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Quantification of Carbon Sequestration in Urban Forests

Klein, Levente and Zhou, Wang and Albrecht, Conrad M. (2021) Quantification of Carbon Sequestration in Urban Forests. Tackling Climate Change with Machine Learning, 2021-07-23, virtual.

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Official URL: https://www.climatechange.ai/papers/icml2021/46

Abstract

Vegetation, trees in particular, sequester carbon by absorbing carbon dioxide from the atmosphere, however, the lack of efficient quantification methods of carbon stored in trees renders it difficult to track the process. Here we present an approach to estimate the carbon storage in trees based on fusing multispectral aerial imagery and LiDAR data to identify tree coverage, geometric shape, and tree species, which are crucial attributes in carbon storage quantification. We demonstrate that tree species information and their three-dimensional geometric shapes can be estimated from remote imagery in order to calculate the tree's biomass. Specifically, for Manhattan, New York City, we estimate a total of 52,000 tons of carbon sequestered in trees.

Item URL in elib:https://elib.dlr.de/143821/
Document Type:Conference or Workshop Item (Speech)
Title:Quantification of Carbon Sequestration in Urban Forests
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Klein, LeventeUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Zhou, WangUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Albrecht, Conrad M.UNSPECIFIEDhttps://orcid.org/0009-0009-2422-7289UNSPECIFIED
Date:2021
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:pp. 1-5
Status:Published
Keywords:carbon sequestration, vegetation management, machine learning, LiDAR, multi-spectral remote sensing, climate change
Event Title:Tackling Climate Change with Machine Learning
Event Location:virtual
Event Type:international Conference
Event Date:23 July 2021
Organizer:International Conference on Machine Learning
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 - Artificial Intelligence
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Albrecht, Conrad M
Deposited On:16 Sep 2021 12:11
Last Modified:24 Apr 2024 20:43

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