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Prescribed fire after thinning increased resistance of sub-Mediterranean pine forests to drought events and wildfires / Lena Vilà-Vilardell in Forest ecology and management, vol 527 (January-1 2023)  

Public [article]  inForest ecology and management > vol 527 (January-1 2023) . - n° 120602 Titre : Prescribed fire after thinning increased resistance of sub-Mediterranean pine forests to drought events and wildfires Type de document : Article/Communication Auteurs : Lena Vilà-Vilardell, Auteur ; Miquel De Cáceres, Auteur ; Míriam Piqué, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 120602 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] brûlis [Termes IGN] éclaircie (sylviculture) [Termes IGN] incendie de forêt [Termes IGN] Pinus nigra [Termes IGN] sécheresse [Termes IGN] sous-étage [Termes IGN] stress hydrique [Termes IGN] structure d'un peuplement forestier [Termes IGN] vulnérabilité [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Vegetation structure affects the vulnerability of a forest to drought events and wildfires. Management decisions, such as thinning intensity and type of understory treatment, influence competition for water resources and amount of fuel available. While heavy thinning effectively reduces tree water stress and intensity of a crown fire, the duration of these benefits may be limited by a fast growth response of the understory. Our aim was to study the effect of forest structure on pine forests vulnerability to extreme drought events and on the potential wildfire behaviour after management, with a special focus on the role of the understory. In three sub-Mediterranean sites of NE Spain dominated by Pinus nigra, two intensities of thinning (light: aiming at 70–75% canopy cover; and heavy: aiming at 50–60% canopy cover) followed by two understory treatments (mechanical only and mechanical plus prescribed burning) were applied, resulting in four differently managed stands plus an untreated control per site. Four to five years after management, we measured forest structure (overstory in one 314 m2 circular plot and understory in 20 quadrats of 1 m2 per treatment unit) and fuel load (in two 10 m transects per treatment unit) and simulated water balance and fire behaviour under extreme weather conditions. Understory contribution was assessed comparing the real structure with a virtual forest stand where understory vegetation equalled the one of the untreated control. Our results suggest that the resulting mid-term structure following treatments effectively reduced water stress and fire behaviour compared with untreated control, and that the most effective treatments were the ones where prescribed burning was applied after light or heavy thinning. While understory clearing contributes to increase the resistance to both disturbances, an additive effect of burning the debris reduced the vulnerability to drought and wildfires after treatments. Our study highlights the importance of managing the understory to further increase forest resistance to both disturbances. Numéro de notice : A2022-858 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2022.120602 Date de publication en ligne : 08/11/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120602 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102109 [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)  

Public [article]  inEuropean Journal of Forest Research > vol 141 n° 6 (December 2022) . - pp 979 - 997 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 climatique Résumé : (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 [article]

A model-based scenario analysis of the impact of forest management and environmental change on the understorey of temperate forests in Europe / Bingbin Wen in Forest ecology and management, vol 522 (October-15 2022)  

Public [article]  inForest ecology and management > vol 522 (October-15 2022) . - n° 120465 Titre : A model-based scenario analysis of the impact of forest management and environmental change on the understorey of temperate forests in Europe Type de document : Article/Communication Auteurs : Bingbin Wen, Auteur ; Haben Blondeel, Auteur ; Dries Landuyt, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 120465 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de sensibilité [Termes IGN] azote [Termes IGN] biodiversité [Termes IGN] changement climatique [Termes IGN] dynamique de la végétation [Termes IGN] Europe centrale [Termes IGN] forêt tempérée [Termes IGN] gestion forestière durable [Termes IGN] impact sur l'environnement [Termes IGN] modèle de simulation [Termes IGN] sous-étage [Termes IGN] système d'aide à la décision [Vedettes matières IGN] Ecologie forestière Résumé : (auteur) The temperate forest understorey is rich in terms of vascular plant diversity and plays a vital functional role. Given the sensitivity of this forest layer to forest management and global environmental change and the limited knowledge on its long-term dynamics, there is a need for decision support systems that can guide temperate forest managers to optimize their management in terms of understorey outcomes. In this study, using understorey resurvey data collected from across temperate Europe, we developed Generalized Additive Models (GAM) to predict four understorey properties based on forest management and environmental change data, and implemented this model in a web-based tool as a prototype understorey Decision Support System (DSS). Using seventy-two combined climate change, nitrogen(N) deposition and forest management scenarios, applied to two case study regions in Europe, we predicted temperate forest understorey biodiversity dynamics between 2020 and 2050. A sensitivity analysis subsequently allowed to quantify the relative importance of canopy opening, N deposition and climate change on understorey dynamics. Our study showed that, regardless of regions, understorey richness and the proportion of forest specialists generally decreased among most scenarios, but the proportion of woody species and the understorey vegetation total cover increased. Climate warming, N deposition, and increases in canopy openness all influenced understorey dynamics. Climate warming will shift composition towards a selection of forest generalists and woody species, but a less open canopy could mitigate this shift by increasing the proportion of forest specialists. The case studies also showed that these responses can be context-dependent, especially in terms of responses to N deposition. Numéro de notice : A2022-710 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2022.120465 Date de publication en ligne : 19/08/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120465 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101587 [article]

