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Article

Evaluating Combinations of Sentinel-2 Data and Machine-Learning Algorithms for Mangrove Mapping in West Africa

1
Department of Geography and Spatial Sciences, University of Delaware, Newark, DE 19716, USA
2
Department of Plant and Soil Sciences, University of Delaware, Newark, DE 19716, USA
3
Center for International Earth Science Information Network (CIESIN), Columbia University, Palisades, NY 10964, USA
4
The Center for Geographic Analysis (CGA), Harvard University, Cambridge, MA 02138, USA
5
NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
6
Department of Coastal Studies, East Carolina University, Wanchese, NC 27948, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(24), 2928; https://doi.org/10.3390/rs11242928
Received: 1 October 2019 / Revised: 21 November 2019 / Accepted: 3 December 2019 / Published: 6 December 2019
(This article belongs to the Special Issue EO Solutions to Support Countries Implementing the SDGs)
Creating a national baseline for natural resources, such as mangrove forests, and monitoring them regularly often requires a consistent and robust methodology. With freely available satellite data archives and cloud computing resources, it is now more accessible to conduct such large-scale monitoring and assessment. Yet, few studies examine the reproducibility of such mangrove monitoring frameworks, especially in terms of generating consistent spatial extent. Our objective was to evaluate a combination of image processing approaches to classify mangrove forests along the coast of Senegal and The Gambia. We used freely available global satellite data (Sentinel-2), and cloud computing platform (Google Earth Engine) to run two machine learning algorithms, random forest (RF), and classification and regression trees (CART). We calibrated and validated the algorithms using 800 reference points collected using high-resolution images. We further re-ran 10 iterations for each algorithm, utilizing unique subsets of the initial training data. While all iterations resulted in thematic mangrove maps with over 90% accuracy, the mangrove extent ranges between 827–2807 km2 for Senegal and 245–1271 km2 for The Gambia with one outlier for each country. We further report “Places of Agreement” (PoA) to identify areas where all iterations for both methods agree (506.6 km2 and 129.6 km2 for Senegal and The Gambia, respectively), thus have a high confidence in predicting mangrove extent. While we acknowledge the time- and cost-effectiveness of such methods for the landscape managers, we recommend utilizing them with utmost caution, as well as post-classification on-the-ground checks, especially for decision making. View Full-Text
Keywords: mangrove; machine-learning algorithms; google earth engine; random forest; CART; Senegal; The Gambia; Africa mangrove; machine-learning algorithms; google earth engine; random forest; CART; Senegal; The Gambia; Africa
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MDPI and ACS Style

Mondal, P.; Liu, X.; Fatoyinbo, T.E.; Lagomasino, D. Evaluating Combinations of Sentinel-2 Data and Machine-Learning Algorithms for Mangrove Mapping in West Africa. Remote Sens. 2019, 11, 2928. https://doi.org/10.3390/rs11242928

AMA Style

Mondal P, Liu X, Fatoyinbo TE, Lagomasino D. Evaluating Combinations of Sentinel-2 Data and Machine-Learning Algorithms for Mangrove Mapping in West Africa. Remote Sensing. 2019; 11(24):2928. https://doi.org/10.3390/rs11242928

Chicago/Turabian Style

Mondal, Pinki, Xue Liu, Temilola E. Fatoyinbo, and David Lagomasino. 2019. "Evaluating Combinations of Sentinel-2 Data and Machine-Learning Algorithms for Mangrove Mapping in West Africa" Remote Sensing 11, no. 24: 2928. https://doi.org/10.3390/rs11242928

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