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  • 2022 Volume 24 Issue 2
    Published: 25 February 2022
      

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  • XIE Hualin, WEN Jiaming, CHEN Qianru, HE Yafen
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    Territorial spatial planning is the spatial blueprint of high-quality social and economic development. With the rapid development of geo-information science and technology, geo-information science and tech- nology has changed the way of territorial spatial planning. Its powerful capability in data acquisition, analysis, prediction, and management provides support in data, method, and platform for territorial spatial planning, thus enabling territorial spatial planning to be more scientific, operable, and forward-looking. Based on literature review, summary, and comparative analysis, this study analyzes the technical requirements of territorial spatial planning compilation, implementation, supervision, public participation, and intelligent transformation, and systematically expounded the application of geo-information science and technology in territorial spatial planning. This study expounds the contributions of geo-information science on China's territorial spatial planning from the following three aspects: (1) Geospatial data, remote sensing data, and socio-economic big data provide data basis for territorial spatial planning; (2) Geographic Information System (GIS) analysis method, geographic simulation method, and artificial intelligence method provide method support for territorial spatial planning; (3) The application of GIS platform, cloud computing, and urban intelligent platform promotes the intelligent transformation of territorial spatial planning. This study also points out shortages of different technologies. However, there are still some problems that need to be further explored: (1) The generation of socio-economic big data and its application scenarios in territorial spatial planning are concentrated in urban space; (2) Both traditional and modern technologies in territorial spatial planning have advantages and disadvantages. These technologies need to be effectively integrated to prepare more scientific territorial spatial planning; (3) The construction of territorial spatial planning platform has not been organically combined with the construction of City Information Modeling (CIM) and other intelligent society platforms, there is a huge space for mining in the future. According to the maturity of its application in territorial spatial planning, these technologies can be divided into mature technology and promising technology. With the promulgation of territorial spatial planning at all levels and types and the initial establishment of Chinese territorial spatial planning system in 2021, attention should be paid to the application of intelligent planning methods in agricultural space and ecological space, technical system construction of intelligent territorial spatial planning, and the improvement of territorial spatial planning intellectualization.

  • LIU Linlin, ZHENG Bohong, LUO Chen
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    For the current territory development planning in China, the Ministry of Natural Resources has put forward a method to evaluate the accessibility of urban centers based on isochrone maps. The use of dynamic traffic data in isochrone maps studies is becoming more and more recurrent, but comparative analyses between dynamic and static data are still rare. In this paper, Nanchang city is taken as a case study to generate the urban center isochrone maps using static and dynamic traffic data. The city is divided into 500 m×500 m grids, with each grid center point representing a given destination while Bayi Square and Greenland Central Square are set as origins. Using the above origins and destinations, the dynamic data were obtained daily from the Baidu open map platform at 15:00 and at 18:00 over nine days-time (Saturday-next Sunday). Subsequently, the confusion matrix and Kappa coefficient are used to test the consistency between the isochrone maps generated by the two datasets. The results suggest that most of Nanchang urban central areas are within a 60 min-circle and most of Nanchang's urban areas are within a 120 min-circle, when taking Bayi Square or Greenland Central Square as the origin. The isochrone maps generated by the static data has just a fair consistency with those generated by the dynamic data at evening peak time on workdays. Within the urban central areas, the isochrone maps generated by the static data have reached a substantial consistency with those generated by the dynamic data at off-peak time on workdays, indicating that the static data is more suitable for evaluating the urban center accessibility at off-peak time on workdays. Besides, the dynamic data can display the temporal characteristics of the isochrone maps. The isochrone maps of the dynamic data at 4 time-points show that the urban center accessibility at 15:00 on workdays is significantly better than others. But the proportions of isochrone surfaces to the total urban areas are found to increase with the drivetime, and their growth curves are in accordance with the trend of the Logistic curve. The key time nodes of each growth curve can provide more targeted division thresholds for isochrone maps. This research highlights the accuracy of the isochrone maps generated by the dynamic data and explores the applicability of the static data. The research also shows that using the key time nodes of the Logistic curve contributes to a more reasonable subdivision of the isochrone map.

