最新刊期
卷 24 ， 期 7 ， 2020
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摘要：Water resources monitoring is the foundation of water resources management. China is implementing the strictest water resources management system toward precise and dynamic management in terms of water quantity, water quality, and water use efficiency, with higher requirements for water resources monitoring. Water resources components are characterized by large spatial and temporal differences. Currently, the water resources monitoring system is based mainly on ground monitoring. However, it is difficult to carry out large-scale and long-term monitoring based solely on ground measurements. There are still knowledge gaps in the fine and dynamic water resources management. With continuous and rapid increases in satellite data, this paper examines the use of multi-mission satellite data, ground observation systems, and land data assimilation systems to jointly develop a stereoscopic monitoring system, which would be of value to address the monitoring gaps for water cycle components and to enhance the monitoring capability of water resources in China, with important implications for stereoscopic water resources monitoring for other countries and regions globally.关键词：remote sensing;multisource data;stereoscopic monitoring;coordinated mechanisms;water resources management- 1058
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发布时间：2021-10-11 -
摘要：Road extraction from remote sensing image has important practical value. It presents a major issue in the field of remote sensing. In recent years, the geometric texture of the target objects of aviation and satellite optical images has been refined in the wake of the rapid development of aviation technology. Thus, this technology provides a sufficient basis for automatic extraction of road information. However, full automation of road extraction through existing methods is difficult. In view of this issue, this study collects and synthesizes the existing methods on the basis of a large number of related literature in recent years. Ultimately, these road extraction methods are divided into four categories: template matching, knowledge-driven, object-oriented, and deep learning.Template matching methods can be generally divided into rule template and variable template according to template type. The difference between the two types is whether the template can be drawn with regular graphics. Template matching mainly consists of the following three steps: template design, measure analysis, and location update. The template design can usually be set manually or by certain rules. Then, the target template is given in the measure analysis. Furthermore, the extreme value of the region is found by the measure function within the defined area. Lastly, the location of the road centerline is dynamically updated.Roads, as artificial ruled features, provide large information. Hence, knowledge-driven methods based on relevant knowledge are used in road extraction work. On the basis of the relationship between knowledge and road, this study divides the knowledge-driven methods by three kinds: geometric knowledge, context knowledge, and auxiliary knowledge. Three methods are described as follows: (1) Geometric knowledge. The model is mainly constructed on the basis of the geometric features of the road. (2) Contextual knowledge. The method utilizes road-related auxiliary knowledge (motor vehicles, trees, zebra crossings, et al.) to assist in identifying the road. (3) Assisting knowledge. Road extraction is guided by using multisource remote sensing data, vector data, GPS data, navigation data, and public source data.With the rapid increase of spatial resolution of optical remote sensing images, object-oriented methods have gradually become one of key methods in road extraction. First, the method selects the spectral and geometric feature criteria to segment image. Second, the geometric radiation similarity criterionis used to classify the segmentation region. Finally, the road extraction results are released by post-processing methods, such as mathematical morphology, matching tracking, and tensor voting.Road extraction research belongs to the problem of remote sensing image interpretation, whereas deep learning supplies a new opportunity for satellite image interpretation in a semantic direction. Deep learning extracts road information with high precision through convolution, pooling, and training. Nevertheless, road breakage and mistaken extraction problems still exist.At the end of the study, the direction of development of road extraction is envisioned. Two main trends are proposed: (1) a multimethod complementary road extraction system and (2) deep integration of deep learning and traditional methods. Thus, the detection area should be studied further.关键词：optic;remote sensing imagery;road extraction;template matching;knowledge-driven;object-oriented;deep learning- 1279
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发布时间：2021-10-11
Review
