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Effects of numbers of observations and predictors for various model types on the performance of forest inventory with airborne laser scanning / Diogo N. Cosenza in Canadian Journal of Forest Research, Vol 52 n° 3 (March 2022)  

Public [article]  inCanadian Journal of Forest Research > Vol 52 n° 3 (March 2022) . - pp 385 - 395 Titre : Effects of numbers of observations and predictors for various model types on the performance of forest inventory with airborne laser scanning Type de document : Article/Communication Auteurs : Diogo N. Cosenza, Auteur ; Petteri Packalen, Auteur ; Matti Maltamo, Auteur ; Petri Varvia, Auteur ; Janne Raty, Auteur ; Paola Soares, Auteur ; Margarida Tomé, Auteur ; Jacob L. Strunk, Auteur ; Lauri Korhonen, Auteur Année de publication : 2022 Article en page(s) : pp 385 - 395 Note générale : bibliographie Langues : Français (fre) Anglais (eng) Descripteur : [Termes IGN] forêt boréale [Termes IGN] lasergrammétrie [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Semi- and nonparametric models are popular in the area-based approach (ABA) using airborne laser scanning. It is unclear, however, how many predictors and training plots are needed to provide accurate predictions without overfitting. This work aims to explore these limits for various approaches: ordinary least squares regression (OLS), generalized additive models (GAM), least absolute shrinkage and selection operator (LASSO), random forest (RF), support vector machine (SVM), and Gaussian process regression (GPR). We modeled timber volume (m3·ha–1) for four boreal sites using ABA with 2–39 predictors and 20–500 training plots. OLS, GAM, LASSO, and SVM overfitted as the number of predictors approached the number of training plots. They required ≥15 plots per predictor to provide accurate predictions (RMSE ≤30%). GAM required ≥250 plots regardless of the number of predictors. The number of predictors only mildly affected RF and GPR, but they required ≥200 and ≥250 training plots, respectively. RF did not overfit in any circumstances, whereas GPR overfit even with 500 training plots. Overall, using up to 39 predictors did not generally result in overfit, and for most model types, it resulted in better accuracy for sufficiently large datasets (≥250 plots). Numéro de notice : A2022-948 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1139/cjfr-2021-0192 En ligne : https://doi.org/10.1139/cjfr-2021-0192 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100413 [article]

A comparison of linear-mode and single-photon airborne LiDAR in species-specific forest inventories / Janne Raty in IEEE Transactions on geoscience and remote sensing, vol 60 n° 1 (January 2022)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 60 n° 1 (January 2022) . - n° 4401514 Titre : A comparison of linear-mode and single-photon airborne LiDAR in species-specific forest inventories Type de document : Article/Communication Auteurs : Janne Raty, Auteur ; Petri Varvia, Auteur ; Lauri Korhonen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 4401514 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] altitude [Termes IGN] analyse comparative [Termes IGN] capteur linéaire [Termes IGN] carte de la végétation [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Finlande [Termes IGN] forêt boréale [Termes IGN] instrumentation Leica [Termes IGN] instrumentation Riegl [Termes IGN] inventaire forestier étranger (données) [Termes IGN] photon [Termes IGN] Pinophyta [Termes IGN] semis de points [Termes IGN] signal laser Résumé : (auteur) Single-photon airborne light detection and ranging (LiDAR) systems provide high-density data from high flight altitudes. We compared single-photon and linear-mode airborne LiDAR for the prediction of species-specific volumes in boreal coniferous-dominated forests. The LiDAR data sets were acquired at different flight altitudes using Leica SPL100 (single-photon, 17 points ⋅ m−2 ), Riegl VQ-1560i (linear-mode, 11 points ⋅ m−2 ), and Leica ALS60 (linear-mode, 0.6 points ⋅ m−2 ) LiDAR systems. Volumes were predicted at the plot-level using Gaussian process regression with predictor variables extracted from the LiDAR data sets and aerial images. Our findings showed that the Leica SPL100 produced a greater mean root-mean-squared error (RMSE) value (41.7 m3 ⋅ ha −1 ) than the Leica ALS60 (39.3 m3 ⋅ ha −1 ) in the prediction of species-specific volumes. Correspondingly, the Riegl VQ-1560i (mean RMSE = 33.0 m3 ⋅ ha −1 ) outperformed both the Leica ALS60 and the Leica SPL100. We found that the cumulative distributions of the first echo heights >1.3 m were rather similar among the data sets, whereas the last echo distributions showed larger differences. We conclude that the Leica SPL100 data set is suitable for area-based LiDAR inventory by tree species although the prediction errors are greater than with data obtained using the modern linear-mode LiDAR, such as Riegl VQ-1560i. Numéro de notice : A2022-026 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1109/TGRS.2021.3060670 Date de publication en ligne : 04/03/2021 En ligne : https://doi.org/10.1109/TGRS.2021.3060670 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99257 [article]