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scientifique

Production potential, biodiversity and soil properties of forest reclamations: Opportunities or risk of introduced coniferous tree species under climate change? / Zdeněk Vacek in European Journal of Forest Research, vol 140 n° 5 (October 2021)  

Public [article]  inEuropean Journal of Forest Research > vol 140 n° 5 (October 2021) . - pp 1243 - 1266 Titre : Production potential, biodiversity and soil properties of forest reclamations: Opportunities or risk of introduced coniferous tree species under climate change? Type de document : Article/Communication Auteurs : Zdeněk Vacek, Auteur ; Jan Cukor, Auteur ; Stanislav Vacek, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1243 - 1266 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] adaptation (biologie) [Termes IGN] biodiversité [Termes IGN] boisement artificiel [Termes IGN] croissance des arbres [Termes IGN] Europe centrale [Termes IGN] Larix decidua [Termes IGN] peuplement forestier [Termes IGN] Pinophyta [Termes IGN] Pinus nigra [Termes IGN] Pinus strobus [Termes IGN] Pinus sylvestris [Termes IGN] précipitation [Termes IGN] Pseudotsuga menziesii [Termes IGN] puits de carbone [Termes IGN] République Tchèque [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) In the time of ongoing climate change and the increasing area of post-mining landscape, the successful afforestation of reclamation sites by suitable adaptive tree species is gaining in importance. One of possible ways may be the use of introduced tree species, which is, however, a controversial topic in relation to risks for forest management and nature conservation. The objective of this study was to comprehensively evaluate the forest stands (age 48 years) of 9 introduced and 3 native coniferous tree species in lowland post-mining coal site of the Czech Republic. The research was focused on production potential, health status, resistance to climate change, carbon sequestration, biodiversity and soil properties. The highest timber production, biomass and carbon stock (49–95% above average), was observed in case of Pinus sylvestris, P. nigra and Pseudotsuga menziesii. On the other hand, unsuitable habitat, insect and pathogens caused poor health status and extremely low production parameters (by 55–62% than average) in Pinus strobus and P. rotundata. In terms of climate, Pinus sylvestris, P. nigra, Larix decidua, Pseudotsuga menziesii and Picea omorika were the most resistance tree species in relation to climatic extremes. Conversely, Pinus rotundata, P. strobus, P. ponderosa, Picea pungens and P. abies were very sensitive to climate events, especially to the lack of precipitation in vegetation period with synergism of high temperature. In terms of soil parameters, the content of plant available nutrients (K, Ca, Mg) was adequate except P deficiency. The highest soil reaction was detected for Pinus nigra and P. sylvestris (pH 6.9–7.1) compared to Picea mariana (pH 4.8). The benefits of “suitable” introduced tree species (Pinus nigra, Pseudotsuga menziesii) are high timber production potential and good adaptation and mitigation of the changing climate; however, native tree species (Pinus sylvestris, Larix decidua) can provide better environmental benefits on reclamation sites. Numéro de notice : A2021-719 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET Nature : Article DOI : 10.1007/s10342-021-01392-x Date de publication en ligne : 18/07/2021 En ligne : https://doi.org/10.1007/s10342-021-01392-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98638 [article]

Early detection of pine wilt disease using deep learning algorithms and UAV-based multispectral imagery / Run Yu in Forest ecology and management, vol 497 (1 October 2021)  

Public [article]  inForest ecology and management > vol 497 (1 October 2021) . - n° 119493 Titre : Early detection of pine wilt disease using deep learning algorithms and UAV-based multispectral imagery Type de document : Article/Communication Auteurs : Run Yu, Auteur ; Youqing Luo, Auteur ; Quan Zhou, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 119493 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] Chine [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] dépérissement [Termes IGN] image captée par drone [Termes IGN] image multibande [Termes IGN] maladie phytosanitaire [Termes IGN] milieu tropical [Termes IGN] peuplement mélangé [Termes IGN] Pinus (genre) [Termes IGN] Pinus massoniana [Termes IGN] réflectance spectrale [Termes IGN] Ulmus (genre) Résumé : (auteur) Pine wilt disease (PWD) is a global devastating threat to forest ecosystems. Therefore, a feasible and effective approach to precisely monitor PWD infection is indispensable, especially at the early stages. However, a precise definition of “early stage” and a rapid and high-efficiency method to detect PWD infection have not been well established. In this study, we systematically divided the PWD infection into green, early, middle, and late stages based on the needle color, the resin secretion, and whether the pine wood nematode (PWN) was carried. Simultaneously, an unmanned aerial vehicle (UAV) equipped with multispectral cameras was used to obtain images. Two target detection algorithms (Faster R-CNN and YOLOv4) and two traditional machine learning algorithms based on feature extraction (random forest and support vector machine) were employed to realize the recognition of infected pine trees. Moreover, we took into consideration of the influence of green broad-leaved trees on the identification of pine trees at the early stage of PWD infection. We obtained the following results: (1) the accuracy of Faster R-CNN (60.98–66.7%) was higher than that of YOLOv4 (57.07–63.55%), but YOLOv4 outperformed in terms of model size, processing speed, training time, and testing time; (2) although the traditional machine learning models had higher accuracy (73.28–79.64%), they were not able to directly identify the object from the images; (3) the accuracy of early detection of PWD infection showed an increase of 3.72–4.29%, from 42.36–44.59% to 46.08–48.88%, when broad-leaved trees were considered. In this study, the combination of UAV-based multispectral images and target detection algorithms allowed us to monitor the occurrence of PWD and obtain the distribution of infected trees at an early stage, which can provide technical support for the prevention and control of PWD. Numéro de notice : A2021-658 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.foreco.2021.119493 En ligne : https://doi.org/10.1016/j.foreco.2021.119493 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98395 [article]