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摘要：As the coronavirus COVID-19 broke out in the Chinese city of Wuhan and spread across China in late January 2020, governments at all levels in China introduced many policies (e.g. closing factories and schools, staying indoors and clearing street) to limit the further transmission of the illness. Chinese industrial activity was impacted and the return-to-work was delayed by the combination of the Chinese New Year holiday and COVID-19 outbreak. And with the abatement of COVID-19 in China, many provinces have downgraded their emergency response levels. Industrial factories stared to re-open, and people gradually returned to their jobs. In this paper, based on the satellite data, we compared and analyzed the temporal and spatial variations of industrial hot spot and tropospheric NO2 VCDs before and after the COVID-19 outbreak to estimate the situation of return-to back rate in China.Generally, several heavy industrial sectors with high-energy consuming (e.g. smelting industries, petrochemical industries and cement industries) have a close relationship with heat-related activities, and the heat emissions can reflect the level of energy consumption to some extent. Therefore, such industrial thermal anomalies have the potential to be detected by satellites sensors with infrared channels. NO2 primarily comes from fossil fuels burning, such as industrial emissions, power plants and vehicles, and it is a key indicator of industrial activities. NO2 measured by satellite from high above Earth’s surface are a good indicator of the geographical location of air pollution because NO2 has a life span of about a day, and thus is concentrated near its sources.Here, the industrial hot spots and the tropospheric NO2 Vertical Column Density (VCD) data over China during the periods from January to February in 2020 and 2019, respectively, were used for evaluating the impact of COVID-19 outbreak on China’s return-to-work. The industrial hot spots were extracted from the fires/thermal anomalies results which were retrieved by the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the new generation of polar-orbiting satellite, Suomi National Polar-orbiting Partnership (NPP). The tropospheric NO2 VCD data were directly obtained from the NO2 standard product of Ozone Monitoring Instrument (OMI) on Aura satellite.Our results indicate that there is a significant reduction of levels of energy consumption and NO2 emissions in February 2020, compared to those of both January 2020 and February 2019. Since the end of the Chinese New Year holiday on February 3, 2020, the number, range of distribution and density of industrial hot spots over China has been steadily rising. Except for a small amount of industrial hot spots still being detected in Wuhan, Huangshi and Ezhou, no more industrial hot spot was monitored in other cities of Hubei province in February 2020, meaning that there is no sign of return-to-work in the whole Hubei province. As the coronavirus epidemic eases in China, although the number of industrial hot spots increased remarkably over Jing-Jin-Ji region, the FRPs and tropospheric NO2 VCDs of most grids with increasing spots were still at low levels, compared to the same period in 2019. It means that production capacities of most heavy industries are limited under the influence of COVID-19 outbreak. Until the early March, both the number and averaged FRP of industrial hot spots began to gradually rise, which reflects the increasing return-to-work rate of heavy industries over most regions of China.关键词：remote sensing;industrial hot spot;energy consumption;NO2;return-to-work;COVID-19 outbreak;OMI;VIIRS- 623
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发布时间：2021-10-11 -
摘要：In the past century, remote sensing has been of particular interest for archaeological specialists and the public because of the combination of three key points of archaeologicalresearch objects, space, and time. Remote sensing has become an important and powerful tool for helping archaeologists to explore and understand cultural and archaeological sites, discovering and monitoring archaeological sites, documenting and preserving cultural heritage, and resolving real archaeological problems. Recently, the focus of remote sensing-based archaeological applications has moved away from survey, mapping, monitoring, and documentation to the deep-mining of archaeological big data, archaeological knowledge (re-) discovery and understanding, and settlement pattern analysis and archaeolandscape reconstruction. These improvements and transformations have been jointly pushing remote sensing archaeology toward a new stage of space archaeology. In this study, the major achievements in remote sensing that is used for cultural heritage conservation are reviewed. Then, a brief dissection of connotations, tasks, methods, and potentials of the new paradigm of space archaeology is provided. The research object of space archaeology is the culture–space or human–land complex containing the remains of anthrophonic production, living activities, and their supporting eco-environments. Space archaeology, a new subfield of archaeology, has the technical advantages and disciplinary characteristics of shaping the unique cognition of cultural heritage. Space archaeology represents an invaluable set of powerful tools for prospecting, monitoring and documentation. It also supports the conservation of archaeological and cultural heritage sites and their supporting environments. The establishment and construction of space archaeology need experimental areas and bases. On the one hand, this study presents the layout of the experimental areas of “three lines and four zones” for domestic research on the basis of the comprehensive consideration of the occurrence conditions of cultural heritage sites in