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Instance segmentation of standing dead trees in dense forest from aerial imagery using deep learning / Aboubakar Sani-Mohammed in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 6 (December 2022)
[article]
Titre : Instance segmentation of standing dead trees in dense forest from aerial imagery using deep learning Type de document : Article/Communication Auteurs : Aboubakar Sani-Mohammed, Auteur ; Wei Yao, Auteur ; Marco Heurich, Auteur Année de publication : 2022 Article en page(s) : n° 100024 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] arbre mort
[Termes IGN] Bavière (Allemagne)
[Termes IGN] bois sur pied
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection automatique
[Termes IGN] gestion forestière durable
[Termes IGN] image à haute résolution
[Termes IGN] image aérienne
[Termes IGN] image infrarouge couleur
[Termes IGN] peuplement mélangé
[Termes IGN] puits de carbone
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Mapping standing dead trees, especially, in natural forests is very important for evaluation of the forest's health status, and its capability for storing Carbon, and the conservation of biodiversity. Apparently, natural forests have larger areas which renders the classical field surveying method very challenging, time-consuming, labor-intensive, and unsustainable. Thus, for effective forest management, there is the need for an automated approach that would be cost-effective. With the advent of Machine Learning, Deep Learning has proven to successfully achieve excellent results. This study presents an adjusted Mask R-CNN Deep Learning approach for detecting and segmenting standing dead trees in a mixed dense forest from CIR aerial imagery using a limited (195 images) training dataset. First, transfer learning is considered coupled with the image augmentation technique to leverage the limitation of training datasets. Then, we strategically selected hyperparameters to suit appropriately our model's architecture that fits well with our type of data (dead trees in images). Finally, to assess the generalization capability of our model's performance, a test dataset that was not confronted to the deep neural network was used for comprehensive evaluation. Our model recorded promising results reaching a mean average precision, average recall, and average F1-Score of 0.85, 0.88, and 0.87 respectively, despite our relatively low resolution (20 cm) dataset. Consequently, our model could be used for automation in standing dead tree detection and segmentation for enhanced forest management. This is equally significant for biodiversity conservation, and forest Carbon storage estimation. Numéro de notice : A2022-871 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.ophoto.2022.100024 Date de publication en ligne : 10/11/2022 En ligne : https://doi.org/10.1016/j.ophoto.2022.100024 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102165
in ISPRS Open Journal of Photogrammetry and Remote Sensing > vol 6 (December 2022) . - n° 100024[article]The contribution of understorey vegetation to ecosystem evapotranspiration in boreal and temperate forests: a literature review and analysis / Philippe Balandier in European Journal of Forest Research, vol 141 n° 6 (December 2022)
[article]
Titre : The contribution of understorey vegetation to ecosystem evapotranspiration in boreal and temperate forests: a literature review and analysis Type de document : Article/Communication Auteurs : Philippe Balandier, Auteur ; Rémy Gobin, Auteur ; Bernard Prévosto, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 979 - 997 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] bilan hydrique
[Termes IGN] écosystème forestier
[Termes IGN] évapotranspiration
[Termes IGN] forêt boréale
[Termes IGN] forêt tempérée
[Termes IGN] gestion forestière
[Termes IGN] Leaf Area Index
[Termes IGN] phénologie
[Termes IGN] sous-bois
[Termes IGN] sous-étage
[Termes IGN] structure d'un peuplement forestier
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) In the context of increasing heat periods and recurrence of droughts, and thus higher soil water depletion, we explored and quantified the role of understorey vegetation in ecosystem evapotranspiration in boreal and temperate forests. We reviewed and analysed about 200 papers that explicitly gave figures of understorey vegetation evapotranspiration relative to different stand features and traits. Understorey vegetation accounted on average for one-third of total ecosystem evapotranspiration during the growing season. Overstorey leaf area index (LAI) is the main variable that drives understorey evapotranspiration through radiation interception. Most data show that below an overstorey LAI of 2–3, the contribution of the understorey vegetation to ecosystem evapotranspiration increases exponentially, following the exponential increase of the climatic demand, i.e. potential evapotranspiration. Different factors have the potential to modulate this effect such as species composition and phenology, root distribution, and interaction with droughts. Consequently, managers must be aware that depending on understorey species present on site and stand structure, understorey vegetation can contribute significantly to a negative stand water balance. Numéro de notice : A2022-857 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s10342-022-01505-0 Date de publication en ligne : 10/10/2022 En ligne : https://doi.org/10.1007/s10342-022-01505-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102108
in European Journal of Forest Research > vol 141 n° 6 (December 2022) . - pp 979 - 997[article]Features predisposing forest to bark beetle outbreaks and their dynamics during drought / M. Müller in Forest ecology and management, vol 523 (November-1 2022)
[article]
Titre : Features predisposing forest to bark beetle outbreaks and their dynamics during drought Type de document : Article/Communication Auteurs : M. Müller, Auteur ; P.O. Olsson, Auteur ; Lars Eklundh, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 120480 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse des risques
