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Landsat Imagery Spectral Trajectories - Important Variables for Spatially Predicting the Risks of Bark Beetle Disturbance

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985939%3A_____%2F16%3A00463567" target="_blank" >RIV/67985939:_____/16:00463567 - isvavai.cz</a>

  • Alternative codes found

    RIV/60077344:_____/16:00463567 RIV/60460709:41330/16:71171 RIV/60076658:12310/16:43890871 RIV/00216208:11310/16:10328356

  • Result on the web

    <a href="http://dx.doi.org/10.3390/rs8080687" target="_blank" >http://dx.doi.org/10.3390/rs8080687</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.3390/rs8080687" target="_blank" >10.3390/rs8080687</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Landsat Imagery Spectral Trajectories - Important Variables for Spatially Predicting the Risks of Bark Beetle Disturbance

  • Original language description

    In this study we introduce pre-disturbance spectral trajectories from Landsat Thematic Mapper (TM) imagery as an indicator of long-term stress into models of bark beetle infestation. Their inclusion improved models predictive ability. Wetness slope had the greatest predictive power, even relative to environmental predictors, and was relatively stable in its power over the years. The pre-disturbance spectral trajectories are valuable not only for assessing the risk of bark beetle infestation, but also for detection of long-term gradual changes even in non-forest ecosystems.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

  • CEP classification

    GK - Forestry

  • OECD FORD branch

Result continuities

  • Project

    <a href="/en/project/LD15158" target="_blank" >LD15158: Modelling of large scale multi-agents disturbances in mountain spruce forests to assess their different scenarios</a><br>

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2016

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Remote Sensing

  • ISSN

    2072-4292

  • e-ISSN

  • Volume of the periodical

    8

  • Issue of the periodical within the volume

    8

  • Country of publishing house

    CH - SWITZERLAND

  • Number of pages

    22

  • Pages from-to

  • UT code for WoS article

    000382458700073

  • EID of the result in the Scopus database