A New Global Mangrove Height Map with a 12 meter spatial resolution.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-01-04 DOI:10.1038/s41597-024-04213-z

Marc Simard, Lola Fatoyinbo, Nathan M Thomas, Atticus E Stovall, Adriana Parra, Abigail Barenblitt, Pete Bunting, Irena Hajnsek

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Abstract

Mangrove forests thrive along global tropical coasts, acting as a barrier that protects coastlines against storm surges and as nurseries for an entire food web. They are also known for their high carbon sequestration rates and soil carbon stocks. We introduce a new global mangrove canopy height map generated from TanDEM-X spaceborne elevation measurements collected during the 2011-2013 period with a 12-meter spatial resolution and an accuracy of 2.4 meters (RMSE). Height was calibrated and validated using GEDI mission data and independently verified with airborne Lidar. The tallest mangrove stands reach nearly 60 meters in Colombia and Gabon, and potentially other countries. The map captures a broader range of canopy heights with finer spatial details than other available global products that use optical imagery. This new global mangrove height dataset can aid in evaluating mangrove ecosystem services at local and regional scales, improving our understanding of factors controlling mangrove structure, and supporting conservation, climate mitigation and adaptation strategies.

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.

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