[Termes IGN] canopée
[Termes IGN] caractérisation
[Termes IGN] changement climatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données météorologiques
[Termes IGN] humidité du sol
[Termes IGN] peuplement mélangé
[Termes IGN] Picea abies
[Termes IGN] Scolytinae
[Termes IGN] sécheresse
[Termes IGN] Suède
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Climate change is estimated to increase the risk of the bark beetle (Ips typographus L.) mass outbreaks in Norway Spruce (Picea abies (L.) Karst) forests. Habitats that are thermally suitable for bark beetles may expand, and an increase in the frequency and intensity of droughts can promote drought stress on host trees. Drought affects tree vigor and in unison with environmental features it influences the local predisposition risk of forest stands to bark beetle attacks. We aimed to study how various environmental features influence the risk of bark beetle attacks during a drought year and the following years with more normal weather conditions but with higher bark beetle populations. We included features representing local forest stand attributes, topography, soil type and wetness, the proximity of clear-cuts and previous bark beetle attacks, and a machine learning algorithm (random forest) was applied to study the variation of predisposition risk across a 48,600 km2 study area in SE Sweden. Forest stands with increased risk of bark beetle attack were distinguished with high accuracy both during drought and in normal weather conditions. The results show that during both study periods, spruce and mixed coniferous forests had elevated risk of attack, while forests with a mix of deciduous and coniferous trees had a lower risk. Forests with high average canopy height were strongly predisposed to bark beetle attacks. However, during the drought year risk was more similar between stands with lower and higher canopy height, suggesting that during drought periods younger trees can be predisposed to bark beetle attacks. The importance of soil moisture and position within the local landscape were highlighted as important features during the drought year. Identifying areas with increased risk, supported by information on how environmental features control the predisposition risk during drought, could aid adaptation strategies and forest management intervention efforts. We conclude that geospatial data and machine learning have the potential to further support the digitalization of the forest industry, facilitating development of methods capable to quantify importance and dynamics of
environmental features controlling the risk in local context. Corresponding methods could help to direct management actions more effectively and offer information for decision-making in changing climate.Numéro de notice : A2022-731 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2022.120480 Date de publication en ligne : 07/09/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120480 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101687
in Forest ecology and management > vol 523 (November-1 2022) . - n° 120480[article]Age-independent diameter increment models for mixed mountain forests / Albert Ciceu in European Journal of Forest Research, vol 141 n° 5 (October 2022)
[article]
Titre : Age-independent diameter increment models for mixed mountain forests Type de document : Article/Communication Auteurs : Albert Ciceu, Auteur ; Karol Bronisz, Auteur ; Juan Garcia-Duro, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 781 - 800 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Abies alba
[Termes IGN] croissance des arbres
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] échantillonnage
[Termes IGN] Fagus sylvatica
[Termes IGN] forêt alpestre
[Termes IGN] forêt inéquienne
[Termes IGN] modèle de croissance végétale
[Termes IGN] modélisation de la forêt
[Termes IGN] peuplement mélangé
[Termes IGN] Picea abies
[Termes IGN] Roumanie
[Vedettes matières IGN] ForesterieRésumé : (auteur) Mixed mountain forests with an uneven-aged structure are characterized by a high tree-growth variability making traditional age-dependent growth models inapplicable. Estimating site productivity is yet another impediment for modelling tree growth in such forests. Uneven-aged mixed-stand forests are known for their high resilience, resistance and productivity, and are being promoted as a suitable alternative to even-aged, pure plantations for climate change adaptation and mitigation. However, their growth must be accurately measured and predicted, but diameter at the breast height (dbh) increment models specifically designed for uneven-aged mixed mountain forests are still rare. Using permanent sampling network data and 465 increment cores, we built two age-independent dbh increment (id) models for the main species of the study area, namely Norway spruce (Picea abies (L.) Karst.), silver fir (Abies alba Mill.) and European beech (Fagus sylvatica L.). Mixed effects models and the algebraic difference approach were employed to develop id models based on empirical and commonly used theoretical growth functions. A past growth index was further developed and introduced in the model in order to explain the id variability. Several mixed effects calibration strategies were assessed in order to obtain the most accurate localized curve for new plots. Tree size, competition and biogeoclimatic variables were found to explain the id through the empirical growth function, while the growth index significantly improved the theoretical growth function for Norway spruce. The optimization of the calibration strategy for the mixed effects modelling framework enables the growth index implementation in forest practice as an accurate method for estimating site productivity. The accuracy of the two id models was similar: the root mean squared error of the empirical growth function varied between 0.940 and 1.042 cm for spruce, beech and fir, while the root mean squared error obtained through the theoretical growth function for spruce only was 1.105 cm. The basal area increment prediction at the plot level based on the theoretical growth function reached a root mean squared error of 0.043 m2 while using the empirical growth function the root mean squared error is 0.047 m2. The high accuracy obtained using age-independent models underlines their suitability for predicting growth in mixed uneven-aged forests. The developed models can be easily integrated into forest practice to accurately obtain id estimates. Numéro de notice : A2022-758 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s10342-022-01473-5 Date de publication en ligne : 13/08/2022 En ligne : https://doi.org/10.1007/s10342-022-01473-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101767
