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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]

Exemplaires(1)

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

Adaptive algorithm for large scale DTM interpolation from lidar data for forestry applications in steep forested terrain / Almasi S. Maguya in ISPRS Journal of photogrammetry and remote sensing, vol 85 (November 2013)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 85 (November 2013) . - pp 74 - 83 Titre : Adaptive algorithm for large scale DTM interpolation from lidar data for forestry applications in steep forested terrain Type de document : Article/Communication Auteurs : Almasi S. Maguya, Auteur ; Virpi Junttila, Auteur ; Tuomo Kauranne, Auteur Année de publication : 2013 Article en page(s) : pp 74 - 83 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] couvert forestier [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] filtrage de points [Termes IGN] fonction spline d'interpolation [Termes IGN] hauteur des arbres [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] modèle numérique de terrain [Termes IGN] morphologie mathématique [Termes IGN] Népal [Termes IGN] pente Résumé : (Auteur) Light Detection and Ranging (lidar) has become a valuable tool in forest inventory because it yields accurate measurements of tree heights. However, tree height can be accurate only if the height of the ground, i. e., the Digital Terrain Model (dtm) is first accurately established. Although great advances have been made in lidar technology over the past decade, filtering lidar data for Digital Terrain Model (dtm) interpolation is still a challenge, especially in steep and complex terrain with forest cover. Several algorithms proposed in the literature address this challenge but their performance deteriorates with the decreasing point density caused by the presence of forest cover and steep slopes. In this paper, we propose a new adaptive algorithm for dtm interpolation from lidar data in steep terrain with forest cover. The algorithm partitions the input data and estimates a section of the dtm by fitting a linear or quadratic trend surface, or uses cubic spline interpolation depending on the complexity of the section of terrain. The performance of the algorithm is tested in three ways: by visual assessment, by comparison of the tree-height estimates produced using the generated dtm with those obtained using field survey, and by use of International Society for Photogrammetry and Remote Sensing (isprs) test data. Test results show that the algorithm can cope well with steep slopes and low lidar point densities, giving a more accurate estimate of average tree height compared to conventional algorithms. The algorithm can be used for dtm extraction in large scale forest inventory projects in challenging environments–complex terrain and low lidar point densities. Numéro de notice : A2013-608 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.08.005 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.08.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32744 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2013111	RAB	Revue	Centre de documentation	En réserve L003	Disponible