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Effects of terrain slope and aspect on the error of ALS-based predictions of forest attributes / Hans Ole Ørka in Forestry, an international journal of forest research, vol 91 n° 2 (April 2018)  

Public [article]  inForestry, an international journal of forest research > vol 91 n° 2 (April 2018) . - pp 225 - 237 Titre : Effects of terrain slope and aspect on the error of ALS-based predictions of forest attributes Type de document : Article/Communication Auteurs : Hans Ole Ørka, Auteur ; Ole Martin Bollandsås, Auteur ; Endre H. Hansen, Auteur ; Erik Naesset, Auteur ; Terje Gobakken, Auteur Année de publication : 2018 Article en page(s) : pp 225 - 237 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de variance [Termes IGN] données dendrométriques [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] inventaire forestier étranger (données) [Termes IGN] modèle de simulation [Termes IGN] Norvège [Termes IGN] pente [Termes IGN] régression non linéaire [Vedettes matières IGN] Inventaire forestier Résumé : (Auteur) Wall-to-wall forest management inventories with the area-based method using airborne laser scanner (ALS) data are operational in many countries. With this method, empirical relationships are established between ALS metrics and ground reference observations of forest attributes, and wall-to-wall predictions can be made over large areas. However, the prediction errors may be influenced by terrain slope and aspect because the properties of the ALS point cloud are dependent on these factors. Two datasets covering wide ranges of terrain slope and aspect, collected in the western part of Norway, were analysed. The first dataset represented sample plots from an ordinary operational forest management inventory and the second dataset were collected as an experimental dataset where clusters of sample plots were distributed on slopes with different inclinations. Six forest attributes were predicted using non-linear regression and the prediction errors were analysed using univariate- and multivariate analysis of variance. The results showed that slope and aspect affected the prediction errors, but that the effects were small in magnitude. Thus, the current study concludes that terrain effects seem to be negligible in operational forest inventories. Numéro de notice : A2018-652 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1093/forestry/cpx058 Date de publication en ligne : 30/01/2018 En ligne : https://doi.org/10.1093/forestry/cpx058 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93238 [article]

Above- and belowground tree biomass models for three mangrove species in Tanzania: a nonlinear mixed effects modelling approach / Marco Andrew Njana in Annals of Forest Science, vol 73 n° 2 (June 2016)  

Public [article]  inAnnals of Forest Science > vol 73 n° 2 (June 2016) . - pp 353 - 369 Titre : Above- and belowground tree biomass models for three mangrove species in Tanzania: a nonlinear mixed effects modelling approach Type de document : Article/Communication Auteurs : Marco Andrew Njana, Auteur ; Ole Martin Bollandsås, Auteur ; Tron Eid, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 353 - 369 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Végétation [Termes IGN] biomasse forestière [Termes IGN] changement climatique [Termes IGN] état de surface du sol [Termes IGN] mangrove [Termes IGN] sol forestier [Termes IGN] sous-sol [Termes IGN] surveillance de la végétation [Termes IGN] Tanzanie [Termes IGN] teneur en carbone Résumé : (auteur) Key message: Tested on data from Tanzania, both existing species-specific and common biomass models developed elsewhere revealed statistically significant large prediction errors. Species-specific and common above- and belowground biomass models for three mangrove species were therefore developed. The species-specific models fitted better to data than the common models. The former models are recommended for accurate estimation of biomass stored in mangrove forests of Tanzania. Context: Mangroves are essential for climate change mitigation through carbon storage and sequestration. Biomass models are important tools for quantifying biomass and carbon stock. While numerous aboveground biomass models exist, very few studies have focused on belowground biomass, and among these, mangroves of Africa are hardly or not represented. Aims: The aims of the study were to develop above- and belowground biomass models and to evaluate the predictive accuracy of existing aboveground biomass models developed for mangroves in other regions and neighboring countries when applied on data from Tanzania. Methods: Data was collected through destructive sampling of 120 trees (aboveground biomass), among these 30 trees were sampled for belowground biomass. The data originated from four sites along the Tanzanian coastline covering three dominant species: Avicennia marina (Forssk.) Vierh, Sonneratia alba J. Smith, and Rhizophora mucronata Lam. The biomass models were developed through mixed modelling leading to fixed effects/common models and random effects/species-specific models. Results: Both the above- and belowground biomass models improved when random effects (species) were considered. Inclusion of total tree height as predictor variable, in addition to diameter at breast height alone, further improved the model predictive accuracy. The tests of existing models from other regions on our data generally showed large and significant prediction errors for aboveground tree biomass. Conclusion: Inclusion of random effects resulted into improved goodness of fit for both above- and belowground biomass models. Species-specific models therefore are recommended for accurate biomass estimation of mangrove forests in Tanzania for both management and ecological applications. For belowground biomass (S. alba) however, the fixed effects/common model is recommended. Numéro de notice : A2016-352 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s13595-015-0524-3 Date de publication en ligne : 14/10/2015 En ligne : https://doi.org/10.1007/s13595-015-0524-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81063 [article]