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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] semis de points [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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Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018081	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2018083	DEP-EXM	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2018082	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Historical land use as a feature for image classification / Jorge Abel Recio in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 4 (April 2011)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 77 n° 4 (April 2011) . - pp 377 - 387 Titre : Historical land use as a feature for image classification Type de document : Article/Communication Auteurs : Jorge Abel Recio, Auteur ; Txomin Hermosilla, Auteur ; L. Ruiz, Auteur ; A. Fernandez-Sarria, Auteur Année de publication : 2011 Article en page(s) : pp 377 - 387 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] changement d'occupation du sol [Termes IGN] classification dirigée [Termes IGN] détection de changement [Termes IGN] données spatiotemporelles [Termes IGN] mise à jour de base de données [Termes IGN] précision de la classification [Termes IGN] utilisation du sol Résumé : (Auteur) This paper analyzes the effect of the addition of historical land-use as a descriptive feature in plot-based image classification when updating land-use/land-cover geospatial databases. Several historical databases have been simulated to assess the influence and significance of this feature in the classification. The causes, nature, and evolution of classification errors as the database currency varies are analyzed; and the impact of these errors on change detection during the updating process is evaluated. The results show that the addition of historical land-use information increases the overall accuracy of image classifications. During a database updating process, changes are detected by comparing the historical land-use with the classification results. The main drawback of employing historical land-use as a descriptive feature in image classification for change detection is that the percentage of undetectable errors significantly increases as more accurate is the database information. Numéro de notice : A2011-129 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.77.4.377 En ligne : https://doi.org/10.14358/PERS.77.4.377 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30908 [article]