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  • Cartography and geographic base data  (47)
  • 11
    Online Resource
    Online Resource
    Mapping shrub cover in grasslands with an object-based approach and investigating the connection to topo-edaphic factors
    Informa UK Limited ; 2024
    In:  Geocarto International Vol. 37, No. 27 ( 2024-02-20), p. 16926-16950
    Details
    In: Geocarto International, Informa UK Limited, Vol. 37, No. 27 ( 2024-02-20), p. 16926-16950
    Type of Medium: Online Resource
    ISSN: 1010-6049 , 1752-0762
    URL: Article
    DOI: 10.1080/10106049.2022.2120549
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2024
    detail.hit.zdb_id: 2109550-4
    SSG: 14
    SSG: 14,1
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  • 12
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    Spatio-Temporal Series Remote Sensing Image Prediction Based on Multi-Dictionary Bayesian Fusion
    MDPI AG ; 2017
    In:  ISPRS International Journal of Geo-Information Vol. 6, No. 11 ( 2017-11-21), p. 374-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 6, No. 11 ( 2017-11-21), p. 374-
    Abstract: Contradictions in spatial resolution and temporal coverage emerge from earth observation remote sensing images due to limitations in technology and cost. Therefore, how to combine remote sensing images with low spatial yet high temporal resolution as well as those with high spatial yet low temporal resolution to construct images with both high spatial resolution and high temporal coverage has become an important problem called spatio-temporal fusion problem in both research and practice. A Multi-Dictionary Bayesian Spatio-Temporal Reflectance Fusion Model (MDBFM) has been proposed in this paper. First, multiple dictionaries from regions of different classes are trained. Second, a Bayesian framework is constructed to solve the dictionary selection problem. A pixel-dictionary likehood function and a dictionary-dictionary prior function are constructed under the Bayesian framework. Third, remote sensing images before and after the middle moment are combined to predict images at the middle moment. Diverse shapes and textures information is learned from different landscapes in multi-dictionary learning to help dictionaries capture the distinctions between regions. The Bayesian framework makes full use of the priori information while the input image is classified. The experiments with one simulated dataset and two satellite datasets validate that the MDBFM is highly effective in both subjective and objective evaluation indexes. The results of MDBFM show more precise details and have a higher similarity with real images when dealing with both type changes and phenology changes.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi6110374
    Language: English
    Publisher: MDPI AG
    Publication Date: 2017
    detail.hit.zdb_id: 2655790-3
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  • 13
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    An Automated Processing Algorithm for Flat Areas Resulting from DEM Filling and Interpolation
    MDPI AG ; 2017
    In:  ISPRS International Journal of Geo-Information Vol. 6, No. 11 ( 2017-11-21), p. 376-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 6, No. 11 ( 2017-11-21), p. 376-
    Abstract: Correction of digital elevation models (DEMs) for flat areas is a critical process for hydrological analyses and modeling, such as the determination of flow directions and accumulations, and the delineation of drainage networks and sub-basins. In this study, a new algorithm is proposed for flat correction/removal. It uses the puddle delineation (PD) program to identify depressions (including their centers and overflow/spilling thresholds), compute topographic characteristics, and further fill the depressions. Three different levels of elevation increments are used for flat correction. The first and second level of increments create flows toward the thresholds and centers of the filled depressions or flats, while the third level of small random increments is introduced to cope with multiple threshold conditions. A set of artificial surfaces and two real-world landscapes were selected to test the new algorithm. The results showed that the proposed method was not limited by the shapes, the number of thresholds, and the surrounding topographic conditions of flat areas. Compared with the traditional methods, the new algorithm simplified the flat correction procedure and reduced the final elevation increments by 5.71–33.33%. This can be used to effectively remove/correct topographic flats and create flat-free DEMs.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi6110376
    Language: English
    Publisher: MDPI AG
    Publication Date: 2017
    detail.hit.zdb_id: 2655790-3
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  • 14
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    QRB-tree Indexing: Optimized Spatial Index Expanding upon the QR-tree Index
    MDPI AG ; 2021
    In:  ISPRS International Journal of Geo-Information Vol. 10, No. 11 ( 2021-10-27), p. 727-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 10, No. 11 ( 2021-10-27), p. 727-
    Abstract: Support for region queries is crucial in geographic information systems, which process exact queries through spatial indexing to filter features and subsequently refine the selection. Although the filtering step has been extensively studied, the refinement step has received little attention. This research builds upon the QR-tree index, which decomposes space into hierarchical grids, registers features to the grids, and builds an R-tree for each grid, to develop a new QRB-tree index with two levels of optimization. In the first level, a bucket is introduced in every grid in the QR-tree index to accelerate the loading and search steps of a query region for the grids within the query region. In the second level, the number of candidate features to be eliminated is reduced by limiting the features to those registered to the grids covering the corners of the query region. Subsequently, an approach for determining the maximal grid level, which significantly affects the performance of the QR-tree index, is proposed. Direct comparisons of time costs with the QR-tree index and geohash index show that the QRB-tree index outperforms the other two approaches for rough queries in large query regions and exact queries in all cases.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi10110727
