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Tree Species Classification in the Bavarian Forest National Park using Hyperspectral Remote Sensing and Site Specific Information

Pinnel, Nicole and Fetik, Yannic and Holzwarth, Stephanie and Heiden, Uta and Carolin, Sommer and Heurich, Marco (2017) Tree Species Classification in the Bavarian Forest National Park using Hyperspectral Remote Sensing and Site Specific Information. 10th EARSeL SIG Imaging Spectroscopy Workshop, 19.-21.April 2017, Zürich, Schweiz.

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Abstract

The Bavarian Forest National Park is a unique area of forest stands that developed with low anthropogenic interference into a landscape with remnants of a primeval forest. There is a high interest of the National Park authority to investigate the composition and development of the forest ecosystem. However conventional forest inventories are time-consuming and focus mainly on the forests economic value. The goal of this study is to develop advanced mapping techniques combining remote sensing and in-situ information that meet the demands of the National Park and emphasize the ecological value of a forest. This study is based on a large scale hyperspectral survey flown in 2013. 16 000 hectares of HySpex VNIR1600 and SWIR 320i data were acquired covering 65% of the whole National Park. A previous hyperspectral species mapping exercise was successfully performed using the HySpex VNIR data only. This new approach was now significantly improved using the additional spectral information of the HySpex SWIR sensor as well as stand density information. In addition to the remote sensing data, forest inventory data from 2002 were provided by the National Park and used for validation and partly as additional training data. Clusters of tree crowns from 11 different tree species were located and identified during a field survey in November 2014 and December 2015. This field data was used as training and validation set. By overlaying the pre-selected and field-demarcated tree canopies with the hyperspectral data the tree species spectral libraries were created and used as input for the analysis. Different input parameters and predictor datasets, consisting of spectral and structural data were investigated to enhance the Random Forest classification model. The impact of spectral coverage, geometric resolution and training data on the quality of classification result was also evaluated by comparing previous and new classification results. This work aims at building a site specific classification model transferable to an area wide mapping approach based on the needs of the Bavarian Forest National Park, which will in future be extended to the Šumava National Park. This study reveals the requirements for tree species mapping and shows which spectral/spatial features and data composition generate the best results.

Item URL in elib:https://elib.dlr.de/112106/
Document Type:Conference or Workshop Item (Speech)
Title:Tree Species Classification in the Bavarian Forest National Park using Hyperspectral Remote Sensing and Site Specific Information
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Pinnel, NicoleNicole.Pinnel (at) dlr.dehttps://orcid.org/0000-0003-1978-3204
Fetik, Yannicyannic.fetik (at) web.deUNSPECIFIED
Holzwarth, Stephaniestephanie.holzwarth (at) dlr.deUNSPECIFIED
Heiden, Utauta.heiden (at) dlr.deUNSPECIFIED
Carolin, Sommercarolin.sommer (at) gaf.deUNSPECIFIED
Heurich, Marcomarco.heurich (at) npv-bw.bayern.deUNSPECIFIED
Date:2017
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:tree species, hyperspectral, HySpex, Bavarian Forest National Park, Random Forest
Event Title:10th EARSeL SIG Imaging Spectroscopy Workshop
Event Location:Zürich, Schweiz
Event Type:Workshop
Event Dates:19.-21.April 2017
Organizer:Remote Sensing Laboratories, University of Zurich
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben Fernerkundung der Landoberfläche (old), R - Vorhaben Spektrometrische Verfahren und Konzepte der Fernerkundung (old), R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center
Deposited By: Pinnel, Dr.rer.nat Nicole
Deposited On:04 Jul 2017 12:06
Last Modified:11 Dec 2018 09:07

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