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Estimating tree height from TanDEM-X data at the northwestern Canadian treeline

Antonova, Sofia and Thiel, Christian and Höfle, Bernhard and Anders, Katharina and Helm, Veit and Zwieback, Simon and Marx, Sabrina and Boike, Julia (2019) Estimating tree height from TanDEM-X data at the northwestern Canadian treeline. Remote Sensing of Environment, 231, p. 111251. Elsevier. DOI: 10.1016/j.rse.2019.111251 ISSN 0034-4257

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Official URL: http://dx.doi.org/10.1016/j.rse.2019.111251

Abstract

The circum-Arctic transitional zone between forest and tundra, i.e. the treeline zone, is shifting northward due to current Arctic warming and, therefore, requires systematic monitoring. Up to now, radar remote sensing was hardly possible in the treeline zone due to spatial resolution and/or temporal decorrelation constraints of preceding satellite missions. The unique constellation of the TanDEM-X satellites with its bistatic mode and very high spatial resolution opens up opportunities for monitoring small (≥0.01 km2) and isolated patches of very sparse forest which are typical for the transitional zone. We focused on an area at the northern edge of the treeline zone in the Northwest Territories, Canada, and evaluated the potential of TanDEM-X bistatic data to characterize the tree height in the forest patches in this region. TanDEM-X data were acquired during the TanDEM-X Science Phase in 2015, when the perpendicular baseline was large (corresponding to the height of ambiguity of approximately 14.6 m) and kept constant. We employed TanDEM-X backscatter, bistatic coherence, and interferometric height from the stack of seven multitemporal bistatic pairs and compared them to maximum vegetation height obtained from full-waveform airborne LiDAR data. We found strong linear relationships between all TanDEM-X metrics and LiDAR vegetation height within the forest patches with r=0.67, r=−0.69, and r=0.78 for the backscatter, coherence, and interferometric height, respectively. Furthermore, we extracted the position of individual trees from the LiDAR data and estimated tree density as the number of trees per unit area. The linear relationships between all TanDEM-X metrics and the tree density were very weak. The relationships between all TanDEM-X metrics and tree height differentiated for three tree density classes (low, medium, and high) remained strong. Random forests regression using all three TanDEM-X metrics predicted the tree height with a mean absolute error of 0.7m (mean forest height in the study area was 2.5 m). CoSSC pairs were generally consistent with each other and the multitemporal averaging slightly improved the performance compared to single pairs. Taking into account the global coverage of bistatic TanDEM-X data acquired for the global digital elevation model, our results show a potential for quantifying the tree height in small forest patches along the circum-Arctic treeline zone.

Item URL in elib:https://elib.dlr.de/131154/
Document Type:Article
Title:Estimating tree height from TanDEM-X data at the northwestern Canadian treeline
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Antonova, SofiaUNSPECIFIEDUNSPECIFIED
Thiel, ChristianChristian.Thiel (at) dlr.dehttps://orcid.org/0000-0001-5144-8145
Höfle, BernhardUNSPECIFIEDUNSPECIFIED
Anders, KatharinaUNSPECIFIEDUNSPECIFIED
Helm, VeitUNSPECIFIEDUNSPECIFIED
Zwieback, SimonUNSPECIFIEDUNSPECIFIED
Marx, SabrinaUNSPECIFIEDUNSPECIFIED
Boike, JuliaUNSPECIFIEDUNSPECIFIED
Date:7 June 2019
Journal or Publication Title:Remote Sensing of Environment
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:231
DOI :10.1016/j.rse.2019.111251
Page Range:p. 111251
Publisher:Elsevier
ISSN:0034-4257
Status:Published
Keywords:Bistatic CoSSC Backscatter Coherence Interferometry InSAR height Forest patches LiDAR ALS
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:other
DLR - Research area:Raumfahrt
DLR - Program:R - no assignment
DLR - Research theme (Project):R - no assignment
Location: Jena
Institutes and Institutions:Institute of Data Science > Citizen Science
Deposited By: Thiel, Christian
Deposited On:25 Nov 2019 08:55
Last Modified:09 Dec 2019 15:31

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