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  • MDPI AG  (60)
  • Gong, Peng  (60)
  • 1
    Online Resource
    Online Resource
    An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends
    MDPI AG ; 2022
    In:  Remote Sensing Vol. 14, No. 8 ( 2022-04-13), p. 1863-
    Details
    In: Remote Sensing, MDPI AG, Vol. 14, No. 8 ( 2022-04-13), p. 1863-
    Abstract: As satellite observation technology develops and the number of Earth observation (EO) satellites increases, satellite observations have become essential to developments in the understanding of the Earth and its environment. However, the current impacts to the remote sensing community of different EO satellite data and possible future trends of EO satellite data applications have not been systematically examined. In this paper, we review the impacts of and future trends in the use of EO satellite data based on an analysis of data from 15 EO satellites whose data are widely used. Articles that reference EO satellite missions included in the Web of Science core collection for 2020 were analyzed using scientometric analysis and meta-analysis. We found the following: (1) the number of publications and citations referencing EO satellites is increasing exponentially; however, the number of articles referencing AVHRR, SPOT, and TerraSAR is tending to decrease; (2) papers related to EO satellites are concentrated in a small number of journals: 43.79% of the articles that were reviewed were published in only 13 journals; and (3) remote sensing impact factor (RSIF), a new impact index, was constructed to measure the impacts of EO satellites and to predict future trends in applications of their data. Landsat, Sentinel, MODIS, Gaofen, and WorldView were found to be the most significant current EO satellite missions and MODIS data to have the widest range of applications. Over the next five years (2021–2025), it is expected that Sentinel will become the satellite mission with the greatest influence.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs14081863
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
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  • 2
    Online Resource
    Online Resource
    Progress and Trends in the Application of Google Earth and Google Earth Engine
    MDPI AG ; 2021
    In:  Remote Sensing Vol. 13, No. 18 ( 2021-09-21), p. 3778-
    Details
    In: Remote Sensing, MDPI AG, Vol. 13, No. 18 ( 2021-09-21), p. 3778-
    Abstract: Earth system science has changed rapidly due to global environmental changes and the advent of Earth observation technology. Therefore, new tools are required to monitor, measure, analyze, evaluate, and model Earth observation data. Google Earth (GE) was officially launched by Google in 2005 as a ”geobrowser”, and Google Earth Engine (GEE) was released in 2010 as a cloud computing platform with substantial computational capabilities. The use of these two tools or platforms in various applications, particularly as used by the remote sensing community, has developed rapidly. In this paper, we reviewed the applications and trends in the use of GE and GEE by analyzing peer-reviewed articles, dating up to January 2021, in the Web of Science (WoS) core collection using scientometric analysis (i.e., by using CiteSpace) and meta-analysis. We found the following: (1) the number of articles describing the use of GE or GEE increased substantially from two in 2006 to 530 in 2020. The number of GEE articles increased much faster than those concerned with the use of GE. (2) Both GE and GEE were extensively used by the remote sensing community as multidisciplinary tools. GE articles covered a broader range of research areas (e.g., biology, education, disease and health, economic, and information science) and appeared in a broader range of journals than those concerned with the use of GEE. (3) GE and GEE shared similar keywords (e.g., “land cover”, “water”, “model”, “vegetation”, and “forest”), which indicates that their application is of great importance in certain research areas. The main difference was that articles describing the use of GE emphasized its use as a visual display platform, while those concerned with GEE placed more emphasis on big data and time-series analysis. (4) Most applications of GE and GEE were undertaken in countries, such as the United States, China, and the United Kingdom. (5) GEE is an important tool for analysis, whereas GE is used as an auxiliary tool for visualization. Finally, in this paper, the merits and limitations of GE and GEE, and recommendations for further improvements, are summarized from an Earth system science perspective.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs13183778
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
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  • 3
    Online Resource
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    Grid-Based Essential Urban Land Use Classification: A Data and Model Driven Mapping Framework in Xiamen City
    MDPI AG ; 2022
    In:  Remote Sensing Vol. 14, No. 23 ( 2022-12-03), p. 6143-
    Details
    In: Remote Sensing, MDPI AG, Vol. 14, No. 23 ( 2022-12-03), p. 6143-
