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Connecting infrared spectra with plant traits to identify species / Maria F. Buitrago in ISPRS Journal of photogrammetry and remote sensing, vol 139 (May 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 139 (May 2018) . - pp 183 - 200 Titre : Connecting infrared spectra with plant traits to identify species Type de document : Article/Communication Auteurs : Maria F. Buitrago, Auteur ; Andrew K. Skidmore, Auteur ; Thomas A. Groen, Auteur ; Christoph A. Hecker, Auteur Année de publication : 2018 Article en page(s) : pp 183 - 200 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse discriminante [Termes IGN] arbre (flore) [Termes IGN] bande infrarouge [Termes IGN] biochimie [Termes IGN] caractérisation [Termes IGN] espèce végétale [Termes IGN] signature spectrale [Termes IGN] teneur en eau de la végétation Résumé : (Auteur) Plant traits are used to define species, but also to evaluate the health status of forests, plantations and crops. Conventional methods of measuring plant traits (e.g. wet chemistry), although accurate, are inefficient and costly when applied over large areas or with intensive sampling. Spectroscopic methods, as used in the food industry and mineralogy, are nowadays applied to identify plant traits, however, most studies analysed visible to near infrared, while infrared spectra of longer wavelengths have been little used for identifying the spectral differences between plant species. This study measured the infrared spectra (1.4–16.0 µm) on individual, fresh leaves of 19 species (from herbaceous to woody species), as well as 14 leaf traits for each leaf. The results describe at which wavelengths in the infrared the leaves’ spectra can differentiate most effectively between these plant species. A Quadratic Discrimination Analysis (QDA) shows that using five bands in the SWIR or the LWIR is enough to accurately differentiate these species (Kappa: 0.93, 0.94 respectively), while the MWIR has a lower classification accuracy (Kappa: 0.84). This study also shows that in the infrared spectra of fresh leaves, the identified species-specific features are correlated with leaf traits as well as changes in their values. Spectral features in the SWIR (1.66, 1.89 and 2.00 µm) are common to all species and match the main features of pure cellulose and lignin spectra. The depth of these features varies with changes of cellulose and leaf water content and can be used to differentiate species in this region. In the MWIR and LWIR, the absorption spectra of leaves are formed by key species-specific traits including lignin, cellulose, water, nitrogen and leaf thickness. The connection found in this study between leaf traits, features and spectral signatures are novel tools to assist when identifying plant species by spectroscopy and remote sensing. Numéro de notice : A2018-116 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.03.013 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.03.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89552 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018051	RAB	Revue	Centre de documentation	En réserve L003	Disponible

The Naïve Overfitting Index Selection (NOIS): A new method to optimize model complexity for hyperspectral data / Alby D. Rocha in ISPRS Journal of photogrammetry and remote sensing, vol 133 (November 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 133 (November 2017) . - pp 61 - 74 Titre : The Naïve Overfitting Index Selection (NOIS): A new method to optimize model complexity for hyperspectral data Type de document : Article/Communication Auteurs : Alby D. Rocha, Auteur ; Thomas A. Groen, Auteur ; Andrew K. Skidmore, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 61 - 74 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] complexité [Termes IGN] image hyperspectrale [Termes IGN] méthode robuste [Termes IGN] modèle de simulation [Termes IGN] optimisation (mathématiques) [Termes IGN] précision [Termes IGN] régression [Termes IGN] validation des données Résumé : (Auteur) The growing number of narrow spectral bands in hyperspectral remote sensing improves the capacity to describe and predict biological processes in ecosystems. But it also poses a challenge to fit empirical models based on such high dimensional data, which often contain correlated and noisy predictors. As sample sizes, to train and validate empirical models, seem not to be increasing at the same rate, overfitting has become a serious concern. Overly complex models lead to overfitting by capturing more than the underlying relationship, and also through fitting random noise in the data. Many regression techniques claim to overcome these problems by using different strategies to constrain complexity, such as limiting the number of terms in the model, by creating latent variables or by shrinking parameter coefficients. This paper is proposing a new method, named Naïve Overfitting Index Selection (NOIS), which makes use of artificially generated spectra, to quantify the relative model overfitting and to select an optimal model complexity supported by the data. The robustness of this new method is assessed by comparing it to a traditional model selection based on cross-validation. The optimal model complexity is determined for seven different regression techniques, such as partial least squares regression, support vector machine, artificial neural network and tree-based regressions using five hyperspectral datasets. The NOIS method selects less complex models, which present accuracies similar to the cross-validation method. The NOIS method reduces the chance of overfitting, thereby avoiding models that present accurate predictions that are only valid for the data used, and too complex to make inferences about the underlying process. Numéro de notice : A2017-722 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.09.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.09.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88407 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2017111	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2017112	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt
081-2017113	DEP-EXM	Revue	Saint-Mandé	Dépôt en unité	Exclu du prêt

