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Evaluation de variables limnologiques grâce à des images Landsat / Danielle Teixeira Alves Da Silva in Géomatique expert, n° 118 (septembre - octobre 2017)
[article]
Titre : Evaluation de variables limnologiques grâce à des images Landsat Type de document : Article/Communication Auteurs : Danielle Teixeira Alves Da Silva, Auteur ; Aziz Serradj, Auteur ; Aline do Vale Figueiredo, Auteur ; Vanessa Becker, Auteur Année de publication : 2017 Article en page(s) : pp 30 - 39 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse spatiale
[Termes IGN] Brésil
[Termes IGN] carte thématique
[Termes IGN] chlorophylle
[Termes IGN] eaux continentales
[Termes IGN] écologie
[Termes IGN] image Landsat
[Termes IGN] limnologie
[Termes IGN] ressources aquatiques
[Termes IGN] teneur en chlorophylle des feuilles
[Termes IGN] zone semi-arideRésumé : (auteur) Utilisation des images Landsat pour estimer la concentration de la chlorophylle-a et de la transparence de l'eau sur un territoire semi-aride du Nord-est brésilien. Numéro de notice : A2017-586 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86728
in Géomatique expert > n° 118 (septembre - octobre 2017) . - pp 30 - 39[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 265-2017051 RAB Revue Centre de documentation En réserve L003 Disponible IFN-001-P001984 PER Revue Nogent-sur-Vernisson Salle périodiques Exclu du prêt Improving the prediction of African savanna vegetation variables using time series of MODIS products / Miriam Tsalyuk in ISPRS Journal of photogrammetry and remote sensing, vol 131 (September 2017)
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Titre : Improving the prediction of African savanna vegetation variables using time series of MODIS products Type de document : Article/Communication Auteurs : Miriam Tsalyuk, Auteur ; Maggi Kelly, Auteur ; Wayne M. Getz, Auteur Année de publication : 2017 Article en page(s) : pp 77 - 91 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Végétation
[Termes IGN] Afrique (géographie physique)
[Termes IGN] biomasse forestière
[Termes IGN] dégradation de la flore
[Termes IGN] Enhanced vegetation index
[Termes IGN] image Terra-MODIS
[Termes IGN] Leaf Area Index
[Termes IGN] Namibie
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] prédiction
[Termes IGN] savane
[Termes IGN] variationRésumé : (Auteur) African savanna vegetation is subject to extensive degradation as a result of rapid climate and land use change. To better understand these changes detailed assessment of vegetation structure is needed across an extensive spatial scale and at a fine temporal resolution. Applying remote sensing techniques to savanna vegetation is challenging due to sparse cover, high background soil signal, and difficulty to differentiate between spectral signals of bare soil and dry vegetation. In this paper, we attempt to resolve these challenges by analyzing time series of four MODIS Vegetation Products (VPs): Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR) for Etosha National Park, a semiarid savanna in north-central Namibia. We create models to predict the density, cover, and biomass of the main savanna vegetation forms: grass, shrubs, and trees. To calibrate remote sensing data we developed an extensive and relatively rapid field methodology and measured herbaceous and woody vegetation during both the dry and wet seasons. We compared the efficacy of the four MODIS-derived VPs in predicting vegetation field measured variables. We then compared the optimal time span of VP time series to predict ground-measured vegetation. We found that Multiyear Partial Least Square Regression (PLSR) models were superior to single year or single date models. Our results show that NDVI-based PLSR models yield robust prediction of tree density (R2 = 0.79, relative Root Mean Square Error, rRMSE = 1.9%) and tree cover (R2 = 0.78, rRMSE = 0.3%). EVI provided the best model for shrub density (R2 = 0.82) and shrub cover (R2 = 0.83), but was only marginally superior over models based on other VPs. FPAR was the best predictor of vegetation biomass of trees (R2 = 0.76), shrubs (R2 = 0.83), and grass (R2 = 0.91). Finally, we addressed an enduring challenge in the remote sensing of semiarid vegetation by examining the transferability of predictive models through space and time. Our results show that models created in the wetter part of Etosha could accurately predict trees’ and shrubs’ variables in the drier part of the reserve and vice versa. Moreover, our results demonstrate that models created for vegetation variables in the dry season of 2011 could be successfully applied to predict vegetation in the wet season of 2012. We conclude that extensive field data combined with multiyear time series of MODIS vegetation products can produce robust predictive models for multiple vegetation forms in the African savanna. These methods advance the monitoring of savanna vegetation dynamics and contribute to improved management and conservation of these valuable ecosystems. Numéro de notice : A2017-537 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.07.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.07.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86575
