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A national fuel type mapping method improvement using sentinel-2 satellite data / Alexandra Stefanidou in Geocarto international, vol 37 n° 4 ([15/02/2022])  

Public [article]  inGeocarto international > vol 37 n° 4 [15/02/2022] . - pp 1022 - 1042 Titre : A national fuel type mapping method improvement using sentinel-2 satellite data Type de document : Article/Communication Auteurs : Alexandra Stefanidou, Auteur ; Ioannis Z. Gitas, Auteur ; Thomas Katagis, Auteur Année de publication : 2022 Article en page(s) : pp 1022 - 1042 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse comparative [Termes IGN] analyse d'image orientée objet [Termes IGN] carte de la végétation [Termes IGN] carte thématique [Termes IGN] combustible [Termes IGN] distribution spatiale [Termes IGN] Grèce [Termes IGN] image Sentinel-MSI [Termes IGN] incendie de forêt [Termes IGN] prévention des risques Résumé : (auteur) Despite the fact that wildland fires have always been an integral part of many ecosystems, their increased frequency and intensity have reinforced the need of fire managers for updated and highly accurate information associated with the spatial distribution of forest fuels. In 2015, a fuel type mapping method was developed in the framework of the “National Observatory of Forest Fires (NOFFi)” project resulting in the generation of a national fuel type map. In this study, we aimed at examining the potential of the newly available Sentinel-2 satellite images for the improvement of the NOFFi’s mapping method in terms of accuracy and update effectiveness of the national fuel type map. Results demonstrate Sentinel-2 data will likely improve the resolution and reliability of national fuel type maps, increasing mapping efficiency for operational purposes. Numéro de notice : A2022-393 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10106049.2020.1756460 Date de publication en ligne : 28/04/2020 En ligne : https://doi.org/10.1080/10106049.2020.1756460 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100687 [article]

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)  

Public [article]  inRemote sensing > vol 13 n° 23 (December-1 2021) . - n° 4827 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 bois Résumé : (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 [article]

A GEOBIA framework for the implementation of national and international forest definitions using very high spatial resolution optical satellite data / M. Tompoulidou in Geocarto international, vol 31 n° 3 - 4 (March - April 2016)  

Public [article]  inGeocarto international > vol 31 n° 3 - 4 (March - April 2016) . - pp 342 - 354 Titre : A GEOBIA framework for the implementation of national and international forest definitions using very high spatial resolution optical satellite data Type de document : Article/Communication Auteurs : M. Tompoulidou, Auteur ; Ioannis Z. Gitas, Auteur ; A. Polychronaki, Auteur ; G. Mallinis, Auteur Année de publication : 2016 Article en page(s) : pp 342 - 354 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse d'image orientée objet [Termes IGN] définition [Termes IGN] forêt [Termes IGN] Grèce [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] occupation du sol [Termes IGN] utilisation du sol [Vedettes matières IGN] Inventaire forestier Résumé : (Auteur) In recent decades, there is an increasing need for harmonised and accurate information on the status and extent of forests. However, delineating the extent of forest areas is a complex task, since the existence of more than 100 definitions of forest worldwide causes considerable discrepancies in forested area estimates. The aim of this work was to examine the potential of geographic object based image analysis (GEOBIA) and very high spatial resolution imagery to discriminate forest areas following two different definitions of forest in northern Greece. In particular, we examined the definition of forest under the Greek law as well as the United Nations Food and Agricultural Organisation definition. Our findings suggest that the developed GEOBIA approach not only performed remarkably well for the discrimination of forest areas but also allowed to estimate rapidly and reliably forest extents when the two aforementioned forest definitions were employed. Numéro de notice : A2016-154 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1047470 Date de publication en ligne : 29/05/2015 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2015.1047470 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80394 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2016021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

vol 81 n° 6 - June 2015 - Special issue on GEOBIA (Bulletin de Photogrammetric Engineering & Remote Sensing, PERS) / Ioannis Z. Gitas  

Public [n° ou bulletin]  est un bulletin de Photogrammetric Engineering & Remote Sensing, PERS / American society for photogrammetry and remote sensing (1975 -)   Titre : vol 81 n° 6 - June 2015 - Special issue on GEOBIA Type de document : Périodique Auteurs : Ioannis Z. Gitas, Éditeur scientifique ; Giogios Mallinis, Éditeur scientifique Année de publication : 2015 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image Numéro de notice : 105-201506 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Numéro de périodique En ligne : http://www.ingentaconnect.com/content/asprs/pers/2015/00000081/00000006 Format de la ressource électronique : URL sommaire Permalink : https://documentation.ensg.eu/index.php?lvl=bulletin_display&id=26505 [n° ou bulletin]

Contient

• Integrating user needs on misclassification error sensitivity into image segmentation quality assessment / Hugo Costa in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 6 (June 2015)  
• Mangrove tree crown delineation from high-resolution imagery / Muditha K. Heenkenda in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 6 (June 2015)  
• Object-based building change detection from a single multispectral image and pre-existing geospatial information / Georgia Doxani in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 6 (June 2015)  
• A fuzzy spatial reasoner for multi-scale GEOBIA ontologies / Argyros Argyridis in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 6 (June 2015)  
• Assessment of wildfire risk in Lebanon using geographic object-based image analysis / George Mitri in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 6 (June 2015)  

Mediterranean forest species mapping using classification of Hyperion imagery / Georgia Galidaki in Geocarto international, vol 30 n° 1 - 2 (January - February 2015)  

Public [article]  inGeocarto international > vol 30 n° 1 - 2 (January - February 2015) . - pp 48 - 61 Titre : Mediterranean forest species mapping using classification of Hyperion imagery Type de document : Article/Communication Auteurs : Georgia Galidaki, Auteur ; Ioannis Z. Gitas, Auteur Année de publication : 2015 Article en page(s) : pp 48 - 61 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte de la végétation [Termes IGN] classification barycentrique [Termes IGN] classification orientée objet [Termes IGN] classification pixellaire [Termes IGN] forêt méditerranéenne [Termes IGN] image EO1-Hyperion [Termes IGN] image hyperspectrale Résumé : (auteur) Regional operational forest species mapping is an active research topic that aims to provide the systematic and updatable information necessary for understanding and monitoring the rapidly changing forest environment. In this study, we investigated the potential of satellite hyperspectral imagery in regional forest species mapping by employing a pixel-based and an object-based nearest neighbour classifier in two different Mediterranean study areas. The overall thematic accuracy of the produced maps was assessed using reference data collected in the field and ranged between 0.72 and 0.83. No approach was found to be superior for the study areas. The McNemar test showed no statistically significant difference at the 95% confidence level in the classification accuracies achieved by the two approaches. Both pixel- and object-based approaches provide useful maps, suggesting that regional forest species mapping from space has much potential. Numéro de notice : A2015-245 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2014.883439 En ligne : https://doi.org/10.1080/10106049.2014.883439 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76243 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2015011	RAB	Revue	Centre de documentation	En réserve L003	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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Wildfires and remote sensing / Ioannis Z. Gitas in Geoinformatics, vol 10 n° 7 (01/11/2007)

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