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Estimation of higher chlorophylla concentrations using field spectral measurement and HJ-1A hyperspectral satellite data in Dianshan Lake, China / Liguo Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)
[article]
Titre : Estimation of higher chlorophylla concentrations using field spectral measurement and HJ-1A hyperspectral satellite data in Dianshan Lake, China Type de document : Article/Communication Auteurs : Liguo Zhou, Auteur ; Dar A. Roberts, Auteur ; Weichun Ma, Auteur ; Hao Zhang, Auteur ; Lin Tang, Auteur Année de publication : 2014 Article en page(s) : pp 41 - 47 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Chine
[Termes IGN] chlorophylle
[Termes IGN] image HJ-1A
[Termes IGN] image hyperspectrale
[Termes IGN] lacRésumé : (Auteur) Based on in situ water sampling and field spectral measurements in Dianshan Lake, a semi-analytical three-band algorithm was used to estimate Chlorophylla (Chla) content in case II waters. The three bands selected to estimate Chla for high concentrations included 653, 691 and 748 nm. An equation, based on the difference in reciprocal reflectance between 653 and 691 nm, multiplied by reflectance at 748 nm as [Rrs-1(653) - Rrs-1(691)] Rrs(748), explained 85.57% of variance in Chla concentration with a root mean square error (RMSE) of Numéro de notice : A2014-084 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.11.016 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.11.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32989
in ISPRS Journal of photogrammetry and remote sensing > vol 88 (February 2014) . - pp 41 - 47[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2014021 RAB Revue Centre de documentation En réserve L003 Disponible Gaussian processes uncertainty estimates in experimental Sentinel-2 LAI and leaf chlorophyll content retrieval / Jochem Verrlest in ISPRS Journal of photogrammetry and remote sensing, vol 86 (December 2013)
[article]
Titre : Gaussian processes uncertainty estimates in experimental Sentinel-2 LAI and leaf chlorophyll content retrieval Type de document : Article/Communication Auteurs : Jochem Verrlest, Auteur ; Juan Pablo Rivera, Auteur ; José Moreno, Auteur ; Gustavo Camps-Valls, Auteur Année de publication : 2013 Article en page(s) : pp 157 - 167 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme de Gauss
[Termes IGN] apprentissage automatique
[Termes IGN] chlorophylle
[Termes IGN] image Sentinel-MSI
[Termes IGN] incertitude des données
[Termes IGN] indice foliaire
[Termes IGN] Leaf Area Index
[Termes IGN] régression
[Termes IGN] surveillance de la végétation
[Termes IGN] teneur en chlorophylle des feuilles
[Termes IGN] variable biophysique (végétation)Résumé : (Auteur) ESA’s upcoming Sentinel-2 (S2) Multispectral Instrument (MSI) foresees to provide continuity to land monitoring services by relying on optical payload with visible, near infrared and shortwave infrared sensors with high spectral, spatial and temporal resolution. This unprecedented data availability leads to an urgent need for developing robust and accurate retrieval methods, which ideally should provide uncertainty intervals for the predictions. Statistical learning regression algorithms are powerful candidats for the estimation of biophysical parameters from satellite reflectance measurements because of their ability to perform adaptive, nonlinear data fitting. In this paper, we focus on a new emerging technique in the field of Bayesian nonparametric modeling. We exploit Gaussian process regression (GPR) for retrieval, which is an accurate method that also provides uncertainty intervals along with the mean estimates. This distinct feature is not shared by other machine learning approaches. In view of implementing the regressor into operational monitoring applications, here the portability of locally trained GPR models was evaluated. Experimental data came from the ESA-led field campaign SPARC (Barrax, Spain). For various simulated S2 configurations (S2-10m, S2-20m and S2-60m) two important biophysical parameters were estimated: leaf chlorophyll content (LCC) and leaf area index (LAI). Local evaluation of an extended training dataset with more variation over bare soil sites led to improved LCC and LAI mapping with reduced uncertainties. GPR reached the 10% precision required by end users, with for LCC a NRMSE of 3.5–9.2% (r2: 0.95–0.99) and for LAI a NRMSE of 6.5–7.3% (r2: 0.95–0.96). The developed GPR models were subsequently applied to simulated Sentinel images over various sites. The associated uncertainty maps proved to be a good indicator for evaluating the robustness of the retrieval performance. The generally low uncertainty intervals over vegetated surfaces suggest that the locally trained GPR models are portable to other sites and conditions. Numéro de notice : A2013-708 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.09.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.09.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32844
in ISPRS Journal of photogrammetry and remote sensing > vol 86 (December 2013) . - pp 157 - 167[article]Active learning methods for biophysical parameter estimation / Edoardo Pasolli in IEEE Transactions on geoscience and remote sensing, vol 50 n° 10 Tome 2 (October 2012)
[article]
Titre : Active learning methods for biophysical parameter estimation Type de document : Article/Communication Auteurs : Edoardo Pasolli, Auteur ; F. Melgani, Auteur ; N. Alajlan, Auteur ; B. Yakoub, Auteur Année de publication : 2012 Article en page(s) : pp 4071 - 4084 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] algorithme de Gauss
[Termes IGN] apprentissage automatique