China, the characteristics of human production and living activities that took place in the sites, and the adaptability and differences of methods and approaches in space archaeology. The three lines are the Grand Canal, the Great Wall, and the Silk Road. The four zones are the desert environment in northwest China, the semi-humid valley landscape in central China, the wetland landscape in south China, and the farming-pastoral ecotone in northeast and northwest China. On the other hand, three international experiment areas are given priority for carrying out the demonstrations of space archaeology based on historical, natural and cultural, social and economic backgrounds and the current physical geographical conditions of Eurasia and Africa. These experimental areas are Southeast China and Southeast Asia, Northwest China and Central and South Asia, North Africa, and the Mediterranean Regions. The delimitation of these experimental areas contribute to the integration and cross innovation of different disciplines in the fields of culture, science, and technology. On the basis of the analysis of the research progress and current development of spatial information technology, this study puts forward the cognition from remote sensing archaeology to space archaeology and describes the connotation of space archaeology, the main research content, and the suggestion on domestic and international experimental regions. Space archaeology not only discusses with the need to adapt to the new tasks, new development, and new disciplines under the era of conservation and sustainable development of cultural heritage. It also deals with the requirement and mission of culture, science, and technology to promote the construction of “One Belt and One Road” and to contribute to improving the soft-power of participating in global governance. Space archaeology should contribute its unique strength and value to the conservation and development of cultural heritage in the new era.关键词：remote sensing archaeology;space archaeology;cultural heritage;One Belt and One Road;spatial information;big archaeological data- 676
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发布时间：2021-10-11
Research Progress
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摘要：A Tropical Cyclone (TC) is one of the most destructive meteorological disasters. The strong winds and heavy precipitation have significant effect on people’slives, property, and social and economic development. Therefore, the accuracy of thepath and intensity prediction of TCs is always an important consideration in meteorological research. However, considering the complexity and variability of typhoon cloud patterns, the existing objective methods are usually based on statistical linear regression.Moreover,they still have deficiencies in expressing the dynamic changes of the complex characteristics of TC cloud patterns. The deep learning algorithm performs well in high-dimensional nonlinear modelingandaccurately identifies the input mode with displacement and slight deformation.This algorithm finds significance in Tropical Cyclone (TC) monitoring with dynamic changes over time. To develop TC intensity estimation technology further in the field of satellite remote sensing, this studyapplied a new machine-learning technology to analyze and to study the TC intensity of FY-4A/AGRI data from China’s second-generation stationary meteorological satellite.First, a deep Convolution Neural Network (CNN) model was used to distinguish effectively and estimate quantitatively the TC intensity level and center wind speed. The images of day and night were placed into the convolution sampling channel of the CNN to obtain and combinesame-size spectral features. Then, multilayer convolution, pooling, nonlinear mapping, and other operations were used to mine the input characteristicsdeeply.Finally, the TC intensity was estimated. The experiment was divided into the TC intensity classification test and the quantitative estimation test of the TC center maximum wind speed. The CNN model was used to convert the recognition of the TC intensity into the pattern recognition of satellite cloud images, which could classify and identify the TC level.The experiment found that the recognition accuracy of the TC intensity was all above 95%regardless of the overall classification accuracy or the respective accuracy of day and night statistics. Compared with k-nearest neighbor, error back-propagation neural network, multiple linear regression, support vector machine, and other classical classification algorithms, itimprovesby 7-16 percentage points. Moreover, the CNN isalso superior to the classical algorithm in terms of classification accuracy. The CNN model comprises two fully connected network layers (each layer has three neurons).The TC wind speed canbequantitatively estimated by prior training samples of the network parameters. Compared with the data of Tropical Cyclone 2017 Yearbook, the mean absolute error of the wind speed was 1.75 m/s, and the root mean square error ofthewind speed was 2.04 m/s, which were lower than the corresponding errors of Deviation Angle Variance Technique (DAVT) by 85.70% and 84.38%.Thus, the CNN algorithm has a high application prospect in the quantitative estimation of typhoon intensity.As the first second-generation Chinese geostationary meteorological satellite to be launched, FY-4A has its advantages of multichannel structure and high spatial and temporal resolution. On the basis of these features, the advantages of the techniquesofthe deep neural network, and theflexible structure of CNN, this study proposesan improved CNN model thatis tailor-made for FY-4A data. The modelhas the capacity to mine the morphological characteristic of typhoons deeply and effectively and achieve high-precision typhoon intensity estimation.Thismodelhas positive research value and application prospect for the quantitative estimation of typhoon intensity.关键词：remote sensing;tropical cyclone;FY-4/AGRI satellite image;CNN;objective intensity estimation- 682