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
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  • 15
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    A Spatio-Temporal Dynamic Visualization Method of Time-Varying Wind Fields Based on Particle System
    MDPI AG ; 2023
    In:  ISPRS International Journal of Geo-Information Vol. 12, No. 4 ( 2023-03-29), p. 146-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 12, No. 4 ( 2023-03-29), p. 146-
    Abstract: The particle system is widely used in vector field feature visualization due to its dynamics and simulation. However, there are some defects of the vector field visualization method based on the Euler fields, such as unclear feature expression and discontinuous temporal expression, so the method cannot effectively express the characteristics of wind field on the temporal scale. We propose a Lagrangian visualization method based on spatio-temporal interpolation to solve these problems, which realizes the fusion and expression of the particle system and the time-varying wind data based on the WebGL shader. Firstly, the linear interpolation algorithm is used to interpolate to obtain continuous and dense wind field data according to the wind field data at adjacent moments. Then, we introduce the Lagrangian analysis method to study the structure of the wind field and optimize the visualization effect of the particle system based on Runge–Kutta algorithms. Finally, we adopt the nonlinear color mapping method with double standard deviation (2SD) to improve the expression effect of wind field features. The experimental results indicate that the wind visualization achieves a comprehensive visual effect and the rendering frame rate is greater than 45. The methods can render the particles smoothly with stable and outstanding uniformity when expressing continuous spatio-temporal dynamic visualization characteristics of the wind field.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi12040146
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2655790-3
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  • 16
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    A Postearthquake Multiple Scene Recognition Model Based on Classical SSD Method and Transfer Learning
    MDPI AG ; 2020
    In:  ISPRS International Journal of Geo-Information Vol. 9, No. 4 ( 2020-04-11), p. 238-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 9, No. 4 ( 2020-04-11), p. 238-
    Abstract: The recognition of postearthquake scenes plays an important role in postearthquake rescue and reconstruction. To overcome the over-reliance on expert visual interpretation and the poor recognition performance of traditional machine learning in postearthquake scene recognition, this paper proposes a postearthquake multiple scene recognition (PEMSR) model based on the classical deep learning Single Shot MultiBox Detector (SSD) method. In this paper, a labeled postearthquake scenes dataset is constructed by segmenting acquired remote sensing images, which are classified into six categories: landslide, houses, ruins, trees, clogged and ponding. Due to the insufficiency and imbalance of the original dataset, transfer learning and a data augmentation and balancing strategy are utilized in the PEMSR model. To evaluate the PEMSR model, the evaluation metrics of precision, recall and F1 score are used in the experiment. Multiple experimental test results demonstrate that the PEMSR model shows a stronger performance in postearthquake scene recognition. The PEMSR model improves the detection accuracy of each scene compared with SSD by transfer learning and data augmentation strategy. In addition, the average detection time of the PEMSR model only needs 0.4565s, which is far less than the 8.3472s of the traditional Histogram of Oriented Gradient + Support Vector Machine (HOG+SVM) method.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi9040238
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2655790-3
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  • 17
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    Characterizing the Up-To-Date Land-Use and Land-Cover Change in Xiong’an New Area from 2017 to 2020 Using the Multi-Temporal Sentinel-2 Images on Google Earth Engine
    MDPI AG ; 2021
    In:  ISPRS International Journal of Geo-Information Vol. 10, No. 7 ( 2021-07-07), p. 464-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 10, No. 7 ( 2021-07-07), p. 464-
    Abstract: Land use and land cover (LULC) are fundamental units of human activities. Therefore, it is of significance to accurately and in a timely manner obtain the LULC maps where dramatic LULC changes are undergoing. Since 2017 April, a new state-level area, Xiong’an New Area, was established in China. In order to better characterize the LULC changes in Xiong’an New Area, this study makes full use of the multi-temporal 10-m Sentinel-2 images, the cloud-computing Google Earth Engine (GEE) platform, and the powerful classification capability of random forest (RF) models to generate the continuous LULC maps from 2017 to 2020. To do so, a novel multiple RF-based classification framework is adopted by outputting the classification probability based on each monthly composite and aggregating the multiple probability maps to generate the final classification map. Based on the obtained LULC maps, this study analyzes the spatio-temporal changes of LULC types in the last four years and the different change patterns in three counties. Experimental results indicate that the derived LULC maps achieve high accuracy for each year, with the overall accuracy and Kappa values no less than 0.95. It is also found that the changed areas account for nearly 36%, and the dry farmland, impervious surface, and other land-cover types have changed dramatically and present varying change patterns in three counties, which might be caused by the latest planning of Xiong’an New Area. The obtained 10-m four-year LULC maps in this study are supposed to provide some valuable information on the monitoring and understanding of what kinds of LULC changes have taken place in Xiong’an New Area.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi10070464
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2655790-3
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  • 18
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    How Has the Recent Climate Change Affected the Spatiotemporal Variation of Reference Evapotranspiration in a Climate Transitional Zone of Eastern China?
