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  • Cartography and geographic base data  (5)
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  • Cartography and geographic base data  (5)
  • 1
    Online Resource
    Online Resource
    Evaluating Natural Ecological Land Change in Function-Oriented Planning Regions Using the National Land Use Survey Data from 2009 to 2018 in China
    MDPI AG ; 2021
    In:  ISPRS International Journal of Geo-Information Vol. 10, No. 3 ( 2021-03-16), p. 172-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 10, No. 3 ( 2021-03-16), p. 172-
    Abstract: The natural ecological lands, such as forest land, grassland, wetland, etc., constitute the most important factor for maintaining and preserving the earth’s ecosystem, which must be well concerned in the regional function-oriented planning for the sustainability of human economic development. We analyzed and evaluated the change of natural ecological land in the function-oriented planning regions where we applied the major function-oriented zones introduced as a new concept in China. Using the land-use data from 2009 to 2018 that were produced by the National Land Use Survey, we re-classified natural ecological land types into the forest, grassland, wetland, and bare land, and then addressed the changes of natural ecological land types from 2009 to 2018 in the major function-oriented zones. As a result, the area of natural ecological lands generally tended to decrease from 2009 to 2018, while the decreasing trend of natural ecological land areas was controlled after 2015 with the implementation of governmental policies for environment protection and eco-logical projects. Especially, the decrease of forest land area significantly tended to be zero in 2018 in optimal development zones. The decreased areas of natural ecological lands were mostly converted from artificial land from 2008 to 2019. On the other side, the forest lands mostly changed from cropland and grassland in key development zones, agricultural production zones, and key ecological function zones, due to the fact that grassland conversed in afforestation during this period. The evaluation of natural ecological land changes, which could be implemented by using the annual updates of national land-use data in China, is significant to support the government’s spatial regulation design, to reshape the planned regions, and make policies for environmental restoration and protection management.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi10030172
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2655790-3
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    Online Resource
  • 2
    Online Resource
    Online Resource
    A Framework of Spatio-Temporal Fusion Algorithm Selection for Landsat NDVI Time Series Construction
    MDPI AG ; 2020
    In:  ISPRS International Journal of Geo-Information Vol. 9, No. 11 ( 2020-11-04), p. 665-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 9, No. 11 ( 2020-11-04), p. 665-
    Abstract: Spatio-temporal fusion algorithms dramatically enhance the application of the Landsat time series. However, each spatio-temporal fusion algorithm has its pros and cons of heterogeneous land cover performance, the minimal number of input image pairs, and its efficiency. This study aimed to answer: (1) how to determine the adaptability of the spatio-temporal fusion algorithm for predicting images in prediction date and (2) whether the Landsat normalized difference vegetation index (NDVI) time series would benefit from the interpolation with images fused from multiple spatio-temporal fusion algorithms. Thus, we supposed a linear relationship existed between the fusion accuracy and spatial and temporal variance. Taking the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM) as basic algorithms, a framework was designed to screen a spatio-temporal fusion algorithm for the Landsat NDVI time series construction. The screening rule was designed by fitting the linear relationship between the spatial and temporal variance and fusion algorithm accuracy, and then the fitted relationship was combined with the graded accuracy selecting rule (R2) to select the fusion algorithm. The results indicated that the constructed Landsat NDVI time series by this paper proposed framework exhibited the highest overall accuracy (88.18%), and lowest omission (1.82%) and commission errors (10.00%) in land cover change detection compared with the moderate resolution imaging spectroradiometer (MODIS) NDVI time series and the NDVI time series constructed by a single STARFM or ESTARFM. Phenological stability analysis demonstrated that the Landsat NDVI time series established by multiple spatio-temporal algorithms could effectively avoid phenological fluctuations in the time series constructed by a single fusion algorithm. We believe that this framework can help improve the quality of the Landsat NDVI time series and fulfill the gap between near real-time environmental monitoring mandates and data-scarcity reality.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi9110665
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2655790-3
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    Online Resource
  • 3
    Online Resource
    Online Resource
    Characterization of spatio-temporal patterns of grassland utilization intensity in the Selinco watershed of the Qinghai-Tibetan Plateau from 2001 to 2019 based on multisource remote sensing and artificial intelligence algorithms
    Informa UK Limited ; 2022
    In:  GIScience & Remote Sensing Vol. 59, No. 1 ( 2022-12-31), p. 2217-2246
    Details
    In: GIScience & Remote Sensing, Informa UK Limited, Vol. 59, No. 1 ( 2022-12-31), p. 2217-2246
    Type of Medium: Online Resource
    ISSN: 1548-1603 , 1943-7226
    URL: Article
    DOI: 10.1080/15481603.2022.2153447
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2209042-3
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  • 4
    Online Resource
    Online Resource
    A Multi-Scale Water Extraction Convolutional Neural Network (MWEN) Method for GaoFen-1 Remote Sensing Images
    MDPI AG ; 2020
    In:  ISPRS International Journal of Geo-Information Vol. 9, No. 4 ( 2020-03-25), p. 189-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 9, No. 4 ( 2020-03-25), p. 189-
    Abstract: Automatic water body extraction method is important for monitoring floods, droughts, and water resources. In this study, a new semantic segmentation convolutional neural network named the multi-scale water extraction convolutional neural network (MWEN) is proposed to automatically extract water bodies from GaoFen-1 (GF-1) remote sensing images. Three convolutional neural networks for semantic segmentation (fully convolutional network (FCN), Unet, and Deeplab V3+) are employed to compare with the water bodies extraction performance of MWEN. Visual comparison and five evaluation metrics are used to evaluate the performance of these convolutional neural networks (CNNs). The results show the following. (1) The results of water body extraction in multiple scenes using the MWEN are better than those of the other comparison methods based on the indicators. (2) The MWEN method has the capability to accurately extract various types of water bodies, such as urban water bodies, open ponds, and plateau lakes. (3) By fusing features extracted at different scales, the MWEN has the capability to extract water bodies with different sizes and suppress noise, such as building shadows and highways. Therefore, MWEN is a robust water extraction algorithm for GaoFen-1 satellite images and has the potential to conduct water body mapping with multisource high-resolution satellite remote sensing data.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi9040189
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2655790-3
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    Online Resource
  • 5
    Online Resource
    Online Resource
    A Knowledge-Driven Geospatially Enabled Framework for Geological Big Data
    MDPI AG ; 2017
    In:  ISPRS International Journal of Geo-Information Vol. 6, No. 6 ( 2017-06-03), p. 166-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 6, No. 6 ( 2017-06-03), p. 166-
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi6060166
    Language: English
    Publisher: MDPI AG
    Publication Date: 2017
    detail.hit.zdb_id: 2655790-3
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