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发布时间：2021-10-11 -
摘要：A Tropical Cyclone (TC) is one of the most destructive meteorological disasters. The strong winds and heavy precipitation have significant effect on people’s lives, property, and social and economic development. Therefore, the accuracy of the path and intensity prediction of TCs is always an important consideration in meteorological research. However, considering the complexity and variability of typhoon cloud patterns, the existing objective methods are usually based on statistical linear regression. Moreover, they still have deficiencies in expressing the dynamic changes of the complex characteristics of TC cloud patterns. The deep learning algorithm performs well in high-dimensional nonlinear modeling and accurately identifies the input mode with displacement and slight deformation. This algorithm finds significance in TC monitoring with dynamic changes over time. To develop TC intensity estimation technology further in the field of satellite remote sensing, this study applied a new machine-learning technology to analyze and to study the TC intensity of FY-4A/AGRI data from China’s second-generation stationary meteorological satellite.First, a deep Convolution Neural Network (CNN) model was used to distinguish effectively and estimate quantitatively the TC intensity level and center wind speed. The images of day and night were placed into the convolution sampling channel of the CNN to obtain and combine same-size spectral features. Then, multilayer convolution, pooling, nonlinear mapping, and other operations were used to mine the input characteristics deeply. Finally, the TC intensity was estimated. The experiment was divided into the TC intensity classification test and the quantitative estimation test of the TC center maximum wind speed. The CNN model was used to convert the recognition of the TC intensity into the pattern recognition of satellite cloud images, which could classify and identify the TC level.The experiment found that the recognition accuracy of the TC intensity was all above 95% regardless of the overall classification accuracy or the respective accuracy of day and night statistics. Compared with k-nearest neighbor, error back-propagation neural network, multiple linear regression, support vector machine, and other classical classification algorithms, it improves by 7-16 percentage points. Moreover, the CNN is also superior to the classical algorithm in terms of classification accuracy. The CNN model comprises two fully connected network layers (each layer has three neurons). The TC wind speed can be quantitatively estimated by prior training samples of the network parameters. Compared with the data of Tropical Cyclone 2017 Yearbook, the mean absolute error of the wind speed was 1.75 m/s, and the root mean square error of the wind speed was 2.04 m/s, which were lower than the corresponding errors of Deviation Angle Variance Technique (DAVT) by 85.70% and 84.38%. Thus, the CNN algorithm has a high application prospect in the quantitative estimation of typhoon intensity.As the first second-generation Chinese geostationary meteorological satellite to be launched, FY-4A has its advantages of multichannel structure and high spatial and temporal resolution. On the basis of these features, the advantages of the techniques of the deep neural network, and the flexible structure of CNN, this study proposes an improved CNN model that is tailor-made for FY-4A data. The model has the capacity to mine the morphological characteristic of typhoons deeply and effectively and achieve high-precision typhoon intensity estimation. This model has positive research value and application prospect for the quantitative estimation of typhoon intensity.关键词：remote sensing;sea surface temperature retrieval;GF-5;thermal infrared remote sensing;two-channel split-window algorithm;three-channel split-window algorithm;four-channel split-window algorithm- 675
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发布时间：2021-10-11
Chinese Satellites
