PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Assessing the Potential of Multi-spectral and Multi-temporal Satellite Images for Classification and Mapping of Plant Communities in a Temperate Region
Sharma, R. C.; Hirayama, H.; Yasuda, M.; Asai, M.; Hara, K. Classification and Mapping of Plant Communities Using Multi-Temporal and Multi-Spectral Satellite Images. Journal of Geography and Geology, 2022, 14, 43. https://doi.org/10.5539/jgg.v14n1p43.
Sharma, R. C.; Hirayama, H.; Yasuda, M.; Asai, M.; Hara, K. Classification and Mapping of Plant Communities Using Multi-Temporal and Multi-Spectral Satellite Images. Journal of Geography and Geology, 2022, 14, 43. https://doi.org/10.5539/jgg.v14n1p43.
Sharma, R. C.; Hirayama, H.; Yasuda, M.; Asai, M.; Hara, K. Classification and Mapping of Plant Communities Using Multi-Temporal and Multi-Spectral Satellite Images. Journal of Geography and Geology, 2022, 14, 43. https://doi.org/10.5539/jgg.v14n1p43.
Sharma, R. C.; Hirayama, H.; Yasuda, M.; Asai, M.; Hara, K. Classification and Mapping of Plant Communities Using Multi-Temporal and Multi-Spectral Satellite Images. Journal of Geography and Geology, 2022, 14, 43. https://doi.org/10.5539/jgg.v14n1p43.
Abstract
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 previous study for satellite-based classification of plant communities. This paper assesses the potential of multi-spectral and multi-temporal images collected by Sentinel-2 satellites. This research was conducted in five representative study sites in a temperate region. It consists of 44 types of plant communities including a few land cover types as well. The plant community types were enumerated in the study sites and ground truth data were prepared 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. Gradient Boosting Decision Trees (GBDT) classifier was employed as an efficient and distributed gradient boosting technique for the supervised classification of big datasets involved in the research. The cross-validation accuracy in terms of kappa coefficient varied from 87% in Oze site with 41 land cover and plant community types to 95% in Hakkoda site with 19 land cover and plant community types; with average performance of 91% across all sites. In addition, the resulting maps demonstrated a clear distribution of plant community types involved in all sites, highlighting the potential of Sentinel-2 multi-spectral and multi-temporal images with GPE classification system for operational and broad-scale mapping of land cover and plant communities.
Keywords
Sentinel-2; Land cover; Vegetation; Mapping; Plant communities; Machine learning; Genus-Physiognomy-Ecosystem; Gradient Boosting Decision Trees
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
