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Features predisposing forest to bark beetle outbreaks and their dynamics during drought / M. Müller in Forest ecology and management, vol 523 (November-1 2022)  

Public [article]  inForest ecology and management > vol 523 (November-1 2022) . - n° 120480 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 climatique Résumé : (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 [article]

GCPs-free photogrammetry for estimating tree height and crown diameter in Arizona cypress plantation using UAV-mounted GNSS RTK / Morteza Pourreza in Forests, vol 13 n° 11 (November 2022)  

Public [article]  inForests > vol 13 n° 11 (November 2022) . - n° 1905 Titre : GCPs-free photogrammetry for estimating tree height and crown diameter in Arizona cypress plantation using UAV-mounted GNSS RTK Type de document : Article/Communication Auteurs : Morteza Pourreza, Auteur ; Fardin Moradi, Auteur ; Mohammad Khosravi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1905 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] cupressus (genre) [Termes IGN] diamètre des arbres [Termes IGN] hauteur de vol [Termes IGN] hauteur des arbres [Termes IGN] image captée par drone [Termes IGN] Iran [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] point d'appui [Termes IGN] positionnement cinématique en temps réel [Termes IGN] structure-from-motion Résumé : (auteur) One of the main challenges of using unmanned aerial vehicles (UAVs) in forest data acquisition is the implementation of Ground Control Points (GCPs) as a mandatory step, which is sometimes impossible for inaccessible areas or within canopy closures. This study aimed to test the accuracy of a UAV-mounted GNSS RTK (real-time kinematic) system for calculating tree height and crown height without any GCPs. The study was conducted on a Cupressus arizonica (Greene., Arizona cypress) plantation on the Razi University Campus in Kermanshah, Iran. Arizona cypress is commonly planted as an ornamental tree. As it can tolerate harsh conditions, this species is highly appropriate for afforestation and reforestation projects. A total of 107 trees were subjected to field-measured dendrometric measurements (height and crown diameter). UAV data acquisition was performed at three altitudes of 25, 50, and 100 m using a local network RTK system (NRTK). The crown height model (CHM), derived from a digital surface model (DSM), was used to estimate tree height, and an inverse watershed segmentation (IWS) algorithm was used to estimate crown diameter. The results indicated that the means of tree height obtained from field measurements and UAV estimation were not significantly different, except for the mean values calculated at 100 m flight altitude. Additionally, the means of crown diameter reported from field measurements and UAV estimation at all flight altitudes were not statistically different. Root mean square error (RMSE Numéro de notice : A2022-838 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f13111905 Date de publication en ligne : 12/11/2022 En ligne : https://doi.org/10.3390/f13111905 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102039 [article]

Multi-level self-adaptive individual tree detection for coniferous forest using airborne LiDAR / Zhenyang Hui in International journal of applied Earth observation and geoinformation, vol 114 (November 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 114 (November 2022) . - n° 103028 Titre : Multi-level self-adaptive individual tree detection for coniferous forest using airborne LiDAR Type de document : Article/Communication Auteurs : Zhenyang Hui, Auteur ; Penggen Cheng, Auteur ; Bisheng Yang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 103028 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse de groupement [Termes IGN] classification par nuées dynamiques [Termes IGN] détection automatique [Termes IGN] détection d'arbres [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] données matricielles [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] optimisation (mathématiques) [Termes IGN] Pinophyta [Termes IGN] segmentation d'image [Termes IGN] segmentation multi-échelle [Termes IGN] semis de points Résumé : (auteur) To obtain satisfying results of individual tree detection from LiDAR points, parameters using traditional methods usually need to be adjusted by trials and errors. When encountering complex forest environments, the detection accuracy cannot be satisfied. To resolve this, a multi-level self-adaptive individual tree detection method was presented in this paper. The proposed method can be seen as a hybrid model, which combined the strength of both raster-based and point-based methods. Raster-based strategy was first used for achieving initial trees detection results, while the point-based strategy was adopted for optimizing the clustered trees. In the proposed method, crown width scales were estimated automatically. Meanwhile, multi-scales segmented results were fused together to take advantage of segmented results of both larger and small scales. Six different coniferous forests were adopted for testing. Experimental result shows that this study achieved the lowest omission and commission errors comparing with other three classical approaches. Meanwhile, the average F1 score in this paper is 0.84, which is much highest out of other methods. Numéro de notice : A2022-784 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.103028 Date de publication en ligne : 24/09/2022 En ligne : https://doi.org/10.1016/j.jag.2022.103028 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101887 [article]

Silvicultural experiment assessment using lidar data collected from an unmanned aerial vehicle / Diogo N. Cosenza in Forest ecology and management, vol 522 (October-15 2022)  

