Space-Based Mapping of Pre- and Post-Hurricane Mangrove Canopy Heights Using Machine Learning with Multi-Sensor Observations

Space-Based Mapping of Pre- and Post-Hurricane Mangrove Canopy Heights Using Machine Learning with Multi-Sensor Observations

Coastal mangrove forests provide numerous ecosystem services, which can be disrupted by natural disturbances, mainly hurricanes. Canopy height (CH) is a key parameter for estimating carbon storage. Airborne Light Detection and Ranging (LiDAR) is widely viewed as the most accurate method for estimati...

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Bibliographic Details
Main Authors: Boya Zhang, Daniel Gann, Shimon Wdowinski, Chaohao Lin, Erin Hestir, Lukas Lamb-Wotton, Khandker S. Ishtiaq, Kaleb Smith, Yuepeng Li
Format: Article
Language:English
Published: MDPI AG 2024-10-01
Series:Remote Sensing
Subjects:
mangrove
canopy height
data fusion
machine learning
transfer learning
natural disturbance
Online Access:https://www.mdpi.com/2072-4292/16/21/3992
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