A comparison of ALS and dense photogrammetric point clouds for individual tree detection in radiata pine plantations / Irfan A. Iqbal in Remote sensing, vol 13 n° 17 (September-1 2021)  

Public [article]  inRemote sensing > vol 13 n° 17 (September-1 2021) . - n° 3536 Titre : A comparison of ALS and dense photogrammetric point clouds for individual tree detection in radiata pine plantations Type de document : Article/Communication Auteurs : Irfan A. Iqbal, Auteur ; Jon Osborn, Auteur ; Christine Stone, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 3536 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] arbre isolé [Termes IGN] détection d'arbres [Termes IGN] données lidar [Termes IGN] image aérienne [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] photogrammétrie aérienne [Termes IGN] Pinus radiata [Termes IGN] semis de points [Termes IGN] Tasmanie Résumé : (auteur) Digital aerial photogrammetry (DAP) has emerged as a potentially cost-effective alternative to airborne laser scanning (ALS) for forest inventory methods that employ point cloud data. Forest inventory derived from DAP using area-based methods has been shown to achieve accuracy similar to that of ALS data. At the tree level, individual tree detection (ITD) algorithms have been developed to detect and/or delineate individual trees either from ALS point cloud data or from ALS- or DAP-based canopy height models. An examination of the application of ITDs to DAP-based point clouds has not yet been reported. In this research, we evaluate the suitability of DAP-based point clouds for individual tree detection in the Pinus radiata plantation. Two ITD algorithms designed to work with point cloud data are applied to dense point clouds generated from small- and medium-format photography and to an ALS point cloud. Performance of the two ITD algorithms, the influence of stand structure on tree detection rates, and the relationship between tree detection rates and canopy structural metrics are investigated. Overall, we show that there is a good agreement between ALS- and DAP-based ITD results (proportion of false negatives for ALS, SFP, and MFP was always lower than 29.6%, 25.3%, and 28.6%, respectively, whereas, the proportion of false positives for ALS, SFP, and MFP was always lower than 39.4%, 30.7%, and 33.7%, respectively). Differences between small- and medium-format DAP results were minor (for SFP and MFP, differences between recall, precision, and F-score were always less than 0.08, 0.03, and 0.05, respectively), suggesting that DAP point cloud data is robust for ITD. Our results show that among all the canopy structural metrics, the number of trees per hectare has the greatest influence on the tree detection rates. Numéro de notice : A2021-689 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs13173536 Date de publication en ligne : 06/09/2021 En ligne : https://doi.org/10.3390/rs13173536 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98425 [article]

Detection of aspen in conifer-dominated boreal forests with seasonal multispectral drone image point clouds / Alwin A. Hardenbol in Silva fennica, vol 55 n° 4 (September 2021)  

Public [article]  inSilva fennica > vol 55 n° 4 (September 2021) . - n° 10515 Titre : Detection of aspen in conifer-dominated boreal forests with seasonal multispectral drone image point clouds Type de document : Article/Communication Auteurs : Alwin A. Hardenbol, Auteur ; Anton Kuzmin, Auteur ; Lauri Korhonen, Auteur ; Pasi Korpelainen, Auteur ; Timo Kumpula, Auteur ; Matti Maltamo, Auteur ; Jari Kouki, Auteur Année de publication : 2021 Article en page(s) : n° 10515 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] aire protégée [Termes IGN] analyse discriminante [Termes IGN] Betula (genre) [Termes IGN] détection d'arbres [Termes IGN] forêt boréale [Termes IGN] image captée par drone [Termes IGN] image multibande [Termes IGN] orthoimage couleur [Termes IGN] peuplement mélangé [Termes IGN] Picea abies [Termes IGN] Pinus sylvestris [Termes IGN] Populus tremula [Termes IGN] semis de points [Termes IGN] variation saisonnière Résumé : (auteur) Current remote sensing methods can provide detailed tree species classification in boreal forests. However, classification studies have so far focused on the dominant tree species, with few studies on less frequent but ecologically important species. We aimed to separate European aspen (Populus tremula L.), a biodiversity-supporting tree species, from the more common species in European boreal forests (Pinus sylvestris L., Picea abies [L.] Karst., Betula spp.). Using multispectral drone images collected on five dates throughout one thermal growing season (May–September), we tested the optimal season for the acquisition of mono-temporal data. These images were collected from a mature, unmanaged forest. After conversion into photogrammetric point clouds, we segmented crowns manually and automatically and classified the species by linear discriminant analysis. The highest overall classification accuracy (95%) for the four species as well as the highest classification accuracy for aspen specifically (user’s accuracy of 97% and a producer’s accuracy of 96%) were obtained at the beginning of the thermal growing season (13 May) by manual segmentation. On 13 May, aspen had no leaves yet, unlike birches. In contrast, the lowest classification accuracy was achieved on 27 September during the autumn senescence period. This is potentially caused by high intraspecific variation in aspen autumn coloration but may also be related to our date of acquisition. Our findings indicate that multispectral drone images collected in spring can be used to locate and classify less frequent tree species highly accurately. The temporal variation in leaf and canopy appearance can alter the detection accuracy considerably. Numéro de notice : A2021-735 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14214/sf.10515 Date de publication en ligne : 14/07/2021 En ligne : https://doi.org/10.14214/sf.10515 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98691 [article]