in ISPRS Journal of photogrammetry and remote sensing > vol 131 (September 2017) . - pp 77 - 91[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017091 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017093 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017092 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Inventaire faune, flore et habitats sur la zone humide de Petelin (Corbelin et Veyrins-Thuellin, Nord-Isère) / Alexandre Gauthier in Lo Parvi, n° 25 (2017)
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Titre : Inventaire faune, flore et habitats sur la zone humide de Petelin (Corbelin et Veyrins-Thuellin, Nord-Isère) Type de document : Article/Communication Auteurs : Alexandre Gauthier, Auteur Année de publication : 2017 Article en page(s) : pp 68 - 80 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Ecologie
[Termes IGN] base de données naturalistes
[Termes IGN] espace naturel sensible
[Termes IGN] habitat (nature)
[Termes IGN] inventaire de la végétation
[Termes IGN] Isère (38)
[Termes IGN] tourbière
[Termes IGN] zone humideNuméro de notice : A2017-911 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET Nature : Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96450
in Lo Parvi > n° 25 (2017) . - pp 68 - 80[article]Documents numériques
en open access
Inventaire faune, flore et habitats sur la zone humide de Petelin - pdf éditeurAdobe Acrobat PDF Spatiotemporal analyses of urban vegetation structural attributes using multitemporal Landsat TM data and field measurements / Zhibin Ren in Annals of Forest Science, vol 74 n° 3 (September 2017)
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Titre : Spatiotemporal analyses of urban vegetation structural attributes using multitemporal Landsat TM data and field measurements Type de document : Article/Communication Auteurs : Zhibin Ren, Auteur ; Ruiliang Pu, Auteur ; Haifeng Zheng, Auteur ; et al., Auteur Année de publication : 2017 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse spatio-temporelle
[Termes IGN] analyse structurale
[Termes IGN] attribut
[Termes IGN] données de terrain
[Termes IGN] données dendrométriques
[Termes IGN] écosystème urbain
[Termes IGN] flore urbaine
[Termes IGN] image Landsat-TM
[Termes IGN] indice foliaire
[Termes IGN] surveillance de la végétationRésumé : (Auteur)
Key message : We conducted spatiotemporal analyses of urban vegetation structural attributes using multitemporal Landsat TM data and field measurements. We showed that multitemporal TM data has the potential of rapidly estimating urban vegetation structural attributes including LAI, CC, and BA at an urban landscape level.
Context : Urban vegetation structural properties/attributes are closely linked to their ecological functions and thus directly affect urban ecosystem process such as energy, water, and gas exchange. Understanding spatiotemporal dynamics of urban vegetation structures is important for sustaining urban ecosystem service and improving the urban environment.
Aims : The purposes of this study were to evaluate the potential of estimating urban vegetation structural attributes from multitemporal Landsat TM imagery and to analyze spatiotemporal changes of the urban structural attributes.
Methods : We first collected three scenes of TM images acquired in 1997, 2004, and 2010 and conducted a field survey to collect urban vegetation structural data (including crown closure (CC), tree height (H), leaf area index (LAI), basal area (BA), stem density (SD), diameter at breast height (DBH), etc.). We then calculated and normalized NDVI maps from the multitemporal TM images. Finally, spatiotemporal urban vegetation structural maps were created using NDVI-based urban vegetation structure predictive models.
Results : The results show that NDVI can be used as a predictor for some selected urban vegetation structural attributes (i.e., CC, LAI, and BA), but not for the other attributes (i.e., H, SD, and DBH) that are well predicted by NDVI in natural vegetation. The results also indicate that urban vegetation structural attributes (i.e., CC, LAI, and BA) in the study area decreased sharply from 1997 to 2004 but increased slightly from 2004 to 2010. The CC, LAI, and BA class distributions were all skewed toward low values in 1997 and 2004. Moreover, LAI, CC, and BA of urban vegetation all present a decreasing trend from suburban areas to urban central areas.
