TROPICAL PEATLAND CLASSIFICATION USING MULTI-SENSOR SENTINEL IMAGERY AND RANDOM FOREST ALGORITHM IN GREATER AMANZULE, GHANA

Alex Owusu Amoakoh, Paul Aplin, Kwame T. Awuah, Irene Delgado-Fernandez, Cherith Moses, Carolina Peña Alonso

Research output: Contribution to conferencePaperpeer-review

4 Citations (Scopus)

Abstract

Tropical peatlands such as Ghana’s Greater Amanzule peatland are important ecosystems due to the magnitude of their greenhouse gas emissions under human and climatic pressures. Accurate measurement of their occurrence and extent is required to facilitate sustainable management. A key challenge however is the high cloud coverage in the tropics that limits optical remote sensing data acquisition. We combined optical, radar and elevation data to optimise Land Use and Land Cover (LULC) classification for the Greater Amanzule tropical peatland. Sentinel-1, Sentinel-2 and SRTM data were acquired, and appropriate features were selected and integrated to develop a machine learning LULC classification using a Random Forest classifier. A total of six LULC classifications were made. Results showed that the best overall accuracy (OA) was found for the integrated Sentinel-1, Sentinel-2 and SRTM features (S1+S2+DEM), significantly outperforming all the other classifications with an OA of 94%, followed by the integrated Sentinel-1 and Sentinel-2 (S1+S2) (92%). Sentinel-1 only (S1) had the worse OA of 70%. The integration of more features systematically increased the classification accuracy. We estimated Ghana’s Greater Amanzule peatland at 60,187ha. Our proposed methodological framework contributes a robust workflow for accurate and detailed landscape-scale monitoring of tropical peatlands, while our findings and research outputs provide timely information critical for the sustainable management of the Greater Amanzule peatland.

Original languageEnglish
Pages5910-5913
Number of pages4
DOIs
Publication statusPublished - 12 Oct 2021
Event2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 - Brussels, Belgium
Duration: 12 Jul 202116 Jul 2021

Conference

Conference2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021
Country/TerritoryBelgium
CityBrussels
Period12/07/2116/07/21

Keywords

  • classification
  • Google Earth Engine
  • Random Forest
  • Sentinel
  • Tropical peatland

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