[Termes IGN] chlorophylle
[Termes IGN] régression
[Termes IGN] séparateur à vaste marge
[Termes IGN] variable biophysique (végétation)Résumé : (Auteur) In this paper, we face the problem of collecting training samples for regression problems under an active learning perspective. In particular, we propose various active learning strategies specifically developed for regression approaches based on Gaussian processes (GPs) and support vector machines (SVMs). For GP regression, the first two strategies are based on the idea of adding samples that are dissimilar from the current training samples in terms of covariance measure, while the third one uses a pool of regressors in order to select the samples with the greater disagreements between the different regressors. Finally, the last strategy exploits an intrinsic GP regression outcome to pick up the most difficult and hence interesting samples to label. For SVM regression, the method based on the pool of regressors and two additional strategies based on the selection of the samples distant from the current support vectors in the kernel-induced feature space are proposed. The experimental results obtained on simulated and real data sets show that the proposed strategies exhibit a good capability to select samples that are significant for the regression process, thus opening the way to the active learning approach for remote-sensing regression problems. Numéro de notice : A2012-528 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2187906 Date de publication en ligne : 17/04/2012 En ligne : https://doi.org/10.1109/TGRS.2012.2187906 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31974
in IEEE Transactions on geoscience and remote sensing > vol 50 n° 10 Tome 2 (October 2012) . - pp 4071 - 4084[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2012101B MANQUANT Revue Centre de documentation Indéterminé Disponible Landscape controls over major nutrients and primary productivity of Arctic lakes / P. Pathak in Cartography and Geographic Information Science, vol 39 n° 4 (October 2012)
[article]
Titre : Landscape controls over major nutrients and primary productivity of Arctic lakes Type de document : Article/Communication Auteurs : P. Pathak, Auteur ; R. Stine, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 187 - 198 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Alaska (Etats-Unis)
[Termes IGN] Arctique
[Termes IGN] azote
[Termes IGN] bassin hydrographique
[Termes IGN] chlorophylle
[Termes IGN] image radar
[Termes IGN] image SPOT
[Termes IGN] interaction spatiale
[Termes IGN] lac
[Termes IGN] macrophyte
[Termes IGN] marais
[Termes IGN] occupation du sol
[Termes IGN] phosphore
[Termes IGN] plante ripicole
[Termes IGN] production primaire brute
[Termes IGN] surveillance
[Termes IGN] zone tamponRésumé : (Auteur) Increasing surface temperatures in the Arctic are affecting the dynamics between lakes and their landscapes. In this paper, we use landscape metrics for land cover and statistical analysis to explore the interactions between such measures as shape and patch density indices for land cover and lake primary productivity. The objective was to identify metrics that could be used to predict lake primary productivity, as measured by chlorophyll a, total nitrogen and total phosphorus estimates. Land cover and landscape metrics for the Toolik region of Alaska were derived using satellite imagery and Fragstats software. The metrics, treated as independent variables in a stepwise regression, were derived for two levels of land cover. The first comprised the entire watershed studied; the second was derived using buffers created around water channels within each watershed. A statistically significant model for each dependent variable was obtained. Results suggest that, of the metrics tested; those related to broad leaf vegetation complexes were most useful in predicting lake primary productivity. The Landscape Shape Index for riparian patches and the Patch Density for heath complex were the two most important metrics for predicting variation in chlorophyll a (p Numéro de notice : A2012-574 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1559/15230406394187 En ligne : https://doi.org/10.1559/15230406394187 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32020
in Cartography and Geographic Information Science > vol 39 n° 4 (October 2012) . - pp 187 - 198[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 032-2012041 RAB Revue Centre de documentation En réserve L003 Disponible In situ estimation of water quality parameters in freshwater aquaculture ponds using hyperspectral imaging system / Amr Abd-Elrahman in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 4 (July - August 2011)
[article]
Titre : In situ estimation of water quality parameters in freshwater aquaculture ponds using hyperspectral imaging system Type de document : Article/Communication Auteurs : Amr Abd-Elrahman, Auteur ; M. Croxton, Auteur ; Roshan Pande-Chhetri, Auteur ; et al., Auteur Année de publication : 2011 Article en page(s) : pp 463 - 472 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] aquaculture
[Termes IGN] chlorophylle
[Termes IGN] cible cachée
[Termes IGN] étang
[Termes IGN] image hyperspectrale
[Termes IGN] qualité des eaux
