Large-Scale High-Resolution Coastal Mangrove Forests Mapping Across West Africa With Machine Learning Ensemble and Satellite Big Data

Abstract

Coastal mangrove forests provide important ecosystem goods and services, including carbon sequestration, biodiversity conservation, and hazard mitigation. However, they are being destroyed at an alarming rate by human activities. To characterize mangrove forest changes, evaluate their impacts, and support relevant protection and restoration decision making, accurate and up-to-date mangrove extent mapping at large spatial scales is essential. Available large-scale mangrove extent data products use a single machine learning method commonly with 30 m Landsat imagery, and significant inconsistencies remain among these data products. With huge amounts of satellite data involved and the heterogeneity of land surface characteristics across large geographic areas, finding the most suitable method for large-scale high-resolution mangrove mapping is a challenge. The objective of this study is to evaluate the performance of a machine learning ensemble for mangrove forest mapping at 20 m spatial resolution across West Africa using Sentinel-2 (optical) and Sentinel-1 (radar) imagery. The machine learning ensemble integrates three commonly used machine learning methods in land cover and land use mapping, including Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Network (NN). The cloud-based big geospatial data processing platform Google Earth Engine (GEE) was used for pre-processing Sentinel-2 and Sentinel-1 data. Extensive validation has demonstrated that the machine learning ensemble can generate mangrove extent maps at high accuracies for all study regions in West Africa (92%–99% Producer’s Accuracy, 98%–100% User’s Accuracy, 95%–99% Overall Accuracy). This is the first-time that mangrove extent has been mapped at a 20 m spatial resolution across West Africa. The machine learning ensemble has the potential to be applied to other regions of the world and is therefore capable of producing high-resolution mangrove extent maps at global scales periodically.

Wetland landscape based on Sentinel-2 images and geo-tagged photographs in Centla, Tabasco

“…This type of algorithms are considered to be, pixel-wise, spectral classifiers and do not consider the spatial dependence on neighboring pixels (Sheykhmousa et al, 2020). In the case of low spatial resolution images, the RF method consistently offers better results, compared to the SVM method (Liu et al 2021); while the SVM allows for better results when using medium to high spatial resolution images (Adam et al, 2014, Noi and Kappas, 2018, Sjögersten et al, 2021. The estuary mangroves are forests along riverbeds, canals and streams of the San Pedro y San Pablo river and along the Grijalva river.…”

Generalized Composite Mangrove Index for Mapping Mangroves Using Sentinel-2 Time Series Data

“…Exploring classification models is an effective way since it can make up the limitations of previous proposed classifiers. Various related studies mainly used ensemble learning [14]- [16], support vector machine (SVM) [17]- [19], decision tree (DT) [20], [21], and deep learning [22]- [24].…”

A Synoptic Review of Forestry and Forest Products Trade and Production in Africa- The Case of Wood and Non-Wood Forest Products in West Africa

“…Forests in West Africa are being destroyed and forest products are being exploited at an alarming rate [1,2]. Land degradation has ensued as a result of this, with the Food and Agricultural Organization (FAO) of the United Nations [3,4] stating that Africa's annual rate of deforestation exceeds 4.5 million hectares, or 0.65 percent annually, despite the dire situation in certain regions.…”