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Mitigating the risk of wind damage at the forest landscape level by using stand neighbourhood and terrain elevation information in forest planning / Roope Ruotsalainen in Forestry, an international journal of forest research, vol 96 n° 1 (January 2023)  

Public [article]  inForestry, an international journal of forest research > vol 96 n° 1 (January 2023) . - pp 121 - 134 Titre : Mitigating the risk of wind damage at the forest landscape level by using stand neighbourhood and terrain elevation information in forest planning Type de document : Article/Communication Auteurs : Roope Ruotsalainen, Auteur ; Timo Pukkala, Auteur ; Veli-Pekka Ikonen, Auteur Année de publication : 2023 Article en page(s) : pp 121 - 134 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] altitude [Termes IGN] canopée [Termes IGN] dommage forestier causé par facteurs naturels [Termes IGN] Finlande [Termes IGN] forêt boréale [Termes IGN] gestion forestière [Termes IGN] inventaire forestier étranger (données) [Termes IGN] modèle numérique de surface [Termes IGN] pondération [Termes IGN] prévention des risques [Termes IGN] topographie locale [Termes IGN] vent [Termes IGN] voisinage (relation topologique) [Vedettes matières IGN] Foresterie Résumé : (auteur) Wind damage and the bark beetle outbreaks associated with it are major threats to non-declining, long-term wood production in boreal forests. We studied whether the risk of wind damage in a forested landscape could be decreased by using stand neighbourhood information in conjunction with terrain elevation information. A reference management plan minimized the differences in canopy height at stand boundaries and did not utilize information on the topography of the terrain, overlooking the possibility that the risk of windthrow may depend on the elevation of the terrain. Alternative management plans were developed by using four different weighting schemes when minimizing differences in canopy height at stand boundaries: (1) no weight (reference); (2) mean terrain elevation at the stand boundary; (3) deviation of the mean elevation of the boundary from the mean elevation of the terrain within a 100-m radius and (4) multipliers that described the effect of topography on wind speed at the stand boundary. For each management plan, we calculated the total number of at-risk trees and the total area of vulnerable stand edge. These statistics were based on the calculated critical wind speeds needed to uproot trees in stand edge zones. Minimization of the weighted mean of canopy height differences between adjacent stands resulted in homogeneous landscapes in terms of canopy height. Continuous cover management was often preferred instead of rotation management due to smaller canopy height differences between adjacent stands and its economical superiority. The best weighting scheme for calculating the mean canopy height difference between adjacent stands was the deviation between the mean elevation of the boundary and the mean elevation of the terrain within 100 m of the boundary. However, the differences between the weighting schemes were small. It was found that reasonably simple methods, based on a digital terrain model, a stand map, and the canopy heights of stands, could be used in forest planning to minimize the risk of wind damage. Validation against actual wind damages is required to assess the reliability of the results and to further develop the methodology presented. Numéro de notice : A2023-114 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1093/forestry/cpac039 Date de publication en ligne : 08/10/2022 En ligne : https://doi.org/10.1093/forestry/cpac039 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102481 [article]

Deep learning for the detection of early signs for forest damage based on satellite imagery / Dennis Wittich in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2022 (2022 edition) . - pp 307 - 315 Titre : Deep learning for the detection of early signs for forest damage based on satellite imagery Type de document : Article/Communication Auteurs : Dennis Wittich, Auteur ; Franz Rottensteiner, Auteur ; Mirjana Voelsen, Auteur ; Christian Heipke, Auteur ; Sönke Müller, Auteur Année de publication : 2022 Article en page(s) : pp 307 - 315 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] dégradation de la flore [Termes IGN] dommage forestier causé par facteurs naturels [Termes IGN] fonction de perte [Termes IGN] image Sentinel-MSI [Termes IGN] régression [Termes IGN] série temporelle [Termes IGN] surveillance forestière Résumé : (auteur) We present an approach for detecting early signs for upcoming forest damages by training a Convolutional Neural Network (CNN) for the pixel-wise prediction of the remaining life-time (RLT) of trees in forests based on Sentinel-2 imagery. We focus on a scenario in which reference data are only available for a related task, namely for a bi-temporal pixel-wise classification of forest degradation. This reference is used to train a CNN for the pixel-wise prediction of forest degradation. In this context, we propose a new sub-sampling-based approach for compensating the effects of a heavy class imbalance in the training data. Using the resulting classification model, we predict semi-labels for images of a Sentinel-2 time series, from which training data for a CNN designed to regress the RLT can be derived after some label cleansing. However, due to data gaps in the time series, e.g. caused by clouds, only intervals can be derived for the target variable to be regressed, and for some training pixels one of the interval limits may even be unknown. Consequently, we propose a new loss function for training a CNN for regressing the RLT that only requires the known interval limits. The method is evaluated on a data set in Germany, covering a time-span of 5 years. We show that the proposed sub-sampling strategy for dealing with strong label imbalance when training the classifier significantly reduces the training time compared to other approaches. We further show that our model predicts the RLT with a maximum error of two months for 80% of the forest pixels that die within one year from the acquisition date of the Sentinel-2 image. Numéro de notice : A2022-432 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.5194/isprs-annals-V-2-2022-307-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2022-307-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100738 [article]