  • YUAN Yuan, MAO Lei, LI Hongqing, ZHAO Xiaofeng
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    Information empowerment to the territorial spatial planning has become a hot research field in the new era. However, research on territorial utilization evaluation using big data integration remains to be explored. The purpose of this paper is to carry out an empirical study on the efficiency assessment of residential land use in new urban area employing Tencent location-based big data. Assessment index of residential land use efficiency in each residential area have been proposed, supported by integration of multi-source geospatial data, to reveal the differences in land use efficiency among different residential areas in Changzhou city. The results show that, firstly, population size of hourly particle statistics within the residential area fluctuates periodically, reaching peak value at 21:00 generally, which is in line with the routine of daily going out and returning home for urban residents. There are also expected differences in population agglomeration degree and population size among residential buildings with different capacity rates. Secondly, the 29 residential areas are divided into five groups by year of construction, 1980s, 1990s, 2000s, 2010—2015, and post-2015. The average population size of efficiency index of group 1980s, 1990s, 2000s, 2010—2015, and post-2015 are 1.74, 2.45, 2.31, 0.95, and 0.91 per 100 m2, respectively. Index values of residential areas built before 2010 are significantly higher than those built after 2010. Furthermore, residential areas built after 2010 are lower than the average level (population size of 2.06 per 100 m2in year 2018) of the entire urban residential areas. Thirdly, it is suggested that lower results of efficiency index is not fully equal to poor level of intensive land use. The main reasons of diverse land use efficiency of residential areas constructed in different periods include the growth periodicity of new urban area development in Changzhou city, and urban residents' desire for better living environment to enhance their quality of habitation. Research shows that location-based big data, as a source of population data with high solution, could reflect the temporal and spatial characteristics of resident aggregations objectively. Index constructed to assess urban residential land efficiency using location-based big data is both innovative and scientific, which could provide a new way for the analysis of high-quality land space utilization. In conclusion, regularity recognition of behavior characteristics from urban residents can provide support for spatial policy formulation during the urbanization process based on "putting people first" policy in China. What's more, new data sources, represented by location-based big data in this paper, will play an important role in decision-making mechanism of territorial spatial planning.

  • HUANG Sheng, LI Weijiang, ZHU Mengru, LIU Zhen
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    In the context of global climate change, extreme precipitation events are becoming more frequent and have an increasing impact on urban commutes. In this study, based on hourly rainfall data and metro OD passenger flow data, we use a prophet time-series model to forecast the regular values of commuting flow under rainfall events, and quantitatively assess the spatial-temporal changes of commuting flow caused by rainfall at station and OD levels. Our results show that (1) the commuting flow generally tends to decrease with increasing hourly rainfall. The fluctuation of commuting flow varies from one type of station to another. Rainfall can delay commuting departure time and lead to surge in metro flow in certain times. The higher the commuting demand for a station, the more its flow fluctuates. Flow fluctuation due to rainfall varies in different time periods. 7:00 and 17:00 show high fluctuation with more flexibility in commuting departure time, while 8:00—9:00 and 18:00—19:00 show high rigidity; (2) Rainfall can induce a significant increase in short commuting flow of less than 15 minutes, averaging to around 7.3%. In contrast, the impact on medium and long commuting flow is modest, with an overall decrease of 1.3%. Of the OD flows across various functional zones, fluctuation from residential to industrial stations is most notable during the morning commute, while less so from commercial to residential stations during the evening commute. Most of the departure stations of rainfall-sensitive metro lines during the morning commute are located around large residential areas, and around large industrial parks and commercial centers during the evening commute. Flow fluctuation in the evening commute is lower than that in the morning commute. Although total commuting flow is not significantly affected by rainfall, its surge in certain local regions and times should be highlighted. Our methodology and results will help to quantify the impact of rainfall on metro commutes and provide a basis for spatialized transport coping strategies.

