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Synthetic RapidEye data used for the detection of area-based spruce tree mortality induced by bark beetles

Latifi, Hooman and Dahms, Thorsten and Beudert, Burkhard and Heurich, Marco and Kübert, Carina and Dech, Stefan (2018) Synthetic RapidEye data used for the detection of area-based spruce tree mortality induced by bark beetles. Giscience & Remote Sensing, 55 (6), pp. 839-859. Taylor & Francis. DOI: doi.org/10.1080/15481603.2018.1458463 ISSN 1548-1603

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Official URL: https://www.tandfonline.com/doi/full/10.1080/15481603.2018.1458463

Abstract

Tree mortality caused by outbreaks of the bark beetle Ips typographus (L.) plays an important role in the natural dynamics of Norway spruce (Picea abies L.) stands, which could cause far-reaching changes in the occurrence and duration of vegetation phenology. Field-based early detection of tree disturbances is hampered by logistic, terrain, and technical shortcomings, and by the inability to continuously monitor disturbances over large areas. Despite achievements in remote mapping of bark-beetle-induced tree mortalities, early warning has been mostly unsuccessful mainly because of the lack of spectral sensitivity and discrepancies in definitions of field- and image-based disturbance classes. Here we applied a method based on inter-annual phenology of Norway spruce stands derived from synthetic multispectral data to part of the Bavarian Forest National Park in Germany. We fused temporally continuous Moderate Resolution Imaging Spectroradiometer and discrete RapidEye data using a flexible spatiotemporal data fusion method to achieve validated 8-day RapidEye-like composites of normalized difference vegetation index for 2011. We assumed that the dead trees delineated on 2012 aerial photographs were those in which bark beetle infestations were initiated in 2011. Samples were drawn with variable-sized buffering to represent the areas prone to infestations and their surroundings. We applied a conditional inference random forest to select the best image date among the entire 46 synthetic datasets to best discriminate between the core infestation patches and their surroundings from the subsequent year. Of the discrete time points identified, day 281 of the year represented the highest discrepancy between aerial image-based dead trees and their surroundings. Classification results were significantly correlated with beetle count data obtained using pheromone traps. Our method provided valuable information for management purposes and enabled wall-to-wall mapping of stands prone to infestation and its uncertainty. The results offer potential implications for rapid and cost-effective monitoring of bark beetle outbreaks using satellite data, which would be of great benefit for both management and research tasks

Item URL in elib:https://elib.dlr.de/122829/
Document Type:Article
Title:Synthetic RapidEye data used for the detection of area-based spruce tree mortality induced by bark beetles
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Latifi, Hoomanhooman.latifi (at) uni-wuerzburg.deUNSPECIFIED
Dahms, Thorstenthorsten.dahms (at) uni-wuerzburg.deUNSPECIFIED
Beudert, BurkhardUNSPECIFIEDUNSPECIFIED
Heurich, MarcoMarco.Heurich (at) npv-bw.bayern.deUNSPECIFIED
Kübert, Carinacarina.kuebert (at) uni-wuerzburg.deUNSPECIFIED
Dech, StefanStefan.Dech (at) dlr.deUNSPECIFIED
Date:2018
Journal or Publication Title:Giscience & Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:55
DOI :doi.org/10.1080/15481603.2018.1458463
Page Range:pp. 839-859
Publisher:Taylor & Francis
ISSN:1548-1603
Status:Published
Keywords:conditional inference trees, forest disturbance, FSDAF, MODIS, RapidEye
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 Geowissenschaftl. Fernerkundungs- und GIS-Verfahren
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center
German Remote Sensing Data Center > Leitungsbereich DFD
Deposited By: Wöhrl, Monika
Deposited On:12 Nov 2018 10:36
Last Modified:06 Sep 2019 15:20

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