Natural disturbances risks in European boreal and temperate forests and their links to climate change : A review of modelling approaches / Joyce Machado Nunes Romeiro in Forest ecology and management, vol 509 (April-1 2022)  

Public [article]  inForest ecology and management > vol 509 (April-1 2022) . - n° 120071 Titre : Natural disturbances risks in European boreal and temperate forests and their links to climate change : A review of modelling approaches Type de document : Article/Communication Auteurs : Joyce Machado Nunes Romeiro, Auteur ; Tron Eid, Auteur ; Clara Antón-Fernández, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 120071 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] changement climatique [Termes IGN] dommage forestier causé par facteurs naturels [Termes IGN] foresterie [Termes IGN] forêt boréale [Termes IGN] forêt tempérée [Termes IGN] gelée [Termes IGN] gestion forestière adaptative [Termes IGN] incendie de forêt [Termes IGN] maladie parasitaire [Termes IGN] modèle de simulation [Termes IGN] modélisation [Termes IGN] risque naturel [Termes IGN] Scolytinae [Termes IGN] sécheresse [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) It is expected that European Boreal and Temperate forests will be greatly affected by climate change, causing natural disturbances to increase in frequency and severity. To detangle how, through forest management, we can make forests less vulnerable to the impact of natural disturbances, we need to include the risks of such disturbances in our decision-making tools. The present review investigates: i) how the most important forestry-related natural disturbances are linked to climate change, and ii) different modelling approaches that assess the risks of natural disturbances and their applicability for large-scale forest management planning. Global warming will decrease frozen soil periods, which increases root rot, snow, ice and wind damage, cascading into an increment of bark beetle damage. Central Europe will experience a decrease in precipitation and increase in temperature, which lowers tree defenses against bark beetles and increases root rot infestations. Ice and wet snow damages are expected to increase in Northern Boreal forests, and to reduce in Temperate and Southern Boreal forests. However, lack of snow cover may increase cases of frost-damaged seedlings. The increased temperatures and drought periods, together with a fuel increment from other disturbances, likely enhance wildfire risk, especially for Temperate forests. For the review of European modelling approaches, thirty-nine disturbance models were assessed and categorized according to their required input variables and to the models’ outputs. Probability models are usually common for all disturbance model approaches, however, models that predict disturbance effects seem to be scarce. Numéro de notice : A2022-190 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article DOI : 10.1016/j.foreco.2022.120071 Date de publication en ligne : 10/02/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120071 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99946 [article]

Detection of windthrown tree stems on UAV-orthomosaics using U-Net convolutional networks / Stefan Reder in Remote sensing, vol 14 n° 1 (January-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 1 (January-1 2022) . - n° 75 Titre : Detection of windthrown tree stems on UAV-orthomosaics using U-Net convolutional networks Type de document : Article/Communication Auteurs : Stefan Reder, Auteur ; J.P. Mund, Auteur ; Nicole Albert, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 75 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] branche (arbre) [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection d'objet [Termes IGN] dommage forestier causé par facteurs naturels [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] image captée par drone [Termes IGN] orthophotoplan numérique [Termes IGN] segmentation sémantique [Termes IGN] tempête [Termes IGN] tronc Résumé : (auteur) The increasing number of severe storm events is threatening European forests. Besides the primary damages directly caused by storms, there are secondary damages such as bark beetle outbreaks and tertiary damages due to negative effects on the market. These subsequent damages can be minimized if a detailed overview of the affected area and the amount of damaged wood can be obtained quickly and included in the planning of clearance measures. The present work utilizes UAV-orthophotos and an adaptation of the U-Net architecture for the semantic segmentation and localization of windthrown stems. The network was pre-trained with generic datasets, randomly combining stems and background samples in a copy–paste augmentation, and afterwards trained with a specific dataset of a particular windthrow. The models pre-trained with generic datasets containing 10, 50 and 100 augmentations per annotated windthrown stems achieved F1-scores of 73.9% (S1Mod10), 74.3% (S1Mod50) and 75.6% (S1Mod100), outperforming the baseline model (F1-score 72.6%), which was not pre-trained. These results emphasize the applicability of the method to correctly identify windthrown trees and suggest the collection of training samples from other tree species and windthrow areas to improve the ability to generalize. Further enhancements of the network architecture are considered to improve the classification performance and to minimize the calculative costs. Numéro de notice : A2022-082 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs14010075 En ligne : https://doi.org/10.3390/rs14010075 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99476 [article]