  • CHEN Ting, XU Weiming, WU Sheng, LIU Jie
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    Under the background of territorial spatial planning in the new period, delimiting the urban development boundary scientifically and reasonably and establishing a sound territorial space use control system are important measures to guide all kinds of territorial space development and protection. Taking Fuzhou City as an example, this paper constructs a global multi-dimensional territorial space control system. Management and control constraints are embedded in the future land use pattern simulation. At the same time, considering the regional spatial heterogeneity and spatial-temporal dependence, this paper designs the Spatial-temporal Cellular Automata (ST-CA) model which integrates geographical partition strategy, deep learning technology, and the functional module of FLUS model to delimit the urban development boundary. Based on the existing achievements, this study integrates three zones and three lines to carry out the application research of spatial management and control under the thinking of "combination of planning and control". The results show that: (1) The ST-CA model considering regional spatial heterogeneity and spatial-temporal dependence can effectively improve the accuracy of land use change simulation and achieve a more realistic and accurate geographical simulation process. The overall accuracy of the model increased from 95.95% to 98.34%; (2) Control constraints are embedded in the process of geographical simulation, which can guide the rational layout and controllable scale of urban, agricultural, and ecological spaces. Delimitation of urban development boundary based on simulation results can effectively avoid occupation on protected land; (3) The future simulation results combined with the control early warning value show that the urban expansion situation in the main urban area and surrounding districts and counties of Fuzhou City is relatively severe. In the future, it is urgent to reasonably regulate the territorial space pattern of Fuzhou City; (4) The characteristics of boundary change trend show that the delimitation results are consistent with the long-term development planning of Fuzhou City, which is in line with regional development demands. The territorial space pattern presents a multi-axis development trend. The research results can provide scientific planning for the development and protection of territorial space and practical reference for territorial space control and optimization in Fuzhou City.

  • REN Guoping, LIU Liming, LI Hongqing, JI Xiang, YIN Gang
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    The essence of integrated territory consolidation is the process of readjusting the relationship between man and land. Under the background of rural revitalization strategy, integrated territory consolidation has been endowed with multiple functions and objectives. Pattern optimization, which contains the comprehensive process of timing arrangement, spatial organization, and goal coordination, is a way to achieve multi-objective integrated territory consolidation. This research analyzes the problem of multi-objective quantification for optimization of spatio-temporal patterns of multi-objective integrated territory consolidation. Using spatial vulnerability and timing urgency as the key points, the index system of social-ecosystem vulnerability and urgency evaluation in rural regions with target function as the main line was constructed. The EW-DEA model and improved multi-factor synthesis model of entropy weight were used to comprehensively evaluate vulnerability efficiency and urgency. Then, the hierarchical objective decomposition method and the attribution method of leading problems were used to construct four kinds of multi-objective pattern optimization of rural integrated territory consolidation. Four plans of prosperity of industrial plan, optimization of structure plan, ecological livability plan, and comprehensive rejuvenation plan, and quantitative analysis of multi-objective optimal decision were carried out using EW-TOPSIS analysis method. This research would achieve the goals of identifying and dividing the types of multi-objective pattern optimization of rural integrated territory consolidation from the perspective of limited multi-objective system selection and ranking, as well as to realize the purpose of constructing multi-objective pattern optimization system for rural integrated territory consolidation. This research took 184 administrative villages in Qingpu District of Shanghai City as examples and the following results were obtained. Firstly, EW-DEA model can effectively overcome the problem of high correlation between multiple administrative villages and improve the application accuracy of spatial suitability. EW-TOPSIS model can accurately divide the multi-objective pattern optimization scheme of rural integrated territory consolidation and refine the heterogeneity among the optimal schemes. Secondly, the mean value of social-ecosystem vulnerability of Qingpu District in the year of 2018 was shown as social subsystem vulnerability (0.605) > economic subsystem vulnerability (0.577)>ecosystem vulnerability (0.549). Vulnerability of social subsystems was an important reason for limiting vulnerability in the region. Thirdly, the vulnerability of social-ecosystem in administrative villages was increasing from central to north-south parts of the region, while the spatial agglomeration of the urgency of rural integrated territory consolidation was significant, with its threshold in [0.435, 0.785]. The urgency of agricultural industry was the maximum. Fourthly, according to the results of multi-objective decision-making evaluation of four multi-objective schemes based on vulnerability and urgency, pattern optimization types of rural integrated territory consolidation in Qingpu District can be divided into 16 types. The research results enrich the theory of rural geography and provide decision basis for rural regional governance, rural revitalization, and regional sustainable development.