Large-area inventory of species composition using airborne laser scanning and hyperspectral data / Hans Ole Ørka in Silva fennica, vol 55 n° 4 (September 2021)  

Public [article]  inSilva fennica > vol 55 n° 4 (September 2021) . - n° 10244 Titre : Large-area inventory of species composition using airborne laser scanning and hyperspectral data Type de document : Article/Communication Auteurs : Hans Ole Ørka, Auteur ; Endre H. Hansen, Auteur ; Michele Dalponte, Auteur ; Terje Gobakken, Auteur ; Erik Naesset, Auteur Année de publication : 2021 Article en page(s) : n° 10244 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] composition d'un peuplement forestier [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] image hyperspectrale [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] Norvège [Termes IGN] Picea abies [Termes IGN] Pinus sylvestris [Termes IGN] régression [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Tree species composition is an essential attribute in stand-level forest management inventories and remotely sensed data might be useful for its estimation. Previous studies on this topic have had several operational drawbacks, e.g., performance studied at a small scale and at a single tree-level with large fieldwork costs. The current study presents the results from a large-area inventory providing species composition following an operational area-based approach. The study utilizes a combination of airborne laser scanning and hyperspectral data and 97 field sample plots of 250 m2 collected over 350 km2 of productive forest in Norway. The results show that, with the availability of hyperspectral data, species-specific volume proportions can be provided in operational forest management inventories with acceptable results in 90% of the cases at the plot level. Dominant species were classified with an overall accuracy of 91% and a kappa-value of 0.73. Species-specific volumes were estimated with relative root mean square differences of 34%, 87%, and 102% for Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), and deciduous species, respectively. A novel tree-based approach for selecting pixels improved the results compared to a traditional approach based on the normalized difference vegetation index. Numéro de notice : A2021-736 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14214/sf.10244 En ligne : https://doi.org/10.14214/sf.10244 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98695 [article]

Forest floor bryophyte and lichen diversity in Scots pine and Norway spruce production forests / Lisa Petersson in Forest ecology and management, vol 493 (1st August 2021)  

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Detecting structural changes induced by Heterobasidion root rot on Scots pines using terrestrial laser scanning / Timo P Pitkänen in Forest ecology and management, vol 492 (15 July 2021)  

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Estimation of biomass increase and CUE at a young temperate scots pine stand concerning drought occurrence by combining eddy covariance and biometric methods / Paulina Dukat in Forests, vol 12 n° 7 (July 2021)  

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Unexpected negative effect of available water capacity detected on recent conifer forest growth trends across wide environmental gradients / Clémentine Ols in Ecosystems, vol 24 n° inconnu ([01/07/2021])  

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Forest cover mapping and Pinus species classification using very high-resolution satellite images and random forest / Laura Alonso-Martinez in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)  

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Characterization of mixed and monospecific stands of Scots pine and Maritime pine: soil profile, physiography, climate and vegetation cover data / Daphne Lopez-Marcos in Annals of Forest Science [en ligne], vol 78 n° 2 (June 2021)  

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Predicting tree species based on the geometry and density of aerial laser scanning point cloud of treetops / Nina Kranjec in Geodetski vestnik, vol 65 n° 2 (June - August 2021)  

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Provisioning forest and conservation science with high-resolution maps of potential distribution of major European tree species under climate change / Debojyoti Chakraborty in Annals of Forest Science [en ligne], vol 78 n° 2 (June 2021)  

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Updating of forest stand data by using recent digital photogrammetry in combination with older airborne laser scanning data / Niels Lindgren in Scandinavian journal of forest research, vol 36 n° 5 ([01/06/2021])  

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Self-thinning tree mortality models that account for vertical stand structure, species mixing and climate / David I. Forrester in Forest ecology and management, Vol 487 ([01/05/2021])  

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