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Transferability and calibration of airborne laser scanning based mixed-effects models to estimate the attributes of sawlog-sized Scots pines / Lauri Korhonen in Silva fennica, vol 53 n° 3 (2019)  

Public [article]  inSilva fennica > vol 53 n° 3 (2019) Titre : Transferability and calibration of airborne laser scanning based mixed-effects models to estimate the attributes of sawlog-sized Scots pines Type de document : Article/Communication Auteurs : Lauri Korhonen, Auteur ; Jaakko Repola, Auteur ; Tomi Karjalainen, Auteur ; Petteri Packalen, Auteur ; Matti Maltamo, Auteur Année de publication : 2019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] détection d'arbres [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] erreur systématique [Termes descripteurs IGN] estimation statistique [Termes descripteurs IGN] étalonnage de modèle [Termes descripteurs IGN] hauteur à la base du houppier [Termes descripteurs IGN] houppier [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] lasergrammétrie [Termes descripteurs IGN] Pinus sylvestris [Termes descripteurs IGN] placette d'échantillonnage [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Airborne laser scanning (ALS) data is nowadays often available for forest inventory purposes, but adequate field data for constructing new forest attribute models for each area may be lacking. Thus there is a need to study the transferability of existing ALS-based models among different inventory areas. The objective of our study was to apply ALS-based mixed models to estimate the diameter, height and crown base height of individual sawlog sized Scots pines (Pinus sylvestrisL.) at three different inventory sites in eastern Finland. Different ALS sensors and acquisition parameters were used at each site. Multivariate mixed-effects models were fitted at one site and the models were validated at two independent test sites. Validation was carried out by applying the fixed parts of the mixed models as such, and by calibrating them using 1–3 sample trees per plot. The results showed that the relative RMSEs of the predictions were 1.2–6.5 percent points larger at the test sites compared to the training site. Systematic errors of 2.4–6.2 percent points also emerged at the test sites. However, both the RMSEs and the systematic errors decreased with calibration. The results showed that mixed-effects models of individual tree attributes can be successfully transferred and calibrated to other ALS inventory areas in a level of accuracy that appears suitable for practical applications. Numéro de notice : A2019-379 Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14214/sf.10179 date de publication en ligne : 31/07/2019 En ligne : https://doi.org/10.14214/sf.10179 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93446 [article]

Increasing precision for French forest inventory estimates using the k-NN technique with optical and photogrammetric data and model-assisted estimators / Dinesh Babu Irulappa Pillai Vijayakumar in Remote sensing, vol 11 n° 8 (August 2019)  

Public [article]  inRemote sensing > vol 11 n° 8 (August 2019) Titre : Increasing precision for French forest inventory estimates using the k-NN technique with optical and photogrammetric data and model-assisted estimators Type de document : Article/Communication Auteurs : Dinesh Babu Irulappa Pillai Vijayakumar, Auteur ; Jean-Pierre Renaud, Auteur ; François Morneau, Auteur ; Ronald E. McRoberts, Auteur ; Cédric Vega , Auteur Année de publication : 2019 Projets : DIABOLO / Packalen, Tuula Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] arbre caducifolié [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] feuillu [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] inférence statistique [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] inventaire forestier national (données France) [Termes descripteurs IGN] modèle numérique de surface de la canopée [Termes descripteurs IGN] Orléans, forêt d' (Loiret) [Termes descripteurs IGN] photogrammétrie numérique [Termes descripteurs IGN] Pinus pinaster [Termes descripteurs IGN] Pinus sylvestris [Termes descripteurs IGN] quercus pedunculata [Termes descripteurs IGN] quercus sessiliflora [Termes descripteurs IGN] Sologne (France) [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Multisource forest inventory methods were developed to improve the precision of national forest inventory estimates. These methods rely on the combination of inventory data and auxiliary information correlated with forest attributes of interest. As these methods have been predominantly tested over coniferous forests, the present study used this approach for heterogeneous and complex deciduous forests in the center of France. The auxiliary data considered included a forest type map, Landsat 8 spectral bands and derived vegetation indexes, and 3D variables derived from photogrammetric canopy height models. On a subset area, changes in canopy height estimated from two successive photogrammetric models were also used. A model-assisted inference framework, using a k nearest-neighbors approach, was used to predict 11 field inventory variables simultaneously. The results showed that among the auxiliary variables tested, 3D metrics improved the precision of dendrometric estimates more than other auxiliary variables. Relative efficiencies (RE) varying from 2.15 for volume to 1.04 for stand density were obtained using all auxiliary variables. Canopy height changes also increased RE from 3% to 26%. Our results confirmed the importance of 3D metrics as auxiliary variables and demonstrated the value of canopy change variables for increasing the precision of estimates of forest structural attributes such as density and quadratic mean diameter. Numéro de notice : A2019-382 Thématique : FORET/IMAGERIE/MATHEMATIQUE Nature : Article DOI : 10.3390/rs11080991 date de publication en ligne : 25/04/2019 En ligne : https://doi.org/10.3390/rs11080991 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93456 [article]