  • LI Jun, LIU Juqing, YOU Lin, DONG Heng, YU Yan, ZHANG Xiaopan, ZHONG Wenjun, YANG Dianhua
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    With the rapid development of urbanization, urban construction land is becoming increasingly scarce. Therefore, as a macro-regulation policy for the intensive utilization and optimal allocation of land resources, land reserve is playing an increasingly important role. However, at present, land reserve decision-making lacks scientific basis and cannot effectively carry out resource allocation. In order to solve this problem, this paper puts forward seven intelligent decision-making models for land reserve through in-depth analysis of the basic services and decision-making processes of land reserves. The models are listed below. Firstly, Stock Land Monitoring Model based on the comprehensive quantitative evaluation method, which can dynamically monitor and discover the city stock land and then make recommendations for land reserve objects. Secondly, Land Reserve Cost Prediction Model based on the market comparison method, which can carry out a large range and efficiently predict the cost of stock land. Thirdly, Land Sale Price Prediction Model based on the Support Vector Machine (SVM), which can predict the reserve income of the land to be sold. Fourthly, Land Reserve Balance Analysis Model based on the gray forecast model, which can predict the amount of land reserve to promote coordinated regional development. Fifthly, Similar Land Query Model based on the comprehensive quantitative evaluation method, which can promote large-scale land development to form an agglomeration effect. Sixthly, Development Sequence Analysis Model based on the comprehensive quantitative evaluation method, which can optimize the spatial structure and formulate a reasonable development sequence to promote the continuous rolling of funds. Seventhly, Abnormal Land Identification Model based on spatial overlay analysis, which can improve the detection efficiency of various problematic plots. The purpose of this model set is to make the land reserve decision-making process scientific, quantitative, and model-based, which focuses on providing instructions for the overall arrangement of total land reserve, benefit, scale, structure, layout, and time sequence. In addition, through theoretical analysis and practical verification, we found that the model set has the characteristics of systematization, high efficiency, flexibility, and intelligence. It can serve the entire chain of land reserve service, meet the needs of real-time decision-making applications, and realize the independent update and evolution to ensure the timeliness of model computation. Finally, the model set has been engineered and applied to the Ningbo Land Reserve Intelligent Decision Support Platform. The effectiveness and practicality of the above decision-making models have been verified by simulating the entire land reserve decision-making processes based on this platform, indicating that the model set can provide a theoretical basis for the scientific decision-making of land reserves.

  • XI Wenqiang, DU Shihong, DU Shouji
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    The extraction and change analysis of cultivated land covers based on multi-temporal images are important means to effectively manage and protect cultivated land resources. However, as far as the classification and extraction of multi-temporal cultivated land covers are concerned, the existing methods have limitations in the comprehensive expression of spatiotemporal features and the accurate modeling of spatiotemporal context relations among geographic objects, leading to the poor extraction accuracy of cultivated land covers. In addition, for the analysis of cultivated land change, the existing methods usually only focus on the statistical areal change of cultivated land cover based on administrative units, while consideration is seldom taken into the spatial correlation distribution characteristics of changes in cultivated land covers. Accordingly, first of all, this paper proposes a multi-temporal spatiotemporal context classification method, which comprehensively expresses and utilizes the multi-temporal spectral, texture, and spatial features of geographic objects, and models the contextual relations of features and semantics among geographic objects in both spatial and temporal dimensions of multi-temporal images, so as to improve the classification accuracy of cultivated land covers. Then, based on the extracted results of cultivated land covers, spatial statistical method of Geographic Information System (GIS) is used to analyze the spatial correlation characteristics of cultivated land changes in regular grids and administrative division units. Finally, Shunyi District of Beijing is taken as the study area while multi-temporal Sentinel-2 images in 2015-2019 are used as the data sources to conduct verification of the proposed method. The results show that, compared with the two existing common multi-temporal classification methods, the proposed method achieves the highest accuracies in the classification of multi-temporal cultivated lands. The average user's accuracy and producer's accuracy reach 91.21% and 90.53%, respectively, while the overall accuracy of all categories is 90.79%, indicating that the proposed method can accurately extract the multi-temporal cultivated land cover information. Furthermore, according to the analysis of the spatial distribution characteristics of cultivated land changes, this study found a phenomenon of regional aggregation of the cultivated land change in Shunyi District from 2015 to 2019, which mainly presents the characteristics of concentrated reduction. The aggregation reduction phenomena of cultivated land covers in Zhaoquanying Town, Gaoliying Town, Mulin Town, and Yang Town are especially obvious, indicating that the problems of cultivated land encroachment and reduction are quite serious in these areas.

