Detection of degraded forests in Guinea, West Africa, based on Sentinel-2 time series by inclusion of moisture-related spectral indices and neighbourhood effect

Abstract

Unlike deforestation, forest degradation implies a change in forest structure, with no modification in land use; thus, it is subtle and less visible than deforestation. Moreover, forest degradation is a major concern, as the potential for carbon sequestration is reduced. Degradation is related to location, anthropogenic drivers, climate, and forest types, and there is no unique detection methodology to map this phenomenon on a global scale. This study proposes a procedure for detecting degraded forests using Sentinel-2 (S2) multispectral imagery in Guinea, West Africa, and demonstrates that consideration of context improves degraded forest detection. In Guinea, the primary degradation process is selective logging in the massif forest, in addition to forest fragmentation caused by land use change. Monitoring forest degradation using remote sensing methods usually requires significant overhead in terms of photo-interpretation expertise. We proposed an approach to monitoring degradation through a semi-automated classification procedure with minimum photo-interpretation costs. First, the radiometric values of the Sentinel-2 data were explored using a photo-interpreted reference map and ground observations carried out with a local forester. Many of the ground observations found that forests considered as intact in the photo-interpreted reference map were degraded. This suggests photo-interpretation can be affected when investing in a single-date image. Our results showed that radiometric moisture-related indices (canopy water content [CWC], moisture stress index [MSI]), associated with leaf area index (LAI) retrieved from S2 data, were strongly related to the degree of degradation. Second, we improved the mapping of degraded forests through photo-interpretation of S2 data. Third, classification methodologies involving LAI, MSI, and CWC were established, and the results were compared with the improved photo-interpreted map. Two types of classification were compared: standard pixel-based classification and a hybrid method of classification that took contextual information into account by including neighbour pixel values in the algorithm to consider the pixel context, similar to the photo-interpretation methods. The inclusion of neighbour pixels in the semi-automated classification process led to a substantial improvement in the results. When contextual information was included, photo-interpretation of 1.6% of the latest year's data was sufficient to reach an overall accuracy above 85% requiring no use of past reference maps. Our method strongly supports the photo-interpretation procedure for less time-consuming and expensive map production, offering a significant foundation for degraded forest delineation in regions under similar conditions of canopy opening.