Utilizing the density of inventory samples to define a hybrid lattice for species distribution models: DISTRIB‐II for 135 eastern U.S. trees / Matthew P. Peters in Ecology and evolution, vol 9 n° 15 (August 2019)  

Public [article]  inEcology and evolution > vol 9 n° 15 (August 2019) . - pp 8876 - 8899 Titre : Utilizing the density of inventory samples to define a hybrid lattice for species distribution models: DISTRIB‐II for 135 eastern U.S. trees Type de document : Article/Communication Auteurs : Matthew P. Peters, Auteur ; Louis R. Iverson, Auteur ; Anantha M. Prasad, Auteur ; Stephen N. Matthews, Auteur Année de publication : 2019 Article en page(s) : pp 8876 - 8899 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] arbre (flore) [Termes descripteurs IGN] densité [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] données météorologiques [Termes descripteurs IGN] inventaire forestier étranger (données) [Termes descripteurs IGN] modèle mathématique [Termes descripteurs IGN] placette d'échantillonnage [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Species distribution models (SDMs) provide useful information about potential presence or absence, and environmental conditions suitable for a species; and high‐resolution models across large extents are desirable. A primary feature of SDMs is the underlying spatial resolution, which can be chosen for many reasons, though we propose that a hybrid lattice, in which grid cell sizes vary with the density of forest inventory plots, provides benefits over uniform grids. We examine how the spatial grain size affected overall model performance for the Random Forest‐based SDM, DISTRIB, which was updated with recent forest inventories, climate, and soil data, and used a hybrid lattice derived from inventory densities. Modeled habitat suitability was compared between a uniform grid of 10 × 10 and a hybrid lattice of 10 × 10 and 20 × 20 km grids to assess potential improvements. The resulting DISTRIB‐II models for 125 eastern U.S. tree species provide information on individual habitat suitability that can be mapped and statistically analyzed to understand current and potential changes. Model performance metrics were comparable among the hybrid lattice and 10‐km grids; however, the hybrid lattice models generally had higher overall model reliability scores and were likely more representative of the inventory data. Our efforts to update DISTRIB models with current information aims to produce a more representative depiction of recent conditions by accounting for the spatial density of forest inventory data and using the latest climate data. Additionally, we developed an approach that leverages a hybrid lattice to maximize the spatial information within the models and recommend that similar modeling efforts be used to evaluate the spatial density of response and predictor data and derive a modeling grid that best represents the environment. Numéro de notice : A2019-374 Thématique : FORET Nature : Article DOI : 10.1002/ece3.5445 date de publication en ligne : 17/07/2019 En ligne : https://doi.org/10.1002/ece3.5445 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93415 [article]

Two contemporary and efficient two-stage sampling methods for estimating the volume of forest stands: a brief overview and unified mathematical description / Aristeidis Georgakis in Open journal of forestry, vol 9 n° 3 (July 2019)  

Public [article]  inOpen journal of forestry > vol 9 n° 3 (July 2019) . - pp 241 - 254 Titre : Two contemporary and efficient two-stage sampling methods for estimating the volume of forest stands: a brief overview and unified mathematical description Type de document : Article/Communication Auteurs : Aristeidis Georgakis, Auteur ; Georgios Stamatellos, Auteur Année de publication : 2019 Article en page(s) : pp 241 - 254 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] échantillonnage [Termes descripteurs IGN] estimation statistique [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] peuplement forestier [Termes descripteurs IGN] surface terrière [Termes descripteurs IGN] volume en bois [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Big Basal Area Factor (Big BAF) and Point-3P are two-stage sampling methods. In the first stage the sampling units, in both methods, are Bitterlich points where the selection of the trees is proportional to their basal area. In the second stage, sampling units are trees which are a subset of the first stage trees. In the Big BAF method, the probability of selecting trees in the second stage is made proportional to the two BAFs’ ratio, with a basal area factor larger than that of the first stage. In the Point-3P method the probability of selecting trees, in the second stage, is based on the height prediction and use of a specific random number table. Estimates of the forest stands’ volume and their sampling errors are based on the theory of the product of two random variables. The increasing error in the second stage is small, but the total cost of measuring the trees is much smaller than simply using the first stage, with all the trees measured. In general, the two sampling methods are modern and cost-effective approaches that can be applied in forest stand inventories for forest management purposes and are receiving the growing interest of researchers in the current decade. Numéro de notice : A2019-381 Thématique : FORET/MATHEMATIQUE Nature : Article DOI : 10.4236/ojf.2019.93013 En ligne : https://doi.org/10.4236/ojf.2019.93013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93460 [article]

