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Integrated water vapour observations in the Caribbean arc from a network of ground-based GNSS receivers during EUREC4A / Olivier Bock in Earth System Science Data, vol 13 n° 5 (May 2021)  

Public [article]  inEarth System Science Data > vol 13 n° 5 (May 2021) . - pp 2407 - 2436 Titre : Integrated water vapour observations in the Caribbean arc from a network of ground-based GNSS receivers during EUREC4A Type de document : Article/Communication Auteurs : Olivier Bock , Auteur ; Pierre Bosser , Auteur ; Cyrille Flamant, Auteur ; Erik Doerflinger, Auteur ; Friedhelm Jansen, Auteur ; Romain Fagès , Auteur ; Sandrine Bony, Auteur ; Sabrina Schnitt, Auteur Année de publication : 2021 Projets : VEGAN / Bock, Olivier, EUREC4A / Article en page(s) : pp 2407 - 2436 Note générale : bibliographie This work was supported by the CNRS program LEFE/INSU through the project VEGAN. The EUREC4A project was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 694768). Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] Caraïbes [Termes IGN] données auxiliaires [Termes IGN] données GNSS [Termes IGN] données météorologiques [Termes IGN] erreur systématique [Termes IGN] humidité de l'air [Termes IGN] retard troposphérique zénithal [Termes IGN] teneur intégrée en vapeur d'eau Résumé : (auteur) Ground-based Global Navigation Satellite System (GNSS) measurements from nearly fifty stations distributed over the Caribbean Arc have been analysed for the period 1 January–29 February 2020 in the framework of the EUREC4A (Elucidate the Couplings Between Clouds, Convection and Circulation) field campaign. The aim of this effort is to deliver high-quality Integrated Water Vapour (IWV) estimates to investigate the moisture environment of mesoscale cloud patterns in the Tradewinds and their feedback on the large-scale circulation and energy budget. This paper describes the GNSS data processing procedures and assesses the quality of the GNSS IWV retrievals from four operational streams and one reprocessed research stream which is the main data set used for offline scientific applications. The uncertainties associated with each of the data sets, including the zenith tropospheric delay (ZTD) to IWV conversion methods and auxiliary data, are quantified and discussed. The IWV estimates from the reprocessed data set are compared to the Vaisala RS41 radiosonde measurements operated from the Barbados Cloud Observatory (BCO) and to the measurements from the operational radiosonde station at Grantley Adams international airport (GAIA). A significant dry bias is found in the GAIA humidity observations with respect to the BCO sondes (−2.9 kg m−2) and the GNSS results (−1.2 kg m−2). A systematic bias between the BCO sondes and GNSS is also observed (1.7 kg m−2) where the Vaisala RS41 measurements are moister than the GNSS retrievals. The IWV estimates from a colocated microwave radiometer agree with the BCO soundings after an instrumental update on 27 January, while they exhibit a dry bias compared to the soundings and to GNSS before that date. IWV estimates from the ECMWF fifth generation reanalysis (ERA5) are overall close to the GAIA observations, probably due to the assimilation of these observations in the reanalysis. However, during several events where strong peaks in IWV occurred, ERA5 is shown to significantly underestimate the GNSS derived IWV peaks. Two successive peaks are observed on 22 January and 23/24 January which were associated with heavy rain and deep moist layers extending from the surface up to altitudes of 3.5 and 5 km, respectively. ERA5 significantly underestimates the moisture content in the upper part of these layers. The origins of the various moisture biases are currently being investigated. We classified the cloud organisation for five representative GNSS stations across the Caribbean Arc and found that the environment of Fish cloud patterns to be moister than that of Flowers cloud patterns which, in turn, is moister than the environment of Gravel cloud patterns. The differences in the IWV means between Fish and Gravel were assessed to be significant. Finally, the Gravel moisture environment was found to be similar to that of clear, cloud-free conditions. The moisture environment associated with the Sugar cloud pattern has not been assessed because it was hardly observed during the first two months of 2020. The reprocessed ZTD and IWV data set from 49 GNSS stations used in this study are available from the AERIS data center (https://doi.org/10.25326/79; Bock (2020b)). Numéro de notice : A2021-172 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/essd-13-2407-2021 date de publication en ligne : 24/02/2021 En ligne : http://dx.doi.org/10.5194/essd-13-2407-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97213 [article]

