Large-Scale High-Resolution Coastal Mangrove Forests Mapping Across West Africa With Machine Learning Ensemble and Satellite Big Data
Large-Scale High-Resolution Coastal Mangrove Forests Mapping Across West Africa With Machine Learning Ensemble and Satellite Big Data
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DOI：
10.3389/feart.2020.560933
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发表时间：
2021-01
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影响因子：
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通讯作者：
Xue Liu;T. Fatoyinbo;N. Thomas;W. Guan;Y. Zhan;P. Mondal;D. Lagomasino;M. Simard;C. Trettin;Rinki Deo;Abigail Barenblitt
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作者：
Xue Liu;T. Fatoyinbo;N. Thomas;W. Guan;Y. Zhan;P. Mondal;D. Lagomasino;M. Simard;C. Trettin;Rinki Deo;Abigail Barenblitt
沿海红树林提供了重要的生态系统商品和服务，包括碳，生物多样性保护和缓解危险，但是，它们以人类活动的惊人速度销毁，以表征红树林的变化，评估其影响和支持相关的保护和相关的保护和保护和保护。恢复决策，准确和最新的红树林在大空间尺度上映射是必不可少的。在这些数据产品中，涉及大量卫星数据的学习方法通常具有30 m的Landsat图像和明显的不一致。映射是一个挑战。非洲使用Sentinel-2（光学）和Sentinel-1（雷达）图像。和神经网络（NN）。机器学习合奏可以为西非的所有研究区域生成高精度的红树林范围（92％–99％的生产商的准确性，98％–100％的用户的准确性，95％–99％的整体准确性）。 - 在整个西非的空间分辨率上，红树林的范围已被映射。高分辨率的红树林范围在全球尺度上定期地图。
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.