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Evaluating the performance of hyperspectral leaf reflectance to detect water stress and estimation of photosynthetic capacities / Jingjing Zhou in Remote sensing, vol 13 n° 11 (June-1 2021)
[article]
Titre : Evaluating the performance of hyperspectral leaf reflectance to detect water stress and estimation of photosynthetic capacities Type de document : Article/Communication Auteurs : Jingjing Zhou, Auteur ; Ya-Hao Zhang, Auteur ; Ze-Min Han, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 2160 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] Chine
[Termes IGN] Citrus (genre)
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] feuille (végétation)
[Termes IGN] image hyperspectrale
[Termes IGN] photosynthèse
[Termes IGN] réflectance végétale
[Termes IGN] rendement agricole
[Termes IGN] stress hydrique
[Termes IGN] surveillance de la végétationRésumé : (auteur) Advanced techniques capable of early, rapid, and nondestructive detection of the impacts of drought on fruit tree and the measurement of the underlying photosynthetic traits on a large scale are necessary to meet the challenges of precision farming and full prediction of yield increases. We tested the application of hyperspectral reflectance as a high-throughput phenotyping approach for early identification of water stress and rapid assessment of leaf photosynthetic traits in citrus trees by conducting a greenhouse experiment. To this end, photosynthetic CO2 assimilation rate (Pn), stomatal conductance (Cond) and transpiration rate (Trmmol) were measured with gas-exchange approaches alongside measurements of leaf hyperspectral reflectance from citrus grown across a gradient of soil drought levels six times, during 20 days of stress induction and 13 days of rewatering. Water stress caused Pn, Cond, and Trmmol rapid and continuous decline throughout the entire drought period. The upper layer was more sensitive to drought than middle and lower layers. Water stress could also bring continuous and dynamic changes of the mean spectral reflectance and absorptance over time. After trees were rewatered, these differences were not obvious. The original reflectance spectra of the four water stresses were surprisingly of low diversity and could not track drought responses, whereas specific hyperspectral spectral vegetation indices (SVIs) and absorption features or wavelength position variables presented great potential. The following machine-learning algorithms: random forest (RF), support vector machine (SVM), gradient boost (GDboost), and adaptive boosting (Adaboost) were used to develop a measure of photosynthesis from leaf reflectance spectra. The performance of four machine-learning algorithms were assessed, and RF algorithm yielded the highest predictive power for predicting photosynthetic parameters (R2 was 0.92, 0.89, and 0.88 for Pn, Cond, and Trmmol, respectively). Our results indicated that leaf hyperspectral reflectance is a reliable and stable method for monitoring water stress and yield increase, with great potential to be applied in large-scale orchards. Numéro de notice : A2021-440 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13112160 Date de publication en ligne : 31/05/2021 En ligne : https://doi.org/10.3390/rs13112160 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97826
in Remote sensing > vol 13 n° 11 (June-1 2021) . - n° 2160[article]Tree height growth modelling using LiDAR-derived topography information / Milan Kobal in ISPRS International journal of geo-information, vol 10 n° 6 (June 2021)
[article]
Titre : Tree height growth modelling using LiDAR-derived topography information Type de document : Article/Communication Auteurs : Milan Kobal, Auteur ; David Hladnik, Auteur Année de publication : 2021 Article en page(s) : n° 419 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] Abies alba
[Termes IGN] croissance des arbres
[Termes IGN] données lidar
[Termes IGN] données topographiques
[Termes IGN] gestion forestière durable
[Termes IGN] hauteur des arbres
[Termes IGN] hétérogénéité environnementale
[Termes IGN] karst
[Termes IGN] modèle de croissance végétale
[Termes IGN] modèle numérique de surface
[Termes IGN] modélisation de la forêt
[Termes IGN] semis de points
