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Effect of occupation time on the horizontal accuracy of a mapping-grade GNSS receiver under dense forest canopy / Robert J. McGaughey in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 12 (December 2017)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 12 (December 2017) . - PP 861 - 868 Titre : Effect of occupation time on the horizontal accuracy of a mapping-grade GNSS receiver under dense forest canopy Type de document : Article/Communication Auteurs : Robert J. McGaughey, Auteur ; Kamal Ahmed, Auteur ; Hans-Erik Andersen, Auteur ; Stephen E. Reutebuch, Auteur Année de publication : 2017 Article en page(s) : PP 861 - 868 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse comparative [Termes IGN] densité de la végétation [Termes IGN] Etats-Unis [Termes IGN] forêt [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] inventaire forestier étranger (données) [Termes IGN] lever topographique [Termes IGN] récepteur bifréquence [Termes IGN] récepteur GPS [Vedettes matières IGN] Inventaire forestier Résumé : (Auteur) A mapping-grade dual frequency GNSS receiver was tested under dense forest canopy to determine the effect of occupation time on horizontal accuracy. The U.S. Forest Service Forest Inventory and Analysis unit in the Pacific Northwest has been using 32 of these units to collect over 7,000 plot locations since 2013. In this study, one-hour GNSS static occupations were collected at 33 ground-surveyed positions with Trimble GeoXH6000 mapping-grade and Javad Triumph1 survey-grade receivers. Rover files were differentially post-processed and horizontal accuracy of each post-processed position was computed. Results indicated that 1.85 m accuracy (n = 990) could be achieved with the GeoXH6000 receiver with 15-minute occupations; however, maximum horizontal error was 7.01 m. Increasing occupation time to 20 minutes did not result in a significant improvement in accuracy. No correlation was found between the horizontal precision of a post-processed position reported by the postprocessing software and the field-measured horizontal accuracy of the positions. Numéro de notice : A2017-805 Affiliation des auteurs : non IGN Thématique : FORET/POSITIONNEMENT Nature : Article DOI : 10.14358/PERS.83.12.861 En ligne : https://doi.org/10.14358/PERS.83.12.861 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89166 [article]

An examination of diameter density prediction with k-NN and airborne lidar / Jacob L. Strunk in Forests, vol 8 n° 11 (November 2017)  

Public [article]  inForests > vol 8 n° 11 (November 2017) Titre : An examination of diameter density prediction with k-NN and airborne lidar Type de document : Article/Communication Auteurs : Jacob L. Strunk, Auteur ; Peter J. Gould, Auteur ; Petteri Packalen, Auteur ; Krishna P. Poudel, Auteur ; Hans-Erik Andersen, Auteur ; Hailemariam Temesgen, Auteur Année de publication : 2017 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Caroline du Sud (Etats-Unis) [Termes IGN] classification barycentrique [Termes IGN] classification par la distance de Mahalanobis [Termes IGN] diamètre des arbres [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] lasergrammétrie [Vedettes matières IGN] Inventaire forestier Résumé : (Auteur) While lidar-based forest inventory methods have been widely demonstrated, performances of methods to predict tree diameters with airborne lidar (lidar) are not well understood. One cause for this is that the performance metrics typically used in studies for prediction of diameters can be difficult to interpret, and may not support comparative inferences between sampling designs and study areas. To help with this problem we propose two indices and use them to evaluate a variety of lidar and k nearest neighbor (k-NN) strategies for prediction of tree diameter distributions. The indices are based on the coefficient of determination (R2), and root mean square deviation (RMSD). Both of the indices are highly interpretable, and the RMSD-based index facilitates comparisons with alternative (non-lidar) inventory strategies, and with projects in other regions. K-NN diameter distribution prediction strategies were examined using auxiliary lidar for 190 training plots distribute across the 800 km2 Savannah River Site in South Carolina, USA. We evaluate the performance of k-NN with respect to distance metrics, number of neighbors, predictor sets, and response sets. K-NN and lidar explained 80% of variability in diameters, and Mahalanobis distance with k = 3 neighbors performed best according to a number of criteria Numéro de notice : A2017-877 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f8110444 Date de publication en ligne : 16/11/2017 En ligne : https://doi.org/10.3390/f8110444 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91213 [article]