Windthrow mapping with Sentinel-2 and PlanetScope in Triglav National Park

Extreme weather increasingly damages forest ecosystems, and affected areas are often remote or inaccessible, limiting field surveys. In such contexts, remote sensing can complement damage assessment. This study presents a regional case study evaluating established multi-temporal optical change detection for windthrow mapping in Triglav National Park (Slovenia) using Sentinel-2 and PlanetScope imagery. Bitemporal index differencing and fixed thresholds were applied, with accuracy quantified via a block bootstrap to account for spatial autocorrelation. Within-sample overall accuracy was 69.2% (95% CI: 67.4–71.2%) for Sentinel-2 and 72.9% (95% CI: 71.2–74.6%) for PlanetScope. Detection was strongly size-dependent: gaps greater than 0.5 ha were consistently detected, whereas gaps smaller than 0.1 ha were frequently omitted, particularly with Sentinel-2. Linking satellite-derived change maps with forest stand data enabled parcel-level estimates of damaged timber volume

Publication Details

Journal
Repository of the University of Ljubljana (University of Ljubljana)
Published
2025-11-05
Primary Topic
Remote Sensing in Agriculture
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article
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article

Windthrow mapping with Sentinel-2 and PlanetScope in Triglav National Park

Repository of the University of Ljubljana (University of Ljubljana)
Remote Sensing in Agriculture
article

Windthrow mapping with Sentinel-2 and PlanetScope in Triglav National Park

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Abstract

Extreme weather increasingly damages forest ecosystems, and affected areas are often remote or inaccessible, limiting field surveys. In such contexts, remote sensing can complement damage assessment. This study presents a regional case study evaluating established multi-temporal optical change detection for windthrow mapping in Triglav National Park (Slovenia) using Sentinel-2 and PlanetScope imagery. Bitemporal index differencing and fixed thresholds were applied, with accuracy quantified via a block bootstrap to account for spatial autocorrelation. Within-sample overall accuracy was 69.2% (95% CI: 67.4–71.2%) for Sentinel-2 and 72.9% (95% CI: 71.2–74.6%) for PlanetScope. Detection was strongly size-dependent: gaps greater than 0.5 ha were consistently detected, whereas gaps smaller than 0.1 ha were frequently omitted, particularly with Sentinel-2. Linking satellite-derived change maps with forest stand data enabled parcel-level estimates of damaged timber volume

Repository of the University of Ljubljana (University of Ljubljana)
Life in Land
Openalex Percentile: Top 33%
Remote Sensing in Agriculture
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