[Termes IGN] SlovénieRésumé : (auteur) The concepts of ecotopes and forest sites are used to describe the correlative complexes defined by landform, vegetation structure, forest stand characteristics and the relationship between soil and physiography. Physically heterogeneous landscapes such as karst, which is characterized by abundant sinkholes and outcrops, exhibit diverse microtopography. Understanding the variation in the growth of trees in a heterogeneous topography is important for sustainable forest management. An R script for detailed stem analysis was used to reconstruct the height growth histories of individual trees (steam analysis). The results of this study reveal that the topographic factors influencing the height growth of silver fir trees can be detected within forest stands. Using topography modelling, we classified silver fir trees into groups with significant differences in height growth. This study provides a sound basis for the comparison of forest site differences and may be useful in the calibration of models for various tree species. Numéro de notice : A2021-515 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10060419 Date de publication en ligne : 19/06/2021 En ligne : https://doi.org/10.3390/ijgi10060419 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97935
in ISPRS International journal of geo-information > vol 10 n° 6 (June 2021) . - n° 419[article]A deep learning model using satellite ocean color and hydrodynamic model to estimate chlorophyll-a concentration / Daeyong Jin in Remote sensing, vol 13 n°10 (May-2 2021)
[article]
Titre : A deep learning model using satellite ocean color and hydrodynamic model to estimate chlorophyll-a concentration Type de document : Article/Communication Auteurs : Daeyong Jin, Auteur ; Eojin Lee, Auteur ; Kyonghwan Kwon, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 2003 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] chlorophylle
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] Corée du sud
[Termes IGN] distribution spatiale
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] hydrodynamique
[Termes IGN] image COMS-GOCIRésumé : (auteur) In this study, we used convolutional neural networks (CNNs)—which are well-known deep learning models suitable for image data processing—to estimate the temporal and spatial distribution of chlorophyll-a in a bay. The training data required the construction of a deep learning model acquired from the satellite ocean color and hydrodynamic model. Chlorophyll-a, total suspended sediment (TSS), visibility, and colored dissolved organic matter (CDOM) were extracted from the satellite ocean color data, and water level, currents, temperature, and salinity were generated from the hydrodynamic model. We developed CNN Model I—which estimates the concentration of chlorophyll-a using a 48 × 27 sized overall image—and CNN Model II—which uses a 7 × 7 segmented image. Because the CNN Model II conducts estimation using only data around the points of interest, the quantity of training data is more than 300 times larger than that of CNN Model I. Consequently, it was possible to extract and analyze the inherent patterns in the training data, improving the predictive ability of the deep learning model. The average root mean square error (RMSE), calculated by applying CNN Model II, was 0.191, and when the prediction was good, the coefficient of determination (R2) exceeded 0.91. Finally, we performed a sensitivity analysis, which revealed that CDOM is the most influential variable in estimating the spatiotemporal distribution of chlorophyll-a. Numéro de notice : A2021-417 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13102003 Date de publication en ligne : 20/05/2021 En ligne : https://doi.org/10.3390/rs13102003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97759
in Remote sensing > vol 13 n°10 (May-2 2021) . - n° 2003[article]Mixture effect on radial stem and shoot growth differs and varies with temperature / Maude Toïgo in Forest ecology and management, vol 488 (May-15 2021)
[article]
Titre : Mixture effect on radial stem and shoot growth differs and varies with temperature Type de document : Article/Communication Auteurs : Maude Toïgo, Auteur ; Gaël Ledoux, Auteur ; Soline Martin-Blangy, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 119046 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Abies alba
[Termes IGN] Alpes (France)
[Termes IGN] climat
[Termes IGN] croissance des arbres
[Termes IGN] diamètre des arbres
[Termes IGN] échantillonnage
[Termes IGN] Fagus sylvatica
[Termes IGN] forêt tempérée
[Termes IGN] houppier
[Termes IGN] indice de stress
[Termes IGN] peuplement mélangé
[Termes IGN] Quercus pubescens
[Termes IGN] température
