Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed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

Version 1 : Received: 24 December 2021 / Approved: 28 December 2021 / Online: 28 December 2021 (10:49:28 CET)

How to cite: Sharma, R.C.; Hirayama, H.; Yasuda, M.; Asai, M.; Hara, K. Assessing the Potential of Multi-spectral and Multi-temporal Satellite Images for Classification and Mapping of Plant Communities in a Temperate Region. Preprints 2021, 2021120443 (doi: 10.20944/preprints202112.0443.v1). Sharma, R.C.; Hirayama, H.; Yasuda, M.; Asai, M.; Hara, K. Assessing the Potential of Multi-spectral and Multi-temporal Satellite Images for Classification and Mapping of Plant Communities in a Temperate Region. Preprints 2021, 2021120443 (doi: 10.20944/preprints202112.0443.v1).

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

EARTH SCIENCES, Geoinformatics

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