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摘要：Sea ice motion derived from remote sensing is a key parameter for sea ice. Systematic comparison and assessment of satellite remote sensing ice motion products are still deficient because of limited data length.This study examines the performance of remote sensing ice velocity products with different time intervals in the central Arctic.The region around Fram Straitare systematically evaluated by employing the International Arctic Buoy Programme(IABP) buoy data from 2009 to 2017.The results show that for the central Arctic, the NSIDC ice velocity with one-day interval hasgreater error in winter than that in summer.In winter, the western icemotion in the southern part of the Beaufort Seais overestimated,whereasthe transpolar drift stream from the Kara Sea to the northern armof Greenland is underestimated. For five ice motion products with two-day interval, the accuracy does not depend completely on resolutions of source data.The improvement of the ice motion retrieval algorithm and the merging method can also improve the accuracy of the ice velocity.The order of the root mean square errors (RMSE) is OSISAF-Merged <OSISAF-AMSR<Ifremer-AMSR< OSISAF-SSM<OSISAF-ASCAT.The RMSEs of ice motion products with three-day interval, which employ the same retrieval algorithm, aredependent on the spatial resolution of the source data. Ifremer-AMSR has the lowest RMSEbecause of its source data has high spatial resolution. Ice velocity products with three-day interval canneglect sea ice movement in a short-time scale.The RMSE of ice velocity acquired is lower than that of thetwo-day interval. In Fram Strait, the other ice velocity products except for Ifremer-AMSR have large meridionalbiases and RMSEs.Moreover, theice velocity is fast in the strait, andthe RMSE of the ice velocity product depends on the resolution of the source data.关键词：Arctic;ice drift buoy;sea ice motion;assessment of remote sensing products;Fram Strait- 711
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发布时间：2021-10-11 -
摘要：The study selected Qingdao Jiaozhou Bay Bridge as the study object and gathered information on deep mining based on InSAR deformation data acquired from Jan 2014 to May 2016.Two new deformation models with seasonal variation and temperature variation were first introduced in data analysis. These two models and the traditional linear model were utilized to decompose the deformation evolution data. Thereafter, the performance of the different models was evaluated by calculating the deviation component among the three models and the measured data. After analyzing the data of a section of the bridge, the deformation information along the longitudinal direction was acquired. The deformation characteristics of the PS points on both sides of the expansion joint and bridge structure were discussed.Results were used to confirm that the periodic deformation component was the main deformation component on the bridge, thus validating the performance of the Linear–Periodical model as the best among the models. Meanwhile, the thermal effect of the bridge panels caused the difference in the deformation between the two sides of the expansion joint.The analysis of measured data confirms that InSAR technology has the capacity to monitor the microdeformation information of the bridge. In the future, it can identify the bridge with deformation risk early, and search the bridge with risk and its corresponding area in advance. At the same time, the measurement data could also be used for risk cause analysis. Finally, it can provide technical support for urban bridge risk management.关键词：deformation monitoring;InSAR;bridge deformation analysis;Jiaozhou bay bridge;deformation decomposition;information analysis- 712
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发布时间：2021-10-11
Doctoral Forum
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摘要：Caragana microphylla is the most representative plant in the shrub-encroached grassland of Inner Mongolia. Accurately estimating the aboveground biomass of Caragana microphylla is critical to study the shrub-encroached grassland ecosystem function further and to monitor the degree of shrub encroachment. Terrestrial Laser Scanning (TLS) can accurately estimate shrub volume by acquiring high-density point cloud data. It is widely used to measure shrub biomass. However, this scanning method has not been effectively applied in shrub-encroached grassland. In this study, the field-measured TLS point cloud data and the shrub biomass of 42 shrubs were first obtained in five plots (10 m × 10 m) from the shrub-encroached grassland vegetation restoration experimental area of the Chinese Academy of Sciences. The volume of shrub was then calculated using the method of global convex hull, convex hull by slices, volume calculation by sections, volumetric surface differencing, and voxels. The regression analysis was also carried out to predict biomass. Finally, the accuracy of biomass estimation models established by the five methods was compared and analyzed by leave-one-out cross validation. Results showed that TLS can achieve the rapid and accurate inversion of Caragana microphylla biomass without destroying vegetation, which is a reliable alternative technology for traditional field investigation methods. The five methods used in the study were able to estimate biomass effectively. Compared with the global convex hull (R2=0.87, p<0.001, RMSE=30.50 g), the convex hull by slices (R2=0.89, p<0.001, RMSE=28.01 g) and the volume calculation by sections (R2=0.88, p<0.001, RMSE=29.03 g) could effectively reduce the overestimation of the volume caused by outliers, thus improving the accuracy of biomass estimation. The volumetric surface differencing fitted best with the measured biomass (R2=0.89, p<0.001, RMSE=28.89 g) when the grid size is 3 cm. The standard deviation of height was selected as the optimal height metric of biomass prediction for Caragana microphylla. The method that used voxels explained 90% of the variation in biomass estimates (R2=0.90, p<0.001, RMSE=26.28 g). Thus, it was the best model for the biomass inversion of Caragana microphylla.关键词：remote sensing;terrestrial laser scanning;shrub encroachment;Caragana microphylla;biomass;volume- 729
