Abstract
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.
Highlights
Mangrove forests cover approximately 0.7% of tropical forest area around the world [1,2,3,4], in more than 118 tropical and sub-tropical countries
Our objective is to evaluate freely available satellite data and machine-learning algorithms, random forest (RF) and classification and regression trees (CART), available on Google Earth Engine (GEE) to predict mangrove extent in the West African countries of Senegal and The Gambia
Since the objective of this study is to evaluate the performance of machine-learning algorithms that can be reproducible in developing countries with limited computing resources, and not to find out the best parameters for these algorithms, we decided to generate simple yet robust models with the default parameters available on GEE
Summary
Mangrove forests cover approximately 0.7% of tropical forest area around the world [1,2,3,4], in more than 118 tropical and sub-tropical countries These forests can store three to four times more carbon per equivalent area compared to tropical forests [5]. Mangrove forests in carbonate, peat-dominated settings are likely to store 25–50% more soil organic carbon compared to mangroves in deltaic and estuarine coastal settings [6]. These forests are known to host 1.6% of the total tropical forest biomass (considering both above- and below-ground biomass) [7]. Most studies do not cover a temporal span suitable for longer-term consistent monitoring, such as those useful for tracking the sustainable development goals (SDG) adopted by the United Nations, especially Goal 15 (‘Life on Land’) indicator 15.1.1 for quantifying “forest area as a proportion of total land area”
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