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Comparison of spatially and nonspatially explicit nonlinear mixed effects models for Norway spruce individual tree growth under single-tree selection / Simone Bianchi in Forests, vol 11 n° 12 (December 2020)
[article]
Titre : Comparison of spatially and nonspatially explicit nonlinear mixed effects models for Norway spruce individual tree growth under single-tree selection Type de document : Article/Communication Auteurs : Simone Bianchi, Auteur ; Mari Myllymäki, Auteur ; Jouni Siipilehto, Auteur ; Hannu Salminen, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 1338 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] arbre (flore)
[Termes IGN] croissance des arbres
[Termes IGN] forêt boréale
[Termes IGN] modèle de croissance végétale
[Termes IGN] modèle non linéaire
[Termes IGN] Picea abies
[Vedettes matières IGN] SylvicultureRésumé : (auteur) Background and Objectives: Continuous cover forestry is of increasing importance, but operational forest growth models are still lacking. The debate is especially open if more complex spatial approaches would provide a worthwhile increase in accuracy. Our objective was to compare a nonspatial versus a spatial approach for individual Norway spruce tree growth models under single-tree selection cutting.
Materials and Methods: We calibrated nonlinear mixed models using data from a long-term experiment in Finland (20 stands with 3538 individual trees for 10,238 growth measurements). We compared the use of nonspatial versus spatial predictors to describe the competitive pressure and its release after cutting. The models were compared in terms of Akaike Information Criteria (AIC), root mean square error (RMSE), and mean absolute bias (MAB), both with the training data and after cross-validation with a leave-one-out method at stand level.
Results: Even though the spatial model had a lower AIC than the nonspatial model, RMSE and MAB of the two models were similar. Both models tended to underpredict growth for the highest observed values when the tree-level random effects were not used. After cross-validation, the aggregated predictions at stand level well represented the observations in both models. For most of the predictors, the use of values based on trees’ height rather than trees’ diameter improved the fit. After single-tree selection cutting, trees had a growth boost both in the first and second five-year period after cutting, however, with different predicted intensity in the two models.
Conclusions: Under the research framework here considered, the spatial modeling approach was not more accurate than the nonspatial one. Regarding the single-tree selection cutting, an intervention regime spaced no more than 15 years apart seems necessary to sustain the individual tree growth. However, the model’s fixed effect parts were not able to capture the high growth of the few fastest-growing trees, and a proper estimation of site potential is needed for uneven-aged stands.Numéro de notice : A2020-578 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article DOI : 10.3390/f11121338 Date de publication en ligne : 16/12/2020 En ligne : https://doi.org/10.3390/f11121338 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97034
in Forests > vol 11 n° 12 (December 2020) . - n° 1338[article]Improving aboveground biomass estimates by taking into account density variations between tree components / Antoine Billard in Annals of Forest Science, vol 77 n° 4 (December 2020)
[article]
Titre : Improving aboveground biomass estimates by taking into account density variations between tree components Type de document : Article/Communication Auteurs : Antoine Billard, Auteur ; Rodolphe Bauer, Auteur ; Frédéric Mothe, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 103 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] allométrie
[Termes IGN] base de données forestières
[Termes IGN] biomasse aérienne
[Termes IGN] bois de chauffage
[Termes IGN] branche (arbre)
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] écorce
[Termes IGN] hauteur des arbres
[Termes IGN] résineux
[Termes IGN] tomographie radar
[Termes IGN] volume en bois
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Key message: Strong density differences were observed between stem wood at 1.30 m and other tree components (stem wood, stem bark, knots, branch stumps and branches). The difference, up to 40% depending on the component, should be taken into account when estimating the biomass available for industrial uses, mainly fuelwood and wood for chemistry.
Context: Basic density is a major variable in the calculation of tree biomass. However, it is usually measured on stem wood only and at breast height.
Aims: The objectives of this study were to compare basic density of stem wood at 1.30 m with other tree components and assess the impact of differences on biomass.
Methods: Three softwood species were studied: Abies alba Mill., Picea abies (L.) H. Karst., Pseudotsuga menziesii (Mirb.) Franco. X-Ray computed tomography was used to measure density.
Results: Large differences were observed between components. Basic density of components was little influenced by tree size and stand density. Overall, bark, knot and branch biomasses were highly underestimated by using basic density measured at 1.30 m.
