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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]

Deriving a tree growth model from any existing stand growth model / Quang V. Cao in Canadian Journal of Forest Research, Vol 52 n° 2 (February 2022)  

Public [article]  inCanadian Journal of Forest Research > Vol 52 n° 2 (February 2022) . - pp 137 - 147 Titre : Deriving a tree growth model from any existing stand growth model Type de document : Article/Communication Auteurs : Quang V. Cao, Auteur Année de publication : 2022 Article en page(s) : pp 137 - 147 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] désagrégation [Termes IGN] Etats-Unis [Termes IGN] méthode des moindres carrés [Termes IGN] modèle de croissance végétale [Termes IGN] modélisation de la forêt [Termes IGN] Pinus taeda [Termes IGN] régression [Termes IGN] surface terrière Résumé : (auteur) In this study, a new method was developed to derive a tree survival and diameter growth model from any existing stand-level model, without the need for individual-tree growth data. Predictions from the derived tree model are constrained to match the number of trees and the basal area per hectare as outputted by the stand model. The tree models derived from three different stand models were evaluated against a tree model, in both unadjusted and disaggregated forms. For the same stand-level model, the derived tree model outperformed its counterpart, the disaggregated tree model. Furthermore, except for one stand model with poor performance, the tree models derived from the remaining two stand models delivered results comparable to those obtained from the unadjusted tree model. The tree model derived from one stand model even performed slightly better than the unadjusted tree model. This result is significant because the coefficients of the unadjusted and disaggregated tree models had to be estimated from tree-level growth data, whereas the derived tree model required no tree growth data at all. The methodology presented in this study should be applicable when there is no ingrowth or recruitment of new trees. Numéro de notice : A2022-311 Affiliation des auteurs : non IGN Autre URL associée : Draft Thématique : FORET/MATHEMATIQUE Nature : Article DOI : 10.1139/cjfr-2021-0106 En ligne : https://doi.org/10.1139/cjfr-2021-0106 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100408 [article]

Automatic detection of planted trees and their heights using photogrammetric rpa point clouds / Kênia Samara Mourão Santos in Boletim de Ciências Geodésicas, vol 27 n° 3 ([01/10/2021])  

Public [article]  inBoletim de Ciências Geodésicas > vol 27 n° 3 [01/10/2021] Titre : Automatic detection of planted trees and their heights using photogrammetric rpa point clouds Type de document : Article/Communication Auteurs : Kênia Samara Mourão Santos, Auteur ; Christel Lingnau, Auteur ; Daniel Rodrigues dos Santos, Auteur Année de publication : 2021 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] détection d'arbres [Termes IGN] hauteur des arbres [Termes IGN] image aérienne [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] inventaire forestier étranger (données) [Termes IGN] Parana (Brésil) [Termes IGN] Pinus taeda [Termes IGN] plantation forestière [Termes IGN] semis de points Résumé : (auteur) This work aims to analyze the potential of the Photogrammetric Point Cloud (PPC) obtained from Remote Piloted Aircraft (RPA) optical images for detecting and obtaining tree heights in a loblolly pine plantation using a global maximum filter. The enhanced algorithm used in this study is then named STD (Single Tree Detection). Field surveys were conducted to count all the trees in the field (Forest Census) and measure the trees’ height with a vertex hypsometer. The results were faced to PCC outcomes. The detection rate (r) was equal to the precision rate (p), indicating that the algorithm reaches a high tree detection performance. In summary, the STD algorithm segmented 2,192 trees, representing 89% of trees recorded in the forest census. The retrieved tree height reached, on average, a height of 17.05 m, whereas slightly higher by the traditional forest inventory (17.42 m). The root-mean-square error (RMSE) and Bias were 47 cm (2.8%) and -37 cm (-2.2%), respectively. The Dunnett test showed that the tree height did not significantly differ between the results obtained by traditional forest inventory from those generated by the STD. It confirms the potential use of PPC for forest inventory procedures. Numéro de notice : A2021-958 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1590/s1982-21702021000300025 En ligne : https://doi.org/10.1590/s1982-21702021000300025 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100075 [article]

