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Multi-sensor aboveground biomass estimation in the broadleaved hyrcanian forest of Iran / Ghasem Ronoud in Canadian journal of remote sensing, vol 47 n° 6 ([01/11/2021])  

Public [article]  inCanadian journal of remote sensing > vol 47 n° 6 [01/11/2021] . - pp 818 - 834 Titre : Multi-sensor aboveground biomass estimation in the broadleaved hyrcanian forest of Iran Titre original : Estimation multi-capteurs de la biomasse aérienne de la forêt de feuillus hyrcanienne d’Iran Type de document : Article/Communication Auteurs : Ghasem Ronoud, Auteur ; Parviz Fatehi, Auteur ; Ali Asghar Darvishsefat, Auteur Année de publication : 2021 Article en page(s) : pp 818 - 834 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse aérienne [Termes IGN] classification barycentrique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] estimation statistique [Termes IGN] Fagus orientalis [Termes IGN] image Landsat-8 [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] Iran [Termes IGN] régression multiple [Vedettes matières IGN] Inventaire forestier Mots-clés libres : Support Vector Regression Résumé : (auteur) In this study, the capability of Landsat-8 (L8), Sentinel-2 (S2), Sentinel-1 (S1), and their combination was investigated for estimating aboveground biomass (AGB). A pure stand of Fagus Orientalis located in the Hyrcanian forest of Iran was selected as the study area. The performance of a parametric approach, i.e., Multiple Linear Regression (MLR) model and non-parametric approaches, i.e., k-Nearest Neighbor (k-NN), Random Forest (RF), and Support Vector Regression (SVR), were also evaluated for AGB estimations. Our results indicated that among S2 metrics, the FAPAR canopy biophysical index and NDVI index based on the red-edge band (NIR-b8a) have the highest correlation coefficient (r) of 0.420 and 0.417, respectively. The results of AGB estimation showed that a combination of S2 and S1 datasets using the k-NN algorithm had the best accuracy (R2 of 0.57 and rRMSE of 14.68%). The best rRMSE using L8, S2, and S1 datasets was 18.95, 16.99, and 19.17% using k-NN, k-NN, and MLR algorithms, respectively. The combination of L8 with S1 dataset also improved the rRMSE relative to L8 and S1 separately by 0.96 and 1.18%, respectively. We concluded that the combination of optical data (L8 or S2) with SAR data (S1) improves the broadleaved Hyrcanian AGB estimation. Numéro de notice : A2021-956 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/MATHEMATIQUE Nature : Article DOI : 10.1080/07038992.2021.1968811 Date de publication en ligne : 07/09/2021 En ligne : https://doi.org/10.1080/07038992.2021.1968811 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99982 [article]

Analysis of plot-level volume increment models developed from machine learning methods applied to an uneven-aged mixed forest / Seyedeh Kosar Hamidi in Annals of Forest Science, vol 78 n° 1 (March 2021)  

