Abstract

Wildfires in the Chornobyl Exclusion Zone (CEZ) and other radioactively contaminated areas threaten human health and well-being with the potential to resuspend radionuclides. Wildfire behavior simulation is a necessary tool to examine the efficiency of fuel treatments in the CEZ, but it requires systematically updated maps of fuel types and canopy metrics. The objective of this study was to demonstrate an effective approach for mapping fuel types, canopy height (CH), and canopy cover (CC) in territories contaminated by radionuclides using Landsat time series (LTS) and Global Ecosystem Dynamics Investigation (GEDI) LiDAR observations. We combined LTS and GEDI data to map fuel types and canopy metrics used in wildfire simulations within the CEZ. Our classifi-cation model showed an adequate overall accuracy (75%) in mapping land covers and associated fuel types. The phenology metrics extracted from LTS reliably distinguished spectrally similar vegetation types (such as grass-lands and croplands) which exhibit different flammability through the year. We also predicted a suite of relative heights metrics and CC at Landsat 30-m pixel level (R2 = 0.23-0.26) using the nearest neighbor technique. The imputed maps adequately captured the dynamics of CH and CC in the CEZ after recent large wildfires occurred in 2015, 2020, and 2022. Thus, we illustrate a LTS processing approach to produce wall-to-wall maps of canopy characteristics that are important for wildfire simulations. We conclude that continuous updating of land cover and canopy fuel data is crucial to ensure relevant fire management of radioactively contaminated landscapes and support local decision-making.

Keywords

Fire behavior; Temporal segmentation; Stand height; Canopy density; Imputation; CCDC

Published in

Journal of Environmental Management
2023, Volume: 345, article number: 118736
Publisher: ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD

SLU Authors

• Matsala, Maksym

  • Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences
    

Associated SLU-program

SLU Forest Damage Center


UKÄ Subject classification

Environmental Sciences
Environmental Management


Publication identifier

DOI: https://doi.org/10.1016/j.jenvman.2023.118736

Permanent link to this page (URI)

https://res.slu.se/id/publ/124428