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Structure Metrics to Generalize Biomass Estimation from Lidar across Forest Types from Different Continents

Knapp, Nikolai and Fischer, Rico and Cazcarra-Bes, Victor and Huth, Andreas (2020) Structure Metrics to Generalize Biomass Estimation from Lidar across Forest Types from Different Continents. Remote Sensing of Environment, 237 (111597), pp. 1-14. Elsevier. doi: 10.1016/j.rse.2019.111597. ISSN 0034-4257.

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Abstract

Forest aboveground biomass is a key variable in remote sensing based forest monitoring. Active sensor systems, such as lidar, can generate detailed canopy height products. Relationships between canopy height and biomass are commonly established via regression analysis using information from ground-truth plots. In this way, many site-specific height-biomass relationships have been proposed in the literature and applied for mapping in regional contexts. However, such relationships are only valid within the specific forest type for which they were calibrated. A generalized relationship would facilitate biomass estimation across forest types and regions. In this study, a combination of lidar-derived and ancillary structural descriptors is proposed as an approach for generalization between forest types. Each descriptor is supposed to quantify a different aspect of forest structure, i.e., mean canopy height, maximum canopy height, maximum stand density, vertical heterogeneity and wood density. Airborne discrete return lidar data covering 194 ha of forest inventory plots from five different sites including temperate and tropical forests from Africa, Europe, North, Central and South America was used. Biomass predictions using the best general model (nRMSE = 12.4%, R2 = 0.74) were found to be almost as accurate as predictions using five site-specific models (nRMSE = 11.6%, R2 = 0.78). The results further allow interpretation about the importance of the employed structure descriptors in the biomass estimation and the mechanisms behind the relationships. Understanding the relationship between canopy structure and aboveground biomass and being able to generalize it across forest types are important steps towards consistent large scale biomass mapping and monitoring using airborne and potentially also spaceborne platforms.

Item URL in elib:https://elib.dlr.de/133102/
Document Type:Article
Title:Structure Metrics to Generalize Biomass Estimation from Lidar across Forest Types from Different Continents
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Knapp, NikolaiDepartment of Ecological Modeling, Helmholtz Centre for Environmental Research (UFZ)UNSPECIFIED
Fischer, RicoDepartment of Ecological Modeling, Helmholtz Centre for Environmental Research (UFZ)UNSPECIFIED
Cazcarra-Bes, VictorVictor.CazcarraBes (at) dlr.dehttps://orcid.org/0000-0002-6776-4553
Huth, AndreasDepartment of Ecological Modeling, Helmholtz Centre for Environmental Research (UFZ)UNSPECIFIED
Date:February 2020
Journal or Publication Title:Remote Sensing of Environment
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:237
DOI :10.1016/j.rse.2019.111597
Page Range:pp. 1-14
Publisher:Elsevier
ISSN:0034-4257
Status:Published
Keywords:forest structure; aboveground biomass; canopy height; lidar; generalization
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Vorhaben Tandem-L Vorstudien (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Microwaves and Radar Institute > Radar Concepts
Deposited By: Cazcarra-Bes, Victor
Deposited On:19 Dec 2019 20:13
Last Modified:14 Jan 2021 11:23

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