Factors affecting winter damage and recovery of newly planted Norway spruce seedlings in boreal forests / Jaana Luoranen in Forest ecology and management, vol 503 (January-1 2022)  

Public [article]  inForest ecology and management > vol 503 (January-1 2022) . - n° 119759 Titre : Factors affecting winter damage and recovery of newly planted Norway spruce seedlings in boreal forests Type de document : Article/Communication Auteurs : Jaana Luoranen, Auteur ; Johanna Riikonen, Auteur ; Timo Saksa, Auteur Année de publication : 2022 Article en page(s) : n° 119759 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] coupe rase (sylviculture) [Termes IGN] dommage forestier causé par facteurs naturels [Termes IGN] Finlande [Termes IGN] forêt boréale [Termes IGN] hiver [Termes IGN] phénomène météorologique [Termes IGN] Picea abies [Termes IGN] régénération (sylviculture) [Termes IGN] semis (sylviculture) [Termes IGN] stockage [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) In boreal and temperate forest zones, snowless winters and springs with varying temperature conditions are becoming more common with climate change. In the spring of 2020, extensive winter damage in Norway spruce (Picea abies (L.) Karst.) seedlings, which had been planted the previous year in Central Finland, was observed. In most cases, the probable reason was winter desiccation. This provided a good opportunity to study the regeneration site, seedling, and weather factors that affect winter damage and the recovery of seedlings from damage. In the study, systematic plot sampling was done in 60 selected regeneration sites where damage was known to have occurred. The prediction models for the probabilities of winter damage and the recovery of seedlings were fit to the data. The risk of winter damage was higher in seedlings packed in a closed package than in seedlings stored in open trays. The risk was especially high if seedlings packed in a closed package were stored for more than a week before planting in the middle of June or later. In open trays, the risk of damage was highest in seedlings planted in September, but even then, the risk was lower than in seedlings packed in a closed package. Long storage duration also increased the damage risk in seedlings stored in open trays and planted in September. Other factors that increased damage were coarse soil and the sample plot being on top of a hill. Factors reducing the risk were a fast chain from clear-cutting to planting, planting in good-quality mounds, a sample plot position on the north slope, and the shading of the forest edge on the southern side of a plot. Recovery of seedlings was weaker when seedlings were stored in a closed package and planted in the fall, in too shallow planting depth, or in humus-covered mounds. Recovery improved when seedlings were planted at a depth of at least 5 cm, or when the coniferous forest edge was on the southern or western side of a plot. Winter damage reduced seedling growth and induced the formation of multiple leaders. In practice, the most important factors to be taken into account were avoiding long storage duration and planting seedlings packed in a closed package after the middle of June. Good-quality site preparation and planting were also important for minimizing the risk of damage and improving recovery. Numéro de notice : A2022-011 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2021.119759 Date de publication en ligne : 07/10/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.119759 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99068 [article]

Estimating timber volume loss due to storm damage in Carinthia, Austria, using ALS/TLS and spatial regression models / Arne Nothdurft in Forest ecology and management, vol 502 (December-15 2021)  

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Growth recovery and phenological responses of juvenile beech (fagus sylvatica L.) exposed to spring warming and late spring frost / Kristine Vander Mijnsbrugge in Forests, vol 12 n° 11 (November 2021)  

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Search for top‐down and bottom‐up drivers of latitudinal trends in insect herbivory in oak trees in Europe / Elena Valdés-Correcher in Global ecology and biogeography, vol 30 n° 3 (March 2021)  

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Simple method for identification of forest windthrows from Sentinel-1 SAR data incorporating PCA / Milan Lazecky in Procedia Computer Science, vol 181 (2021)  

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État sanitaire et croissance radiale des arbres / Clara Tallieu (2020)  

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Forest fire / Janusz Szmyt (2018)  

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The effects of forest structure on the risk of wind damage at a landscape level in a boreal forest ecosystem / Hongcheng Zeng in Annals of Forest Science, vol 67 n° 1 (January-February 2010)  

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Estimation des dégâts de tempête : l'oeil, l'avion et le satellite / Nicolas Stach (2009)

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Facteurs de risques de chablis dans les peuplements forestiers : les leçons tirées des tempêtes de 1999 / Francis Colin (2009)

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Frost damage in Pinus sylvestris L. stems assessed by chlorophyll fluorescence in cortical bark chlorenchyma / José Javier Peguero-Pina in Annals of Forest Science, Vol 65 n° 8 (December 2008)  

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