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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引用次数: 0

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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一个新的12米空间分辨率的全球红树林高度图。

红树林在全球热带海岸繁茂生长，作为一道屏障保护海岸线免受风暴潮的侵袭，也是整个食物网的托儿所。它们还以高碳固存率和土壤碳储量而闻名。我们介绍了一个新的全球红树林冠层高度图，该图是由2011-2013年期间收集的TanDEM-X星载高程测量数据生成的，空间分辨率为12米，精度为2.4米（RMSE）。高度使用GEDI任务数据进行校准和验证，并使用机载激光雷达进行独立验证。在哥伦比亚和加蓬，以及潜在的其他国家，最高的红树林高达近60米。与其他使用光学图像的全球产品相比，该地图捕获了更大范围的树冠高度和更精细的空间细节。这一新的全球红树林高度数据集有助于在地方和区域尺度上评估红树林生态系统服务，提高我们对红树林结构控制因素的理解，并支持保护、气候减缓和适应战略。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.