[Vedettes matières IGN] SylvicultureRésumé : (auteur) The effect of species diversity on forest productivity and its temporal stability is known to be species-, climate- and site- dependent and is mostly apprehended through stem diameter. Therefore, it remains largely unknown whether the mixture effect on the growth of tree crowns is similar to its effect on the growth of tree diameter. However, it is commonly accepted that changes in crown architecture are an important component of tree response to tree species diversity. Moreover, the mixture effect on species is often asymmetric, i.e. the effect of a species A on a species B is not equal to the effect of species B on A. It then appears that considering the effects of both species mixture and climate on shoot growth could contrast the results coming mainly from stem growth. We studied the effects of tree species mixture and temperature on the annual growth of shoots and basal area of stems in Fagus sylvatica-Quercus pubescens and Fagus sylvatica-Abies alba stands along a Mediterranean-Alpine gradient, for four years in five sites. The sample design was organized in 10 triplets: four triplets of mono- and bi-specific plots of Quercus pubescens and Fagus sylvatica and six triplets of mono- and bi-specific plots of Abies alba and Fagus sylvatica along an altitudinal gradient ranging from 725 m to 1431 m. We found that the mixture effect on annual shoot volume increment (SVI) and on basal area increment (BAI) was asymmetrical in seven out of 10 cases and not significant in the three remaining cases. Mixture effect on SVI ranked from −56% to 157% and on BAI it ranked from −40% to 252%. Eventually we found that mixture effect was dependent on the type of limiting factor for growth, with at the driest sites a predominance of competition effects and at the coldest site a positive mixture effect on the two species studied. Branch growth appears as a variable that can be at least as informative as radial growth regarding the tree response to species interactions. This implies that considering only stem diameter in the diversity-productivity relationship can lead to biased conclusions on the global mixture effect on tree growth, which calls for a comprehensive approach of the tree response to tree species diversity. Our results are discussed in the light of the species stress tolerances and strategies to cope with competition. Numéro de notice : A2021-357 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2021.119046 Date de publication en ligne : 26/02/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.119046 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97615
in Forest ecology and management > vol 488 (May-15 2021) . - n° 119046[article]Inversion of solar-induced chlorophyll fluorescence using polarization measurements of vegetation / Haiyan Yao in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 5 (May 2021)
[article]
Titre : Inversion of solar-induced chlorophyll fluorescence using polarization measurements of vegetation Type de document : Article/Communication Auteurs : Haiyan Yao, Auteur ; Ziying Li, Auteur ; Yang Han, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 331-338 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] chlorophylle
[Termes IGN] couvert végétal
[Termes IGN] données polarimétriques
[Termes IGN] fluorescence
[Termes IGN] polarisationRésumé : (Auteur) In vegetation remote sensing, the apparent radiation of the vegetation canopy is often combined with three components derived from different parts of vegetation that have different production mechanisms and optical properties: volume scattering Lvol, polarized light Lpol, and chlorophyll fluorescence ChlF. The chlorophyll fluorescence plays a very important role in vegetation remote sensing, and the polarization information in vegetation remote sensing has become an effective way to characterize the physical characteristics of vegetation. This study analyzes the difference between these three types of radiation flux and utilizes polarization radiation to separate them from the apparent radiation of the vegetation canopy. Specifically, solar-induced chlorophyll fluorescence is extracted from vegetation canopy radiation data using standard Fraunhofer-line discrimination. The results show that polarization measurements can quantitatively separate Lvol, Lpol, and ChlF and extract the solar-induced chlorophyll fluorescence. This study improves our understanding of the light-scattering properties of vegetation canopies and provides insights for developing building models and research algorithms. Numéro de notice : A2021-365 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.5.331 Date de publication en ligne : 01/05/2021 En ligne : https://doi.org/10.14358/PERS.87.5.331 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97694
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 5 (May 2021) . - pp 331-338[article]Réservation
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