Testing the Contribution of Multi-Source Remote Sensing Features for Random Forest Classification of the Greater Amanzule Tropical Peatland

Testing the Contribution of Multi-Source Remote Sensing Features for Random Forest Classification of the Greater Amanzule Tropical Peatland

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 clo...

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Bibliographic Details
Main Authors: Alex O. Amoakoh, Paul Aplin, Kwame T. Awuah, Irene Delgado-Fernandez, Cherith Moses, Carolina Peña Alonso, Stephen Kankam, Justice C. Mensah
Format: Article
Language:English
Published: MDPI AG 2021-05-01
Series:Sensors
Subjects:
tropical peatland
random forest
feature selection
classification
Sentinel
Google Earth Engine
Online Access:https://www.mdpi.com/1424-8220/21/10/3399

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