Towards low vegetation identification: A new method for tree crown segmentation from LiDAR data based on a symmetrical structure detection algorithm (SSD) / Langning Huo in Remote sensing of environment, vol 270 (March 2022)  

Public [article]  inRemote sensing of environment > vol 270 (March 2022) . - n° 112857 Titre : Towards low vegetation identification: A new method for tree crown segmentation from LiDAR data based on a symmetrical structure detection algorithm (SSD) Type de document : Article/Communication Auteurs : Langning Huo, Auteur ; Eva Lindberg, Auteur ; Johan Holmgren, Auteur Année de publication : 2022 Article en page(s) : n° 112857 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forêt boréale [Termes IGN] hauteur à la base du houppier [Termes IGN] houppier [Termes IGN] inventaire forestier étranger (données) [Termes IGN] segmentation [Termes IGN] semis de points [Termes IGN] sous-bois [Termes IGN] sous-étage [Termes IGN] strate végétale [Termes IGN] structure d'un peuplement forestier [Termes IGN] Suède Résumé : (auteur) Obtaining low vegetation data is important in order to quantify the structural characteristics of a forest. Dense three-dimensional (3D) laser scanning data can provide information on the vertical profile of a forest. However, most studies have focused on the dominant and subdominant layers of the forest, while few studies have tried to delineate the low vegetation. To address this issue, we propose a framework for individual tree crown (ITC) segmentation from laser data that focuses on both overstory and understory trees. The framework includes 1) a new algorithm (SSD) for 3D ITC segmentation of dominant trees, by detecting the symmetrical structure of the trees, and 2) removing points of dominant trees and mean shift clustering of the low vegetation. The framework was tested on a boreal forest in Sweden and the performance was compared 1) between plots with different stem density levels, vertical complexities, and tree species composition, and 2) using airborne laser scanning (ALS) data, terrestrial laser scanning (TLS) data, and merged ALS and TLS data (ALS + TLS data). The proposed framework achieved detection rates of 0.87 (ALS + TLS), 0.86 (TLS), and 0.76 (ALS) when validated with field-inventory data (of trees with a diameter at breast height ≥ 4 cm). When validating the estimated number of understory trees by visual interpretation, the framework achieved 19%, 21%, and 39% root-mean-square error values with ALS + TLS, TLS, and ALS data, respectively. These results show that the SSD algorithm can successfully separate laser points of overstory and understory trees, ensuring the detection and segmentation of low vegetation in forest. The proposed framework can be used with both ALS and TLS data, and achieve ITC segmentation for forests with various structural attributes. The results also illustrate the potential of using ALS data to delineate low vegetation. Numéro de notice : A2022-127 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112857 Date de publication en ligne : 03/01/2022 En ligne : https://doi.org/10.1016/j.rse.2021.112857 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99707 [article]