    Abstract: Accurate and timely mapping of essential urban land use categories (EULUC) is vital to understanding urban land use distribution, pattern, and composition. Recent advances in leveraging big open data and machine learning algorithms have demonstrated the possibility of large-scale mapping of EULUC in a new cost-effective way. However, they are still limited by the transferability of samples, models, and classification results across space, particularly across different cities. Given the heterogeneities of environmental and socioeconomic conditions among cities, in-depth studies of data and model adaptation towards city-specific EULUC mappings are highly required to support policy making, and urban renewal planning and management practices. In addition, the trending need for timely and detailed small land unit data processing with finer data granularity becomes increasingly important. We proposed a City Meta Unit (CMU) data model and classification framework driven by multisource data and artificial intelligence (AI) algorithms to address these challenges. The CMU Framework was innovatively applied to systematically set up a grid-based data model and classify urban land use with an improved AI algorithm by applying Moore neighborhood correlations. Specifically, we selected Xiamen, Fujian, in China, a coastal city, as the typical testbed to implement this proposed framework and apply an AI transfer learning technique for grid and parcel land-use study. Experimental results with our proposed CMU framework showed that the grid-based land use classification performance achieves overall accuracies of 81.17% and 76.55% for level I (major classes) and level II (minor classes), which is much higher than the parcel-based land use classification (overall accuracies of 72.37% for level I, and 68.99% for level II). We further investigated the relationship between training sample size and classification performance and quantified the contribution of different data sources to urban land use classifications. The CMU framework makes data collections and processing intelligent and efficient, with finer granularity, saving time and cost by using existing open social data. Incorporating the CMU framework with the proposed grid-based model is an effective and new approach for urban land use classification, which can be flexibly extended and applied to various cities.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs14236143
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
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  • 4
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    Visualizing the Nucleotide Addition Cycle of Viral RNA-Dependent RNA Polymerase
    MDPI AG ; 2018
    In:  Viruses Vol. 10, No. 1 ( 2018-01-04), p. 24-
    Details
    In: Viruses, MDPI AG, Vol. 10, No. 1 ( 2018-01-04), p. 24-
    Type of Medium: Online Resource
    ISSN: 1999-4915
    URL: Article
    DOI: 10.3390/v10010024
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2516098-9
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  • 5
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    Deformation Control Technology of Gob-Side Entry Retaining with Large Volume CFST Roadway Side Support in Top-Coal Caving Longwall and Stability Analysis: A Case Study
    MDPI AG ; 2023
    In:  Applied Sciences Vol. 13, No. 15 ( 2023-07-26), p. 8610-
    Details
    In: Applied Sciences, MDPI AG, Vol. 13, No. 15 ( 2023-07-26), p. 8610-
    Abstract: Considering the factors affecting the surrounding rock stability of gob-side entry retaining, the applicability of a large-diameter, concrete-filled steel tube roadside support body in a top-coal caving fully mechanized face is discussed, and a new approach to gob-side entry retaining is proposed in this study. The mechanical model of the surrounding rock structure of gob-side entry retaining in a top-coal caving fully mechanized face was established, the critical state of column–roof contact shear slip instability was clarified through Prandtl foundation failure theory, and the deformation mechanism of the surrounding rock of the retained roadway was analyzed through numerical simulation. The results indicated that the range of the tensile stress zone and extreme tensile stress of the roof between columns are closely related to the spacing of columns, which is the key factor influencing the deformation of the retained roadway. In addition, besides uncontrollable factors, the stability of the contact interface between the roof and columns is directly related to the area of the contact interface between the concrete-filled steel tubes and the roof, and the size of the critical contact area is directly related to the properties of top-coal mass. Finally, a field test was carried out in 91–101 working panels in the Wang-Zhuang Coal Mine; the maximum convergence of the roof and floor was 510 mm, and the area of the retained roadway section reached 12.9 m2, which is within a reasonable range.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app13158610
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2704225-X
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  • 6
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    Patterns of Bacillary Dysentery in China, 2005–2010
    MDPI AG ; 2016
    In:  International Journal of Environmental Research and Public Health Vol. 13, No. 2 ( 2016-01-27), p. 164-
    Details
    In: International Journal of Environmental Research and Public Health, MDPI AG, Vol. 13, No. 2 ( 2016-01-27), p. 164-
    Type of Medium: Online Resource
    ISSN: 1660-4601
    URL: Article
    DOI: 10.3390/ijerph13020164
    Language: English
    Publisher: MDPI AG
    Publication Date: 2016
    detail.hit.zdb_id: 2175195-X
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  • 7
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    Mapping Mountain Pine Beetle Mortality through Growth Trend Analysis of Time-Series Landsat Data
    MDPI AG ; 2014
    In:  Remote Sensing Vol. 6, No. 6 ( 2014-06-18), p. 5696-5716
    Details
    In: Remote Sensing, MDPI AG, Vol. 6, No. 6 ( 2014-06-18), p. 5696-5716
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs6065696
    Language: English
    Publisher: MDPI AG
    Publication Date: 2014
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  • 8
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    Improving 3-m Resolution Land Cover Mapping through Efficient Learning from an Imperfect 10-m Resolution Map
    MDPI AG ; 2020
    In:  Remote Sensing Vol. 12, No. 9 ( 2020-04-30), p. 1418-
    Details