Conclusion: Using available wood density databases mainly based on breast height measurements would lead to important biases (up to more than 40%) on biomass estimates for some tree components. Further work is necessary to complete available databases.Numéro de notice : A2020-714 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s13595-020-00999-1 Date de publication en ligne : 26/10/2020 En ligne : https://doi.org/10.1007/s13595-020-00999-1 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96282
in Annals of Forest Science > vol 77 n° 4 (December 2020) . - n° 103[article]Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks / Felix Schiefer in ISPRS Journal of photogrammetry and remote sensing, vol 170 (December 2020)
[article]
Titre : Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks Type de document : Article/Communication Auteurs : Felix Schiefer, Auteur ; Teja Kattenborn, Auteur ; Annett Frick, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 205-215 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond
[Termes IGN] arbre (flore)
[Termes IGN] carte forestière
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] espèce végétale
[Termes IGN] Forêt-Noire, massif de la
[Termes IGN] image à haute résolution
[Termes IGN] image captée par drone
[Termes IGN] image RVB
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] inventaire forestier local
[Termes IGN] segmentation sémantique
[Vedettes matières IGN] Inventaire forestierRésumé : (Auteur) The use of unmanned aerial vehicles (UAVs) in vegetation remote sensing allows a time-flexible and cost-effective acquisition of very high-resolution imagery. Still, current methods for the mapping of forest tree species do not exploit the respective, rich spatial information. Here, we assessed the potential of convolutional neural networks (CNNs) and very high-resolution RGB imagery from UAVs for the mapping of tree species in temperate forests. We used multicopter UAVs to obtain very high-resolution ( Numéro de notice : A2020-706 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.10.015 Date de publication en ligne : 03/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.10.015 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96236
in ISPRS Journal of photogrammetry and remote sensing > vol 170 (December 2020) . - pp 205-215[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2020121 RAB Revue Centre de documentation En réserve L003 Disponible A meta-analysis of changes in soil organic carbon stocks after afforestation with deciduous broadleaved, sempervirent broadleaved, and conifer tree species / Guolong Hou in Annals of Forest Science, vol 77 n° 4 (December 2020)
[article]
Titre : A meta-analysis of changes in soil organic carbon stocks after afforestation with deciduous broadleaved, sempervirent broadleaved, and conifer tree species Type de document : Article/Communication Auteurs : Guolong Hou, Auteur ; Claudio O. Delang, Auteur ; Xixi Lu, Auteur ; Lei Gao, Auteur Année de publication : 2020 Article en page(s) : 13 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] afforestation
[Termes IGN] arbre caducifolié
[Termes IGN] arbre sempervirent
[Termes IGN] boisement artificiel
[Termes IGN] feuillu
[Termes IGN] Pinophyta
[Termes IGN] puits de carbone
[Termes IGN] sol
[Vedettes matières IGN] Ecologie forestièreRésumé : (auteur) Key message: Different tree species have dissimilar capacities to sequester soil organic carbon (SOC). Deciduous broadleaved trees show the most stable increase in SOC stock after afforestation than other tree species, while sempervirent conifer trees show the lowest rate of SOC stock change. Sempervirent broadleaved trees show the greatest increase in SOC stock 20 years after afforestation.
Context: The rate at which soil organic carbon (SOC) stock changes after afforestation varies considerably with the tree species. A better understanding of the role of tree species in SOC change dynamic is needed to evaluate the SOC sequestration potential of afforestation programs.
Aims: The aim of this paper is to identify the dissimilar rates at which different tree species sequester SOC, following afforestation.
Methods: We complete a meta-analysis with 544 data points from 261 sites in 90 papers. We group tree species into decidious broadleved, sempervirent broadleaved and sempervirent conifer. We use standardization and/or extrapolation methods to standardize soil depths. Statistical analysis test the main effects of tree species and their interactions with previous land use and plantation age on SOC stock change after afforestation.
Results: Deciduous broadleaved trees show a stable increase in SOC stock, and are especially suited for afforestation of grassland or soils with high initial SOC. Sempervirent broadleaved afforestation results in loss of SOC stock in young stands, but greater SOC stock in mature stands. Sempervirent conifer trees show the lowest rate of SOC stock change, but are suitable for nutrient-poor soil.