Ultrahigh-resolution boreal forest canopy mapping: Combining UAV imagery and photogrammetric point clouds in a deep-learning-based approach / Linyuan Li in International journal of applied Earth observation and geoinformation, vol 107 (March 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 107 (March 2022) . - n° 102686 Titre : Ultrahigh-resolution boreal forest canopy mapping: Combining UAV imagery and photogrammetric point clouds in a deep-learning-based approach Type de document : Article/Communication Auteurs : Linyuan Li, Auteur ; Xihan Mu, Auteur ; Francesco Chianucci, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102686 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] algorithme SLIC [Termes IGN] apprentissage profond [Termes IGN] canopée [Termes IGN] carte forestière [Termes IGN] Chine [Termes IGN] classification par maximum de vraisemblance [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] couvert forestier [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] données lidar [Termes IGN] faisceau laser [Termes IGN] forêt boréale [Termes IGN] image captée par drone [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] modèle numérique de terrain [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Termes IGN] sous-étage [Termes IGN] structure-from-motion Résumé : (auteur) Accurate wall-to-wall estimation of forest crown cover is critical for a wide range of ecological studies. Notwithstanding the increasing use of UAVs in forest canopy mapping, the ultrahigh-resolution UAV imagery requires an appropriate procedure to separate the contribution of understorey from overstorey vegetation, which is complicated by the spectral similarity between the two forest components and the illumination environment. In this study, we investigated the integration of deep learning and the combined data of imagery and photogrammetric point clouds for boreal forest canopy mapping. The procedure enables the automatic creation of training sets of tree crown (overstorey) and background (understorey) data via the combination of UAV images and their associated photogrammetric point clouds and expands the applicability of deep learning models with self-supervision. Based on the UAV images with different overlap levels of 12 conifer forest plots that are categorized into “I”, “II” and “III” complexity levels according to illumination environment, we compared the self-supervised deep learning-predicted canopy maps from original images with manual delineation data and found an average intersection of union (IoU) larger than 0.9 for “complexity I” and “complexity II” plots and larger than 0.75 for “complexity III” plots. The proposed method was then compared with three classical image segmentation methods (i.e., maximum likelihood, Kmeans, and Otsu) in the plot-level crown cover estimation, showing outperformance in overstorey canopy extraction against other methods. The proposed method was also validated against wall-to-wall and pointwise crown cover estimates using UAV LiDAR and in situ digital cover photography (DCP) benchmarking methods. The results showed that the model-predicted crown cover was in line with the UAV LiDAR method (RMSE of 0.06) and deviate from the DCP method (RMSE of 0.18). We subsequently compared the new method and the commonly used UAV structure-from-motion (SfM) method at varying forward and lateral overlaps over all plots and a rugged terrain region, yielding results showing that the method-predicted crown cover was relatively insensitive to varying overlap (largest bias of less than 0.15), whereas the UAV SfM-estimated crown cover was seriously affected by overlap and decreased with decreasing overlap. In addition, canopy mapping over rugged terrain verified the merits of the new method, with no need for a detailed digital terrain model (DTM). The new method is recommended to be used in various image overlaps, illuminations, and terrains due to its robustness and high accuracy. This study offers opportunities to promote forest ecological applications (e.g., leaf area index estimation) and sustainable management (e.g., deforestation). Numéro de notice : A2022-192 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102686 Date de publication en ligne : 05/02/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102686 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99951 [article]

Conservation zones increase habitat heterogeneity of certified Mediterranean oak woodlands / Teresa Mexia in Forest ecology and management, vol 504 (January-15 2022)  

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The long-term development of temperate woodland creation sites: from tree saplings to mature woodlands / Elisa Fuentes-Montemayor in Forestry, an international journal of forest research, vol 95 n° 1 (January 2022)  

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Understory plant community responses to widespread spruce mortality in a subalpine forest / Trevor A. Carter in Journal of vegetation science, vol 33 n° 1 (January 2022)  

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Vegetation changes in the understory of nitrogen-sensitive temperate forests over the past 70 years / Marina Roth in Forest ecology and management, vol 503 (January-1 2022)  

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A density-based algorithm for the detection of individual trees from LiDAR data / Melissa Latella in Remote sensing, Vol 13 n° 2 (January-2 2021)  

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Soil biodiversity as affected by different thinning intensities in a pinus laricio stand of Calabrian Apennine, South Italy / Adele Muscolo in Forests, vol 12 n° 1 (January 2021)  

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Can mixed pine forests conserve understory richness by improving the establishment of understory species typical of native oak forests? / Daphne Lopez-Marcos in Annals of Forest Science, Vol 77 n° 1 (March 2020)  

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Assessing the impacts of canopy openness and flight parameters on detecting a sub-canopy tropical invasive plant using a small unmanned aerial system / Ryan L. Perroy in ISPRS Journal of photogrammetry and remote sensing, vol 125 (March 2017)  

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Lidar imagery and InSAR for digital forestry / Benoît Saint-Onge in GIM international [en ligne], vol 30 n° 7 (July 2016)

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Optimizing the spatial resolution of WorldView-2 imagery for discriminating forest vegetation at subspecies level in KwaZulu-Natal, South Africa / Romano Lottering in Geocarto international, vol 31 n° 7 - 8 (July - August 2016)  

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