Conclusion : The experimental results demonstrate that Landsat TM imagery could provide a fast and cost-effective method to obtain a spatiotemporal 30-m resolution urban vegetation structural dataset (including CC, LAI, and BA).Numéro de notice : A2017-353 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article DOI : 10.1007/s13595-017-0654-x Date de publication en ligne : 05/07/2017 En ligne : https://doi.org/10.1007/s13595-017-0654-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85719
in Annals of Forest Science > vol 74 n° 3 (September 2017)[article]Reducing classification error of grassland overgrowth by combing low-density lidar acquisitions and optical remote sensing data / Timo P Pitkänen in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)
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Titre : Reducing classification error of grassland overgrowth by combing low-density lidar acquisitions and optical remote sensing data Type de document : Article/Communication Auteurs : Timo P Pitkänen, Auteur ; Niina Käyhkö, Auteur Année de publication : 2017 Article en page(s) : pp 150 - 161 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse diachronique
[Termes IGN] arbre (flore)
[Termes IGN] boisement naturel
[Termes IGN] carte de la végétation
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] densité des points
[Termes IGN] détection de changement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] erreur de classification
[Termes IGN] image Landsat
[Termes IGN] orthoimage
[Termes IGN] prairie
[Termes IGN] structure de données localiséesRésumé : (Auteur) Mapping structural changes in vegetation dynamics has, for a long time, been carried out using satellite images, orthophotos and, more recently, airborne lidar acquisitions. Lidar has established its position as providing accurate material for structure-based analyses but its limited availability, relatively short history, and lack of spectral information, however, are generally impeding the use of lidar data for change detection purposes. A potential solution in respect of detecting both contemporary vegetation structures and their previous trajectories is to combine lidar acquisitions with optical remote sensing data, which can substantially extend the coverage, span and spectral range needed for vegetation mapping. In this study, we tested the simultaneous use of a single low-density lidar data set, a series of Landsat satellite frames and two high-resolution orthophotos to detect vegetation succession related to grassland overgrowth, i.e. encroachment of woody plants into semi-natural grasslands. We built several alternative Random Forest models with different sets of variables and tested the applicability of respective data sources for change detection purposes, aiming at distinguishing unchanged grassland and woodland areas from overgrown grasslands. Our results show that while lidar alone provides a solid basis for indicating structural differences between grassland and woodland vegetation, and orthophoto-generated variables alone are better in detecting successional changes, their combination works considerably better than its respective parts. More specifically, a model combining all the used data sets reduces the total error from 17.0% to 11.0% and omission error of detecting overgrown grasslands from 56.9% to 31.2%, when compared to model constructed solely based on lidar data. This pinpoints the efficiency of the approach where lidar-generated structural metrics are combined with optical and multitemporal observations, providing a workable framework to identify structurally oriented and dynamically organized landscape phenomena, such as grassland overgrowth. Numéro de notice : A2017-513 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.05.016 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.05.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86459
in ISPRS Journal of photogrammetry and remote sensing > vol 130 (August 2017) . - pp 150 - 161[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017081 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017083 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017082 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Retrieving grassland canopy water content by considering the information from neighboring pixels / Binbin He in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 8 (August 2017)PermalinkUsing Landsat time series for characterizing forest disturbance dynamics in the coupled human and natural systems of Central Europe / Cornelius Senf in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)PermalinkL’identification et la mobilisation des peuplements pauvres / Fabienne Benest in Forêt entreprise, n° 235 (juillet - août 2017)PermalinkNorthern conifer forest species classification using multispectral data acquired from an unmanned aerial vehicle / Steven E. Franklin in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 7 (July 2017)PermalinkChange detection in forests and savannas using statistical analysis based on geographical objects / Lucilia Rezende Leite in Boletim de Ciências Geodésicas, vol 23 n° 2 (abr - jun 2017)PermalinkEffects of environmental factors on the species richness, composition and community horizontal structure of vascular plants in Scots pine forests on fixed sand dunes / Mari Tilk in Silva fennica, vol 51 n° 3 (2017)PermalinkNatura 2000 protected habitats, Massaciuccoli Lake (northern Tuscany, Italy) / Daniele Viciani in Journal of maps, vol 13 n° 2 ([01/06/2017])PermalinkRecent growth changes in Western European forests are driven by climate warming and structured across tree species climatic habitats / Marie Charru in Annals of Forest Science, vol 74 n° 2 (June 2017)PermalinkTM-Based SOC models augmented by auxiliary data for carbon crediting programs in semi-arid environments / Salahuddin M. Jaber in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 6 (June 2017)PermalinkAssessing future suitability of tree species under climate change by multiple methods: a case study in southern Germany / Helge Walentowski in Annals of forest research, vol 60 n° 1 (January - June 2017)Permalink