote Sensing Imagery
    Chi Wenfeng, Kuang Wenhui, Dang Xiaohong, Pan Tao, Liu Zhengjia
    Remote Sensing Technology and Application. 2019, 34(1): 33-45. https://doi.org/10.11873/j.issn.1004-0323.2019.1.0033
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    The urban land cover structures play an important role in providing urban ecological service and altering the quality of human settlements environment.In this study，2000~2015 Landsat TM series satellite data along with fine resolution remote sensing images were used to capture information of each 5-year land cover structures in 12 prefecture-level cities of Inner Mongolia Autonomous Region.Subsequently，there land cover information was used to monitor and analyze the spatiotemporal dynamics of urban expansion，and differences of land cover structures and expansion types.The results showed that：in 2000~2015，the overall changes of land cover structures in 12 prefecture level cities of Inner Mongolia Autonomous Region were rapid.Specifically，the urban area expanded by 278.93 km2.By comparison，the proportion of urban area expansion in 2010~2015 was 1.61 times and 1.91 times than that of the first two periods (in 2000~2005 and in 2005~2010).Since 2010，the most dramatic changes has been observed.Particularly，obvious urban impervious surface expansion was found.Also urban vegetation showed obviously increased with varying degrees.At the past decade，urban expansion has undergone three stages.Specifically，the main process experienced from urban interior filling to urban interior filling and then to urban extension，of which Baotou and Hulunbeier belonging to the internal filling city.Population growth and socio-economic development are responsible for these differences.
  • Study on Surface Albedo of Spectral Radiation of Different Land Use Types in China
    Liu Qinqin, Cui Yaoping, Liu Sujie, Li Nan
    Remote Sensing Technology and Application. 2019, 34(1): 46-56. https://doi.org/10.11873/j.issn.1004-0323.2019.1.0046
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    The change of surface albedo directly affects the radiation balance and surface heat budget，but it is limited by the lack of temporal and spatial discontinuity of remote sensing inversion data，the analysis of surface albedo on large-scale areas still faces many uncertainties.Moreover，the current researchesalways focus on short-wave albedo and few studies on surface albedo involving the visible and near-infrared bands.Select surface albedo data of 2000~2015 remote sensing inversion and land use data for 2000 and 2015，the classical statistical method was used to analyze the characteristics of surface albedo of different land use types and their inter-annual variation trend.Provide a scientific basis for understanding the albedo characteristics of land use types，the physical processes of the relevant spectral splitting variables of energy modules in cognitive regional climates or land surface models.The results show that different land use types have different surface albedo characteristics，and the difference in surface albedo of the same land use type even exceeds the difference between different types of land use types，indicating the spatial heterogeneity of the surface albedo sexuality.Most land use types meet the surface albedo of short-wave total radiation and spectral radiation： near-infrared>short-wave>visible，indicating that the upper limit of short-wave surface albedo depends more on the surface albedo in the near-infrared.During the period，the albedo of each land use type showed different trends in the three bands，but the annual inter-annual rate of surface albedo of most land use types was relatively small and remained basically stable.
  • Comparison of Machine Learning Methods for Land Use/Land Cover Classification in the Complicated Terrain Regions
    Gu Xiaotian, Gao Xiaohong, Ma Huijuan, Shi Feifei, Liu Xuemei, Cao Xiaomin
    Remote Sensing Technology and Application. 2019, 34(1): 57-67. https://doi.org/10.11873/j.issn.1004-0323.2019.1.0057
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     Aiming at the characteristics of varied and complex geomorphic types，crisscross network of ravines and broken terrain in high altitude complicated terrain regions，it is very important to study and find the rapid and effective land use/land cover classification method for obtaining and timely updating of land use information.Taking the Huangshui river basin located in the transitional zone between the Loess Plateau and the Qinghai-Tibet Plateau as acasestudy area，the objective of this study is to explore a kind of effective information extraction method from comparison of four kinds machine learning methods for complicated terrain regions.based on Landsat 8 OLI satellite data，DEM and combined with various thematic features，on the basis of geographical division of the study area，artificial neural network，decision tree，support vector machine and random forest four machine learning methods for land use information extraction were used to obtain land use data，and confusion matrix was constructed to evaluate classification accuracy.The results showed that the classification accuracies of random forest and decision tree are obviously higher than those of support vector machine and artificial neural network.The random forest method has the highest classification accuracy，the overall classification accuracy is 85.65%，the Kappa coefficient is 0.84.based on the above classification，Random forest classification method was chose to further classify Landsat 8 fusion datafrom panchromatic 15 meter and multispectral 30 meter image，the overall classification accuracy is 86.49% and the Kappa coefficient is 0.85.This indicated that the random forest classification method can obtain higher classification efficiency while ensuring the classification accuracy.It is very effective for the extraction of land use information in complicated terrain regions.Data fusion can improve the classification accuracy to a certain extent.