    In: Remote Sensing, MDPI AG, Vol. 12, No. 9 ( 2020-04-30), p. 1418-
    Abstract: Substantial progress has been made in the field of large-area land cover mapping as the spatial resolution of remotely sensed data increases. However, a significant amount of human power is still required to label images for training and testing purposes, especially in high-resolution (e.g., 3-m) land cover mapping. In this research, we propose a solution that can produce 3-m resolution land cover maps on a national scale without human efforts being involved. First, using the public 10-m resolution land cover maps as an imperfect training dataset, we propose a deep learning based approach that can effectively transfer the existing knowledge. Then, we improve the efficiency of our method through a network pruning process for national-scale land cover mapping. Our proposed method can take the state-of-the-art 10-m resolution land cover maps (with an accuracy of 81.24% for China) as the training data, enable a transferred learning process that can produce 3-m resolution land cover maps, and further improve the overall accuracy (OA) to 86.34% for China. We present detailed results obtained over three mega cities in China, to demonstrate the effectiveness of our proposed approach for 3-m resolution large-area land cover mapping.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs12091418
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
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  • 9
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    Automatic High-Resolution Land Cover Production in Madagascar Using Sentinel-2 Time Series, Tile-Based Image Classification and Google Earth Engine
    MDPI AG ; 2020
    In:  Remote Sensing Vol. 12, No. 21 ( 2020-11-08), p. 3663-
    Details
    In: Remote Sensing, MDPI AG, Vol. 12, No. 21 ( 2020-11-08), p. 3663-
    Abstract: Madagascar, one of Earth’s biodiversity hotpots, is characterized by heterogeneous landscapes and huge land cover change. To date, fine, reliable and timely land cover information is scarce in Madagascar. However, mapping high-resolution land cover map in the tropics has been challenging due to limitations associated with heterogeneous landscapes, the volume of satellite data used, and the design of methodology. In this study, we proposed an automatic approach in which the tile-based model was used on each tile (defining an extent of 1° × 1° as a tile) for mapping land cover in Madagascar. We combined spectral-temporal, textural and topographical features derived from all available Sentinel-2 observations (i.e., 11,083 images) on Google Earth Engine (GEE). We generated a 10-m land cover map for Madagascar, with an overall accuracy of 89.2% based on independent validation samples obtained from a field survey and visual interpretation of very high-resolution (0.5–5 m) images. Compared with the conventional approach (i.e., the overall model used in the entire study area), our method enables reduce the misclassifications between several land cover types, including impervious land, grassland and wetland. The proposed approach demonstrates a great potential for mapping land cover in other tropical or subtropical regions.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs12213663
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
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  • 10
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    Analysing the Driving Forces and Environmental Effects of Urban Expansion by Mapping the Speed and Acceleration of Built-Up Areas in China between 1978 and 2017
    MDPI AG ; 2020
    In:  Remote Sensing Vol. 12, No. 23 ( 2020-11-30), p. 3929-
    Details
    In: Remote Sensing, MDPI AG, Vol. 12, No. 23 ( 2020-11-30), p. 3929-
    Abstract: Abundant data sets produced from long-term series of high-resolution remote sensing data have made it possible to explore urban issues across different spatiotemporal scales. Based on a 40-year impervious area data set released by Tsinghua University, a method was developed to map the speed and acceleration of urban built-up areas. With the mapping results of the two indices, we characterised the spatiotemporal dynamics of built-up area expansion and captured different types of expansion. Combined with socioeconomic data, we examined the temporal changes and spatial heterogeneity of driving forces with an ordinary least square (OLS) model and a panel data model, as well as exploring the environmental effects of the expansion. Our results reveal that China has experienced drastic urban expansion over the last four decades. Among all cities, megacities and large cities in eastern China, as well as megacities in central and northeast China have experienced the most dramatic urban expansion. A growing number of cities are categorised as thriving, which means that they have both high expansion speed and acceleration. The overall driving force of urban expansion has significantly increased. More specifically, it was associated with population increase in the early stages; however, since 2000, it has been substantially associated with increases in GDP and fixed asset investments. The major driving factors also differ between regions and urban sizes. Urban expansion is identified as being closely associated with environmental deterioration; thus, speed and acceleration should be included as key indicators in exploring the environmental effects of urban expansion. In summary, the results of the presented case study, based on a data set of China, indicate that speed and acceleration are useful in analysing the driving forces of urban expansion and its environmental effects, and may generate more interest in related research.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs12233929
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
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