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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)
[article]
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-motionRésumé : (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
in International journal of applied Earth observation and geoinformation > vol 107 (March 2022) . - n° 102686[article]Unexpected negative effect of available water capacity detected on recent conifer forest growth trends across wide environmental gradients / Clémentine Ols in Ecosystems, vol 25 n° 2 (March 2022)
[article]
Titre : Unexpected negative effect of available water capacity detected on recent conifer forest growth trends across wide environmental gradients Type de document : Article/Communication Auteurs : Clémentine Ols , Auteur ; Thomas Gschwantner, Auteur ; Klemens Schadauer, Auteur ; Jean-Daniel Bontemps , Auteur Année de publication : 2022 Projets : ARBRE / AgroParisTech (2007 -), LUE / Université de Lorraine Article en page(s) : pp 404 - 421 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Abies alba
[Termes IGN] analyse diachronique
[Termes IGN] Autriche
[Termes IGN] cerne
[Termes IGN] changement climatique
[Termes IGN] croissance des arbres
[Termes IGN] gradient d'altitude
[Termes IGN] hétérogénéité environnementale
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] inventaire forestier national (données France)
[Termes IGN] Larix decidua
[Termes IGN] modèle de croissance végétale
[Termes IGN] Picea abies
[Termes IGN] Pinus sylvestris
[Termes IGN] ressources en eau
[Termes IGN] structure d'un peuplement forestier
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) National Forest Inventories (NFIs) perform systematic forest surveys across space and time. They are hence powerful tools to understand climate controls on forest growth at wide geographical scales and account for the effects of local abiotic and biotic interactions. To investigate the effects of climate change upon growth dynamics of four major European conifer species along elevation and continentality gradients, we herein provide an original harmonization of the French and Austrian NFI datasets. The growth of Norway spruce, Scots pine, silver fir and European larch over the 1996–2016 period was studied in pure and even-aged plots across different ecological regions. We derived climate-driven growth trends from > 65, 000 radial increment series filtered out from major biotic and abiotic influences using statistical modeling. We further identified primary environmental drivers of conifer growth by regressing growth trends against regionally aggregated biotic and abiotic forest attributes. Negative growth trends were observed in continental regions undergoing the most rapid warming and thermal amplitude contraction over the study period. Negative trends were also associated with lower forest structural heterogeneity and, surprisingly, with greater available water capacity. Remarkably, we observed these associations both at the inter- and intra-species levels, suggesting the universality of these primary growth determinants. Our study shows that harmonized NFI data at the transnational level provide reliable information on climate–growth interactions. Here, greater forest structural complexity and greater water resource limitation were highlighted as drivers of greater forest resilience to climate change at large-scale. This result forms crucial bases to implementing climate-smart forest management. Numéro de notice : A2022-023 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10021-021-00663-3 En ligne : https://doi.org/10.1007/s10021-021-00663-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98116
in Ecosystems > vol 25 n° 2 (March 2022) . - pp 404 - 421[article]Competition and climate influence in the basal area increment models for Mediterranean mixed forests / Diego Rodríguez de Prado in Forest ecology and management, vol 506 (February-15 2022)
[article]
Titre : Competition and climate influence in the basal area increment models for Mediterranean mixed forests Type de document : Article/Communication Auteurs : Diego Rodríguez de Prado, Auteur ; José Riofrio, Auteur ; Jorge Aldea, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 119955 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] climat aride
[Termes IGN] climat méditerranéen
[Termes IGN] croissance des arbres
[Termes IGN] Espagne
[Termes IGN] forêt méditerranéenne
[Termes IGN] gestion forestière durable
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] méthode du maximum de vraisemblance (estimation)
[Termes IGN] modélisation de la forêt
[Termes IGN] peuplement mélangé
[Termes IGN] surface terrière
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Competition plays a key role controlling tree growth in mixed forests. Contrary to monocultures, quantifying species mixing influence on tree growth suppose a challenge since the presence of two or more species requires to estimate the degree of intra- and inter-specific competition among trees. Moreover, it is well known that aridity can also influence tree growth, especially in the Mediterranean Basin. In the present context of climate change, it is essential to take into account species mixing and aridity uncertainty in the design of sustainable management guidelines for Mediterranean mixed forests. To achieve that, data from Spanish National Forest Inventory was used in this study to fit new mixed-effects basal area increment (BAI) models for 29 two-species compositions in Spain. A wide range of different competition structures (intra-specific, inter-specific, size-symmetric and size-asymmetric) and aridity conditions (in terms of the De Martonne Index) were included and tested into the BAI models. Parameter estimations were obtained for all possible species, mixtures and combinations by Maximum Likelihood (ML). Models with all the coefficients being significant (p Numéro de notice : A2022-059 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2021.119955 Date de publication en ligne : 28/12/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.119955 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99470
in Forest ecology and management > vol 506 (February-15 2022) . - n° 119955[article]Multi-species individual tree segmentation and identification based on improved mask R-CNN and UAV imagery in mixed forests / Chong Zhang in Remote sensing, vol 14 n° 4 (February-2 2022)
[article]
Titre : Multi-species individual tree segmentation and identification based on improved mask R-CNN and UAV imagery in mixed forests Type de document : Article/Communication Auteurs : Chong Zhang, Auteur ; Jiawei Zhou, Auteur ; Huiwen Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 874 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection de contours