Demonstrating the transferability of forest inventory attribute models derived using airborne laser scanning data / Piotr Tompalski in Remote sensing of environment, vol 227 (15 June 2019)  

Public [article]  inRemote sensing of environment > vol 227 (15 June 2019) . - pp 110 - 124 Titre : Demonstrating the transferability of forest inventory attribute models derived using airborne laser scanning data Type de document : Article/Communication Auteurs : Piotr Tompalski, Auteur ; Joanne C. White, Auteur ; Nicholas C. Coops, Auteur ; Michael A. Wulder, Auteur Année de publication : 2019 Article en page(s) : pp 110 - 124 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] densité des points [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] méthode des moindres carrés [Termes descripteurs IGN] méthode robuste [Termes descripteurs IGN] modèle mathématique [Termes descripteurs IGN] régression [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Airborne laser scanning (ALS) is a reliable source of accurate information for forest stand inventory attributes including height, cover, basal area, and volume. The commonly applied area-based approach (ABA) allows the derivation of wall-to-wall geospatial coverages representing each of the modeled attributes at a grid-cell level, with spatial resolutions typically between 20 and 30 m. The ABA predictive models are developed using stratified inventory data from field plots, the requirement for which can increase the overall cost of the ALS-based inventory. Parsimonious use of ground plots is a key means to control variable costs in the operational implementation of the ABA. In this paper, we demonstrate how the prediction accuracy of Lorey's height (HL, m), quadratic mean diameter (QMD, cm), and gross volume (V, m3) vary when existing ABA models are transferred to different areas or are applied to point cloud data with different characteristics than those on which the original model was developed. Specifically, we consider three scenarios of model transferability: (i) same point cloud characteristics, different areas; (ii) different point cloud characteristics, same areas; and (iii) different point cloud characteristics, different areas. We generated area-based models using three modeling approaches: linear regression (OLS), random forests (RF), and k-nearest neighbour (kNN) imputation. Results indicated that the prediction accuracy of area-based models varied by attribute and by modeling approach. We found that when the models were transferred their prediction accuracy decreased, with an average increase in relative bias up to 22.04%, and increase in relative RMSE up to 29.31%. Prediction accuracies for HL were higher than those of QMD or V when models were transferred, and had the lowest average increase in relative bias and relative RMSE of Numéro de notice : A2019-227 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2019.04.006 date de publication en ligne : 13/04/2019 En ligne : https://doi.org/10.1016/j.rse.2019.04.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92741 [article]

Estimating forest stand density and structure using Bayesian individual tree detection, stochastic geometry, and distribution matching / Kasper Kansanen in ISPRS Journal of photogrammetry and remote sensing, vol 152 (June 2019)  

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A general method for the classification of forest stands using species composition and vertical and horizontal structure / Miquel de Cáceres in Annals of Forest Science [en ligne], vol 76 n° 2 (June 2019)  

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Tree and stand level estimations of Abies alba Mill. aboveground biomass / Andrzej M. Jagodzinski in Annals of Forest Science [en ligne], vol 76 n° 2 (June 2019)  

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Partition idéalisée et régionalisée de la composition en espèces ligneuses des forêts françaises / Jean-Daniel Bontemps in Ecoscience, vol 26 n° inconnu (2019)  

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Efficiency of post-stratification for a large-scale forest inventory : case Finnish NFI / Helena Haakana in Annals of Forest Science [en ligne], vol 76 n° 1 (March 2019)  

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Height-diameter allometry for tree species in Tanzania mainland / Wilson Ancelm Mugasha in International journal of forestry research, vol 2019 (2019)  

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Temporal and spatial high-resolution climate data from 1961 to 2100 for the German National Forest Inventory (NFI) / Helge Dietrich in Annals of Forest Science [en ligne], vol 76 n° 1 (March 2019)  

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Thinking outside the square: Evidence that plot shape and layout in forest inventories can bias estimates of stand metrics / Thomas S. H. Paul in Methods in ecology and evolution, vol 10 n° 3 (March 2019)  

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Combining potentially incompatible community datasets when harmonizing forest inventories in subarctic Alaska, USA / Robert J. Smith in Journal of vegetation science, vol 30 n° 1 (January 2019)  

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Integrating urban and national forest inventory data in support of rural–urban assessments / James A. Westfall in Forestry, an international journal of forest research, vol 91 n° 5 (December 2018)  

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