Centre de documentation
scientifique

Linear models for airborne-laser-scanning-based operational forest inventory with small field sample size and highly correlated LiDAR data / Virpi Junttila in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 10 (October 2015) . - pp 5600 - 5612 Titre : Linear models for airborne-laser-scanning-based operational forest inventory with small field sample size and highly correlated LiDAR data Type de document : Article/Communication Auteurs : Virpi Junttila, Auteur ; Tuomo Kauranne, Auteur ; Andrew O. Finley, Auteur ; John B. Bradford, Auteur Année de publication : 2015 Article en page(s) : pp 5600 - 5612 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] appariement d'images [Termes IGN] décomposition d'image [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] inventaire forestier étranger (données) [Termes IGN] modèle linéaire [Termes IGN] placette d'échantillonnage [Termes IGN] précision des données [Vedettes matières IGN] Inventaire forestier Résumé : (Auteur) Modern operational forest inventory often uses remotely sensed data that cover the whole inventory area to produce spatially explicit estimates of forest properties through statistical models. The data obtained by airborne light detection and ranging (LiDAR) correlate well with many forest inventory variables, such as the tree height, the timber volume, and the biomass. To construct an accurate model over thousands of hectares, LiDAR data must be supplemented with several hundred field sample measurements of forest inventory variables. This can be costly and time consuming. Different LiDAR-data-based and spatial-data-based sampling designs can reduce the number of field sample plots needed. However, problems arising from the features of the LiDAR data, such as a large number of predictors compared with the sample size (overfitting) or a strong correlation among predictors (multicollinearity), may decrease the accuracy and precision of the estimates and predictions. To overcome these problems, a Bayesian linear model with the singular value decomposition of predictors, combined with regularization, is proposed. The model performance in predicting different forest inventory variables is verified in ten inventory areas from two continents, where the number of field sample plots is reduced using different sampling designs. The results show that, with an appropriate field plot selection strategy and the proposed linear model, the total relative error of the predicted forest inventory variables is only 5%-15% larger using 50 field sample plots than the error of a linear model estimated with several hundred field sample plots when we sum up the error due to both the model noise variance and the model's lack of fit. Numéro de notice : A2015-748 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2425916 Date de publication en ligne : 14/05/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2425916 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78757 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2015101	SL	Revue	Centre de documentation	Revues en salle	Disponible

Delineation of forest/nonforest land use classes using nearest neighbor methods / R. Haapanen in Remote sensing of environment, vol 89 n° 3 (15/02/2004)  

Public [article]  inRemote sensing of environment > vol 89 n° 3 (15/02/2004) . - pp 265 - 271 Titre : Delineation of forest/nonforest land use classes using nearest neighbor methods Type de document : Article/Communication Auteurs : R. Haapanen, Auteur ; A.R. Ek, Auteur ; Andrew O. Finley, Auteur ; M.E. Bauer, Auteur Année de publication : 2004 Article en page(s) : pp 265 - 271 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification barycentrique [Termes IGN] délimitation [Termes IGN] forêt [Termes IGN] image Landsat-ETM+ [Termes IGN] image Landsat-TM [Termes IGN] Minnesota (Etats-Unis) [Termes IGN] occupation du sol Résumé : (Auteur) The k-Nearest Neighbor (kNN) method of forest attribute estimation and mapping has become an integral part of national forest inventory methods in Finland in the last decade. This success of kNN method in facilitating multisource inventory has encouraged trials of the method in the Great Lakes Region of the United States. Here we present results from applying the method to Landsat TM and ETM+ data and land cover data collected by the USDA Forest Service's Forest Inventory and Analysis (FIA) program. In 1999, the FIA program in the state of Minnesota moved to a new annual inventory design to reach its targeted full sampling intensity over a 5-year period. This inventory design also utilizes a new 4-subplot cluster plot configuration. Using this new plot design together with 1 year of field plot observations, the kNN classification of forest/nonforest/water achieved overall accuracies ranging from 87% to 91%. Our analysis revealed several important behavioral features associated with kNN classification using the new FIA sample plot design. Results demonstrate the simplicity and utility of using kNN to produce FIA defined forest/nonforest/water classifications. Numéro de notice : A2004-017 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2003.10.002 En ligne : https://doi.org/10.1016/j.rse.2003.10.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26545 [article]