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Mapping Canopy Cover in African Dry Forests from the Combined Use of Sentinel-1 and Sentinel-2 Data: Application to Tanzania for the Year 2018

Authors :
Astrid Verhegghen
Klara Kuzelova
Vasileios Syrris
Hugh Eva
Frédéric Achard
Source :
Remote Sensing, Vol 14, Iss 6, p 1522 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

High-resolution Earth observation data is routinely used to monitor tropical forests. However, the seasonality and openness of the canopy of dry tropical forests remains a challenge for optical sensors. In this study, we demonstrate the potential of combining Sentinel-1 (S1) SAR and Sentinel-2 (S2) optical sensors in order to map the tree cover in East Africa. The overall methodology consists of: (i) the generation of S1 and S2 layers, (ii) the collection of an expert-based training/validation dataset and (iii) the classification of the satellite data. Three different classification workflows, together with different approaches to incorporating the spatial information to train the classifiers, are explored. Two types of maps were derived from these mapping approaches over Tanzania: (i) binary tree cover–no tree cover (TC/NTC) maps, and (ii) maps of the canopy cover classes. The overall accuracy of the maps is >95% for the TC/NTC maps and >85% for the forest types maps. Considering the neighboring pixels for training the classification improved the mapping of the areas that are covered by 1–10% tree cover. The study relied on open data and publicly available tools and can be integrated into national monitoring systems.

Details

Language :
English
ISSN :
20724292
Volume :
14
Issue :
6
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.689c4007b32f4cb1b254077586db630c
Document Type :
article
Full Text :
https://doi.org/10.3390/rs14061522