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Northern conifer forest species classification using multispectral data acquired from an unmanned aerial vehicle / Steven E. Franklin in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 7 (July 2017)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 7 (July 2017) . - pp 501 - 507 Titre : Northern conifer forest species classification using multispectral data acquired from an unmanned aerial vehicle Type de document : Article/Communication Auteurs : Steven E. Franklin, Auteur ; Oumer S. Ahmed, Auteur ; Griffin Williams, Auteur Année de publication : 2017 Article en page(s) : pp 501 - 507 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] Canada [Termes IGN] classification automatique [Termes IGN] drone [Termes IGN] espèce végétale [Termes IGN] image aérienne [Termes IGN] image multibande [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] Ontario (Canada) [Termes IGN] Pinophyta [Termes IGN] semis de points Résumé : (auteur) Object-based image analysis and machine learning classification procedures, after field calibration and photogrammetric processing of consumer-grade unmanned aerial vehicle (UAV) digital camera data, were implemented to classify tree species in a conifer forest in the Great Lakes/St Lawrence Lowlands Ecoregion, Ontario, Canada. A red-green-blue (RGB) digital camera yielded approximately 72 percent classification accuracy for three commercial tree species and one conifer shrub. Accuracy improved approximately 15 percent, to 87 percent overall, with higher radiometric quality data acquired separately using a digital camera that included near infrared observations (at a lower spatial resolution). Interpretation of the point cloud, spectral, texture and object (tree crown) classification Variable Importance (VI) selected by a machine learning algorithm suggested a good correspondence with the traditional aerial photointerpretation cues used in the development of well-established large-scale photography northern conifer elimination keys, which use three-dimensional crown shape, spectral response (tone), texture derivatives to quantify branching characteristics, and crown size, development and outline features. These results suggest that commonly available consumer-grade UAV-based digital cameras can be used with object-based image analysis to obtain acceptable conifer species classification accuracy to support operational forest inventory applications. Numéro de notice : A2017-434 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.83.7.501 En ligne : https://doi.org/10.14358/PERS.83.7.501 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86338 [article]

Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm / Oumer S. Ahmed in ISPRS Journal of photogrammetry and remote sensing, vol 101 (March 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 101 (March 2015) . - pp 89 - 101 Titre : Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm Type de document : Article/Communication Auteurs : Oumer S. Ahmed, Auteur ; Steven E. Franklin, Auteur ; Michael A. Wulder, Auteur ; Joanne C. White, Auteur Année de publication : 2015 Article en page(s) : pp 89 - 101 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] canopée [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] composition d'un peuplement forestier [Termes IGN] couvert forestier [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] hauteur des arbres [Termes IGN] image Landsat [Termes IGN] régression multiple [Termes IGN] série temporelle [Termes IGN] Vancouver (Colombie britannique) Résumé : (auteur) Many forest management activities, including the development of forest inventories, require spatially detailed forest canopy cover and height data. Among the various remote sensing technologies, LiDAR (Light Detection and Ranging) offers the most accurate and consistent means for obtaining reliable canopy structure measurements. A potential solution to reduce the cost of LiDAR data, is to integrate transects (samples) of LiDAR data with frequently acquired and spatially comprehensive optical remotely sensed data. Although multiple regression is commonly used for such modeling, often it does not fully capture the complex relationships between forest structure variables. This study investigates the potential of Random Forest (RF), a machine learning technique, to estimate LiDAR measured canopy structure using a time series of Landsat imagery. The study is implemented over a 2600 ha area of industrially managed coastal temperate forests on Vancouver Island, British Columbia, Canada. We implemented a trajectory-based approach to time series analysis that generates time since disturbance (TSD) and disturbance intensity information for each pixel and we used this information to stratify the forest land base into two strata: mature forests and young forests. Canopy cover and height for three forest classes (i.e. mature, young and mature and young (combined)) were modeled separately using multiple regression and Random Forest (RF) techniques. For all forest classes, the RF models provided improved estimates relative to the multiple regression models. The lowest validation error was obtained for the mature forest strata in a RF model (R2 = 0.88, RMSE = 2.39 m and bias = −0.16 for canopy height; R2 = 0.72, RMSE = 0.068% and bias = −0.0049 for canopy cover). This study demonstrates the value of using disturbance and successional history to inform estimates of canopy structure and obtain improved estimates of forest canopy cover and height using the RF algorithm. Numéro de notice : A2015-470 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.11.007 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.11.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77172 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2015031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Integration of Lidar and Landsat to estimate forest canopy cover in coastal British Columbia / Oumer S. Ahmed in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 10 (October 2014)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 10 (October 2014) . - pp 953 - 961 Titre : Integration of Lidar and Landsat to estimate forest canopy cover in coastal British Columbia Type de document : Article/Communication Auteurs : Oumer S. Ahmed, Auteur ; Steven E. Franklin, Auteur ; Michael A. Wulder, Auteur Année de publication : 2014 Article en page(s) : pp 953 - 961 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] analyse des mélanges spectraux [Termes IGN] canopée [Termes IGN] Colombie-Britannique (Canada) [Termes IGN] couvert forestier [Termes IGN] données lidar [Termes IGN] feuillu [Termes IGN] image Landsat [Termes IGN] occupation du sol [Termes IGN] Pinophyta Résumé : (auteur) Airborne Light Detection and Ranging (lidar) data provide useful measurements of forest canopy structure but are often limited in spatial coverage. Satellite remote sensing data from Landsat can provide extensive spatial coverage of generalized forest information. A forest survey approach that integrates airborne lidar and satellite data would potentially capitalize upon these distinctive characteristics. In this study in coastal forests of British Columbia, the main objective was to determine the potential of Landsat imagery to accurately estimate forest canopy cover measured from small-footprint airborne lidar data in order to expand the lidar measurements to a larger area. Landsat-derived Tasseled Cap Angle (TCA) and spectral mixture analysis (SMA) endmember fractions (i.e., sunlit canopy, non-phofosynthetic vegetation (NPV), shade and exposed soil) were compared to lidar-derived canopy cover estimates. Pixel-based analysis and object-based area-weighted error calculations were used to assess regression model performance. The best canopy cover estimate was obtained (in the object-based deciduous forest models) with a mean object size (MOS) of 2.5 hectares (adjusted R2 = O.86 and RMSE = 0.28). Overall, lower canopy cover estimation accuracy was obtained for coniferous forests compared to deciduous forests in both the pixel and object-based approaches. Numéro de notice : A2014-672 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.80.10.953 En ligne : https://doi.org/10.14358/PERS.80.10.953 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75152 [article]