Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Application of Advanced Land Observing Satellite 3 (ALOS-3) Data to Land Cover and Vegetation Mapping

Version 1 : Received: 26 December 2021 / Approved: 27 December 2021 / Online: 27 December 2021 (13:43:39 CET)
Version 2 : Received: 13 May 2022 / Approved: 13 May 2022 / Online: 13 May 2022 (14:45:48 CEST)

How to cite: Sharma, R.C.; Hirayama, H.; Hara, K. Application of Advanced Land Observing Satellite 3 (ALOS-3) Data to Land Cover and Vegetation Mapping . Preprints 2021, 2021120428 (doi: 10.20944/preprints202112.0428.v1). Sharma, R.C.; Hirayama, H.; Hara, K. Application of Advanced Land Observing Satellite 3 (ALOS-3) Data to Land Cover and Vegetation Mapping . Preprints 2021, 2021120428 (doi: 10.20944/preprints202112.0428.v1).

Abstract

Advanced Land Observing Satellite 3 (ALOS-3) is capable of observing global land areas with wide swath (4000 km along-track direction and 70 km cross-track direction) at high spatial resolution (panchromatic: 0.8m, multispectral: 3.2m). Maintenance and updating of Land Cover and Vegetation (LCV) information at national level is one of the major goals of the ALOS-3 mission. This paper presents the potential of simulated ALOS-3 images for the classification and mapping of LCV types. We simulated WorldView-3 images according to the configuration of the ALOS-3 satellite sensor and the ALOS-3 simulated (ALOS-3S) images were utilized for the classification and mapping of LCV types in two cool temperate ecosystems. This research dealt with classification and mapping of 17 classes in the Hakkoda site and 25 classes in the Zao site. We employed a Gradient Boosted Decision Tree (GBDT) classifier with 10-fold cross-validation method for assessing the potential of ALOS-3S images. In the Hakkoda site, we obtained overall accuracy, 0.811 and kappa coefficient, 0.798. In the Zao site, overall accuracy and kappa coefficient were 0.725 and 0.711 respectively. Regardless of limited temporal scenes available in the research, ALOS-3S images showed high potential (at least 0.711 kappa-coefficient) for the LCV classification. The availability of more temporal scenes from ALOS-3 satellite is expected for improved classification and mapping of LCV types in the future.

Keywords

ALOS-3; Land Cover; Vegetation; Machine learning; Classification; Mapping; Ge-nus-Physiognomy-Ecosystem level

Subject

EARTH SCIENCES, Atmospheric Science

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