Conclusion: The results emphasize the importance of considering tree species when estimating SOC stock change, in particular when carbon sequestration is an objective of afforestation programs.Numéro de notice : A2020-590 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-020-00997-3 Date de publication en ligne : 25/09/2020 En ligne : https://doi.org/10.1007/s13595-020-00997-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95921
in Annals of Forest Science > vol 77 n° 4 (December 2020) . - 13 p.[article]A novel intelligent classification method for urban green space based on high-resolution remote sensing images / Zhiyu Xu in Remote sensing, vol 12 n° 22 (December-1 2020)
[article]
Titre : A novel intelligent classification method for urban green space based on high-resolution remote sensing images Type de document : Article/Communication Auteurs : Zhiyu Xu, Auteur ; Yi Zhou, Auteur ; Shixin Wang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 3845 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] apprentissage profond
[Termes IGN] arbre urbain
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] espace vert
[Termes IGN] image à haute résolution
[Termes IGN] image Gaofen
[Termes IGN] milieu urbain
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Pékin (Chine)
[Termes IGN] phénologie
[Termes IGN] précision de la classification
[Termes IGN] urbanismeRésumé : (auteur) The real-time, accurate, and refined monitoring of urban green space status information is of great significance in the construction of urban ecological environment and the improvement of urban ecological benefits. The high-resolution technology can provide abundant information of ground objects, which makes the information of urban green surface more complicated. The existing classification methods are challenging to meet the classification accuracy and automation requirements of high-resolution images. This paper proposed a deep learning classification method for urban green space based on phenological features constraints in order to make full use of the spectral and spatial information of green space provided by high-resolution remote sensing images (GaoFen-2) in different periods. The vegetation phenological features were added as auxiliary bands to the deep learning network for training and classification. We used the HRNet (High-Resolution Network) as our model and introduced the Focal Tversky Loss function to solve the sample imbalance problem. The experimental results show that the introduction of phenological features into HRNet model training can effectively improve urban green space classification accuracy by solving the problem of misclassification of evergreen and deciduous trees. The improvement rate of F1-Score of deciduous trees, evergreen trees, and grassland were 0.48%, 4.77%, and 3.93%, respectively, which proved that the combination of vegetation phenology and high-resolution remote sensing image can improve the results of deep learning urban green space classification. Numéro de notice : A2020-792 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs12223845 Date de publication en ligne : 23/11/2020 En ligne : https://doi.org/10.3390/rs12223845 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96565
in Remote sensing > vol 12 n° 22 (December-1 2020) . - n° 3845[article]The crown condition of Norway spruce and occurrence of symptoms caused by Armillaria spp. in mixed stands / Petr Čermák in Journal of forest science, vol 66 n° 12 (December 2020)PermalinkUrban tree species identification and carbon stock mapping for urban green planning and management / MD Abdul Choudhury in Forests, vol 11 n°11 (November 2020)PermalinkUsing climate-sensitive 3D city modeling to analyze outdoor thermal comfort in urban areas / Rabeeh Hosseinihaghighi in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)PermalinkAssessing the effects of thinning on stem growth allocation of individual Scots pine trees / Ninni Saarinen in Forest ecology and management, vol 474 ([15/10/2020])PermalinkHierarchical instance recognition of individual roadside trees in environmentally complex urban areas from UAV laser scanning point clouds / Yongjun Wang in ISPRS International journal of geo-information, vol 9 n° 10 (October 2020)PermalinkA preliminary exploration of the cooling effect of tree shade in urban landscapes / Qiuyan Yu in International journal of applied Earth observation and geoinformation, vol 92 (October 2020)PermalinkTree species classification using structural features derived from terrestrial laser scanning / Louise Terryn in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)Permalink3D reconstruction of internal wood decay using photogrammetry and sonic tomography / Junjie Zhang in Photogrammetric record, vol 35 n° 171 (September 2020)PermalinkL-band SAR for estimating aboveground biomass of rubber plantation in Java Island, Indonesia / Bambang H Trisasongko in Geocarto international, vol 35 n° 12 ([01/09/2020])PermalinkHomogeneous tree height derivation from tree crown delineation using Seeded Region Growing (SRG) segmentation / Muhamad Farid Ramli in Geo-spatial Information Science, vol 23 n° 3 (September 2020)Permalink