Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed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.v2). 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.v2).

Abstract

Japan Aerospace Exploration Agency (JAXA) is going to launch Advanced Land Observing Satellite 3 (ALOS-3) after 2022. ALOS-3 satellite 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 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 land cover and vegetation types at Genus-Physiognomy-Ecosystem (GPE) level. We acquired and simulated WorldView-3 images according to the configuration of the ALOS-3 satellite sensor and the simulated ALOS-3 images were utilized for the classification and mapping of land cover and vegetation types in three sites (Hakkoda, Zao, and Shiranuka) in northern Japan. This research dealt with 17 land cover and vegetation types in Hakkoda site, 25 land cover and vegetation types in Zao site, and 12 land cover and vegetation types in Shiranuka site. Ground truth data were newly collected in three sites, and we employed eXtreme Gradient Boosting (XGBoost) classifier with the implementation of 10-fold cross-validation method for assessing the potential of ALOS-3S images. The classification accuracies obtained in Hakkoda, Zao, and Shiranuka sites in terms of f1-score were 0.810, 0.729, and 0.805 respectively. The fine scale (3.2m) land cover and vegetation maps produced in the study sites showed clear and detailed view of the distribution of plant communities. Regardless of the limited number of the temporal images, ALOS-3S images showed high potential (at least 0.729 F1-score) for the land cover and vegetation classification in all three sites. The availability of more cloud free temporal scenes is expected for improved classification and mapping in the future.

Keywords

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

Subject

EARTH SCIENCES, Environmental Sciences

Comments (1)

Comment 1
Received: 13 May 2022
Commenter: Ram C. Sharma
Commenter's Conflict of Interests: Author
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