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European-wide forest monitoring substantiate the neccessity for a joint conservation strategy to rescue European ash species (Fraxinus spp.) / Jan-Peter George in Scientific reports, vol 12 (2022)  

Public [article]  inScientific reports > vol 12 (2022) . - n° 4764 Titre : European-wide forest monitoring substantiate the neccessity for a joint conservation strategy to rescue European ash species (Fraxinus spp.) Type de document : Article/Communication Auteurs : Jan-Peter George, Auteur ; Tanja G.M. Sanders, Auteur ; Volkmar Timmermann, Auteur ; Nenad Potočić, Auteur ; Mait Lang, Auteur Année de publication : 2022 Article en page(s) : n° 4764 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] défoliation [Termes IGN] Europe (géographie politique) [Termes IGN] Fraxinus angustifolia [Termes IGN] Fraxinus excelsior [Termes IGN] mortalité [Termes IGN] politique de conservation (biodiversité) [Termes IGN] régression [Termes IGN] surveillance forestière [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) European ash (Fraxinus excelsior) and narrow-leafed ash (F. angustifolia) are keystone forest tree species with a broad ecological amplitude and significant economic importance. Besides global warming both species are currently under significant threat by an invasive fungal pathogen that has been spreading progressively throughout the continent for almost three decades. Ash dieback caused by the ascomycete Hymenoscyphus fraxineus is capable of damaging ash trees of all age classes and often ultimately leads to the death of a tree after years of progressively developing crown defoliation. While studies at national and regional level already suggested rapid decline of ash populations as a result of ash dieback, a comprehensive survey at European level with harmonized crown assessment data across countries could shed more light into the population decline from a pan-European perspective and could also pave the way for a new conservation strategy beyond national boarders. Here we present data from the ICP Forests Level I crown condition monitoring from 27 countries resulting in > 36,000 observations. We found a substantial increase in defoliation and mortality over time indicating that crown defoliation has almost doubled during the last three decades. Hotspots of mortality are currently situated in southern Scandinavia and north-eastern Europe. Overall survival probability after nearly 30 years of infection has already reached a critical value of 0.51, but with large differences among regions (0.20–0.86). Both a Cox proportional hazard model as well as an Aalen additive regression model strongly suggest that survival of ash is significantly lower in locations with excessive water regime and which experienced more extreme precipitation events during the last two decades. Our results underpin the necessity for fast governmental action and joint rescue efforts beyond national borders since overall mean defoliation will likely reach 50% as early as 2030 as suggested by time series forecasting. Numéro de notice : A2022-309 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1038/s41598-022-08825-6 Date de publication en ligne : 19/03/2022 En ligne : http://dx.doi.org/10.1038/s41598-022-08825-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100404 [article]

Planning of commercial thinnings using machine learning and airborne Lidar data / Tauri Arumäe in Forests, vol 13 n° 2 (February 2022)  

Public [article]  inForests > vol 13 n° 2 (February 2022) . - n° 206 Titre : Planning of commercial thinnings using machine learning and airborne Lidar data Type de document : Article/Communication Auteurs : Tauri Arumäe, Auteur ; Mait Lang, Auteur ; Allan Sims, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 206 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage automatique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] éclaircie (sylviculture) [Termes IGN] Estonie [Termes IGN] gestion forestière [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] modèle linéaire [Termes IGN] planification [Termes IGN] semis de points Résumé : (auteur) The goal of this study was to predict the need for commercial thinning using airborne lidar data (ALS) with random forest (RF) machine learning algorithm. Two test sites (with areas of 14,750 km2 and 12,630 km2) were used with a total of 1053 forest stands from southwestern Estonia and 951 forest stands from southeastern Estonia. The thinnings were predicted based on the ALS measurements in 2019 and 2017. The two most important ALS metrics for predicting the need for thinning were the 95th height percentile and the canopy cover. The prediction accuracy based on validation stands was 93.5% for southwestern Estonia and 85.7% for southeastern Estonia. For comparison, the general linear model prediction accuracy was less for both test sites—92.1% for southwest and 81.8% for southeast. The selected important predictive ALS metrics differed from those used in the RF algorithm. The cross-validation of the thinning necessity models of southeastern and southwestern Estonia showed a dependence on geographic regions. Numéro de notice : A2022-122 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f13020206 Date de publication en ligne : 29/01/2022 En ligne : https://doi.org/10.3390/f13020206 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99674 [article]