Classification and Mapping of Plant Communities Using Multi-Temporal and Multi-Spectral Satellite Images

  •  Ram C. Sharma    
  •  Hidetake Hirayama    
  •  Masatsugu Yasuda    
  •  Miki Asai    
  •  Keitarou Hara    


Classification and mapping of plant communities is an essential step for conservation and management of ecosystems and biodiversity. We adopt the Genus-Physiognomy-Ecosystem (GPE) system developed in the previous study for satellite-based classification of plant communities at a broad scale. This paper assesses the potential of multi-spectral and multi-temporal images collected by Sentinel-2 satellites for the classification and mapping of GPE types. This research was conducted in seven representative study sites in different climatic regions ranging from one warm-temperate site in Aya to six cool-temperate sites in Hakkoda, Zao, Oze, Shirakami, Kitakami and Shiranuka. The GPE types were enumerated in all study sites and ground truth data were collected with reference to extant vegetation surveys, visual interpretation of high-resolution images, and onsite field observations. We acquired all Sentinel-2 Level-1C product images available for the study sites between 2017-2019 and generated monthly median composite images consisting of ten spectral and twelve spectral-indices. The Gradient Boosting Decision Trees (GBDT) classifier was employed for the supervised classification of the satellite data with the support of ground truth data. The cross-validation accuracy in terms of kappa coefficient varied from 87% in Oze site with 41 GPE types to 95% in Hakkoda site with 19 GPE types; with average performance of 91% across all sites. The GPE maps produced in this research demonstrated a clear distribution of plant communities in all seven sites, highlighting the potential of Sentinel-2 multi-spectral and multi-temporal images with GPE classification system for operational and broad-scale mapping of plant communities.

This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1916-9779
  • ISSN(Online): 1916-9787
  • Started: 2009
  • Frequency: semiannual

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