Public [article]  inAnnals of Forest Science > vol 78 n° 1 (March 2021) . - n° 4 Titre : Analysis of plot-level volume increment models developed from machine learning methods applied to an uneven-aged mixed forest Type de document : Article/Communication Auteurs : Seyedeh Kosar Hamidi, Auteur ; Eric K. Zenner, Auteur ; Mahmoud Bayat, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Acer velutinum [Termes IGN] Alnus cordata [Termes IGN] analyse comparative [Termes IGN] apprentissage automatique [Termes IGN] Carpinus betulus [Termes IGN] classification barycentrique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par réseau neuronal [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] dynamique de la végétation [Termes IGN] écosystème forestier [Termes IGN] Fagus orientalis [Termes IGN] forêt inéquienne [Termes IGN] inventaire forestier étranger (données) [Termes IGN] Iran [Termes IGN] modèle de croissance végétale [Termes IGN] modèle de simulation [Termes IGN] peuplement mélangé [Termes IGN] régression linéaire [Termes IGN] volume en bois [Vedettes matières IGN] Sylviculture Résumé : (auteur) Key message: We modeled 10-year net stand volume growth with four machine learning (ML) methods, i.e., artificial neural networks (ANN), support vector machines (SVM), random forests (RF), and nearest neighbor analysis (NN), and with linear regression analysis. Incorporating interactions of multiple variables, the ML methods ANN and SVM predicted nonlinear system behavior and unraveled complex relations with greater accuracy than regression analysis. Context: Investigating the quantitative and qualitative characteristics of short-term forest dynamics is essential for testing whether the desired goals in forest-ecosystem conservation and restoration are achieved. Inventory data from the Jojadeh section of the Farim Forest located in the uneven-aged, mixed Hyrcanian Forest were used to model and predict 10-year net annual stand volume increment with new machine learning technologies. Aims: The main objective of this study was to predict net annual stand volume increment as the preeminent factor of forest growth and yield models. Methods: In the current study, volume increment was modeled from two consecutive inventories in 2003 and 2013 using four machine learning techniques that used physiographic data of the forest as input for model development: (i) artificial neural networks (ANN), (ii) support vector machines (SVM), (iii) random forests (RF), and (iv) nearest neighbor analysis (NN). Results from the various machine learning technologies were compared against results produced with regression analysis. Results: ANNs and SVMs with a linear kernel function that incorporated field-measurements of terrain slope and aspect as input variables were able to predict plot-level volume increment with a greater accuracy (94%) than regression analysis (87%). Conclusion: These results provide compelling evidence for the added utility of machine learning technologies for modeling plot-level volume increment in the context of forest dynamics and management. Numéro de notice : A2021-071 Affiliation des auteurs : non IGN Thématique : FORET/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-020-01011-6 Date de publication en ligne : 12/01/2021 En ligne : https://doi.org/10.1007/s13595-020-01011-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96794 [article]

Classification of European beech forests: a Gordian Knot? / Wolfgang Willner in Applied Vegetation Science, vol 20 n° 3 (July 2017)  

Public [article]  inApplied Vegetation Science > vol 20 n° 3 (July 2017) . - pp 494 - 512 Titre : Classification of European beech forests: a Gordian Knot? Type de document : Article/Communication Auteurs : Wolfgang Willner, Auteur ; Borja Jimenez-Alfaro, Auteur ; Emiliano Agrillo, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 494 - 512 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Europe (géographie politique) [Termes IGN] Fagus orientalis [Termes IGN] Fagus sylvatica [Termes IGN] phytosociologie [Termes IGN] Turquie [Termes IGN] TWINSPAN [Termes IGN] unité phytosociologique [Vedettes matières IGN] Ecologie forestière Résumé : (auteur) Questions : What are the main floristic patterns in European beech forests? Which classification at the alliance and suballiance level is the most convincing? Location : Europe and Asia Minor. Methods : We applied a TWINSPAN classification to a data set of 24 605 relevés covering the whole range of Fagus sylvatica forests and the western part of Fagus orientalis forests. We identified 24 ‘operational phytosociological units’ (OPUs), which were used for further analysis. The position of each OPU along the soil pH and temperature gradient was evaluated using Ellenberg Indicator Values. Fidelity of species to OPUs was calculated using the phi coefficient and constancy ratio. We compared alternative alliance concepts, corresponding to groups of OPUs, in terms of number and frequency of diagnostic species. We also established formal definitions for the various alliance concepts based on comparison of the total cover of the diagnostic species groups, and evaluated alternative geographical subdivisions of beech forests. Results : The first and second division levels of TWINSPAN followed the temperature and soil pH gradients, while lower divisions were mainly geographical. We grouped the 22 OPUs of Fagus sylvatica forests into acidophytic, meso-basiphytic and thermo-basiphytic beech forests, and separated two OPUs of F. orientalis forests. However, a solution with only two ecologically defined alliances of F. sylvatica forests (acidophytic vs basiphytic) was clearly superior with regard to number and frequency of diagnostic species. In contrast, when comparing groupings with three to six geographical alliances of basiphytic beech forests, respectively, we did not find a strongly superior solution. Conclusions : We propose to classify F. sylvatica forests into 15 suballiances – three acidophytic and 12 basiphytic ones. Separating these two groups at alliance or order level was clearly supported by our results. Concerning the grouping of the 12 basiphytic suballiances into ecological or geographical alliances, as advocated by many authors, we failed to find an optimal solution. Therefore, we propose a multi-dimensional classification of basiphytic beech forests, including both ecological and geographical groups as equally valid concepts which may be used alternatively depending on the purpose and context of the classification. Numéro de notice : A2017-661 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1111/avsc.12299 En ligne : http://doi.org/10.1111/avsc.12299 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87085 [article]