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Auteur Samuli Launiainen |
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Towards dynamic forest trafficability prediction using open spatial data, hydrological modelling and sensor technology / Aura Salmivaara in Forestry, an international journal of forest research, vol 93 n° 5 (October 2020)
[article]
Titre : Towards dynamic forest trafficability prediction using open spatial data, hydrological modelling and sensor technology Type de document : Article/Communication Auteurs : Aura Salmivaara, Auteur ; Samuli Launiainen, Auteur ; Jari Perttunen, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 662 - 674 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Environnement
[Termes IGN] apprentissage automatique
[Termes IGN] chemin forestier
[Termes IGN] classification barycentrique
[Termes IGN] dégradation des sols
[Termes IGN] dommage
[Termes IGN] données localisées libres
[Termes IGN] exploitation forestière
[Termes IGN] Finlande
[Termes IGN] humidité du sol
[Termes IGN] modèle dynamique
[Termes IGN] modèle hydrographiqueRésumé : (auteur) Forest harvesting operations with heavy machinery can lead to significant soil rutting. Risks of rutting depend on the soil bearing capacity which has considerable spatial and temporal variability. Trafficability prediction is required in the selection of suitable operation sites for a given time window and conditions, and for on-site route optimization during the operation. Integrative tools are necessary to plan and carry out forest operations with minimal negative ecological and economic impacts. This study demonstrates a trafficability prediction framework that utilizes a spatial hydrological model and a wide range of spatial data. Trafficability was approached by producing a rut depth prediction map at a 16 × 16 m grid resolution, based on the outputs of a general linear mixed model developed using field data from Southern Finland, modelled daily soil moisture, spatial forest inventory and topography data, along with field measured rolling resistance and information on the mass transported through the grid cells. Dynamic rut depth prediction maps were produced by accounting for changing weather conditions through hydrological modelling. We also demonstrated a generalization of the rolling resistance coefficient, measured with harvester CAN-bus channel data. Future steps towards a nationwide prediction framework based on continuous data flow, process-based modelling and machine learning are discussed. Numéro de notice : A2020-790 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1093/forestry/cpaa010 Date de publication en ligne : 05/10/2020 En ligne : https://doi.org/10.1093/forestry/cpaa010 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96559
in Forestry, an international journal of forest research > vol 93 n° 5 (October 2020) . - pp 662 - 674[article]