  • A Hierarchical Classification and Iterative Model based Methodfor Remote Sensing Classification of Land Cover
    Wu Wei, Zhang Yuan, Li Qiangzi, Huang Huiping
    Remote Sensing Technology and Application. 2019, 34(1): 68-78. https://doi.org/10.11873/j.issn.1004-0323.2019.1.0068
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    Land cover classification based on remote sensing is an important means to analyze the change and spatial pattern of land use.In order to further improve the classification accuracy，this paper proposed a hierarchical classification and iterative CART model based method for remote sensing classification of landcover.Firstly，the extraction order of land cover classes was determined based on the class separability evaluation，which was water，vegetation，bare soil and built-up land.Secondly，we selected the optimal image segmentation parameters and a set of sensitive features for each class during the hierarchical classification process.Finally，object-based training samples were selected to be fed into the iterative CART algorithm for the successive extraction of the first three classes，with the remaining unclassified objects being directly assigned to the last class.Results demonstrated that the proposed method can significantly reduce the mixture between bare soil and built-up land，and is capable of achieving landcover classification with much higher accuracy.The proposed method achieved an overall accuracy of 85.76% and a Kappa efficient of 0.72，with the performance improvements ranging from 10.67% to 16.5% and 0.15 to 0.21 as compared SVM and CART single classification methods.The classification accuracy of a specific class can be flexibly adjusted using this method，giving different purposes of classification.This method can also be easily extended to other districts and disciplines involving remote sensing image classification.
  • Land Cover Classification and Mapping based on Segmentation Optimized Hierarchical Object Classification System
    Liu Li, Liu Yong
    Remote Sensing Technology and Application. 2019, 34(1): 79-89. https://doi.org/10.11873/j.issn.1004-0323.2019.1.0079
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    With the wide application of high-resolution satellite，Object based Image Analysis (OBIA) has gradually become main stream of extracting land cover information.Segmentation optimization is a fundamental step in OBIA.Different land cover types usually have different optimized segmentation parameters.How to make full use of the optimal Multi-Resolution Segmentation (MRS) to establish a segmentation classification hierarchy and to achieve high-precision land cover mapping，is a challenge in object-oriented image analysis.based on the optimal segmentation parameters of different land cover types，this paper explores a method to construct a segmentation optimized hierarchical classification system based on the minimum optimized segmentation unit.Experiments show that this method can effectively reduce the dependence on the operator's personal experience when setting up the classification hierarchy system，improve the classification accuracy，and meet the requirements of automatic drawing.
