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FOSTER - An R package for forest structure extrapolation / Martin Queinnec in Plos one, vol 16 n° 1 (January 2021)  

Public [article]  inPlos one > vol 16 n° 1 (January 2021) . - n° 0244846 Titre : FOSTER - An R package for forest structure extrapolation Type de document : Article/Communication Auteurs : Martin Queinnec, Auteur ; Piotr Tompalski, Auteur ; Douglas K. Bolton, Auteur ; Nicholas C. Coops, Auteur Année de publication : 2021 Article en page(s) : n° 0244846 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] autocorrélation spatiale [Termes IGN] classification barycentrique [Termes IGN] Colombie-Britannique (Canada) [Termes IGN] données localisées 3D [Termes IGN] extrapolation [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] R (langage) [Termes IGN] structure d'un peuplement forestier [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) The uptake of technologies such as airborne laser scanning (ALS) and more recently digital aerial photogrammetry (DAP) enable the characterization of 3-dimensional (3D) forest structure. These forest structural attributes are widely applied in the development of modern enhanced forest inventories. As an alternative to extensive ALS or DAP based forest inventories, regional forest attribute maps can be built from relationships between ALS or DAP and wall-to-wall satellite data products. To date, a number of different approaches exist, with varying code implementations using different programming environments and tailored to specific needs. With the motivation for open, simple and modern software, we present FOSTER (Forest Structure Extrapolation in R), a versatile and computationally efficient framework for modeling and imputation of 3D forest attributes. FOSTER derives spectral trends in remote sensing time series, implements a structurally guided sampling approach to sample these often spatially auto correlated datasets, to then allow a modelling approach (currently k-NN imputation) to extrapolate these 3D forest structure measures. The k-NN imputation approach that FOSTER implements has a number of benefits over conventional regression based approaches including lower bias and reduced over fitting. This paper provides an overview of the general framework followed by a demonstration of the performance and outputs of FOSTER. Two ALS-derived variables, the 95th percentile of first returns height (elev_p95) and canopy cover above mean height (cover), were imputed over a research forest in British Columbia, Canada with relative RMSE of 18.5% and 11.4% and relative bias of -0.6% and 1.4% respectively. The processing sequence developed within FOSTER represents an innovative and versatile framework that should be useful to researchers and managers alike looking to make forest management decisions over entire forest estates. Numéro de notice : A2021-306 Affiliation des auteurs : non IGN Thématique : FORET/INFORMATIQUE/MATHEMATIQUE Nature : Article DOI : 10.1371/journal.pone.0244846 Date de publication en ligne : 28/01/2021 En ligne : https://doi.org/10.1371/journal.pone.0244846 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97656 [article]

The utility of terrestrial photogrammetry for assessment of tree volume and taper in boreal mixedwood forests / Christopher Mulverhill in Annals of Forest Science [en ligne], Vol 76 n° 3 (September 2019)  

Public [article]  inAnnals of Forest Science [en ligne] > Vol 76 n° 3 (September 2019) . - pp 76 - 83 Titre : The utility of terrestrial photogrammetry for assessment of tree volume and taper in boreal mixedwood forests Type de document : Article/Communication Auteurs : Christopher Mulverhill, Auteur ; Nicholas C. Coops, Auteur ; Piotr Tompalski, Auteur ; Christopher W. Bater, Auteur ; Adam R. Dick, Auteur Année de publication : 2019 Article en page(s) : pp 76 - 83 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] Abies balsamea [Termes IGN] Alberta (Canada) [Termes IGN] allométrie [Termes IGN] betula papyrifera var. papyrifera [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] diamètre des arbres [Termes IGN] données dendrométriques [Termes IGN] forêt boréale [Termes IGN] hauteur des arbres [Termes IGN] image terrestre [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] peuplement mélangé [Termes IGN] photogrammétrie terrestre [Termes IGN] Picea glauca [Termes IGN] Picea mariana [Termes IGN] Pinus contorta [Termes IGN] Populus tremuloides [Termes IGN] semis de points [Termes IGN] volume en bois Résumé : (auteur) Key Message: This study showed that digital terrestrial photogrammetry is able to produce accurate estimates of stem volume and diameter across a range of species and tree sizes that showed strong correspondence when compared with traditional inventory techniques. This paper demonstrates the utility of the technology for characterizing trees in complex habitats such as boreal mixedwood forests. Context: Accurate knowledge of tree stem taper and volume are key components of forest inventories to manage and study forest resources. Recent developments have seen the increasing use of ground-based point clouds, including from digital terrestrial photogrammetry (DTP), to provide accurate estimates of these key forest attributes. Aims: In this study, we evaluated the utility of DTP based on a small set of photos (12 per tree) for estimating stem volume and taper on a set of 15 trees from 6 different species (Populus tremuloides, Picea glauca, Pinus contorta latifolia, Betula papyrifera, Picea mariana, Abies balsamea) in a boreal mixedwood forest in Alberta, Canada. Methods: We constructed accurate photogrammetric point clouds and derived taper and volume from three point cloud–based methods, which were then compared with estimates from conventional, field-based measurements. All methods were evaluated for their accuracy based on field-measured taper and volume of felled trees. Results: Of the methods tested, we found that the point cloud–derived diameters in a taper curve matching approach performed the best at estimating diameters at the lowest parts of the stem ( 50% of total height). Using the field-measured DBH and height as inputs to calculate stem volume yielded the most accurate predictions; however, these were not significantly different from the best point cloud-based estimates. Conclusion: The methodology confirmed that using a small set of photographs provided accurate estimates of individual tree DBH, taper, and volume across a range of species and size gradients (10.8–40.4 cm DBH). Numéro de notice : A2019-303 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-019-0852-9 Date de publication en ligne : 08/08/2019 En ligne : https://doi.org/10.1007/s13595-019-0852-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93226 [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 IGN] classification barycentrique [Termes IGN] classification par forêts aléatoires [Termes IGN] densité des points [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] méthode des moindres carrés [Termes IGN] méthode robuste [Termes IGN] modèle mathématique [Termes 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 Affiliation des auteurs : non IGN 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]