  • ZHANG Wenyuan, LIU Runhua, WAN Junbi, TAN Guoxin
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    In recent years, a large amount of 3D building models of Chinese ancient architecture have been created to preserve and protect cultural heritages by different organizations and persons. However, most of these existing 3D models are lack of semantic information, which are predominantly used for 3D visualization, and they cannot meet the requirements of fine management and other intelligent applications. To address this problem, the standard building model defined by City Geography Markup Language (CityGML) is extended based on the features of Chinese ancient architecture. Moreover, a novel 3D semantic modeling approach is proposed to automatically identify semantic surfaces from mesh models and generate extended CityGML model in this paper. Firstly, we summarize the common features of Chinese ancient architecture components in Ming and Qing dynasties. We develop a CityGML extension to explicitly represent the geometric and semantic information of typical components of ancient architectures using CityGML Application Domain Extensions (ADE). Bracket, beam, column, base, and other new objects are added into this designed CityGML building model. Secondly, a model transformation algorithm is proposed to generate extended CityGML building models from mesh models, in which face normal and coordinate range of each triangle face from mesh model are calculated, together with other defined shape and position rules for different components. The algorithm is able to automatically recognize different semantic surfaces from roof, wall, column, and other objects of an ancient building. Thirdly, all these extracted triangular faces are further refined via topological validation and merged into polygon geometries, so as to accurately represent an ancient building according to geometric and semantic principles of CityGML. Two public mesh models of ancient buildings with different construction structures are selected to automatically extract semantic objects and generate corresponding extended CityGML models using the proposed method. Nine kinds of semantic objects such as RoofSurface, WallSurface, and Base are successfully recognized. More than 400 planar polygons with semantic information for each CityGML building model are generated. The percentage of correct face recognition is as high as 95%. Experimental results indicate that the extended CityGML model can effectively support the explicit semantic representation of typical components for ancient architecture. Most of planar faces from these mesh models can be automatically extracted and converted into extended CityGML semantic objects using the explored geometric rules and transformation approach. Therefore, the presented algorithm is beneficial for 3D semantic modeling of Chinese ancient architectures in an automated manner. It is useful for the fine management of ancient building models as well.

  • QI Duo, MAO Zhengyuan
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    Short-term traffic flow prediction with high accuracy and efficiency plays an important role in Intelligent Transportation Systems, which is a prerequisite for traffic guidance, management, and control. Due to the time-varying and non-stationary characteristics of the dynamic change of traffic flow, it is difficult to predict traffic flow with high accuracy, which needs to be resolved urgently in the transportation field. In order to improve the accuracy and efficiency of short-term traffic flow prediction, the paper develops a short-term traffic flow predicting algorithm based on adaptive time slice and the improved KNN model (A-TS-KNN), which is then implemented successfully in short-term traffic flow predicting experiments. In the first, the Dynamic Time Warping (DTW) algorithm is used to dynamically slice the daytime sequence of traffic flow into different traffic patterns. Secondly, the mutual information method is used to solve the maximum threshold of the time delays of traffic flow at each time in different traffic patterns. Then the traffic flow state vectors of different time delays is constructed, which generates a history database of traffic flow. Thirdly, the method of ten times ten-fold cross-validation is used to solve the orthogonal error distribution of different time delays and K values of traffic flow at each time. The orthogonal result with the smallest error is selected, and the parameters combination of adaptive time delay and K value are obtained. In the end, the weighted value of the reciprocal Euclidean distance of the K most similar neighbors is used for predicting traffic flow of next time. The forecasting accuracies of the improved A-TS-KNN and other four models including K-Nearest Neighbors (KNN) model, Support Vector Regression (SVR) model, Long-Short Term Memory (LSTM) neural networks, and Gate Recurrent Unit (GRU) neural networks are compared. The experimental results indicate that the improved A-TS-KNN model is more appropriate for short-term traffic flow forecasting than the other models. In addition, the A-TS-KNN algorithm is used for short-term traffic flow predicting at other four different intersections in the urban road network of Fuzhou, which has been shown good generalization ability.

  • TIAN Xin, ZHAO Wenji
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    Nowadays, web cartography or web mapping has become an important direction for the development of web GIS. The production of thematic maps on the web is extremely important. The SLD/SE standard proposed by the OGC is currently the standard and specification of web mapping. However, SLD/SE standard is often at a fundamental level, which leads to poor quality of maps. With web cartography development, besides the SLD/SE standard, some new web styling languages are emerging. Compared with the SLD/SE standard, the emerging map styling languages have advantages in making thematic maps. This research selects Mapbox GL and CartoCSS from the existing emerging map styling languages for comparison with the SLD/SE standard, and then summarizes the defects of the SLD/SE standard in expressing thematic maps. The study found that the SLD/SE standard lacks the function of setting a classification method. Meanwhile, it also lacks the function of accurately expressing the unclassified proportional symbol map. Because of the shortcomings, this study proposed the extensions of Classify and ProportionalSymbol. Example data of Overijssel province in Netherlands were used to prove that the new extensions were feasible, while the verification data of Beijing were used to prove that the extensions were applicable to all vector data used to create thematic maps. The SLD/SE standard is still in the stage of continuous development. In future standardisation, the two extensions proposed in this research can greatly simplify the work of cartographers and improve the ability of current standard to express unclassified proportional symbol maps.

