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Comparison of two parameter recovery methods for the transformation of Pinus sylvestris yield tables into a diameter distribution model / Francisco Mauro in Annals of Forest Science, vol 78 n° 1 (March 2021)  

Public [article]  inAnnals of Forest Science > vol 78 n° 1 (March 2021) . - n° 12 Titre : Comparison of two parameter recovery methods for the transformation of Pinus sylvestris yield tables into a diameter distribution model Type de document : Article/Communication Auteurs : Francisco Mauro, Auteur ; Antonio Garcia-Abril, Auteur ; Esperanza Ayuga-Téllez, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 12 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse comparative [Termes IGN] croissance des arbres [Termes IGN] densité de la végétation [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] diamètre des arbres [Termes IGN] distribution spatiale [Termes IGN] Espagne [Termes IGN] Pinus sylvestris [Termes IGN] structure d'un peuplement forestier [Vedettes matières IGN] Sylviculture Résumé : (auteur) Key message: We successfully transformed Pinus sylvestris yield tables into diameter distribution models. The best results were obtained with the parameter recovery method based on both mean and quadratic mean diameter, which explained 70% of the variability of frequencies by diameter classes and provided better results in the analysis of errors. On the other hand, the method based on stand density, dominant diameter and quadratic mean diameter explained less variability of frequencies by diameter classes (64.4%). Context: Old datasets used to develop yield table models can be recovered to transform those yield tables into diameter distribution models that provide a more detailed description of size variability and forest structure. Methods: We compared two different parameter recovery methods, one based on both mean and quadratic mean diameter and another one based on dominant diameter, stand density and quadratic mean diameter and used a set of 104 even aged plots to analyze the performance of the said methods for the transformation of Pinus sylvestris L yield tables in central Spain into a diameter distribution model. Results: The parameter recovery method based on both mean and quadratic mean diameter explained 70% of the variability of frequencies by diameter classes and provided better results than the method based on stand density, dominant diameter and quadratic mean diameter that explained 64.4% of the variability of frequencies by diameter classes. However, more important than the method itself were the errors that propagated from the models predicting the different variables used in the parameter recovery. Conclusion: Based on the results from the analysis of errors by diameter classes, the method using both mean and quadratic mean diameter outperformed the method using dominant diameter, stand density and quadratic mean diameter and is the best option to transform P. sylvestris yield tables into diameter distribution models. Numéro de notice : A2021-164 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-021-01028-5 Date de publication en ligne : 28/01/2021 En ligne : https://doi.org/10.1007/s13595-021-01028-5 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97094 [article]

A simple approach to forest structure classification using airborne laser scanning that can be adopted across bioregions / Syed Adnan in Forest ecology and management, vol 433 (15 February 2019)  

Public [article]  inForest ecology and management > vol 433 (15 February 2019) . - pp 111 - 121 Titre : A simple approach to forest structure classification using airborne laser scanning that can be adopted across bioregions Type de document : Article/Communication Auteurs : Syed Adnan, Auteur ; Matti Maltamo, Auteur ; David A. Coomes, Auteur ; Antonio Garcia-Abril, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 111 - 121 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] canopée [Termes IGN] classification ascendante hiérarchique [Termes IGN] classification barycentrique [Termes IGN] classification et arbre de régression [Termes IGN] coefficient de Gini [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] dynamique de la végétation [Termes IGN] écorégion [Termes IGN] hétérogénéité environnementale [Termes IGN] inventaire forestier étranger (données) [Termes IGN] Pinophyta [Termes IGN] structure d'un peuplement forestier Résumé : (auteur) Reliable assessment of forest structural types (FSTs) aids sustainable forest management. We developed a methodology for the identification of FSTs using airborne laser scanning (ALS), and demonstrate its generality by applying it to forests from Boreal, Mediterranean and Atlantic biogeographical regions. First, hierarchal clustering analysis (HCA) was applied and clusters (FSTs) were determined in coniferous and deciduous forests using four forest structural variables obtained from forest inventory data – quadratic mean diameter , Gini coefficient , basal area larger than mean and density of stems –. Then, classification and regression tree analysis (CART) were used to extract the empirical threshold values for discriminating those clusters. Based on the classification trees, and were the most important variables in the identification of FSTs. Lower, medium and high values of and characterize single storey FSTs, multi-layered FSTs and exponentially decreasing size distributions (reversed J), respectively. Within each of these main FST groups, we also identified young/mature and sparse/dense subtypes using and . Then we used similar structural predictors derived from ALS – maximum height (), L-coefficient of variation (), L-skewness (), and percentage of penetration (), – and a nearest neighbour method to predict the FSTs. We obtained a greater overall accuracy in deciduous forest (0.87) as compared to the coniferous forest (0.72). Our methodology proves the usefulness of ALS data for structural heterogeneity assessment of forests across biogeographical regions. Our simple two-tier approach to FST classification paves the way toward transnational assessments of forest structure across bioregions. Numéro de notice : A2019-007 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.foreco.2018.10.057 Date de publication en ligne : 03/11/2018 En ligne : https://doi.org/10.1016/j.foreco.2018.10.057 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91600 [article]