[Termes IGN] turbidité des eauxRésumé : (Auteur) Knowledge of water quality parameters is integral to sustainability of freshwater aquaculture operations that raise ornamental fish. Our objective in this study is to evaluate the ability of a mobile, ground-based hyperspectral (HS) imaging sensor to determine chlorophyll-a (Chl-a) concentrations in working aquaculture ponds, which represent manipulated, shallow, nutrient-rich systems, and to determine the effect of using submerged reflectance targets on the accuracy of Chl-a estimation. We collected Chl-a measurements from aquaculture ponds ranging from 0.8 to 494 ug/L.. Chl-a measurements showed a strong correlation with two-band and three-band spectral indices computed from the HS image reflectance. Coefficient of determination (R2) values of 0.975 and 0.982 were obtained for the two- and three-band models, respectively, using spectra captured from the submerged target at 10 cm depth. Using spectra captured from water (no submerged targets), R2 values were slightly lower at 0.833 and 0.862 for two- and three-band models. Data from the submerged target at 30 cm depth had the lowest correlation with measured chlorophyll-a concentrations, potentially due to variations in water column properties and shadows cast by the platform. Modeling total Phosphorous (P) and Nitrogen (N) concentrations of the collected samples with the spectral indices sensitive to Chl-a concentrations showed a moderate level of correlation. Removing a model outlier (observation with maximum N and P concentrations) led to a significant increase in the models’ coefficient of determination (e.g. from 0.478 to 0.823 for the P model using three-band index values), which highlighted the possibility of using HS imagery to estimate N and P concentrations and the need for more research to model the interrelationships between Chl-a and nutrient concentrations in aquaculture water systems. Numéro de notice : A2011-298 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2011.02.005 En ligne : https://doi.org/10.1016/j.isprsjprs.2011.02.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31077
in ISPRS Journal of photogrammetry and remote sensing > vol 66 n° 4 (July - August 2011) . - pp 463 - 472[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2011041 SL Revue Centre de documentation Revues en salle Disponible vol 31 n° 17 - 18 - September 2010 - Pan ocean remote sensing : oceanic manifestation of global changes (Bulletin de International Journal of Remote Sensing IJRS) / G. LevyPermalinkFrost damage in Pinus sylvestris L. stems assessed by chlorophyll fluorescence in cortical bark chlorenchyma / José Javier Peguero-Pina in Annals of Forest Science, Vol 65 n° 8 (December 2008)PermalinkWavelet decomposition of hyperspectral data: a novel approach to quantifying pigment concentrations in vegetation / G. Blackburn in International Journal of Remote Sensing IJRS, vol 28 n°11-12 (June 2007)PermalinkExtending the MODIS 1 km ocean colour atmospheric correction to the MODIS 500 m bands and 500 m chlorophyll-a estimation towards coastal and estuarine monitoring / J.D. Shutler in Remote sensing of environment, vol 107 n° 4 (30/04/2007)PermalinkA new algorithm for estimating chlorophyll-a concentration from multi-spectral satellite data in case 2 waters: a simulation based on a controlled laboratory experiment / Y. Oyoma in International Journal of Remote Sensing IJRS, vol 28 n°7-8 (April 2007)PermalinkRegional products for the Baltic Sea using MERIS data / H. Krawczyk in International Journal of Remote Sensing IJRS, vol 28 n°3-4 (February 2007)PermalinkNeural network estimation of LAI, fAPAR, fCover and LAI*Cab, from top of canopy MERIS reflectance data: principles and validation / Cédric Bacour in Remote sensing of environment, vol 105 n° 4 (30/12/2006)PermalinkRelationship between herbicide concentration during the 1960s and 1970s and the contemporary MERIS terrestrial chlorophyll index (MTCI) for southern Vietnam / J. Dash in International journal of geographical information science IJGIS, vol 20 n° 8 (september 2006)PermalinkDynamique urbaine et télédétection : le choix de l'indicateur végétal, les cas de Montréal, Paris et Pékin / I. Biraud-Burot in Photo interprétation, vol 41 n° 4 (Novembre 2005)PermalinkMarine GIS: identification of mesoscale oceanic thermal fronts / V.D. Valavanis in International journal of geographical information science IJGIS, vol 19 n° 10 (november 2005)PermalinkAssessing the potential of SeaWiFS and MODIS for estimating chlorophyll concentration in turbid productive waters using red and near-infrared bands / G. Dall'olmo in Remote sensing of environment, vol 96 n° 2 (30/05/2005)PermalinkAnalysis of simultaneous chlorophyll measurements by lidar fluorosensor, MODIS and SeaWiFS / R. Bardini in International Journal of Remote Sensing IJRS, vol 25 n° 11 (June 2004)PermalinkSeaWIFS validation in European coastal waters using optical and bio-geochemical measurements / S.J. Lavender in International Journal of Remote Sensing IJRS, vol 25 n° 7 (April 2004)PermalinkHyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modelling and validation in the context of precision agriculture / D. Haboudane in Remote sensing of environment, vol 90 n° 3 (15/04/2004)PermalinkToward universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements / G. Le Maire in Remote sensing of environment, vol 89 n° 1 (15/01/2004)PermalinkAutomated subpixel photobathymetry and water quality mapping / R.L. Huguenin in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 1 (January 2004)PermalinkRemote sensing techniques to assess water quality / J.C. Ritchie in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 6 (June 2003)PermalinkSchätzung von Vegetationsparametern aus multispektralen Fernerkundungsdaten / F. Kurz (2003)PermalinkUtilisation des images et données multisources pour caractériser l'état de palmeraies industrielles au Gabon / Marcellin Nziengui (2000)PermalinkOcean colour analysis in coastal waters by airborne sensors / G. Zibordi in International Journal of Remote Sensing IJRS, vol 11 n° 5 (May 1990)Permalink