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Mapping forest windthrows using high spatial resolution multispectral satellite images

Acceso Abierto
ID Minciencias: ART-0000000651-40
Ranking: ART-ART_D

Abstract:

Wind disturbances represent the main source of damage in European forests, affecting them directly (windthrows) or indirectly due to secondary damages (insect outbreaks and forest fires). The assessment of windthrows damages is very important to establish adequate management plans and remote sensing can be very useful for this purpose. Many types of optical remote sensing data are available with different spectral, spatial and temporal resolutions, and many options are possible for data acquisition, i.e. immediately after the event or after a certain time. The objective of this study is to compare the windthrows mapping capabilities of two multispectral satellite constellations (i.e. Sentinel-2 and PlanetScope) characterized by very different spectral, spatial and temporal resolutions, and to evaluate the impact of the acquisition conditions on the mapping results. The analysed area, with an extent of 732 km2, is located in the Trentino-South Tyrol region (Italy) which was affected by the Vaia storm on the 27th-30th of October 2018, causing serious forest damages. The change vector analysis technique was used to detect the windthrows. For each data source, two pairs of images were considered: 1) pre- and post- event images acquired as close as possible to the event; 2) pre- and post- event images acquired at optimal conditions, i.e. at similar phenological state and similar illumination conditions. The results obtained with the two satellite constellations are very similar despite their different resolutions. Data acquired in optimal conditions allowed having the best detection rate (accuracy above 80 %), while data acquired just after the event showed many limitations. Improved spatial resolution (PlanetScope data) allows for a better delineation of the borders of the windthrow areas and of the detection of smaller windthrow patches.

Tópico:

Remote Sensing in Agriculture

Citaciones:

Citations: 51
51

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Paperbuzz Score: 0
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Información de la Fuente:

SCImago Journal & Country Rank
FuenteInternational Journal of Applied Earth Observation and Geoinformation
Cuartil año de publicaciónNo disponible
Volumen93
IssueNo disponible
Páginas102206 - 102206
pISSN1569-8432
ISSNNo disponible

Enlaces e Identificadores:

Minciencias IDART-0000000651-40Scienti ID0000000651-40Doi URLhttps://doi.org/10.1016/j.jag.2020.102206
Openalex URLhttps://openalex.org/W3045120308Open_access URLhttps://doi.org/10.1016/j.jag.2020.102206
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