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Thermal unmixing based downscaling for fine resolution diurnal land surface temperature analysis / Jiong Wang in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 161 (March 2020) . - pp 76 - 89 Titre : Thermal unmixing based downscaling for fine resolution diurnal land surface temperature analysis Type de document : Article/Communication Auteurs : Jiong Wang, Auteur ; Olivier Schmitz, Auteur ; Meng Lu, Auteur ; Derek Karssenberg, Auteur Année de publication : 2020 Article en page(s) : pp 76 - 89 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] données spatiotemporelles [Termes descripteurs IGN] factorisation de matrice non-négative [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] image MODIS [Termes descripteurs IGN] mise à l'échelle [Termes descripteurs IGN] Pays-Bas [Termes descripteurs IGN] radiance [Termes descripteurs IGN] réduction [Termes descripteurs IGN] température de surface [Termes descripteurs IGN] variation diurne Résumé : (Auteur) Due to the limitation in the availability of airborne imagery data that are high in both spatial and temporal resolution, land surface temperature (LST) dense in both space and time can only be obtained through downscaling of frequently acquired LST with coarse resolution. Many conventional downscaling techniques are only feasible in an ideal situation, where land surface factors as LST predictors are continuously available for downscaling the LST. These techniques are also applied only at large scales ignoring sub-regional variations. Based upon unmixing based approaches, this study presents an LST downscaling workflow, where only the coarse resolution of 1 km LST image at the prediction time is required. The conceptual backbone of the study is assuming that the LST patterns are governed by thermal behaviors of a fixed set of temperature sensitive land surface components. In operation, the study focuses on central Netherlands covering an area of 90 × 90 km. The MODIS and Landsat imagery acquired simultaneously are used as a coarse-fine resolution pair to derive downscaling mechanism which is then applied to coarse imagery at a time with missing fine resolution imagery. First, an optimal number of thermal components are extracted at fine resolution through the application of the non-negative matrix factorization (NMF). These components are assumed to possess unique temperature change patterns caused by combined effects of land cover change, radiance change, or both. Given the LST change and thermal components at coarse resolution, the LST change load of each component can then be obtained at the coarse resolution by solving a system of linear equations encoding thermal component-LST relationship. Such LST change load of thermal components is further unmixed to fine resolution and linearly weighted by the component distribution at fine resolution to obtain the fine resolution LST change. During the process, the coarse LST data is used directly without any resampling practice as shown in previous studies. Thus the technique is less time consuming even with a large downscaling factor of 30. The downscaled fine resolution LST represents an R-squared of over 0.7 outperforming classic downscaling techniques. The downscaled LST differentiates temperature over major land types and captures both seasonal and diurnal LST dynamics. Numéro de notice : A2020-063 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.01.014 date de publication en ligne : 16/01/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.01.014 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94580 [article]

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Xylem anatomy of Robinia pseudoacacia L. and Quercus robur L. is differently affected by climate in a temperate alluvial forest / Paola Nola in Annals of Forest Science [en ligne], Vol 77 n° 1 (mars 2020)  

Public [article]  inAnnals of Forest Science [en ligne] > Vol 77 n° 1 (mars 2020) . - 16 p. Titre : Xylem anatomy of Robinia pseudoacacia L. and Quercus robur L. is differently affected by climate in a temperate alluvial forest Type de document : Article/Communication Auteurs : Paola Nola, Auteur ; Francesco Bracco, Auteur ; Silvia Assini, Auteur ; Georg von Arx, Auteur ; Daniele Catagneri, Auteur Année de publication : 2020 Article en page(s) : 16 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] cerne [Termes descripteurs IGN] dendrochronologie [Termes descripteurs IGN] espèce exotique envahissante [Termes descripteurs IGN] espèce pionnière [Termes descripteurs IGN] forêt ripicole [Termes descripteurs IGN] gestion forestière [Termes descripteurs IGN] orthoptère [Termes descripteurs IGN] quercus pedunculata [Termes descripteurs IGN] Robinia pseudoacacia [Termes descripteurs IGN] sécheresse [Termes descripteurs IGN] variation saisonnière [Termes descripteurs IGN] zone tempérée [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Key message: Xylem hydraulic traits of native Quercus robur are more sensitive to previous-summer drought than those of alien Robinia pseudoacacia. The latter modulates vessel traits and ring porosity to cope with inter-annual climate variability, and is less affected by extreme events. This suggests that R. pseudoacacia might be more competitive under future drier conditions. Context: Forest management strategies require knowledge on how co-occurring native and alien species respond to unprecedented climate conditions, which can severely affect xylem conductivity and tree performance. Aims: We aimed at quantitatively comparing xylem anatomical traits of co-occurring native Quercus robur and alien Robinia pseudoacacia and assessing similarities and differences in their response to climate variability. Methods: We analyzed tree-ring anatomy and built chronologies of several parameters related to vessel number, size, and theoretical conductivity. Mean chronologies for each parameter were correlated to monthly temperature and precipitation data for the period 1954–2005 and within 30-year moving windows. We also assessed responses to extreme conditions in 2003. Results: Quercus robur showed typical ring-porous vessel distribution, while R. pseudoacacia modulated vessel size and number year by year, frequently showing semi-ring porous appearance. Previous rainy summers increased size of large vessels in Q. robur, and number of large vessels in R. pseudoacacia. In winter, R. pseudoacacia was sensitive to water excess. High temperature in March increased vessel size in Q. robur, but reduced it in R. pseudoacacia. The 2003 summer heatwave strongly reduced vessel size and number in the following year in Q. robur, but had much less effect on R. pseudoacacia. Conclusion: Quercus robur xylem traits are more influenced by both inter-annual climate variability and extreme events than those of R. pseudoacacia. Lower performance under dry conditions might reduce competitiveness of Q. robur in the future, slowing down the natural replacement of the invasive pioneer R. pseudoacacia by later-stage Q. robur. Numéro de notice : A2020-068 Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : https://doi.org/10.1007/s13595-019-0906-z date de publication en ligne : 10/01/2020 En ligne : https://doi.org/10.1007/s13595-019-0906-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94581 [article]

Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods / Elisa Kamir in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 160 (February 2020) . - pp 124 - 135 Titre : Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods Type de document : Article/Communication Auteurs : Elisa Kamir, Auteur ; François Waldner, Auteur ; Zvi Hochman, Auteur Année de publication : 2020 Article en page(s) : pp 124 - 135 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] Australie [Termes descripteurs IGN] blé (céréale) [Termes descripteurs IGN] carte agricole [Termes descripteurs IGN] climat [Termes descripteurs IGN] estimation de précision [Termes descripteurs IGN] fonction de base radiale [Termes descripteurs IGN] image satellite [Termes descripteurs IGN] modèle de croissance [Termes descripteurs IGN] modèle non linéaire [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] régression [Termes descripteurs IGN] rendement agricole [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] variation saisonnière Résumé : (Auteur) Closing the yield gap between actual and potential wheat yields in Australia is important to meet the growing global demand for food. The identification of hotspots of the yield gap, where the potential for improvement is the greatest, is a necessary step towards this goal. While crop growth models are well suited to quantify potential yields, they lack the ability to provide accurate large-scale estimates of actual yields, owing to the sheer quantity of data they require for parameterisation. In this context, we sought to provide accurate estimates of actual wheat yields across the Australian wheat belt based on machine-learning regression methods, climate records and satellite image time series. Out of nine base learners and two ensembles, support vector regression with radial basis function emerged as the single best learner (root mean square error of 0.55 t ha−1 and R2 of 0.77 at the pixel level). At national scale, this model explained 73% of the yield variability observed across statistical units. Benchmark approaches based on peak Normalised Difference Vegetation Index (NDVI) and on a harvest index were largely outperformed by the machine-learning regression models (R2  Numéro de notice : A2020-046 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.11.008 date de publication en ligne : 20/12/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.11.008 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94556 [article]

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Modelling of the timeseries of GNSS coordinates and their interaction with average magnitude earthquakes / Sanja Tucikesic in Geodetski vestnik, Vol 63 n° 4 (décembre 2019)  

Public [article]  inGeodetski vestnik > Vol 63 n° 4 (décembre 2019) . - pp 525 - 540 Titre : Modelling of the timeseries of GNSS coordinates and their interaction with average magnitude earthquakes Type de document : Article/Communication Auteurs : Sanja Tucikesic, Auteur ; Dragan Blagojevic, Auteur Année de publication : 2019 Article en page(s) : pp 525 - 540 Note générale : bibliographie Langues : Anglais (eng) Slovène (slv) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] analyse spectrale [Termes descripteurs IGN] Bosnie-Herzégovine [Termes descripteurs IGN] bruit (théorie du signal) [Termes descripteurs IGN] bruit blanc [Termes descripteurs IGN] compensation par moindres carrés [Termes descripteurs IGN] coordonnées GNSS [Termes descripteurs IGN] déformation de la croute terrestre [Termes descripteurs IGN] modèle stochastique [Termes descripteurs IGN] séisme [Termes descripteurs IGN] Serbie [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] station GNSS [Termes descripteurs IGN] variation temporelle Résumé : (auteur) In this article the time series data of GNSS station coordinates are analysed, using least-squares spectral analysis (LSSA). One type of LSSA, the method of estimating a frequency spectrum, is the Lomb–Scargle method. Because of the presence of discontinuities in GNSS measurements, we applied Lomb–Scargle model for detecting and characterizing periodicity. We analyzed time series data from the station SRJV (Sarajevo), for a period of about 20 years, and BEOG (Belgrade), for a period of about 5 years. The spectral analysis is used to determine quickly the predominant noise in the position time series. Analyzed spectral indices of noise (α) of GNSS coordinate time series of SRJV and BEOG are in the range of -1 Numéro de notice : A2019-579 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.15292/geodetski-vestnik.2019.04.525-540 date de publication en ligne : 24/05/2019 En ligne : https://doi.org/10.15292/geodetski-vestnik.2019.04.525-540 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94467 [article]

