Amoakoh, AO, Aplin, P, Awuah, KT, Delgado-Fernandez, I, Moses, C, Alonso, CP, Kankam, S and Mensah, JC (2021) Testing the Contribution of Multi-Source Remote Sensing Features for Random Forest Classification of the Greater Amanzule Tropical Peatland. Sensors, 21 (10). pp. 1-25. ISSN 1424-8220

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Abstract

Tropical peatlands such as Ghana's Greater Amanzule peatland are highly valuable ecosystems and under great pressure from anthropogenic land use activities. Accurate measurement of their occurrence and extent is required to facilitate sustainable management. A key challenge, however, is the high cloud cover in the tropics that limits optical remote sensing data acquisition. In this work we combine optical imagery with radar and elevation data to optimise land cover classification for the Greater Amanzule tropical peatland. Sentinel-2, Sentinel-1 and Shuttle Radar Topography Mission (SRTM) imagery were acquired and integrated to drive a machine learning land cover classification using a random forest classifier. Recursive feature elimination was used to optimize high-dimensional and correlated feature space and determine the optimal features for the classification. Six datasets were compared, comprising different combinations of optical, radar and elevation features. Results showed that the best overall accuracy (OA) was found for the integrated Sentinel-2, Sentinel-1 and SRTM dataset (S2+S1+DEM), significantly outperforming all the other classifications with an OA of 94%. Assessment of the sensitivity of land cover classes to image features indicated that elevation and the original Sentinel-1 bands contributed the most to separating tropical peatlands from other land cover types. The integration of more features and the removal of redundant features systematically increased classification accuracy. We estimate Ghana's Greater Amanzule peatland covers 60,187 ha. Our proposed methodological framework contributes a robust workflow for accurate and detailed landscape-scale monitoring of tropical peatlands, while our findings provide timely information critical for the sustainable management of the Greater Amanzule peatland.

Item Type:	Article
Uncontrolled Keywords:	Google Earth Engine; Sentinel; classification; feature selection; random forest; tropical peatland; Machine Learning and Artificial Intelligence; 15 Life on Land; 0301 Analytical Chemistry; 0502 Environmental Science and Management; 0602 Ecology; 0805 Distributed Computing; 0906 Electrical and Electronic Engineering; Analytical Chemistry
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences S Agriculture > SD Forestry
Divisions:	Biological and Environmental Sciences (from Sep 19)
Publisher:	MDPI
SWORD Depositor:	A Symplectic
Date Deposited:	16 Jan 2025 12:27
Last Modified:	16 Jan 2025 12:30
DOI or ID number:	10.3390/s21103399
URI:	https://researchonline.ljmu.ac.uk/id/eprint/25296

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