[Termes IGN] échantillonnage de données
[Termes IGN] entropie
[Termes IGN] estimation quantitative
[Termes IGN] feuillu
[Termes IGN] hauteur des arbres
[Termes IGN] image captée par drone
[Termes IGN] peuplement mélangé
[Termes IGN] Pinophyta
[Termes IGN] segmentation d'imageRésumé : (auteur) High-resolution UAV imagery paired with a convolutional neural network approach offers significant advantages in accurately measuring forestry ecosystems. Despite numerous studies existing for individual tree crown delineation, species classification, and quantity detection, the comprehensive situation in performing the above tasks simultaneously has rarely been explored, especially in mixed forests. In this study, we propose a new method for individual tree segmentation and identification based on the improved Mask R-CNN. For the optimized network, the fusion type in the feature pyramid network is modified from down-top to top-down to shorten the feature acquisition path among the different levels. Meanwhile, a boundary-weighted loss module is introduced to the cross-entropy loss function Lmask to refine the target loss. All geometric parameters (contour, the center of gravity and area) associated with canopies ultimately are extracted from the mask by a boundary segmentation algorithm. The results showed that F1-score and mAP for coniferous species were higher than 90%, and that of broadleaf species were located between 75%–85.44%. The producer’s accuracy of coniferous forests was distributed between 0.8–0.95 and that of broadleaf ranged in 0.87–0.93; user’s accuracy of coniferous was distributed between 0.81–0.84 and that of broadleaf ranged in 0.71–0.76. The total number of trees predicted was 50,041 for the entire study area, with an overall error of 5.11%. The method under study is compared with other networks including U-net and YOLOv3. Results in this study show that the improved Mask R-CNN has more advantages in broadleaf canopy segmentation and number detection. Numéro de notice : A2022-168 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs14040874 Date de publication en ligne : 11/02/2022 En ligne : https://doi.org/10.3390/rs14040874 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99793
in Remote sensing > vol 14 n° 4 (February-2 2022) . - n° 874[article]Scorch height and volume modeling in prescribed fires: Effects of canopy gaps in Pinus pinaster stands in Southern Europe / J.R. Molina in Forest ecology and management, vol 506 (February-15 2022)
[article]
Titre : Scorch height and volume modeling in prescribed fires: Effects of canopy gaps in Pinus pinaster stands in Southern Europe Type de document : Article/Communication Auteurs : J.R. Molina, Auteur ; M. Ortega, Auteur ; F. Rodríguez y Silva, Auteur Année de publication : 2022 Article en page(s) : n° 119979 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] brûlis
[Termes IGN] canopée
[Termes IGN] échantillonnage de données
[Termes IGN] Espagne
[Termes IGN] gestion des risques
[Termes IGN] gestion forestière
[Termes IGN] incendie de forêt
[Termes IGN] Pinus pinaster
[Vedettes matières IGN] ForesterieRésumé : (auteur) The use of prescribed fire has been on the rise in recent years owing to its effectiveness in surface fuel reduction, its implementation cost, and the possibility of firefighter training. However, greater knowledge regarding the effects of fire on woodlands is required by forest managers. Scorch height and scorch volume are the most widely used variables for evaluating the effects of burning on trees. This study proposes a scorch height model for the prescribed fires of pine stands in Southern Europe. Although the two main variables of the existing models (fire-line intensity and air temperature) were considered, our model achieved a coefficient of determination of 89% with the incorporation of the canopy base height. A decision tree for scorch volume was also developed using the three independent variables. The presence of canopy gaps in the lower, mid-, and upper slopes resulted in significant differences in the scorch height. The scorch height increased between 0.33 m and 2.08 m because of the canopy gaps in the upper slope. These findings can play an important role in the implementation and improvement of prescribed burn windows. Numéro de notice : A2022-058 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2021.119979 Date de publication en ligne : 24/12/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.119979 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99469
in Forest ecology and management > vol 506 (February-15 2022) . - n° 119979[article]A stand-level growth and yield model for thinned and unthinned even-aged Scots pine forests in Norway / Christian Kuehne in Silva fennica, vol 56 n° 1 (January 2022)PermalinkPourquoi la forêt française a besoin d’un traitement de fond / Guillaume Decocq in The Conversation France, vol 2022 ([10/02/2022])PermalinkAfforestation with Pinus nigra Arn ssp salzmannii along an elevation gradient: controlling factors and implications for climate change adaptation / Manuel Esteban Lucas-Borja in Trees, vol 36 n° 1 (February 2022)PermalinkAn open science and open data approach for the statistically robust estimation of forest disturbance areas / Saverio Francini in International journal of applied Earth observation and geoinformation, vol 106 (February 2022)PermalinkEvaluation of mapped-plot variance estimators across a range of partial nonresponse in a post-stratified national forest inventory / James A. Westfall in Canadian Journal of Forest Research, Vol 52 n° 2 (February 2022)PermalinkFive decades of ground flora changes in a temperate forest: The good, the bad and the ambiguous in biodiversity terms / K.J. Kirby in Forest ecology and management, vol 505 (February-1 2022)PermalinkGrowing stock monitoring by European National Forest Inventories: Historical origins, current methods and harmonisation / Thomas Gschwantner in Forest ecology and management, vol 505 (February-1 2022)PermalinkHow much does it take to be old? Modelling the time since the last harvesting to infer the distribution of overmature forests in France / Lucie Thompson in Diversity and distributions, vol 28 n° 2 (February 2022)PermalinkLandsat-based monitoring of southern pine beetle infestation severity and severity change in a temperate mixed forest / Ran Meng in Remote sensing of environment, vol 269 (February 2022)PermalinkMapping burn severity in the western Italian Alps through phenologically coherent reflectance composites derived from Sentinel-2 imagery / Donato Morresi in Remote sensing of environment, vol 269 (February 2022)Permalink