  • WANG Wenqi, LI Zongchun, FU Yongjian, XIONG Feng, ZHAO Zhaoming, HE Hua
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    The single primitive classification method is difficult to fully describe the complex scene of point cloud, and multiple primitives classification is becoming a trend. A point cloud classification method combining point, voxel, and object features is proposed in this study. This method mainly includes the following four procedures: (1) Determining the classification primitives at each level. The point primitive adopts a method of optimal neighborhood, and the voxel primitive uses octree to carry out voxel division. In the aspect of object primitive, the improved multi-factor segmentation method is used to realize the point cloud segmentation; (2) Extracting the classification features of each primitive. Firstly, the classification features of point primitive are obtained, and then the Locality-constrained Linear Coding (LLC) is carried out. Secondly, the features of Latent Dirichlet Allocation (LDA) and Max Pooling (MP) are extracted; (3) Reducing the dimension of classification features. The variable importance algorithm of random forest is used to select classification features and reduce its dimension; (4) Completing point cloud classification. The point cloud classification is achieved using random forest algorithms. Three different types of point cloud data are used for the experiment. The result shows that the classification accuracy of multiple primitives is increased by 1.43%, 7.02%, and 2.48%, respectively on the basis of the point primitive classification. The feature dimension reduction can effectively reduce the feature redundancy, and the time cost of the classifier is reduced by about 70%. Compared with other algorithms, this proposed algorithm has a higher classification accuracy and is suitable for the classification of point cloud data acquired from different scenes.

  • LIU Hengzi, Lü Ning, JIANG Hou, YAO Ling
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    Affected by cloud layer, sensor error, and other factors, there are large area of data gaps in time and space of Land Surface Temperature (LST) products acquired by Moderate-resolution Imaging Spectroradiometer (MODIS), which seriously affects the analysis and application of LST data set. In this paper, a fully automatic smoothing algorithm for filling MODIS LST data sets is introduced. Based on a Penalized Least Squares (PLS) method, the algorithm allows fast smoothing of data in one and more dimensions by means of the Discrete Cosine Transform (DCT). By optimizing smoothing parameters, the DCT-PLS algorithm is a fast multidimensional data smoothing method, which uses the spatial and temporal information of LST data set to fill data gaps. This study carries out an empirical study in Guangdong-Hong Kong-Macao Greater Bay Area (GBA). The DCT-PLS algorithm is used to fill data gaps of monthly MODIS LST data set from January 2001 to December 2017 in this area. To analyze its performance, the error analysis and accuracy verification of the algorithm are performed by introducing artificial simulation data gaps. The results of error analysis show that errors in gap filling are mainly due to the use of biased LST time information in data set, which causes significantly overestimation or underestimation in filling results. In response to this problem, an optimized DCT-PLS algorithm is proposed to automatically obtain effective auxiliary LST layers from the time series of MODIS LST and provide unbiased temporal information. Meanwhile, the three-dimensional algorithm is changed to two-dimensional space to reduce computing consumption by transforming auxiliary temporal information into spatial information. The results of accuracy verification on the optimized DCT-PLS algorithm show that the computational efficiency and accuracy are optimized simultaneously. The average computing time decreases from 12.0s to 1.7s, average R increases from 0.94 to 0.97, and average RMSE decreases from 1.94 K to 0.74 K (compared with the three-dimensional algorithm). The optimized DCT-PLS algorithm is applied in GBA. The accuracy of gap filling of monthly MODIS LST data set from January 2001 to December 2017 is R=0.98 and RMSE=0.79 K in daytime, and R=0.99 and RMSE=0.56 K in nighttime. The results of accuracy verification in GBA show that the accuracy of gap filling is not affected by heterogeneous surface. The optimized DCT-PLS algorithm transforms the MODIS LST data in the time domain and frequency domain to two-dimensional space, and retains high-frequency information to fill data gaps. It is a fast and robust method for gap filling, which is completely independent of external auxiliary data sets, enabling gap filling on long-time sequence MODIS LST data set.