Digital aerial photogrammetry for assessing cumulative spruce budworm defoliation and enhancing forest inventories at a landscape-level / Tristan R.H. Goodbody in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 142 (August 2018) . - pp 1 - 11 Titre : Digital aerial photogrammetry for assessing cumulative spruce budworm defoliation and enhancing forest inventories at a landscape-level Type de document : Article/Communication Auteurs : Tristan R.H. Goodbody, Auteur ; Nicholas C. Coops, Auteur ; Txomin Hermosilla, Auteur ; Piotr Tompalski, Auteur ; Grant MacCartney, Auteur ; David A. MacLean, Auteur Année de publication : 2018 Article en page(s) : pp 1 - 11 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] défoliation [Termes IGN] dégradation de la flore [Termes IGN] échantillonnage d'image [Termes IGN] insecte nuisible [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] méthode des moindres carrés [Termes IGN] Ontario (Canada) [Termes IGN] photogrammétrie aérienne [Termes IGN] photogrammétrie numérique [Termes IGN] Picea abies [Termes IGN] régression linéaire [Termes IGN] surface terrière [Termes IGN] surveillance forestière [Vedettes matières IGN] Inventaire forestier Résumé : (Auteur) Spruce budworm (Choristoneura fumiferana [Clem.], Lepidoptera: Tortricidae) is a native defoliating insect with an important disturbance role in the eastern boreal forests of North America. With an extensive history of outbreaks and associated impacts on forest structural changes and timber supply, the mapping of spruce budworm defoliation has been of major management importance. In this study we assessed the ability of high spatial resolution digital aerial photogrammetric (DAP) data to predict cumulative defoliation as well as basal area and merchantable volume in spruce budworm host stands in the Gordon Cosens Forest south of Kapuskasing, Ontario, Canada. To do so, DAP derived structural and spectral metrics were incorporated to implement a stratified sampling design to improve the efficiency and cost-effectiveness of field surveying. Standard forest inventory measurements including diameter and height, as well as ocular and branch level defoliation assessments were undertaken on thirty 400 m2 radius plots. A partial least squares analysis was performed to determine whether structural metrics from a DAP point cloud could be influenced by spruce budworm defoliation, as well as determine the relative effectiveness of spectral (e.g. mean NDVI) vs. structural (e.g. 90th percentile of height) metrics, or their combination, for predicting cumulative defoliation. Results indicated that spectral metrics were the most effective for predicting cumulative defoliation (R2 = 0.79), while structural metrics were the least effective (R2 = 0.49). Metrics characterizing variance of the spectral values were found to be the most important predictors. Structural metrics and linear regression were also used to estimate landscape-level volume and basal area per hectare yielding R2 = 0.80 and R2 = 0.90, respectively. Outcomes of this analysis indicate that DAP-derived spectral metrics were more capable of modeling cumulative defoliation, while structural metrics were effective for landscape-level estimations of standard forest inventory attributes. This analysis indicated that the provision of both spectral and structural metrics from a single aerial imagery survey has potential to enhance defoliation monitoring and forest attribute modeling at a landscape-level. Numéro de notice : A2018-290 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.05.012 Date de publication en ligne : 01/08/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.05.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90408 [article]

