elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Carbon Sequestration and Urban Heat Island Mitigation by Urban Forests

Klein, Levente J and Albrecht, Conrad M and Marianno, Fernando (2022) Carbon Sequestration and Urban Heat Island Mitigation by Urban Forests. 2022 ESA Living Planet Symposium, 23.-27. May 2022, Bonn, Germany.

[img] PDF
3MB

Abstract

Nature-based carbon sequestration is one of the most straightforward ways to extract and to store carbon dioxide from the atmosphere. Urban forests hold the promise of optimized carbon storage and temperature reduction in cities. Remote sensing imagery can identify tree location and size, classify trees based on their species, and track tree health. Using multi- and hyperspectral overhead imagery, green vegetation can be separated from various land use types. Moreover, through further refinement of models by texture and contextual information, trees can get spatially separated from bushes and grass covered surfaces. While spectral-based tree identification can achieve accuracy of 90%, additional deep learning models using even noisy labeled data can further improve tree identification models. Once trees are identified in two-dimensional remote sensing images, allometric models allow to extract tree height and tree growth based on climate data, topography, and soil properties. The biomass of the trees is calculated for tree species using geometrical and phenological models. The carbon stored in trees can be quantified at individual tree level. Furthermore, the models allow to identify areas densely covered by trees to pinpoint bare land where further trees may be planted. Exploiting land surface temperature maps from satellite thermal measurements of, e.g., the Sentinel or Landsat missions, urban heat island can be mapped out at city scale. Urban heat islands may vary based on season and weather conditions; areas persistently warmer when compared to average city temperature background can be identified from time series of data. The correlation of local temperature, tree cover, and land perviousness helps to identify local climate zones. It also may refine and re-evaluate the definition of Local Climate Zones (LCZ). We employ the PAIRS geospatial information platform to demonstrate a scalable solution for tree delineation, carbon sequestration, and urban heat island identification for three global cities: Madrid, New York City, and Dallas, TX.

Item URL in elib:https://elib.dlr.de/186651/
Document Type:Conference or Workshop Item (Poster)
Title:Carbon Sequestration and Urban Heat Island Mitigation by Urban Forests
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Klein, Levente Jkleinl (at) us.ibm.comUNSPECIFIED
Albrecht, Conrad MConrad.Albrecht (at) dlr.deUNSPECIFIED
Marianno, Fernandofjmarian (at) us.ibm.comUNSPECIFIED
Date:26 May 2022
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:carbon sequestration, urban forests, machine learning, LiDAR, tree species identification, Big GeoData Processing
Event Title:2022 ESA Living Planet Symposium
Event Location:Bonn, Germany
Event Type:international Conference
Event Dates:23.-27. May 2022
Organizer:European Space Agency
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:03 Jun 2022 10:45
Last Modified:07 Jun 2022 17:52

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Website and database design: Copyright © German Aerospace Center (DLR). All rights reserved.