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发布时间：2021-10-11 -
摘要：The Tibetan Plateau (TP) area is recognized as the “Water Tower of Asia” owing to its significant climatic and hydrological characteristics. However, land surface freeze/thaw transition can hardly be detected in this area because of its harsh and complex geographical environment. This study intends to establish an algorithm to identify near-surface freeze/thaw state using the AMSR-2 satellite data, including discrimination function and seasonal threshold. The 6.925 GHz horizontal polarization Quasi-emissivity with high sensitivity to near-surface freeze/thaw cycle should replace the relative Frost Factor (FFrel). To minimize the effect of small-scale threshold selection, Min-Max normalization can be replaced with a new normalization method, namely, standard deviation normalization method. The parameterization of the discrimination function algorithm to the TP area is proposed to improve algorithm accuracy using four soil moisture and temperature dense observation network data.Results reveal that the classification accuracy of the discriminant function algorithm exhibits the most advantage during ascend period. In addition, this algorithm reduces misclassification points due to complex changes of surface emissivity during summer. The seasonal threshold algorithm based on the standard deviation normalization method shows optimum performance during descend period. Moreover, the amplitude of surface emissivity (initial liquid water content) exhibits an important influence on the algorithm accuracy.关键词：remote sensing;Tibetan Plateau;soil freeze/thaw;microwave remote sensing;AMSR-2;discriminant function algorithm;seasonal threshold algorithm- 706
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发布时间：2021-10-11 -
摘要：With the deepening research of global change, it is increasingly recognized that at the regional scale, changes in land use/land cover caused by human activities such as deforestation, urban expansion, and farmland reclamation affected the land surface characteristics and thus play a dominant factor in land surface radiative budget and energy balance, which is a key link in understanding the impact of human activities on global climate change.Based on the 30 m Globe Land 30 dataset and 1000 m MODIS albedo products, this study quantitatively analyzed the spatial variations of surface albedo at interannual and seasonal scales, and compared its characteristics between farmland, forest, grassland and artificial surfaces in different climate zones among China, the United States, India, and Brazil. Then the changes in albedo due to reclamation and urbanization were further simulated.Results show that (1) the interannual surface albedo from 2000 to 2015 showed a slight decline in India and a weak increase in Brazil, which had a obvious spatial heterogeneity in China and the United States. The arid and semi-arid zones in China and humid zones in middle and low latitudes of the United States showed decline trends, however the humid subtropical zones in China and the microthermal as well as the arid zones in the United States were opposite. (2) Under the snow-free condition, the interannual surface albedo of the farmland, forest, grassland and artificial surfaces have country differences. The surface albedo of the four types were high in summer and low in winter, and were obviously higher in arid and semi-arid zones than in humid zones. The albedo of humid subtropical zones in China has increased which is contrary to the arid and semiarid zones. Except for the farmland in arid zones in the United States that showed a relatively strong interannual trend, others generally declined. All types have decreased in India, however raised in Brazil. (3) Compared to the snow-free condition, when under the snow condition, the surface albedo of the four land cover types have raised. The albedo changes in forest are 0.06 to 0.26, which is the smallest, and 0.17 to 0.38 in cultivated land, which is the highest. And the albedo changes of forest in China is higher than that in the United States. (4) Reclamation and urbanization on forest will increase the albedo in all months, which will be more aggravated in arid zones than in humid zones. Reclamation on grassland will increase the albedo in India, Brazil, humid subtropical zones in China, and the United States. But the changes due to urbanization further demonstrate the variations between countries and zones that impacted by the original land cover types, seasons and background climate.Therefore, even under the same climatic background, the variation of surface albedo has different directions and magnitudes in different countries. The spatial heterogeneity of albedo changes can reflect the differences in natural geographic background and can also reflect the differentiation in human disturbances such as different land use patterns and eco-environment policies which may helps the further analysis and understanding of the driving mechanism of the impacts on land surface characteristics caused by land use/land cover changes.关键词：remote sensing;land cover changes;surface albedo;inter-national comparison;climate zone differentiation;spatial variations- 581
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发布时间：2021-10-11
Remote Sensing Applications
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