Mapping the height and spatial cover of features beneath the forest canopy at small-scales using airborne scanning discrete return Lidar / Matthew Sumnall in ISPRS Journal of photogrammetry and remote sensing, vol 133 (November 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 133 (November 2017) . - pp 186 - 200 Titre : Mapping the height and spatial cover of features beneath the forest canopy at small-scales using airborne scanning discrete return Lidar Type de document : Article/Communication Auteurs : Matthew Sumnall, Auteur ; Thomas R. Fox, Auteur ; Randolph H. Wynne, Auteur ; Valerie A. Thomas, Auteur Année de publication : 2017 Article en page(s) : pp 186 - 200 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] couvert forestier [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] estimation statistique [Termes IGN] Etats-Unis [Termes IGN] hauteur des arbres [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] lidar à retour d'onde complète [Termes IGN] Pinophyta [Termes IGN] Pinus taeda [Termes IGN] sous-bois Résumé : (Auteur) The objective of the current study was to develop methods for estimating the height and horizontal coverage of the forest understorey using airborne Lidar data in three managed pine plantation forest typical of the south eastern USA. The current project demonstrates a two-step approach applied automatically across a given study site extent. The first operation divided the study site extent into a regularly spaced grid (25 × 25 m) and identified the potential height range of the main Loblolly pine canopy layer for each grid-cell through aggregating Lidar return height measurements into a ‘stack’ of vertical height bins describing the frequency of returns by height. Once height bins were created, the resulting vertical distributions were smoothed with a regression curve line function and the main canopy vertical layer was identified through the detection of local maxima and minima. The second operation sub-divided the 25 × 25 m grid-cell into 1 × 1 m horizontal grid, for which height-bin stacks were created for each cell. Vertical features below the main canopy were then identified at this scale in the same manner as in the previous step, and classified as understorey features if they were lower in height than the 25 × 25 m estimate of the main canopy layer. The heights of the tallest understorey and sub-canopy layers were kept, and used to produce a rasterized map of the understorey layer height at the 1 × 1 m scale. Lidar derived estimates of the 25 × 25 m lowest vertical extent of the coniferous canopy correlated highly with field data (R2 0.87; RMSE 2.1 m). Estimates of understorey horizontal cover ranged from R2 0.80 to 0.90 (RMSE 6.6–11.7%), and maximum understorey layer height ranged from R2 0.69 to 0.80 (RMSE 1.6–3.4 m) for the three study sites. The automated method deployed within the current study proved sufficient in determining the presence and absence of vegetation and artificial structures within the understorey portion of the current forest context, in addition to height and horizontal cover to a reasonable accuracy. Issues were encountered within older stands (e.g. more than 30 years old) where understorey deciduous vegetation layers intersected with the coniferous canopy layer, resulting in an underestimation of sub-dominant heights. Numéro de notice : A2017-726 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.10.002 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.10.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88411 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2017111	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2017112	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt
081-2017113	DEP-EXM	Revue	Saint-Mandé	Dépôt en unité	Exclu du prêt

Predicting stem total and assortment volumes in an industrial pinus taeda L. forest plantation using airborne laser scanning data and random forest / Carlos Alberto Silva in Forests, vol 8 n° 7 (July 2017)  

Public [article]  inForests > vol 8 n° 7 (July 2017) Titre : Predicting stem total and assortment volumes in an industrial pinus taeda L. forest plantation using airborne laser scanning data and random forest Type de document : Article/Communication Auteurs : Carlos Alberto Silva, Auteur ; Carine Klauberg, Auteur ; Andrew Thomas Hudak, Auteur ; Lee Alexander Vierling, Auteur ; Wan Shafrina Wan Mohd Jaafar, Auteur ; et al., Auteur Année de publication : 2017 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Brésil [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] modèle de simulation [Termes IGN] Pinus taeda [Termes IGN] plantation forestière [Termes IGN] volume en bois Résumé : (Auteur) Improvements in the management of pine plantations result in multiple industrial and environmental benefits. Remote sensing techniques can dramatically increase the efficiency of plantation management by reducing or replacing time-consuming field sampling. We tested the utility and accuracy of combining field and airborne lidar data with Random Forest, a supervised machine learning algorithm, to estimate stem total and assortment (commercial and pulpwood) volumes in an industrial Pinus taeda L. forest plantation in southern Brazil. Random Forest was populated using field and lidar-derived forest metrics from 50 sample plots with trees ranging from three to nine years old. We found that a model defined as a function of only two metrics (height of the top of the canopy and the skewness of the vertical distribution of lidar points) has a very strong and unbiased predictive power. We found that predictions of total, commercial, and pulp volume, respectively, showed an adjusted R2 equal to 0.98, 0.98 and 0.96, with unbiased predictions of −0.17%, −0.12% and −0.23%, and Root Mean Square Error (RMSE) values of 7.83%, 7.71% and 8.63%. Our methodology makes use of commercially available airborne lidar and widely used mathematical tools to provide solutions for increasing the industry efficiency in monitoring and managing wood volume. Numéro de notice : A2017-875 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f8070254 Date de publication en ligne : 17/07/2017 En ligne : https://doi.org/10.3390/f8070254 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91214 [article]

The impact of integrating WorldView-2 sensor and environmental variables in estimating plantation forest species aboveground biomass and carbon stocks in uMgeni Catchment, South Africa / Timothy Dube in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)  

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Investigating the robustness of the new Landsat-8 Operational Land Imager derived texture metrics in estimating plantation forest aboveground biomass in resource constrained areas / Timothy Dube in ISPRS Journal of photogrammetry and remote sensing, vol 108 (October 2015)  

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Evaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in uMgeni catchment, South Africa / Timothy Dube in ISPRS Journal of photogrammetry and remote sensing, vol 101 (March 2015)  

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Impact of management on nutrients, carbon, and energy in aboveground biomass components of mid-rotation loblolly pine (pinus taeda L.) plantations / Dehai Zhao in Annals of Forest Science, vol 71 n° 8 (December 2014)  

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Basal area and biomass estimates of loblolly pine stands using L-band UAVSAR / William L. Marks in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 1 (January 2014)  

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Computer-based synthetic data to assess the tree delineation algorithm from airborne LiDAR survey / L. Wang in Geoinformatica, vol 17 n° 1 (January 2013)  

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Site preparation and competing vegetation control affect loblolly pine long-term productivity in the southern Piedmont/Upper Coastal Plain of the United States / Dehai Zhao in Annals of Forest Science, Vol 66 n° 7 (October - November 2009)  

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Estimating forest biomass using small footprint LiDAR data: An individual tree-based approach that incorporates training data / Z.J. Bortolot in ISPRS Journal of photogrammetry and remote sensing, vol 59 n° 6 (November 2005)  

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