Public [article]  inForest ecology and management > vol 522 (October-15 2022) . - n° 120489 Titre : Silvicultural experiment assessment using lidar data collected from an unmanned aerial vehicle Type de document : Article/Communication Auteurs : Diogo N. Cosenza, Auteur ; Jason Vogel, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 120489 Langues : Anglais (eng) Descripteur : [Termes IGN] croissance végétale [Termes IGN] données allométriques [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] modélisation de la forêt [Termes IGN] Pinus taeda [Termes IGN] plantation forestière [Termes IGN] sylviculture [Vedettes matières IGN] Foresterie Résumé : (auteur) Collecting field data in silvicultural experiments can be challenging and time-consuming. Alternatively, unmanned aerial vehicles using laser scanners (UAV-lidar) can be used for cost-effective data collection in forest stands. This work aims to assess the capability of UAV-lidar to estimate biophysical forest attributes in silvicultural experiments. The showcase experiment refers to the IMPAC II (Intensive Management Practices Assessment Center II), a long-term project of 24 plots aiming to assess the effects of fertilization and weed control on forest growth and nutrient cycling in past and ongoing silvicultural treatments in a second rotation of loblolly pine (Pinus taeda L.) plantation at age 12 years. Treatment performances were assessed based on four biometric attributes related to forest productivity: Growing stock biomass (Mg ha−1), stem volume (m3 ha−1), dominant height (m), and leaf area index (LAI, m2 m−2). We used the area-based approach (ABA) and multiple linear models to characterize these forest attributes in the different silvicultural treatments and use their predictions to run the experiment analysis. Two groups of ALS-derived metrics were tested in the modeling, traditional metrics and a novel group of metrics based on plant area density (PAD) distribution. Models using PAD-based metrics increased the correlation between observed and predicted values (R2) from 0.27–0.40 to 0.50–0.85 when compared to the same models using traditional metrics, while the relative root mean square errors (RMSE%) of the predictions were reduced from 6–18% to 4–12%. Experiment analysis using UAV-lidar data and PAD-based model predictors led to the same results as those using field observations: i) fertilization was the most effective treatment for enhancing stand attributes, especially in terms of biomass, stem volume, and LAI; ii) weed control alone provided marginal improvements in the stands; iii) actively retreating stands in both first and second rotation led to increased growth when compared to the carryover effects. UAV-lidar using PAD-based metrics was effective in characterizing enhanced silvicultural treatments and might benefit studies involving understory assessment. Numéro de notice : A2022-314 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.foreco.2022.120489 En ligne : https://doi.org/10.1016/j.foreco.2022.120489 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102250 [article]

Age-independent diameter increment models for mixed mountain forests / Albert Ciceu in European Journal of Forest Research, vol 141 n° 5 (October 2022)  

Public [article]  inEuropean Journal of Forest Research > vol 141 n° 5 (October 2022) . - pp 781 - 800 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] Foresterie Résumé : (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 [article]

Canopy self-replacement in Pinus sylvestris rear-edge populations following drought-induced die-off and mortality / Jordi Margalef- Marrase in Forest ecology and management, vol 521 (October-1 2022)  

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Detecting overmature forests with airborne laser scanning (ALS) / Marc Fuhr in Remote sensing in ecology and conservation, vol 8 n° 5 (October 2022)  

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Regional 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)  

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Classification of pine wilt disease at different infection stages by diagnostic hyperspectral bands / Niwen Li in Ecological indicators, vol 142 (September 2022)  

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Experimental precipitation reduction slows down litter decomposition but exhibits weak to no effect on soil organic carbon and nitrogen stocks in three Mediterranean forests of Southern France / Mathieu Santonja in Forests, vol 13 n° 9 (september 2022)  

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Using multi-temporal tree inventory data in eucalypt forestry to benchmark global high-resolution canopy height models. A showcase in Mato Grosso, Brazil / Adrián Pascual in Ecological Informatics, vol 70 (September 2022)  

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Evapotranspiration mapping of cotton fields in Brazil: comparison between SEBAL and FAO-56 method / Juan Vicente Liendro Moncada in Geocarto international, Vol 37 n° 17 ([20/08/2022])  

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Assessing 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)  

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Climatic sensitivities derived from tree rings improve predictions of the forest vegetation simulator growth and yield model / Courtney L. Giebink in Forest ecology and management, vol 517 (August-1 2022)  

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Crown allometry and growing space requirements of four rare domestic tree species compared to oak and beech: implications for adaptive forest management / Julia Schmucker in European Journal of Forest Research, vol 141 n° 4 (August 2022)  

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