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Quasi-circular Vegetation Patch Mapping with Multitemporal Kauth-Thomas Transformation of the mIHS Pansharpened GF-2 Images

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Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2020)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 88))

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Abstract

The quasi-circular vegetation patches (QVPs) are better objects for studying the ecosystem evolution, functioning, and maintenance in the Yellow River Delta, China. Remote sensing with linear change detection technique is an effective approach for mapping the vegetation dynamics. In this paper, the multitemporal Kauth-Thomas transformation (MKT) change detection technique with the decision tree classifier was used to map the QVPs based on the modified intensity-hue-saturation pansharpened April and August Gaofen-2 images. Results indicated that mapping the QVPs could be performed well using the approaches used in this paper. The precision, recall rate, and F-measure were 66.7%, 52.9%, and 59.0%, respectively. In the future, patch splitting techniques and more test areas should be tested for improving the detection accuracy of the QVPs. In addition, an assessment on the possibility of change in brightness and greenness between multitemporal images for mapping the dominant communities of the QVPs should be performed, which were important for the establishment, evolution, and disappearance of the QVPs.

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Acknowledgment

This research was jointly financially supported by the National Natural Science Foundation of China (Project No. 41671422, 41661144030, 41561144012), the Strategic Priority Research Program of Chinese Academy of Sciences (Project No. XDA 20030302), the Innovation Project of LREIS (Project No. 088RA20CYA, 08R8A010YA), and the National Mountain Flood Disaster Investigation Project (Project No. SHZH-IWHR-57). Thanks to China Center for Resources Satellite Data and Application for providing the GF-2 data products.

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Authors and Affiliations

  1. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China

    Qingsheng Liu

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  1. Qingsheng Liu
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Correspondence to Qingsheng Liu .

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Editors and Affiliations

  1. College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge, UK

    Hongying Meng

  2. School of Electronical Information and Artificial Engineering, Shaanxi University of Science and Technology, Xi’an, China

    Tao Lei

  3. College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge, UK

    Maozhen Li

  4. College of Electrical and Information, Hunan University, Changsha, China

    Kenli Li

  5. Division of Intelligent Future Technologies, Mälardalen University, Västerås, Västmanlands Län, Sweden

    Ning Xiong

  6. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

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Liu, Q. (2021). Quasi-circular Vegetation Patch Mapping with Multitemporal Kauth-Thomas Transformation of the mIHS Pansharpened GF-2 Images. In: Meng, H., Lei, T., Li, M., Li, K., Xiong, N., Wang, L. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 88. Springer, Cham. https://doi.org/10.1007/978-3-030-70665-4_2

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