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Accurate modelling of canopy traits from seasonal Sentinel-2 imagery based on the vertical distribution of leaf traits / Tawanda W. Gara in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 157 (November 2019) . - pp 108 - 123 Titre : Accurate modelling of canopy traits from seasonal Sentinel-2 imagery based on the vertical distribution of leaf traits Type de document : Article/Communication Auteurs : Tawanda W. Gara, Auteur ; Roshanak Darvishzadeh, Auteur ; Andrew K. Skidmore, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 108 - 123 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Bavière (Allemagne) [Termes descripteurs IGN] canopée [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] écosystème forestier [Termes descripteurs IGN] hétérogénéité spatiale [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] indice foliaire [Termes descripteurs IGN] Leaf mass per area [Termes descripteurs IGN] photosynthèse [Termes descripteurs IGN] variation saisonnière Résumé : (Auteur) Leaf traits at canopy level (hereinafter canopy traits) are conventionally expressed as a product of total canopy leaf area index (LAI) and leaf trait content based on samples collected from the exposed upper canopy. This traditional expression is centered on the theory that absorption of incident photosynthetically active radiation (PAR) follow a bell-shaped function skewed to the upper canopy. However, the validity of this theory has remained untested for a suite of canopy traits in a temperate forest ecosystem across multiple seasons using multispectral imagery. In this study, we examined the effect of canopy traits expression in modelling canopy traits using Sentinel-2 multispectral data across the growing season in Bavaria Forest National Park (BFNP), Germany. To achieve this, we measured leaf mass per area (LMA), chlorophyll (Cab), nitrogen (N) and carbon content and LAI from the exposed upper and shaded lower canopy respectively over three seasons (spring, summer and autumn). Subsequently, we estimated canopy traits using two expressions, i.e. the traditional expression-based on the product of LAI and leaf traits content of samples collected from the sunlit upper canopy (hereinafter top-of-canopy expression) and the weighted expression - established on the proportion between the shaded lower and sunlit upper canopy LAI and their respective leaf traits content. Using a Random Forest machine-learning algorithm, we separately modelled canopy traits estimated from the two expressions using Sentinel-2 spectral bands and vegetation indices. Our results showed that dry matter related canopy traits (LMA, N and carbon) estimated based on the weighted canopy expression yield stronger correlations and higher prediction accuracy (NRMSECV  0.48 µg/cm2) across all seasons. We also developed a generalized model that explained 52.57–67.82% variation in canopy traits across the three seasons. Using the most accurate Random Forest model for each season, we demonstrated the capability of Sentinel-2 data to map seasonal dynamics of canopy traits across the park. Results presented in this study revealed that canopy trait expression can have a profound effect on modelling the accuracy of canopy traits using satellite imagery throughout the growing seasons. These findings have implications on model accuracy when monitoring the dynamics of ecosystem functions, processes and services. Numéro de notice : A2019-493 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.09.005 date de publication en ligne : 11/09/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.09.005 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93725 [article]

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Vulnerability of forest ecosystems to fire in the French Alps / Sylvain Dupire in European Journal of Forest Research, Vol 138 n° 5 (octobre 2019)  

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Decomposition of geodetic time series: A combined simulated annealing algorithm and Kalman filter approach / Feng Ming in Advances in space research, vol 64 n°5 (1 September 2019)  

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Monitoring of extreme land hydrology events in central Poland using GRACE, land surface models and absolute gravity data / Joanna Kuczynska-Siehien in Journal of applied geodesy, vol 13 n° 3 (July 2019)  

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A novel method for separating woody and herbaceous time series / Qiang Zhou in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 7 (July 2019)  

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Long-term soil moisture content estimation using satellite and climate data in agricultural area of Mongolia / Enkhjargal Natsagdorj in Geocarto international, vol 34 n° 7 ([01/06/2019])  

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Seasonal pattern in time series of variances of GPS residual errors Anova estimates / Darko Anđić in Geodetski vestnik, vol 63 n° 2 (June - August 2019)  

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Evaluation of the IRI-2016 and NeQuick electron content specification by COSMIC GPS radio occultation, ground-based GPS and Jason-2 joint altimeter/GPS observations / Iurii Cherniak in Advances in space research, vol 63 n° 6 (15 March 2019)  

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Tree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis / Matheus Pinheiro Ferreira in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)  

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Learning scene geometry for visual localization in challenging conditions / Nathan Piasco (2019)    

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Identification and extraction of seasonal geodetic signals due to surface load variations / Stacy Larochelle in Journal of geophysical research : Solid Earth, vol 123 n° 12 (December 2018)  

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