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Tracking forest and open area effects on snow accumulation by unmanned aerial vehicle photogrammetry

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F16%3A10328025" target="_blank" >RIV/00216208:11310/16:10328025 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.5194/isprsarchives-XLI-B1-917-2016" target="_blank" >http://dx.doi.org/10.5194/isprsarchives-XLI-B1-917-2016</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.5194/isprsarchives-XLI-B1-917-2016" target="_blank" >10.5194/isprsarchives-XLI-B1-917-2016</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Tracking forest and open area effects on snow accumulation by unmanned aerial vehicle photogrammetry

  • Original language description

    The availability of low-cost Unmanned Aerial Vehicle (UAV) platforms well adopted for digital photography and progress in software development now gives rise to apply this technique to different areas of research. Especially in determining snow depth spatial distributions, where repetitive mapping of cryosphere dynamics is crucial. Here, we introduce UAV-based digital photogrammetry as a rapid and robust approach for evaluating snow accumulation over small local areas and to reveal impacts related to changes in forest and snowpack. Due to the advancement of the technique, snow depth of selected study areas such as of healthy forest, disturbed forest, succession, dead forest, and of open areas can be estimated at a 1 cm spatial resolution. The approach is performed in two steps: 1) developing a high resolution Digital Elevation Model during snow-free and 2) during snow-covered conditions. By substracting these two models the snow depth can be accurately retrieved and volumetric changes of snow depth distribution can be achieved. This is a first proof-of-concept study combining snow depth determination and Leaf Area Index (LAI) retrieval to monitor the impact of forest canopy metrics on snow accumulation in coniferous forest within the Šumava National Park, Czech Republic. Both, downward-looking UAV images and upward-looking LAI-2200 canopy analyser measurements were applied to reveal the LAI, controlling interception and transmitting radiation. In contrast to the classical determination of LAI by hemispherical photography or by LAI plant canopy analyser, our approach will also test the accuracy of LAI measurements by UAV that are taken simultaneously during the snow cover mapping campaigns. Since the LAI parameter is important for snowpack modelling, this method presents the potential of simplifying LAI retrieval and mapping of snow dynamics while reducing running costs and time.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    DA - Hydrology and limnology

  • OECD FORD branch

Result continuities

  • Project

    <a href="/en/project/LD15130" target="_blank" >LD15130: Impact of landscape disturbance on stream and basin connectivitz</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>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

  • Article name in the collection

    The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

  • ISBN

  • ISSN

    1682-1750

  • e-ISSN

  • Number of pages

    7

  • Pages from-to

    917-923

  • Publisher name

    International Society of Photogrammetry and Remote Sensing (ISPRS)

  • Place of publication

    Neuveden

  • Event location

    Praha

  • Event date

    Jul 12, 2016

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000392750100141