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Predicting temperate forest stand types using only structural profiles from discrete return airborne lidar / Melissa Fedrigo in ISPRS Journal of photogrammetry and remote sensing, vol 136 (February 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 136 (February 2018) . - pp 106 - 119 Titre : Predicting temperate forest stand types using only structural profiles from discrete return airborne lidar Type de document : Article/Communication Auteurs : Melissa Fedrigo, Auteur ; Glenn J. Newnham, Auteur ; Nicholas C. Coops, Auteur ; Darius S. Culvenor, Auteur ; Douglas K. Bolton, Auteur ; Craig R. Nitschke, Auteur Année de publication : 2018 Article en page(s) : pp 106 - 119 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse en composantes principales [Termes IGN] analyse linéaire des mélanges spectraux [Termes IGN] Australie [Termes IGN] classification par forêts aléatoires [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Eucalyptus (genre) [Termes IGN] forêt tempérée [Termes IGN] peuplement forestier [Termes IGN] prédiction [Termes IGN] strate forestière Résumé : (Auteur) Light detection and ranging (lidar) data have been increasingly used for forest classification due to its ability to penetrate the forest canopy and provide detail about the structure of the lower strata. In this study we demonstrate forest classification approaches using airborne lidar data as inputs to random forest and linear unmixing classification algorithms. Our results demonstrated that both random forest and linear unmixing models identified a distribution of rainforest and eucalypt stands that was comparable to existing ecological vegetation class (EVC) maps based primarily on manual interpretation of high resolution aerial imagery. Rainforest stands were also identified in the region that have not previously been identified in the EVC maps. The transition between stand types was better characterised by the random forest modelling approach. In contrast, the linear unmixing model placed greater emphasis on field plots selected as endmembers which may not have captured the variability in stand structure within a single stand type. The random forest model had the highest overall accuracy (84%) and Cohen’s kappa coefficient (0.62). However, the classification accuracy was only marginally better than linear unmixing. The random forest model was applied to a region in the Central Highlands of south-eastern Australia to produce maps of stand type probability, including areas of transition (the ‘ecotone’) between rainforest and eucalypt forest. The resulting map provided a detailed delineation of forest classes, which specifically recognised the coalescing of stand types at the landscape scale. This represents a key step towards mapping the structural and spatial complexity of these ecosystems, which is important for both their management and conservation. Numéro de notice : A2018-074 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.11.018 Date de publication en ligne : 29/12/2017 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.11.018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89438 [article]

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Remote sensing technologies for enhancing forest inventories: A review / Joanne C. White in Canadian journal of remote sensing, vol 42 n° 5 ([01/05/2016])  

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Evaluating the impact of leaf-on and leaf-off airborne laser scanning data on the estimation of forest inventory attributes with the area-based approach / Joanne C. White in Canadian Journal of Forest Research, vol 45 n° 11 (November 2015)  

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Spatial data, analysis approaches, and information needs for spatial ecosystem service assessments: a review / Margaret E. Andrew in GIScience and remote sensing, vol 52 n° 3 (2015)  

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Using multi-frequency radar and discrete-return LiDAR measurements to estimate above-ground biomass and biomass components in a coastal temperate forest / O. Tsui in ISPRS Journal of photogrammetry and remote sensing, vol 69 (April 2012)

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Development of a simulation model to predict Lidar interception in forested environments / N.R. Goodwin in Remote sensing of environment, vol 111 n° 4 (28/12/2007)

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Assessment of forest structure with airborne LiDAR and the effects of platform altitude / N.R. Goodwin in Remote sensing of environment, vol 103 n° 2 (30/07/2006)

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MODIS enhanced vegetation index predicts tree species richness across forested ecoregions in the contiguous USA / R.H. Waring in Remote sensing of environment, vol 103 n° 2 (30/07/2006)

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Assessment of Quickbird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation / Nicholas C. Coops in Remote sensing of environment, vol 103 n° 1 (15 July 2006)

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Estimating fragmentation effects on simulated forest net primary productivity derived from satellite imagery / Nicholas C. Coops in International Journal of Remote Sensing IJRS, vol 25 n° 4 (February 2004)

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Assessment of regional forest and scrub productivity using a coupled vegetation process model with remote sensing / Nicholas C. Coops in Geocarto international, vol 17 n° 4 (December 2002 - February 2003)  

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