  • Urban Expansion and Driving Force Analysis of Ulaanbaatar in Mongolia in Recent Thirty Years
    Cheng Kai, Wang Juanle, Jaahanaa Davaadorj, Han Xuehua
    Remote Sensing Technology and Application. 2019, 34(1): 90-100. https://doi.org/10.11873/j.issn.1004-0323.2019.1.0090
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    Since the reform of the regime in the 1990s，Mongolian urban experienced a rapid development.Understanding the characteristics of urbanization and development in Mongolia is much of significance to China’s implementation of the “Belt and Road” strategy and “China-Mongolia-Russia Economic Corridor”.This study was based on theLandsat TM/OLI remote sensing image，using object-oriented classification method，and obtained 1990，2001，2010，2017 land cover data set，the overall classification accuracy were 86%，89%，91.6%，94.80% respectively，Kappa coefficient were 0.83，0.869，0，898，0，935.based on the transfer matrix，the information of land cover change from 1990 to 2017 in Ulaanbaatar was mined，the results showed that：① the areas of built area，barren，and water showed an increasing trend，and the built area was increased most.On the contrary，the area of forest，cropland，and grassland showed a tendency to decrease，and the forest area decreased most.② the transfer between grassland and forest，grassland and built area，forest and grassland played a major role in land cover change of Ulaanbaatar.The change area from 1990 to 2001，2001 to 2010 and 2010 to 2017 accounted for nearly 71%，74%，and 79% of the total change area.③ the expansion trend of built area was significant，the area has increased from 99.87 km2to 216.16 km2，the growth rate has reached 216%，the expansion rate was 8.01 km2/a，which belonged to the rapid expansion mode.The middle-north with the type of summer house，northeast with the types of traditional houses based on the structure of home-household-yard，mixture of Mongolian yurts and low buildings，west with the types of industrial land and residential land.The urbanization in Ulaanbaatar caused by the interaction of external social economic development and national policies，among of which，land privatization and market economic were the main policy driving forces of urbanization
  • Quantification Error Analysis of Digital Correlator on Fully Polarization Microwave Radiometer
    Huang Yishen, Wang Zhenzhan, Lu Hao
    Remote Sensing Technology and Application. 2019, 34(1): 101-106. https://doi.org/10.11873/j.issn.1004-0323.2019.1.0101
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    Fullypolarization microwave radiometer is a passive remote sensing device that measures the target’s microwave radiation characteristics.It is a complete extractiontechnology of polarization information such as amplitude，frequency and phase based on the traditional microwave radiometer.Digital correlator utilizes high-speed analog-to-digital converter and programmable logic device FPGA to realize quantification processing and related operations of horizontal and vertical channel signal.Compared to analog correlators，digital correlators have the advantage of high bandwidth and immunity from DC interference，but quantification errors are also introduced in the quantification process.This paperanalyzes the influence of 8 bit quantification on the correlation coefficient before and after quantification on digital correlation fully polarization radiometer whichdeveloped by NSSC.By determining the appropriate A/D quantification threshold，it is ensured that the error of the digital correlator has the least influence on the radiometer system.The reliability of the analysis conclusion is verified by modeling and simulation.The results show that the quantification error of 8 bit digital correlator developed by the project can be neglected to the system error.
  • Extracting Building Height Information based on the Same-name Feature from High-resolution Satellite Image
    Long En, Wang Yuan, Meng Gang, Wang Weiyang, Chen Xu, Lian Cuiping
    Remote Sensing Technology and Application. 2019, 34(1): 107-114. https://doi.org/10.11873/j.issn.1004-0323.2019.1.0107
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    Based on the idea of the same-name feature，the distance of the same-name straight edge and the main direction parameters of the building are introduced，and the altimetry model of the flat-top straight-edged building is constructed.The feature information extraction technology combining object-oriented and expert knowledge is used to realize the height extraction of the building.Firstly，using the spectral and shape features of the shadow，the detection and localization of the same-name straight edge of the building is carried out from high spatial resolution image.Secondly，based on the detected same-name straight edge vector and the parameter calculation model，the distance of the same-name straight edge parameter is extracted.Finally，based on Building imaging conditions and a new altimetry model to extract building height.The experiment of using the IKONOS and GF-2 image in Beijing area show that the scheme solves the problem that the shadow length of the conventional method is not easy to measure，and the precision is high，reaching the level of the meter，with good human-computer interaction and strong practicality in business operation.
