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

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.

Keywords: Google Earth Engine; Sentinel; classification; feature selection; random forest; tropical peatland.

Figure 1

The Greater Amanzule landscape showing identified patchy peatlands and communities fringing the wetland resources. Peatland information was obtained from Hen Mpoano’s data repository and is based on participatory GIS and ground truthing approach.

Figure 2

Satellite image data of the study area: (a) Sentinel-2 true colour composite, (b) Sentinel-1 dual-polarization and (c) SRTM DEM showing estimated elevation in metres above sea level.

Figure 3

Process flowchart for land cover classification of Greater Amanzule using individual and integrated Sentinel-2, Sentinel-1 and SRTM datasets.

Figure 4

Optimal Features selected for the classification of each datasets using RFE algorithm.

Figure 5

Land cover classification results of a small part of the study area (zoomed in for ease of viewing) using the (a) S2, (b) S2+, (c) S1, (d) S1+, (e) S2+S1+ and (f) S2+S1+DEM datasets.

Figure 6

F-score of landcover classes from the classification results of various datasets.

Figure 7

Important predictor variables from RF classification results: (A) S2, (B) S2+, (C) S1, (D) S1+, (E) S2+S1+ and (F) S2+S1+DEM.

Figure 8

Land cover classification of the Greater Amanzule peatland based on the S2+S1+DEM dataset.