in European Journal of Forest Research > vol 141 n° 5 (October 2022) . - pp 781 - 800[article]Detecting overmature forests with airborne laser scanning (ALS) / Marc Fuhr in Remote sensing in ecology and conservation, vol 8 n° 5 (October 2022)
[article]
Titre : Detecting overmature forests with airborne laser scanning (ALS) Type de document : Article/Communication Auteurs : Marc Fuhr, Auteur ; Etienne Lalechère, Auteur ; Jean-Matthieu Monnet, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 731 - 743 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] Abies alba
[Termes IGN] âge du peuplement forestier
[Termes IGN] Bootstrap (statistique)
[Termes IGN] canopée
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] coefficient de corrélation
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Fagus sylvatica
[Termes IGN] Picea abies
[Termes IGN] Préalpes (France)
[Termes IGN] semis de points
[Termes IGN] structure d'un peuplement forestierRésumé : (auteur) Building a network of interconnected overmature forests is crucial for the conservation of biodiversity. Indeed, a multitude of plant and animal species depend on forest structural maturity attributes such as very large living trees and deadwood. LiDAR technology has proved to be powerful when assessing forest structural parameters, and it may be a promising way to identify existing overmature forest patches over large areas. We first built an index (IMAT) combining several forest structural maturity attributes in order to characterize the structural maturity of 660 field plots in the French northern Pre-Alps. We then selected or developed LiDAR metrics and applied them in a random forest model designed to predict the IMAT. Model performance was evaluated with the root mean square error of prediction obtained from a bootstrap cross-validation and a Spearman correlation coefficient calculated between observed and predicted IMAT. Predictors were ranked by importance based on the average increase in the squared out-of-bag error when the variable was randomly permuted. Despite a non-negligible RMSEP (0.85 for calibration and validation data combined and 1.26 for validation data alone), we obtained a high correlation (0.89) between the observed and predicted IMAT values, indicating an accurate ranking of the field plots. LiDAR metrics for height (maximum height and height heterogeneity) were among the most important metrics for predicting forest maturity, together with elevation, slope and, to a lesser extent, with metrics describing the distribution of echoes' intensities. Our framework makes it possible to reconstruct a forest maturity gradient and isolate maturity hot spots. Nevertheless, our approach could be considerably strengthened by taking into consideration site fertility, collecting other maturity attributes in the field or developing adapted LiDAR metrics. Including additional spectral or textural metrics from optical imagery might also improve the predictive capacity of the model. Numéro de notice : A2022-880 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1002/rse2.274 Date de publication en ligne : 15/07/2022 En ligne : https://doi.org/10.1002/rse2.274 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102197
in Remote sensing in ecology and conservation > vol 8 n° 5 (October 2022) . - pp 731 - 743[article]Synthèse des résultats de la littérature scientifique sur les peuplements mélangés / Jordan Bello in Rendez-vous techniques, n° 76 (automne 2022)PermalinkRegional climate moderately influences species-mixing effect on tree growth-climate relationships and drought resistance for beech and pine across Europe / Géraud de Streel in Forest ecology and management, vol 520 (September-15 2022)PermalinkAssessing structural complexity of individual scots pine trees by comparing terrestrial laser scanning and photogrammetric point clouds / Noora Tienaho in Forests, Vol 13 n° 8 (August 2022)PermalinkAnalysis of structure from motion and airborne laser scanning features for the evaluation of forest structure / Alejandro Rodríguez-Vivancos in European Journal of Forest Research, vol 141 n° 3 (June 2022)PermalinkFunding for planting missing species financially supports the conversion from pure even-aged to uneven-aged mixed forests and climate change mitigation / Joerg Roessinger in European Journal of Forest Research, vol 141 n° 3 (June 2022)PermalinkCharacterizing stream morphological features important for fish habitat using airborne laser scanning data / Spencer Dakin Kuiper in Remote sensing of environment, vol 272 (April 2022)PermalinkComparison of neural networks and k-nearest neighbors methods in forest stand variable estimation using airborne laser data / Andras Balazs in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 4 (April 2022)PermalinkDrought impacts in forest canopy and deciduous tree saplings in Central European forests / Mirela Beloiu in Forest ecology and management, vol 509 (April-1 2022)PermalinkEstimation and testing of linkages between forest structure and rainfall interception characteristics of a Robinia pseudoacacia plantation on China’s Loess Plateau / Changkun Ma in Journal of Forestry Research, vol 33 n° 2 (April 2022)PermalinkProblems with models assessing influences of tree size and inter-tree competitive processes on individual tree growth: a cautionary tale / P.W. West in Journal of Forestry Research, vol 33 n° 2 (April 2022)Permalink