A new small area estimation algorithm to balance between statistical precision and scale / Cédric Vega in International journal of applied Earth observation and geoinformation, vol 97 (May 2021)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 97 (May 2021) . - n° 102303 Titre : A new small area estimation algorithm to balance between statistical precision and scale Type de document : Article/Communication Auteurs : Cédric Vega , Auteur ; Jean-Pierre Renaud, Auteur ; Ankit Sagar , Auteur ; Olivier Bouriaud , Auteur Année de publication : 2021 Projets : LUE / Université de Lorraine, DIABOLO / Packalen, Tuula, ARBRE/CHM-era / Jolly, Anne Article en page(s) : n° 102303 Note générale : bibliographie This research was funded by The French Environmental Management Agency (ADEME), grant number 16-60-C0007. The methods and algorithms for processing photogrammetric data were supported by DIABOLO project from the European Union’s Horizon 2020 research and innovation program under grant agreement No 633464, as well as CHM-ERA project from the French National Research Agency (ANR) as part of the “Investissements d’Avenir” program (ANR-11-LABX-0002-01, Lab of Excellence ARBRE). Ankit Sagar received the financial support of the French PIA project “Lorraine Université d’Excellence”, reference ANR-15-IDEX-04-LUE, through the project Impact DeepSurf. Langues : Anglais (eng) Descripteur : [Termes IGN] arbre BSP [Termes IGN] capital sur pied [Termes IGN] données auxiliaires [Termes IGN] données de terrain [Termes IGN] estimation bayesienne [Termes IGN] inventaire forestier national (données France) [Termes IGN] réduction d'échelle [Termes IGN] seuillage [Termes IGN] surface terrière [Vedettes matières IGN] Sylviculture Résumé : (auteur) Combining national forest inventory (NFI) data with auxiliary information allows downscaling and improving the precision of NFI estimates for small domains, where normally too few field plots are available to produce reliable estimates. In most situations, small domains represent administrative units that could greatly vary in size and forested area. In small and poorly sampled domains, the precision of estimates often drop below expected standards. To tackle this issue, we introduce a downscaling algorithm generating the smallest possible groups of domains satisfying prescribed sampling density and estimation error. The binary space partitioning algorithm recursively divides the population of domains in two groups while the prescribed precision conditions are fulfilled. The algorithm was tested on two major forest attributes (i.e. growing stock and basal area) in an area of 7,500 km2 dominated by hardwood forests in the centre of France. The estimation domains consisted in 157 municipalities. The field data included 819 NFI plots surveyed during a 5 years period. The auxiliary data consisted in 48 metrics derived from a forest map, photogrammetric models and Landsat images. A model-assisted framework was used for estimation. For each forest attribute, the best model was selected using a best-subset approach using a Bayesian Information Criteria. The retained models explained 58% and 41% of the observed variance for the growing stocks and basal areas respectively. The performance of the algorithm was evaluated using a minimum of 3 NFI points per domain and estimation errors varying from 10 to 50%. For a target estimation error set to 10%, the algorithm led to a limited number of estimation domains ( The algorithm provides a flexible estimation framework for small area estimation. The key advantages of the approach are relying on its capacity to produce estimations based on a preselected precision threshold and to produce results over the whole area of interest, avoiding areas without any estimates. The algorithm could also be used on any kind of polygon layers (not only administrative ones), provided that the field sampling design enable estimation. This makes the proposed algorithm a convenient tool notably for decision makers and forest managers. Numéro de notice : A2021-067 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2021.102303 date de publication en ligne : 25/01/2021 En ligne : https://doi.org/10.1016/j.jag.2021.102303 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96992 [article]

