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Auteur Dimitris G. Stavrakoudis |
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Estimation of individual tree stem biomass in an uneven-aged structured coniferous forest using multispectral LiDAR data / Nikos Georgopoulos in Remote sensing, vol 13 n° 23 (December-1 2021)
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Titre : Estimation of individual tree stem biomass in an uneven-aged structured coniferous forest using multispectral LiDAR data Type de document : Article/Communication Auteurs : Nikos Georgopoulos, Auteur ; Ioannis Z. Gitas, Auteur ; Alexandra Stefanidou, Auteur ; Lauri Korhonen, Auteur ; Dimitris G. Stavrakoudis, Auteur Année de publication : 2021 Article en page(s) : n° 4827 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] Abies (genre)
[Termes IGN] biomasse aérienne
[Termes IGN] capteur multibande
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt inéquienne
[Termes IGN] Grèce
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] montagne
[Termes IGN] Pinophyta
[Termes IGN] régression
[Termes IGN] tronc
[Termes IGN] volume en boisRésumé : (auteur) Stem biomass is a fundamental component of the global carbon cycle that is essential for forest productivity estimation. Over the last few decades, Light Detection and Ranging (LiDAR) has proven to be a useful tool for accurate carbon stock and biomass estimation in various biomes. The aim of this study was to investigate the potential of multispectral LiDAR data for the reliable estimation of single-tree total and barkless stem biomass (TSB and BSB) in an uneven-aged structured forest with complex topography. Destructive and non-destructive field measurements were collected for a total of 67 dominant and co-dominant Abies borisii-regis trees located in a mountainous area in Greece. Subsequently, two allometric equations were constructed to enrich the reference data with non-destructively sampled trees. Five different regression algorithms were tested for single-tree BSB and TSB estimation using height (height percentiles and bicentiles, max and average height) and intensity (skewness, standard deviation and average intensity) LiDAR-derived metrics: Generalized Linear Models (GLMs), Gaussian Process (GP), Random Forest (RF), Support Vector Regression (SVR) and Extreme Gradient Boosting (XGBoost). The results showcased that the RF algorithm provided the best overall predictive performance in both BSB (i.e., RMSE = 175.76 kg and R2 = 0.78) and TSB (i.e., RMSE = 211.16 kg and R2 = 0.65) cases. Our work demonstrates that BSB can be estimated with moderate to high accuracy using all the tested algorithms, contrary to the TSB, where only three algorithms (RF, SVR and GP) can adequately provide accurate TSB predictions due to bark irregularities along the stems. Overall, the multispectral LiDAR data provide accurate stem biomass estimates, the general applicability of which should be further tested in different biomes and ecosystems. Numéro de notice : A2021-953 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs13234827 Date de publication en ligne : 27/11/2021 En ligne : https://doi.org/10.3390/rs13234827 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99955
in Remote sensing > vol 13 n° 23 (December-1 2021) . - n° 4827[article]Classification of remotely sensed images using the geneSIS fuzzy segmentation algorithm / Stelios Mylonas in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)
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Titre : Classification of remotely sensed images using the geneSIS fuzzy segmentation algorithm Type de document : Article/Communication Auteurs : Stelios Mylonas, Auteur ; Dimitris G. Stavrakoudis, Auteur ; John B. Theocharis, Auteur ; Paris A. Mastorocostas, Auteur Année de publication : 2015 Article en page(s) : pp 5352 - 5376 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] algorithme génétique
[Termes IGN] classification floue
[Termes IGN] classification spectrale
[Termes IGN] regroupement de données
[Termes IGN] segmentation d'imageRésumé : (Auteur) In this paper, we propose an integrated framework of the recently proposed Genetic Sequential Image Segmentation (GeneSIS) algorithm. GeneSIS segments the image in an iterative manner, whereby at each iteration, a single object is extracted via a genetic algorithm-based object extraction method. This module evaluates the fuzzy content of candidate regions, and through an effective fitness function design provides objects with optimal balance between fuzzy coverage, consistency and smoothness. GeneSIS exhibits a number of interesting properties, such as reduced over-/undersegmentation, adaptive search scale, and region-based search. To enhance the capabilities of GeneSIS, we incorporate here several improvements of our initial proposal. On one hand, two modifications are introduced pertaining to the object extraction algorithm. Specifically, we consider a more flexible representation of the structural elements used for the object's extraction. Furthermore, in view of its importance, the consistency criterion is redefined, thus providing a better handling of the ambiguous areas of the image. On the other hand we incorporate three tools properly devised, according to the fuzzy principles characterizing GeneSIS. First, we develop a marker selection strategy that creates reliable markers, particularly when dealing with ambiguous components of the image. Furthermore, using GeneSIS as the essential part, we consider a generalized experimental setup embracing two different classification schemes for remote sensing images: the spectral-spatial classification and the supervised segmentation methods. Finally, exploiting the inherent property of GeneSIS to produce multiple segmentations, we propose a segmentation fusion scheme. The effectiveness of the proposed methodology is validated after thorough experimentation on four data sets. Numéro de notice : A2015-750 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2421640 Date de publication en ligne : 08/05/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2421640 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78759
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 10 (October 2015) . - pp 5352 - 5376[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015101 SL Revue Centre de documentation Revues en salle Disponible A genetic fuzzy-rule-based classifier for land cover classification from hyperspectral imagery / Dimitris G. Stavrakoudis in IEEE Transactions on geoscience and remote sensing, vol 50 n° 1 (January 2012)
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Titre : A genetic fuzzy-rule-based classifier for land cover classification from hyperspectral imagery Type de document : Article/Communication Auteurs : Dimitris G. Stavrakoudis, Auteur ; G. Galidaki, Auteur ; Ioannis Z. Gitas, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 130 - 148 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par algorithme génétique
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectrale
[Termes IGN] occupation du solRésumé : (Auteur) This paper proposes the use of a genetic fuzzy-rule-based classification system for land cover classification from hyperspectral images. The proposed classifier, namely, Feature Selective Linguistic Classifier, is constructed through a three-stage learning process. The first stage produces a preliminary fuzzy rule base in an iterative fashion. During this stage, a local feature selection scheme is employed, designed to guide the genetic evolution, through the evaluation of deterministic information about the relevance of each feature with respect to its classification ability. The structure of the model is then simplified in a subsequent postprocessing stage. The performance of the classifier is finally optimized through a genetic tuning stage. An extensive comparative analysis, using an Earth Observing-1 Hyperion satellite image, highlights the quality advantages of the proposed system, when compared with nonfuzzy classifiers, commonly employed in hyperspectral classification tasks. Numéro de notice : A2012-032 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2159613 Date de publication en ligne : 29/07/2011 En ligne : https://doi.org/10.1109/TGRS.2011.2159613 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31480
in IEEE Transactions on geoscience and remote sensing > vol 50 n° 1 (January 2012) . - pp 130 - 148[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2012011 RAB Revue Centre de documentation En réserve L003 Disponible