    MDPI AG ; 2022
    In:  ISPRS International Journal of Geo-Information Vol. 11, No. 5 ( 2022-05-06), p. 300-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 11, No. 5 ( 2022-05-06), p. 300-
    Abstract: Reference evapotranspiration (ET0) is essential for agricultural production and crop water management. The recent climate change affecting the spatiotemporal variation of ET0 in eastern China continues to still be less understood. For this purpose, the latest observed data from 77 meteorological stations in Anhui province were utilized to determine the spatiotemporal variations of ET0 by the use of the Penman–Monteith FAO 56 (PMF-56) model. Furthermore, the Theil–Sen estimator and the Mann–Kendall (M–K) test were adopted to analyze the trends of ET0 and meteorological factors. Moreover, the differential method was employed to explore the sensitivity of ET0 to meteorological factors and the contributions of meteorological factors to ET0 trends. Results show that the ET0 decreased significantly before 1990, and then increased slowly. The ET0 is commonly higher in the north and lower in the south. ET0 is most sensitive to relative humidity (RH), except in summer. However, in summer, net radiation (Rn) is the most sensitive factor. During 1961–1990, Rn was the leading factor annually, during the growing season and summer, while wind speed (u2) played a leading role in others. All meteorological factors provide negative contributions to ET0 trends, which ultimately lead to decreasing ET0 trends. During 1991–2019, the leading factor of ET0 trends changed to the mean temperature (Ta) annually, during the growing season, spring and summer, and then to Rn in others. Overall, the negative contributions from u2 and Rn cannot offset the positive contributions from Ta and RH, which ultimately lead to slow upward ET0 trends. The dramatic drop in the amount of u2 that contributes to the changes in ET0 in Region III is also worth noting.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi11050300
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2655790-3
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  • 19
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    Correction: Li et al. How Has the Recent Climate Change Affected the Spatiotemporal Variation of Reference Evapotranspiration in a Climate Transitional Zone of Eastern China? ISPRS Int. J. Geo-Inf. 2022, 11, 300
    MDPI AG ; 2023
    In:  ISPRS International Journal of Geo-Information Vol. 12, No. 3 ( 2023-03-08), p. 114-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 12, No. 3 ( 2023-03-08), p. 114-
    Abstract: In the published publication [...]
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi12030114
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2655790-3
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  • 20
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    Forecasting of Short-Term Daily Tourist Flow Based on Seasonal Clustering Method and PSO-LSSVM
    MDPI AG ; 2020
    In:  ISPRS International Journal of Geo-Information Vol. 9, No. 11 ( 2020-11-13), p. 676-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 9, No. 11 ( 2020-11-13), p. 676-
    Abstract: The accurate prediction of tourist flow is essential to appropriately prepare tourist attractions and inform the decisions of tourism companies. However, tourist flow in scenic spots is a dynamic trend with daily changes, and specialized methods are necessary to measure it accurately. For this purpose, a tourist flow forecasting method is proposed in this research based on seasonal clustering. The experiment employs the K-means algorithm considering seasonal variations and the particle swarm optimization-least squares support vector machine (PSO-LSSVM) algorithm to forecast the tourist flow in scenic spots. The LSSVM is also used to compare the performance of the proposed model with that of the existing ones. Experiments based on a dataset comprising the daily tourist data for Mountain Huangshan during the period between 2014 and 2017 are conducted. Our results show that seasonal clustering is an effective method to improve tourist flow prediction, besides, the accuracy of daily tourist flow prediction is significantly improved by nearly 3 percent based on the hybrid optimized model combining seasonal clustering. Compared with other algorithms which provide predictions at monthly intervals, the method proposed in this research can provide more timely analysis and guide professionals in the tourism industry towards better daily management.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi9110676
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2655790-3
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