Bridging the gap: toward a French MS-NFI for territories / Jean-Pierre Renaud (2019)

Public Titre : Bridging the gap: toward a French MS-NFI for territories Type de document : Article/Communication Auteurs : Jean-Pierre Renaud, Auteur ; Dinesh Babu Irulappa Pillai Vijayakumar , Auteur ; François Morneau , Auteur ; Cédric Vega , Auteur Editeur : Paris [France] : Office national des forêts ONF Année de publication : 2019 Conférence : Conference 2019, A century of national forest inventories – informing past, present and future decisions 19/05/2019 21/05/2019 Oslo Norvège programme sans actes Langues : Anglais (eng) Descripteur : [Termes IGN] carte forestière [Termes IGN] classification barycentrique [Termes IGN] densité de la végétation [Termes IGN] données auxiliaires [Termes IGN] données de terrain [Termes IGN] feuillu [Termes IGN] forêt tempérée [Termes IGN] image aérienne [Termes IGN] image Landsat [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] placette d'échantillonnage [Termes IGN] surface terrière [Termes IGN] volume en bois [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Introduction: National forest inventories are designed to produce statistics about forest attributes at a national to regional scales. Beyond these administrative units, the amount of points become limiting in terms of precision. In France, the establishment of regional programs for forest and wood (PRFB) require estimates at a smaller scale. The multisource inventory approaches allowed to bridge this gap (Tomppo et al. 2008). The methods rely on the combination of field plot information with auxiliary data (Kangas et al. 2018). The objective was to set up a multisource inventory workflow for the French Forest and to evaluate the gain in precision obtain at different administrative levels. Materials and methods: This research was conducted over a 7500 km2 area located in centre of France, of which 50 % is covered by forests dominated by broadleaved species. The forest area included 775 NFI plots collected during the 2009-2014 period. The auxiliary data were acquired in 2013-2014 and selected to fulfil the following criteria: Relevant, i.e. well correlated with the forest attributed under survey; Actualized Regularly for updating; Exhaustive over the whole territory; and Economical (RARE2). In this regard, we used the following data sources: Landsat images, 3D models derived from aerial photographs and a forest thematic map. We further evaluated the contribution of 3D models acquired 5 years apart in a subset area. The multisource approach relies on the non-parametric k-nearest neighbours (k-nn) approach owing to its multivariate capabilities. The k-nn was optimised for variable selection, number of neighbours (k) and distance metrics. Its performance was tested under a model-assisted framework using estimators from Mandallaz (2013) for various administrative levels. Results: Among the auxiliary variables tested, the 3D data source from aerial photographs performed best, as compared to Landsat, or forest thematic maps. The best combination of data included all sources and provide relative efficiencies (RE) varying from 2.05 for volume to 1.03 for stand density. Over the subset area, the diachronic data allow to improve the RE from 3-26 %. The diachronic data markedly improved the efficiency in estimations of forest type volumes, basal area and stand density. Similar RE were obtained for small area estimation at the scale of Canton and Municipalities. Conclusion: Our results confirmed the importance of 3D models of forest canopies and demonstrated the interest of canopy changes to improve precision of some forest attributes such as production volume and density, which are associated with fluxes. Numéro de notice : C2019-064 Affiliation des auteurs : LIF+Ext (2012-2019) Thématique : FORET Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96975