  • Comparative Study of ELM and SVM in Hyperspectral Image Supervision Classification
    Mou Duoduo, Liu Lei
    Remote Sensing Technology and Application. 2019, 34(1): 115-124. https://doi.org/10.11873/j.issn.1004-0323.2019.1.0115
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    Combining the spatial features and spectral feature of hyperspectral remote sensing image in supervised classification can effectively improve the classification time and accuracy.In this study，the spatial information extraction method，named watershed transform，was combined with the Extreme Learning Machine(ELM)and Support Vector Machine(SVM)methods.The classification results of the datasets with the spatial features and without the spatial features were synthetically evaluated and compared.Two hyperspectral datasets，the ROSIS data of Pavia university and the Hyperion data of Okavango Delta(Botswana)，were selected to test the methods.After preprocessing，the training samples were selected from the images as the reference areas for each type，and the spectral features of each type were analyzed.The two classification methods were utilized to classify the hyperspectral datasets and relevant classification results were obtained.based on the validation samples selected from the images，the classification results were evaluated using the confusion matrix and the execution times.After that，the spectral features and spatial features were combined to classify the data.The results show that the Extreme Learning Machine(ELM) is superior to the Support Vector Machine(SVM)in the classification time and precision，and the spatial features are introduced in the classification process，which can effectively improve the classification accuracy.
  • A Regional-Scale Performance Evaluation of SMOS and AMSR2 Soil Moisture Products over Asia
    Li Ruijuan, Li Zhaofu, Hao Rui, Zhang Shuyu, Pan Jianjun
    Remote Sensing Technology and Application. 2019, 34(1): 125-134. https://doi.org/10.11873/j.issn.1004-0323.2019.1.0125
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    Soil Moisture (SM)products derived from satellite missions have been widely applied in agriculture，meteorology，hydrology，and other fields.It is very necessary to assess the reliability of satellite soil moisture products over regional scale before using them.Two passive satellite soil moisture products，obtained from SMOS (the Soil Moisture Ocean Salinity；L3)and AMSR2 (the Advanced Microwave Scanning Radiometer 2；LPRM)，were evaluated over Asia with reference to MERRA-2 (the Modern Era Retrospective-analysis data for Research and Applications，Version 2)simulated products.The evaluation was performed by adopting a classic statistical method (including Pearson correlation (R)for original SM data and anomalies，bias and unbiased root mean square root)and Triple Collocation (TC)approach based on the SMOS-L3 and AMSR-LPRM daily SM products during July 2012~July 2016.The results reveal that：(1)in space，the performance of SMOS-L3-SM is better than AMSR2-LPRM-SM both for the original SM data and anomalies.Because the SMOS-L3-SM performed consistent correlation over Asia and the smaller unbiased root mean squared difference (ubRMSD)of SMOS-L3-SM is found in most parts of Asia；(2)the correlation between satellite and simulated SM is better in the wet season than in the dry season.Additionally there is a quite high probability of a lack of value in high latitude (about >55°)areas in the dry season to appear；(3)SMOS-L3-SM and AMSR2-LPRM-SM have relatively similar TC errors distribution with a mean error of 0.076 m3/m3 for both of them.Overall，both SMOS-L3-SM and AMSR2-LPRM-SM give reasonable results on correlation and TC error，thus providing an additional reference for application of soil moisture in agriculture，meteorology，hydrology and other studies.

  • Remote Sensing Estimation of Forest Coverage based on Histogram and Pixel Decomposition Model
    Dong Lixin
    Remote Sensing Technology and Application. 2019, 34(1): 136-145. https://doi.org/10.11873/j.issn.1004-0323.2019.1.0136
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    Forest coverage is the percentage of forest cover for hooking out the forest stand boundary，and quantitative coverage information can be used for describing the temporal and spatial variability of vegetation in the horizontal scale.Pixel decomposition model has been widely used in remote sensing estimation of vegetation coverage.However，there are still many problems.For example，it is difficult to find a pure spectrum of tree canopy coverage to estimate the crown coverage with high accuracy in forest vegetation applications.In this paper，combining land use and soil type data，it is proposed to determine the endmember parameters of the different vegetation-soil types by using the histogram method based on pixel decomposition model for estimating the regional scale forest coverage in the Three Gorges.And the results were verified using the 161 samples of field data in the Three Gorges Reservoir area，R2 was found to be 0.742 4~0.853 6，the estimated results are satisfactory.This method will provide a reference for remote sensing estimation of high-resolution forest coverage at regional scale.
