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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]

Horvitz-Thompson–like estimation with distance-based detection probabilities for circular plot sampling of forests / Kasper Kansanen in Biometrics, vol 77 n° 2 (June 2021)  

Public [article]  inBiometrics > vol 77 n° 2 (June 2021) . - pp 715 - 728 Titre : Horvitz-Thompson–like estimation with distance-based detection probabilities for circular plot sampling of forests Type de document : Article/Communication Auteurs : Kasper Kansanen, Auteur ; Petteri Packalen, Auteur ; Matti Maltamo, Auteur ; Lauri Mehtätalo, Auteur Année de publication : 2021 Article en page(s) : pp 715 - 728 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] distribution de Poisson [Termes IGN] erreur systématique [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] placette d'échantillonnage [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) In circular plot sampling, trees within a given distance from the sample plot location constitute a sample, which is used to infer characteristics of interest for the forest area. If the sample is collected using a technical device located at the sampling point, eg, a terrestrial laser scanner, all trees of the sample plot cannot be observed because they hide behind each other. We propose a Horvitz-Thompson–like estimator with distance-based detection probabilities derived from stochastic geometry for estimation of population totals such as stem density and basal area in such situation. We show that our estimator is unbiased for Poisson forests and give estimates of variance and approximate confidence intervals for the estimator, unlike any previous methods. We compare the estimator to two previously published benchmark methods. The comparison is done through a simulation study where several plots are simulated either from field measured data or different marked point processes. The simulations show that the estimator produces lower or comparable error values than the other methods. In the sample plots based on the field measured data, the bias is relatively small—relative mean of errors for stem density, for example, varying from 0.3% to 2.2%, depending on the detection condition. The empirical coverage probabilities of the approximate confidence intervals are either similar to the nominal levels or conservative in these sample plots. Numéro de notice : A2021-987 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1111/biom.13312 Date de publication en ligne : 07/06/2020 En ligne : https://doi.org/10.1111/biom.13312 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103237 [article]

Volumes by tree species can be predicted using photogrammetric UAS data, Sentinel-2 images and prior field measurements / Mikko Kukkonen in Silva fennica, vol 55 n° 1 (January 2021)  

Public [article]  inSilva fennica > vol 55 n° 1 (January 2021) . - n° 10360 Titre : Volumes by tree species can be predicted using photogrammetric UAS data, Sentinel-2 images and prior field measurements Type de document : Article/Communication Auteurs : Mikko Kukkonen, Auteur ; Eetu Kotivuori, Auteur ; Matti Maltamo, Auteur ; Lauri Korhonen, Auteur ; Petteri Packalen, Auteur Année de publication : 2021 Article en page(s) : n° 10360 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] classification barycentrique [Termes IGN] données de terrain [Termes IGN] données localisées 3D [Termes IGN] Finlande [Termes IGN] forêt boréale [Termes IGN] image captée par drone [Termes IGN] image Sentinel-MSI [Termes IGN] inventaire forestier local [Termes IGN] modèle de simulation [Termes IGN] régression [Termes IGN] semis de points [Termes IGN] volume en bois Résumé : (auteur) Photogrammetric point clouds obtained with unmanned aircraft systems (UAS) have emerged as an alternative source of remotely sensed data for small area forest management inventories (FMI). Nonetheless, it is often overlooked that small area FMI require considerable field data in addition to UAS data, to support the modelling of forest attributes. In this study, we propose a method whereby tree volumes by species are predicted with photogrammetric UAS data and Sentinel-2 images, using models fitted with airborne laser scanning data. The study area is in a managed boreal forest area in Eastern Finland. First, we predicted total volume with UAS point cloud metrics using a prior regression model fitted in another area with ALS data. Tree species proportions were then predicted by k nearest neighbor (k-NN) imputation based on bi-seasonal Sentinel-2 images without measuring new field plot data. Species-specific volumes were then obtained by multiplying the total volume by species proportions. The relative root mean square error (RMSE) values for total and species-specific volume predictions at the validation plot level (30 m × 30 m) were 9.0%, and 33.4–62.6%, respectively. Our approach appears promising for species-specific small area FMI in Finland and in comparable forest conditions in which suitable field plots are available. Numéro de notice : A2021-738 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14214/sf.10360 En ligne : https://doi.org/10.14214/sf.10360 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98703 [article]

The utility of fused airborne laser scanning and multispectral data for improved wind damage risk assessment over a managed forest landscape in Finland / Ranjith Gopalakrishnan in Annals of Forest Science, vol 77 n° 4 (December 2020)  

