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Using LiDAR-modified topographic wetness index, terrain attributes with leaf area index to improve a single-tree growth model in south-eastern Finland / Cheikh Mohamedou in Forestry, an international journal of forest research, vol 92 n° 3 (July 2019)  

Public [article]  inForestry, an international journal of forest research > vol 92 n° 3 (July 2019) . - pp 253 - 263 Titre : Using LiDAR-modified topographic wetness index, terrain attributes with leaf area index to improve a single-tree growth model in south-eastern Finland Type de document : Article/Communication Auteurs : Cheikh Mohamedou, Auteur ; Lauri Korhonen, Auteur ; Kalle Eerikäinen, Auteur ; Timo Tokola, Auteur Année de publication : 2019 Article en page(s) : pp 253 - 263 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] croissance des arbres [Termes IGN] diamètre des arbres [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] erreur systématique [Termes IGN] Finlande [Termes IGN] humidité du sol [Termes IGN] indice d'humidité [Termes IGN] Leaf Area Index [Termes IGN] modèle de croissance végétale [Termes IGN] Perceptron multicouche Résumé : (Auteur) Tree growth information is crucial in forest management and planning. Terrain-derived attributes such as the topographic wetness index (TWI), in addition to leaf area index (LAI) are closely related to tree growth, but are not commonly used in empirical growth models. In this study, we examined if modified TWI and LAI estimated from airborne light detection and ranging (LiDAR) data could be used to improve the predictions of a national single-tree diameter growth model. Altogether 1118 sample trees were selected within 197 subjectively placed plots in randomly selected forest stands in south-eastern Finland. Linear mixed effect (LME) and multilayer perceptron models were used to model the bias of 5-year growth predictions of the model and thus ultimately improve its predictions. The root mean square error (RMSE) of the national model was 0.604 cm. LME modelling reduced this value to 0.404 cm and MLP to 0.568 cm. The predictors included in the best-performing LME model were modified TWI, LAI estimated from LiDAR intensities, and elevation. Without an LAI estimate, the best RMSE was 0.436 cm. When applied as such, original and modified TWIs produced similar accuracy. We conclude that both TWI and LAI obtained from LiDAR data improve the diameter growth predictions of the national model. Numéro de notice : A2019-293 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1093/forestry/cpz010 Date de publication en ligne : 28/02/2019 En ligne : https://doi.org/10.1093/forestry/cpz010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93184 [article]

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)  

Public [article]  inEcological indicators > vol 60 (January 2016) . - pp 574 - 585 Titre : Gini coefficient predictions from airborne lidar remote sensing display the effect of management intensity on forest structure Type de document : Article/Communication Auteurs : Rubén Valbuena, Auteur ; Kalle Eerikäinen, Auteur ; Petteri Packalen, Auteur ; Matti Maltamo, Auteur Année de publication : 2016 Article en page(s) : pp 574 - 585 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse comparative [Termes IGN] analyse diachronique [Termes IGN] coefficient de Gini [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Finlande [Termes IGN] hauteur des arbres [Termes IGN] parc naturel national [Termes IGN] structure d'un peuplement forestier [Termes IGN] surveillance forestière [Termes IGN] sylviculture Résumé : (auteur) In this study, two forest sites located in Finland were compared by means of predictions of Gini coefficient (GC) obtained from airborne laser scanning (ALS). We discuss the potential of the proposed method for identifying differences in structural complexity in relation with the management history of forests. The first study site (2200 ha), the Koli National Park (NP), includes areas where human intervention was restricted after 1907, in addition to forests which were protected only after the 1990s. The second study site in the municipality of Kiihtelysvaara (800 ha) has been under intensive management. These are commercial forests which include areas with different types of ownership: a large estate owned by an industrial company together with smaller private properties. We observed that GC predictions may be used to evaluate the effects of management practice on forest structure. Conservation and commercial forests showed significant differences, with the old-protected area of Koli having the highest, and the most intensively managed area in Kiihtelysvaara the lowest GC values. The effect of management history was revealed, as the 1990s’ extensions of Koli NP were more similar to unprotected areas than to forests contained within the original borders of the 1907s’ state property. Yet, their conservation status for almost two decades has been sufficient for developing significant differences against the outside of the NP. In Kiihtelysvaara, we found significant differences in GC according to the type of ownership. Moreover, the ALS predictions of GC also detected differences near lakeshores, which are driven by limitations on logging governed by Finnish law. Estimating this indicator with ALS remote sensing allowed to observe its spatial distribution and to detect peculiarities which would otherwise be unavailable from field plot sampling. Consequently, the method presented appears to be well suited for monitoring the effects of management practice, as well as verifying its compliance with legal restrictions. Numéro de notice : A2016-338 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.ecolind.2015.08.001 En ligne : http://dx.doi.org/10.1016/j.ecolind.2015.08.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81022 [article]

Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions / Matti Maltamo in Remote sensing of environment, vol 90 n° 3 (15/04/2004)  

Public [article]  inRemote sensing of environment > vol 90 n° 3 (15/04/2004) . - pp 319 - 330 Titre : Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions Type de document : Article/Communication Auteurs : Matti Maltamo, Auteur ; Kalle Eerikäinen, Auteur ; et al., Auteur Année de publication : 2004 Article en page(s) : pp 319 - 330 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] couvert forestier [Termes IGN] Finlande [Termes IGN] forêt [Termes IGN] hauteur des arbres [Termes IGN] houppier [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] mesure de précision [Termes IGN] Pinophyta [Termes IGN] sylviculture [Termes IGN] télémétrie laser aéroporté [Termes IGN] tronc Résumé : (Auteur) Laser scanners of small footprint diameter and high sampling density provide possibility to obtain accurate height information on the forest canopy. When applying tree crown segmentation methods, individual single trees can be recognised and tree height as well as crown area can be detected. Detection of suppressed trees from a height model based on laser scanning is difficult; however, it is possible to predict these trees by using theoretical distribution functions. In this study, two different methods are used to predict small trees. In the first method, the parameter prediction method is utilised with the complete Weibull distribution, the parameters of which are predicted with separate parameter prediction models; thus, small trees are determined from the predicted tree height distribution. In the second method, the twoparameter left-truncated Weibull distribution is fitted to the detected tree height distribution. The results are presented by using timber volume and stem density as predicted stand characteristics. The results showed that the root mean square error (RMSE) for the timber volume is about 25% when using only information obtained from laser scanning, whereas the RMSE for the number of stems per ha is about 75%. Predictions for both characteristics are also highly biased and the underestimates are 24% and 62%, respectively. The use of the parameter prediction method to describe small trees improved the accuracy considerably; the RMSE figures for estimates of timber volume and number of stems are 16.0% and 49.2%, respectively. The bias for the estimates is also decreased to 6.3% for timber volume and 8.2% for the number of stems. When a left-truncated height distribution is used to predict the heights of the missing small trees, the RMSEs for the estimates of timber volume and number of stems are 22.5% and 72.7%, respectively. In the case of the timber volume, the reliability figures for both the original laser scanning-based estimates and for the estimates that also contain small trees are comparable to those obtained by conventional compartment-wise Finnish field inventories. Numéro de notice : A2004-199 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2004.01.006 En ligne : https://doi.org/10.1016/j.rse.2004.01.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26726 [article]