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Mapping individual silver fir trees using hyperspectral and LiDAR data in a Central European mixed forest

Shi, Yifang and Wang, Tiejun and Skidmore, Andrew and Holzwarth, Stefanie and Heiden, Uta and Heurich, Marco (2021) Mapping individual silver fir trees using hyperspectral and LiDAR data in a Central European mixed forest. International Journal of Applied Earth Observation and Geoinformation. Elsevier. ISSN 0303-2434.

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Abstract

Mapping a specific tree species at individual tree level across landscapes using remote sensing is challenging, especially in forests where co-occurring tree species exhibit similar characteristics. In Central European mixed forests, silver fir and Norway spruce have been identified as a pair of coniferous tree species with similar spectral and structural characteristics, typically leading to a major misclassification error in mapping studies. Here, we aimed to accurately map individual silver fir trees in a spruce-dominated natural forest in the Bavarian Forest National Park using integrated airborne hyperspectral and LiDAR data. To accomplish this goal, we extracted a set of relevant spectral and structural features from the hyperspectral and LiDAR data and used them to build machine learning classification models. Specifically, we compared the performance of three one-class classification algorithms (i.e. one-class support vector machine, biased support vector machine, and maximum entropy) for mapping individual silver fir trees. Our results showed that the biased support vector machine classifier yielded the highest mapping accuracy, with the area under the curve for positive and unlabeled samples (puAUC) achieving 0.95 (kappa 0.90). We found that the intensity value of 95th percentile of normalized tree height and the percentage of first returns above 2 m high were the most influential structural features, capturing the main morphological difference between silver fir and Norway spruce at the top tree crown. We also found that the wavebands at 700.1 nm, 714.5 nm, and 1201.6 nm were the most robust spectral bands, which are strongly affected by chlorophyll and foliar water content. Our study suggests that discovering links between spectral and structural features captured by different remotely sensed data and species-specific traits can significantly improve the mapping accuracy of a focal species at the individual tree level.

Item URL in elib:https://elib.dlr.de/140983/
Document Type:Article
Title:Mapping individual silver fir trees using hyperspectral and LiDAR data in a Central European mixed forest
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Shi, Yifangy.shi-1 (at) utwente.nlUNSPECIFIED
Wang, Tiejunt.wang (at) utwente.nlUNSPECIFIED
Skidmore, Andrewa.k.skidmore (at) utwente.nlUNSPECIFIED
Holzwarth, StefanieStefanie.Holzwarth (at) dlr.dehttps://orcid.org/0000-0001-7364-7006
Heiden, Utauta.heiden (at) dlr.dehttps://orcid.org/0000-0002-3865-1912
Heurich, Marcomarco.heurich (at) npv-bw.bayern.deUNSPECIFIED
Date:17 February 2021
Journal or Publication Title:International Journal of Applied Earth Observation and Geoinformation
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
Publisher:Elsevier
ISSN:0303-2434
Status:Published
Keywords:Specific tree species, Silver fir, Hyperspectral, LiDAR, One-class classification
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 - Remote Sensing and Geo Research
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface Dynamics
Deposited By: Holzwarth, Stefanie
Deposited On:18 Feb 2021 14:47
Last Modified:18 Feb 2021 14:47

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