  • Ground-based Automatic Radiometric Calibration of Land Observation Satellite Optical Sensors and Cross Validation Analysis
    Pang Bo, Ma Lingling, Liu Yaokai, Wang Ning, Zhao Yongguang, Han Qijin
    Remote Sensing Technology and Application. 2019, 34(1): 146-154. https://doi.org/10.11873/j.issn.1004-0323.2019.1.0146
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    Due to the low cost-effectiveness and large uncertainty of single calibration for traditional ground-based radiometric calibration methods，it is difficult to meet the requirement ofhigh-precision radiometric calibration of satellite payloads.Aroutinely-operated ground-based automatic radiometric calibration method was developed and applied on radiometric calibration and cross validation analysis of Landsat-8/OLI opticalsensor based on the “National Calibration and Validation Site for High Resolution Remote Sensors” (hereinafter referred to as the “Baotou Site”) in Baotou.The comparison of 11 observation results (from May 2016 to April 2017) between on-board calibration and ground-based calibrationare in good agreement：for the four bands of blue，green，red and near infrared，the average relative deviation between ground-based calibration and on-board calibration was 0.83%，-0.21%，-0.20%，and -1.37%，respectively，while the standard deviation was 2.78%，2.89%，2.94%，and 2.20%，respectively.Further，quantitative analyses on the sources of errors in the process of ground-based automatic radiometric calibration was conducted.The results showed that the final uncertainties of ground-based automatic radiometric calibration in the four bands of blue，green，red，and near infrared were 5.06%，4.65%，4.80%，and 4.98%，respectively.Good consistency between ground-based calibration and on-board calibration proved the reliability of this method，which can dramatically promote the frequency and timeliness of satellite radiometric calibration.
  • GlobalLeaf Spectral Characteristics of Typical Vegetation and It’s Impacts on LAI Inversion
    Liu Jie, Li Jing, Liu Qinhuo, He Bingbing, Yu Wentao
    Remote Sensing Technology and Application. 2019, 34(1): 155-165. https://doi.org/10.11873/j.issn.1004-0323.2019.1.0155
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    Long time series LAI remote sensing inversion algorithms use only a few leaves spectra to represent the global leaf spectral characteristics throughout the year.while due to the variation of leaf spectra，it may introduce uncertainties to LAI remote sensing products.An amount of spectrum databases containing leaf spectrum of different vegetation species，geographical locations and time phase and corresponding biochemical parameters have been constructed to provide support for the analysis of spectral characteristics of leaves.This paper mainly uses the leaf spectral database LOPEX’93，ANGERS’03，Spectral library of typical ground objects in China and field experimental data to analyze the effects of spectral characteristics of different plant species and different climate zones on MODIS reflectance of specific channels and further to provide prior information for the development of LAI inversion algorithms with consideration of leaf spetra differences.The result suggests that：There exists diversity in vegetation leaf spectra.The spectral differences mainly affect the reflectance in red and green band (green band is most sensitive to leaf spectra variation).Only considering vegetation types without taking leaf spectral variation into account may induce error over 3 in remote sensing LAI inversion algorithms.