Public [article]  inAnnals of Forest Science > vol 77 n° 4 (December 2020) . - 18 p. Titre : The utility of fused airborne laser scanning and multispectral data for improved wind damage risk assessment over a managed forest landscape in Finland Type de document : Article/Communication Auteurs : Ranjith Gopalakrishnan, Auteur ; Petteri Packalen, Auteur ; Veli-Pekka Ikonen, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 18 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] cartographie des risques [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Finlande [Termes IGN] forêt [Termes IGN] forêt boréale [Termes IGN] image multibande [Termes IGN] modèle de simulation [Termes IGN] risque naturel [Termes IGN] tempête [Termes IGN] vent [Termes IGN] zone à risque Résumé : (auteur) Key message: The potential of airborne laser scanning (ALS) and multispectral remote sensing data to aid in generating improved wind damage risk maps over large forested areas is demonstrated. This article outlines a framework to generate such maps, primarily utilizing the horizontal structural information contained in the ALS data. Validation was done over an area in Eastern Finland that had experienced sporadic wind damage. Context: Wind is the most prominent disturbance element for Finnish forests. Hence, tools are needed to generate wind damage risk maps for large forested areas, and their possible changes under planned silvicultural operations. Aims: (1) How effective are ALS-based forest variables (e.g. distance to upwind forest stand edge, gap size) for identifying high wind damage risk areas? (2) Can robust estimates of predicted critical wind speeds for uprooting of trees be derived from these variables? (3) Can these critical wind speed estimates be improved using wind multipliers, which factor in topography and terrain roughness effects? Methods: We first outline a framework to generate several wind damage risk–related parameters from remote sensing data (ALS + multispectral). Then, we assess if such parameters have predictive power. That is, whether they help differentiate between damaged and background points. This verification exercise used 42 wind damaged points spread over a large area. Results: Parameters derived from remote sensing data are shown to have predictive power. Risk models based on critical wind speeds are not that robust, but show potential for improvement. Conclusion: Overall, this work described a framework to get several wind risk–related parameters from remote sensing data. These parameters are shown to have potential in generating wind damage risk maps over large forested areas. Numéro de notice : A2020-629 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-020-00992-8 Date de publication en ligne : 09/10/2020 En ligne : https://doi.org/10.1007/s13595-020-00992-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96045 [article]

Transferability and calibration of airborne laser scanning based mixed-effects models to estimate the attributes of sawlog-sized Scots pines / Lauri Korhonen in Silva fennica, vol 53 n° 3 (2019)  

Public [article]  inSilva fennica > vol 53 n° 3 (2019) Titre : Transferability and calibration of airborne laser scanning based mixed-effects models to estimate the attributes of sawlog-sized Scots pines Type de document : Article/Communication Auteurs : Lauri Korhonen, Auteur ; Jaakko Repola, Auteur ; Tomi Karjalainen, Auteur ; Petteri Packalen, Auteur ; Matti Maltamo, Auteur Année de publication : 2019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] détection d'arbres [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] erreur systématique [Termes IGN] estimation statistique [Termes IGN] étalonnage de modèle [Termes IGN] hauteur à la base du houppier [Termes IGN] houppier [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] lasergrammétrie [Termes IGN] Pinus sylvestris [Termes IGN] placette d'échantillonnage [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Airborne laser scanning (ALS) data is nowadays often available for forest inventory purposes, but adequate field data for constructing new forest attribute models for each area may be lacking. Thus there is a need to study the transferability of existing ALS-based models among different inventory areas. The objective of our study was to apply ALS-based mixed models to estimate the diameter, height and crown base height of individual sawlog sized Scots pines (Pinus sylvestrisL.) at three different inventory sites in eastern Finland. Different ALS sensors and acquisition parameters were used at each site. Multivariate mixed-effects models were fitted at one site and the models were validated at two independent test sites. Validation was carried out by applying the fixed parts of the mixed models as such, and by calibrating them using 1–3 sample trees per plot. The results showed that the relative RMSEs of the predictions were 1.2–6.5 percent points larger at the test sites compared to the training site. Systematic errors of 2.4–6.2 percent points also emerged at the test sites. However, both the RMSEs and the systematic errors decreased with calibration. The results showed that mixed-effects models of individual tree attributes can be successfully transferred and calibrated to other ALS inventory areas in a level of accuracy that appears suitable for practical applications. Numéro de notice : A2019-641 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14214/sf.10179 Date de publication en ligne : 31/07/2019 En ligne : https://doi.org/10.14214/sf.10179 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93446 [article]

Comparing nearest neighbor configurations in the prediction of species-specific diameter distributions / Janne Raty in Annals of Forest Science, vol 75 n° 1 (March 2018)  

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Airborne laser scanning for tree diameter distribution modelling: a comparison of different modelling alternatives in a tropical single-species plantation / Matti Maltamo in Forestry, an international journal of forest research, vol 91 n° 1 (January 2018)  

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

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Image matching as a data source for forest inventory – Comparison of semi-global matching and next-generation automatic terrain extraction algorithms in a typical managed boreal forest environment / Mari Kukkonen in International journal of applied Earth observation and geoinformation, vol 60 (August 2017)  

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Effect of flying altitude, scanning angle and scanning mode on the accuracy of ALS based forest inventory / Juha Keränen in International journal of applied Earth observation and geoinformation, vol 52 (October 2016)  

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Nationwide airborne laser scanning based models for volume, biomass and dominant height in Finland / Eetu Kotivuori in Silva fennica, vol 50 n° 4 (2016)  

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Gini coefficient predictions from airborne lidar remote sensing display the effect of management intensity on forest structure / Rubén Valbuena in Ecological indicators, vol 60 (January 2016)  

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Stand volume models based on stable metrics as from multiple ALS acquisitions in Eucalyptus plantations / Eric Bastos Görgens in Annals of Forest Science, vol 72 n° 4 (June 2015)  

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Comparison of airborne laser scanning methods for estimating forest structure indicators based on Lorenz curves / Rubén Valbuena in ISPRS Journal of photogrammetry and remote sensing, vol 95 (September 2014)  

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Experiences and possibilities of ALS based forest inventory in Finland / Matti Maltamo (2007)  

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