Stand-volume estimation from multi-source data for coppiced and high forest Eucalyptus spp. silvicultural systems in KwaZulu-Natal, South Africa / Timothy Dube in ISPRS Journal of photogrammetry and remote sensing, vol 132 (October 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 132 (October 2017) . - pp 162 - 169 Titre : Stand-volume estimation from multi-source data for coppiced and high forest Eucalyptus spp. silvicultural systems in KwaZulu-Natal, South Africa Type de document : Article/Communication Auteurs : Timothy Dube, Auteur ; Mbulisi Sibanda, Auteur ; Cletah Shoko, Auteur ; Onisimo Mutanga, Auteur Année de publication : 2017 Article en page(s) : pp 162 - 169 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse comparative [Termes IGN] cubage de peuplement [Termes IGN] données auxiliaires [Termes IGN] écosystème forestier [Termes IGN] Eucalyptus camaldulensis [Termes IGN] image SPOT 5 [Termes IGN] KwaZulu-Natal (Afrique du Sud) [Termes IGN] peuplement forestier [Termes IGN] régression [Termes IGN] taillis Résumé : (Auteur) Forest stand volume is one of the crucial stand parameters, which influences the ability of these forests to provide ecosystem goods and services. This study thus aimed at examining the potential of integrating multispectral SPOT 5 image, with ancillary data (forest age and rainfall metrics) in estimating stand volume between coppiced and planted Eucalyptus spp. in KwaZulu-Natal, South Africa. To achieve this objective, Partial Least Squares Regression (PLSR) algorithm was used. The PLSR algorithm was implemented by applying three tier analysis stages: stage I: using ancillary data as an independent dataset, stage II: SPOT 5 spectral bands as an independent dataset and stage III: combined SPOT 5 spectral bands and ancillary data. The results of the study showed that the use of an independent ancillary dataset better explained the volume of Eucalyptus spp. growing from coppices (adjusted R2 (R2Adj) = 0.54, RMSEP = 44.08 m3/ha), when compared with those that were planted (R2Adj = 0.43, RMSEP = 53.29 m3/ha). Similar results were also observed when SPOT 5 spectral bands were applied as an independent dataset, whereas improved volume estimates were produced when using combined dataset. For instance, planted Eucalyptus spp. were better predicted adjusted R2 (R2Adj) = 0.77, adjusted R2Adj = 0.59, RMSEP = 36.02 m3/ha) when compared with those that grow from coppices (R2 = 0.76, R2Adj = 0.46, RMSEP = 40.63 m3/ha). Overall, the findings of this study demonstrated the relevance of multi-source data in ecosystems modelling. Numéro de notice : A2017-643 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.09.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.09.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87002 [article]

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TM-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)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 6 (June 2017) . - pp 447 - 457 Titre : TM-Based SOC models augmented by auxiliary data for carbon crediting programs in semi-arid environments Type de document : Article/Communication Auteurs : Salahuddin M. Jaber, Auteur ; Mohammed I. Al-Qinna, Auteur Année de publication : 2017 Article en page(s) : pp 447 - 457 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse comparative [Termes IGN] données auxiliaires [Termes IGN] image Landsat-TM [Termes IGN] Jordanie [Termes IGN] matière organique [Termes IGN] prédiction [Termes IGN] sol [Termes IGN] teneur en carbone [Termes IGN] zone semi-aride Résumé : (Auteur) This study aimed at testing the hypothesis that augmenting Landsat TM-based models for predicting soil organic carbon (SOC) with auxiliary data about variables that might affect the spatial distribution of SOC might improve the predictability of these models in the Zarqa Basin in Jordan (a typical semi-arid watershed) and enable them to be used for implementing carbon crediting programs in semi-arid environments. Six modeling procedures, namely stepwise regression, partial least squares, recursive partitioning analysis, screening regression analysis, artificial neural networks, and combined models, were calibrated and validated for the basin and for the land cover types that exist in the basin. Although none of the developed models was powerful for predicting SOC, artificial neural networks models were more applicable specifically in agricultural lands. However, the margins of error associated with the best models were high, and hence hindered the applicability of these models in carbon crediting programs in semi-arid environments. Numéro de notice : A2017-350 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.6.447 En ligne : https://doi.org/10.14358/PERS.83.6.447 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85635 [article]

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