  • ZHAN Qiqi, ZHAO Wei, YANG Mengjiao, FU Hao, LI Xinjuan, XIONG Donghong
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    As the political, cultural, and economic core zone of Tibet, the middle part of the Yarlung Zangbo River basin has suffered from serious land desertification for a long time, posing obvious negative impacts for the local socio-economic development and natural environmental protection. It is the basic precondition for land desertification prevention and control to obtain spatial distribution, track the status quo, and analyze dynamic development of sandy land desertification. Remote sensing images have been widely used in the dynamical monitoring of sandy land information due to its characteristics of fast, large-scale, and high precision. In order to reduce the uncertainty caused by the fragmented distribution of sandy land and the large area of sparsely vegetation surfaces in this region, this study developed an object-oriented integrated classification method, combining decision tree classifier and nearest neighbor classifier. The method is based on the Landsat cloud-free images from Google Earth Engine platform. The spectral, geometrical, and topographic features of sandy land were extracted as the inputs of the method to differentiate sandy land from other land cover types, including the sparsely vegetated surfaces with similar spectral pattern as sandy land. The results indicated that, firstly, with the validation sample data collected from the Google Earth high-resolution images and field investigation, the integrated classification method has the highest overall accuracy of 92.38 % and the Kappa coefficient of 0.82. Secondly, compared with other single classifier classification methods, such as supported vector machine, nearest neighbor, and decision tree, the integrated classification method achieved the best classification results in identifying sandy land with small area. In addition, it also reduced the confusion between sandy land and sparsely vegetated surfaces, thus increased the reliability of the classification results. Thirdly, the sandy land in the middle part of the Yarlung Zangbo River basin in 2019 was mapped based on the proposed method with an area of 299.66 km2, displaying a zonal and fragmented pattern along river valleys and concentrating on the northern bank of rivers and the regions with southern aspect, low altitude, and close to riverways. This study provides a new direction for sandy land desertification monitoring with remote sensing data, and its application can also serve the prevention and management of sandy land desertification in the middle part of the Yarlung Zangbo River basin.

  • GAO Shupeng, LIU Xiaolong, SONG Jinling, SHI Zhengtao, YANG Lei, GUO Libiao
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    As an important data source in remote sensing application, high spatiotemporal resolution NDVI time series data is of great significance for dynamic change monitoring of land cover, especially in tropical mountainous areas, where the surface elevation changes significantly, climate conditions are complex and spatiotemporally heterogeneous. Many multi-spatiotemporal data fusion models have been proposed by scholars. However, it is rare to analyze the fusion accuracy of these models and their influencing factors in tropical mountainous areas. This study takes the Naban River Watershed in the tropical mountainous area of Southwest China as the study area. Four representative models have been selected from three types of spatiotemporal data fusion methods, namely weight function-based method, Bayesian-based method, and Hybrid method. The four models are Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), Spatial and Temporal Adaptive Reflectance Fusion Model (RASTFM), and Bayesian Spatiotemporal Fusion Model (BSFM). Among them, STARFM and ESTARFM are weight function-based method, BSFM is Bayesian-based method, and RASTFM is Hybrid method. This study carries out analysis of data source selection, terrain of the study area, landscape spatial heterogeneity, pixel numerical accuracy of fusion model, and atmospheric conditions such as thin clouds and haze. The results show that, firstly, the fusion accuracy decreases with the increase of time interval and its relative variation. A better match in sensor spectrum between the two input data results in a higher fusion accuracy. OLI is better than Sentinel-2 while MODIS is better than VIIRS. Compared with unadjusted data, data adjusted by the Bidirectional Reflectance Distribution Function (BRDF) can effectively improve fusion accuracy.Secondly, fusion accuracy is negatively correlated with spatial heterogeneity. Fusion accuracy decreases when spatial heterogeneity increases. There is a strong negative correlation between fusion accuracy and spatial heterogeneity at elevations. Fusion accuracy decreases when slope increases. In comparison, slope aspect has little influence on fusion accuracy. The influence of terrain on RASTFM is smaller when compared with models. Thirdly, the more high-quality high-resolution raw data as input data for the model, the higher the fusion accuracy will be. Fourthly, thin clouds and haze have a significant negative impact on the fusion accuracy. The results have important values as references for improving the high spatial-temporal data fusion model in tropical mountainous areas and establishing high spatiotemporal resolution NDVI data sets in complex geographical environment.