  • Analysis of the Forest Dynamic Changes in Southeast Asia based on Remote Sensing and Meteorological Data
    Yin Siyang, Wu Wenjin, Li Xinwu
    Remote Sensing Technology and Application. 2019, 34(1): 166-175. https://doi.org/10.11873/j.issn.1004-0323.2019.1.0166
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    To further understand the relationship between dynamic changes of tropical forest and human activities as well as climate changes，we use methods of time series analysis and correlation analysis to study the temporal and spatial changes of forest net primary productivity(NPP) and their correlation with tree coverage(VCF)，temperature，precipitation and photosynthetically active radiation(PAR) in 11 countries in Southeast Asia from 2001 to 2013 based on MODIS remote sensing data and ERA-Interim reanalysis of meteorological data.The main conclusions are as follows：①the NPP in Southeast Asia is increasing from the equator to the north and the south;②NPP in most areas of the study area show a decreasing trend，and regions where have a more dramatic change of NPP usually have a higher coefficient of variation which showsa more unstable carbon sequestration capacity of forest ecosystem;③the tree cover in study areais generally high(60%~80%) and most of thearea have an increasing trend，in addition，the partial correlation coefficient between VCF and NPP was higher than correlation coefficient，indicating that human activities have a greater impacton forest NPP;④the temperature，precipitation and PAR in study area are relatively high，and as for the correlation between NPP and meteorological factors，countries with tropical forest climate have a better correlation than countries with tropical monsoon climate，whose NPP is generally negatively correlated with the temperature and positively correlated with precipitation and PAR.
  • Temporal and Spatial Variation and Influencing Factors of Water Storage on the Loess Plateau
    Hu Pengfei, Li Jing, Zhang Yanli, Zhu Guofeng, He Panxing, Cao Yongpan
    Remote Sensing Technology and Application. 2019, 34(1): 176-186. https://doi.org/10.11873/j.issn.1004-0323.2019.1.0176
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    In recent years，the conditions of the underlying surface of the Loess Plateau have changed greatly.We researched the changes of water storage by using multi-source data to further reveal the region’s water cycle process.GRACE data were used to study the temporal and spatial characteristics of Terrestrial Water Storage Changes (TWSC) in the Loess Plateau for 2003~2015 years，combined with the atmospheric circulation data，TRMM (3B43) precipitation，GLDAS evaporation and MODIS surface temperature data to analyze the impact of climate change and human activities on TWSC.The results shown that：①in the 2003~2015 years，the TWSC of Loess Plateau showed a decreasing trend with the rate of -5.16±1.51 mm/a，and the seasonal variation shown autumn＞winter＞summer＞spring.②in the past 13 years，the TWSC of Loess Plateau were decreasing from west to east，and the whole were in the state of loss，the minimum value was up to -4.5 cm.③Precipitation has a greater influence on the TWSC in the southwest and south of Loess Plateau，but the surface temperature plays dominated role in the southeast and east.④Human activities have a greater impact on TWSC in Shanxi province and the border zone of Shaanxi，Shanxi and Henan.The comparative study of multi-source data can more accurately reflect the spatial and temporal distribution of water storage changes in the region，and it also have great significant for further research of water cycle process.
  • The Spatial Transformation and Hotspot Detection of Land Use and Cover in Arid Basin：A Case of the Middle Reaches of Heihe River
    Li Jie, Wang Fuhong, Song Xiaoyu, Shi Peiji, Zhao Ruifeng
    Remote Sensing Technology and Application. 2019, 34(1): 187-196. https://doi.org/10.11873/j.issn.1004-0323.2019.1.0187
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    Based on the data of land use/cover change in the middle reaches of the Heihe River in 1987，2001 and 2016，with the support of remote sensing technology，the Markov transfer matrix and land use dynamic estimation model were used to simulate the land use transformation direction and spatial distribution characteristics，detect hotspots of land use/cover change，analyze the relevant driving factors，and propose the countermeasures for development bottlenecks during the study period in the middle reaches of the Heihe River between 1987~2001 and 2001~2016.Its main findings are as follows：the main changes of land use in the middle reaches of Heihe River as the land of human activities gradually increasing，the ecological land decreasing continuously.The areas with sharp changes in land use are mainly concentrated on both sides of the river，the types of transformation are mainly in the conversion of waters into grassland，grassland into cultivated land，and the unused land to construction land;The scope of land use transformation in the study area have obvious spatial differences，the frequency and expansion are significantly higher than the previous period;as a whole，the hot region are located in the oasis area of agriculture in Zhangye，but the early hot region is more dispersed，small，no benefit，the later is concentrated，and has more central tendency in the larger space.