Climatic niche breadth can explain variation in geographical range size of alpine and subalpine plants / Fangyuan Yu in International journal of geographical information science IJGIS, vol 31 n° 1-2 (January - February 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 1-2 (January - February 2017) . - pp 190 - 212 Titre : Climatic niche breadth can explain variation in geographical range size of alpine and subalpine plants Type de document : Article/Communication Auteurs : Fangyuan Yu, Auteur ; Thomas A. Groen, Auteur ; Tiejun Wang, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 190 - 212 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] aire de répartition [Termes IGN] changement climatique [Termes IGN] Chine [Termes IGN] climat de montagne [Termes IGN] croissance des arbres [Termes IGN] distribution spatiale [Termes IGN] entropie maximale [Termes IGN] région [Termes IGN] Rhododendron (genre) [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Understanding the environmental factors determining the distribution of species with different range sizes can provide valuable insights for evolutionary ecology and conservation biology in the face of expected climate change. However, little is known about what determines the variation in geographical and elevational ranges of alpine and subalpine plant species. Here, we examined the relationship between geographical and elevational range sizes for 80 endemic rhododendron species in China using Spearman’s rank-order correlation. We ran the species distribution model – maximum entropy modelling (MaxEnt) – with 27 environmental variables. The importance of each variable to the model prediction was compared for species groups with different geographical and elevational range sizes. Our results showed that the correlation between geographical and elevational range sizes of rhododendron species was not significant. Climate-related variables were found to be the most important factors in shaping the distributional ranges of alpine and subalpine plant species across China. Species with geographically and elevationally narrow ranges had distinct niche requirements. For geographical ranges, the narrow-ranged species showed less tolerance to niche conditions than the wide-ranged species. For elevational ranges, compared with the wide-ranged species, the narrow-ranged species showed an equivalent niche breadth, but occurred at different niche position along the environmental gradient. Our findings suggest that over large spatial extents the elevational range size can be a complementary trait of alpine and subalpine plant species to geographical range size. Climatic niche breadth, especially the range of seasonal variability, can explain species’ geographical range sizes. Changes in climate may influence the distribution of rhododendrons, with the effects likely being felt most by species with either a narrow geographical or narrow elevational range. Numéro de notice : A2017-031 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1195502 En ligne : http://dx.doi.org/10.1080/13658816.2016.1195502 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84024 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2017011	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-2017012	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Retrieval of leaf area index in different plant species using thermal hyperspectral data / Elnaz Neinavaz in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 119 (September 2016) . - pp 390 - 401 Titre : Retrieval of leaf area index in different plant species using thermal hyperspectral data Type de document : Article/Communication Auteurs : Elnaz Neinavaz, Auteur ; Andrew K. Skidmore, Auteur ; Roshanak Darvishzadeh, Auteur ; Thomas A. Groen, Auteur Année de publication : 2016 Article en page(s) : pp 390 - 401 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Buxus sempervirens [Termes IGN] classification par réseau neuronal [Termes IGN] espèce végétale [Termes IGN] Euonymus japonicus [Termes IGN] image hyperspectrale [Termes IGN] indice foliaire [Termes IGN] Leaf Area Index [Termes IGN] méthode des moindres carrés [Termes IGN] photo-interprétation [Termes IGN] rayonnement infrarouge thermique [Termes IGN] régression [Termes IGN] Rhododendron (genre) Résumé : (Auteur) Leaf area index (LAI) is an important variable of terrestrial ecosystems because it is strongly correlated with many ecosystem processes (e.g., water balance and evapotranspiration) and directly related to the plant energy balance and gas exchanges. Although LAI has been accurately predicted using visible and short-wave infrared hyperspectral data (0.3–2.5 μm), LAI estimation using thermal infrared (TIR, 8–14 μm) measurements has not yet been addressed. The novel approach of this study is to evaluate the retrieval of LAI using TIR hyperspectral data. The leaf area indices were destructively acquired for four plant species: Azalea japonica, Buxus sempervirens, Euonymus japonicus, and Ficus benjamina. Canopy emissivity spectral measurements were obtained under controlled laboratory conditions using a MIDAC (M4401-F) spectrometer. The LAI retrieval was assessed using a partial least squares regression (PLSR), artificial neural networks (ANNs), and narrow band indices calculated from all possible combinations of waveband pairs for three vegetation indices including simple difference, simple ratio, and normalized difference. ANNs retrieved LAI more accurately than PLSR and vegetation indices (0.67 Numéro de notice : A2016-789 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.07.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.07.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82505 [article]

Thermal infrared reveals vegetation stress / Thomas A. Groen in GIM international, vol 30 n° 6 (June 2016)

Public [article]  inGIM international > vol 30 n° 6 (June 2016) . - pp 29 - 31 Titre : Thermal infrared reveals vegetation stress Type de document : Article/Communication Auteurs : Thomas A. Groen, Auteur ; Christoph A. Hecker, Auteur ; Maria Buitrago, Auteur Année de publication : 2016 Article en page(s) : pp 29 - 31 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Végétation [Termes IGN] image infrarouge [Termes IGN] image proche infrarouge [Termes IGN] indice de stress [Termes IGN] photo-interprétation Résumé : (éditeur) Climate change or other environmental changes may affect the health of plants. Conventional methods for determining how vegetation responds to changes in temperature and humidity measure the reflectance of the visible and near-infrared part of the electromagnetic (EM) spectrum on the leaves. By using a non-destructive thermal infrared spectrometer, the authors demonstrate that persistent stress also affects the thermal infrared emissivity of plants. This finding paves the way for using thermal spectroscopy to monitor responses of vegetation to climate change. Numéro de notice : A2016-315 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80933 [article]

Changes in thermal infrared spectra of plants caused by temperature and water stress / Maria F. Buitrago in ISPRS Journal of photogrammetry and remote sensing, vol 111 (January 2016)  

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