  • Study on the Pattern of Land Cover Hierarchy in Hohhot-Baotou-Ordos Cities in the Semi-arid Region of China
    Zhuang Yuan, Xue Dongqian, Kuang Wenhui, Chi Wenfeng, Pan Tao
    Remote Sensing Technology and Application. 2019, 34(1): 197-206. https://doi.org/10.11873/j.issn.1004-0323.2019.1.0197
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    Taking the Hohhot-Baotou-Ordos city group in semiarid area as an example，constructs a system of surface hierarchical structure in the semi-arid area for urbanization.It reveals the urbanization process of different scales since twenty-first Century.The results show that：(1) the process of urbanization in the study area is intense in 2000~2015，especially during the period of 2005~2010.Among them，the expansion of Hohhot city presents extension type，Baotou for filling type，and ordos for enclaving type.(2) The impermeable space continued to increase (low density -medium density -high density increasing)，and its proportion in urban areas increased from 62.46% in 2000 to 75.40% in 2015.The green space shows an increasing trend of fluctuation，which reflects that the urbanization process in semi-arid area focuses on the coordinated construction of urbanization and green space.Although the development of the city includes the bare soil space，the process of urbanization obviously reduces the spatial scope of the bare soil，which effectively improves the ecosystem service and human well-being.(3) the change order of impermeable components density is low，medium and high.During this period，the composition of the green space was reduced first and then increased，and the overall micro amplitude increased，indicating that the green space in the urban area was promoted.At the same time，the density of bare soil components showed a trend of high，middle and low，indicating that urbanization was expanded into a large number of bare soil.Then bare soil was further transformed into impervious surface and vegetation，and the ecological environment in the urban area was significantly improved.
  • Study of Urban Expansion and Driving Factors in Xi’an City based on Nighttime Light Data
    Gao Ning, Ge Yingchun, Song Xiaoyu
    Remote Sensing Technology and Application. 2019, 34(1): 207-215. https://doi.org/10.11873/j.issn.1004-0323.2019.1.0207
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    The dynamic monitoring of urban expansion information is of great significance to understand the social and economic activities of cities and the implementation and feedback of urban planning.Using DMSP / OLS night-time light image as data source，the urban expansion data were corrected and extracted by using rational function model and threshold dichotomy.By using the light index，urban spatial expansion rate and intensity index and the index of center of gravity migration，The results show that the urban built-up area in Xi’an has expanded 2.2 times from 148 km2 in 1993 to 473 km2 in 2013.The direction of urban expansion expands from southwest to south and north，and the spatial expansion mode also increases from the early stage The single-core concentric expansion mode gradually transformed into a multi-level nuclear growth expansion mode；thus using gray relational analysis to analyze the socio-economic indicators of the suburbs in Xi’an in 2008~2013 (Weiyang district，Yanta district，Baqiao district and Chang’an district) It is confirmed that secondary industry，population density，tertiary industry and fixed assets of the whole society are the main driving factors of urban expansion in four districts respectively.
  • Evaluation of Urban Expansion in Chongqing City based on the Nighttime Light Data
    Bai Heting, Ma Mingguo, Yan Ran, Liu Kangning, Juan Chuhan
    Remote Sensing Technology and Application. 2019, 34(1): 216-224. https://doi.org/10.11873/j.issn.1004-0323.2019.1.0216
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    Urbanization is an important embodiment of regional economic development.Its progress reflects the level of economic development in the region.Since Chongqing being directly under the Central Government in 1997，it has enjoyed rapid economic growth and rapid urbanization.Looking for an intuitive and efficient method of urbanization is of great practical significance to correctly grasp the achievements of urbanization in Chongqing from the overall situation.Through the comparison of statistical data on night lighting data，we can extract the built-up area of Chongqing year after year.The city gravity model is based on the results.A night light index is established and used to calculate the speed of urban expansion.The result indicates that the urban distributions in Chongqing is spatially distributed and expanded in all directions.There are great differences between urban areas and rural areas and urban area urbanization level is higher.The city gravity gradually shifted from Yuzhong district to Yubei district.The speed of city expansion maintains high.Our study can afford refers for the planning and construction of the Chongqing city.
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