• Home
  • Nathalie Saint-Geours
Nathalie Saint-Geours

Nathalie Saint-Geours
Data Terrae

Dr. Ing.
Data Scientist @ DataTerrae

About

44
Publications
4,103
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
278
Citations
Additional affiliations
January 2013 - March 2015
French National Institute for Agriculture, Food, and Environment (INRAE)
Position
  • Research Associate
September 2009 - November 2012
Université de Montpellier
Position
  • PhD Student
July 2009 - October 2009
Manaaki Whenua - Landcare Research
Position
  • Research intern
Description
  • Master thesis

Publications

Publications (44)
Article
Full-text available
This study investigated the influence of sampling design parameters on biomass prediction accuracy obtained from airborne lidar data. A one-factor-at-a-time and a global sensitivity analyses were applied to identify the parameters most impacting model accuracy. We focused on several lidar and field survey parameters that can be easily controlled by...
Presentation
Full-text available
We reviewed the available knowledge about measurements and modelling of N fluxes and losses in oil palm plantations.
Chapter
This section presents several sensitivity analysis methods to deal with spatial and/or temporal models. Focusing on the variance-based approach, solutions are proposed to perform global sensitivity analysis with functional inputs and outputs. Some of these solutions are illustrated on two industrial case studies: an environmental model for flood ri...
Article
Full-text available
Oil palm is the most rapidly expanding tropical perennial crop. Its cultivation raises environmental concerns, notably related to the use of nitrogen (N) fertilisers and the associated pollution and greenhouse gas emissions. While numerous and diverse models exist to estimate N losses from agriculture, very few are currently available for tropical...
Article
Full-text available
Oil palm is the most rapidly expanding tropical perennial crop. Its cultivation raises environmental concerns, notably related to the use of nitrogen (N) fertilisers and the associated pollution and greenhouse gas emissions. While numerous and diverse models exist to estimate N losses from agriculture, very few are currently available for tropical...
Presentation
Full-text available
N2O emissions from agriculture greatly contribute to climate change. In palm plantations on mineral soils, these emissions are mostly due to fertiliser inputs. This raises environmental concerns as oil palm is the most rapidly expanding tropical perennial crop. There is hence a critical need to quantify and model N2O emissions in order to explore s...
Data
Conference paper. 5th International Conference on Oil Palm and Environment (ICOPE), 16-18th Mar 2016, Bali, Indonesia
Data
Oral presentation. Conference: 5th International Conference on Oil Palm and Environment (ICOPE), 16-18th Mar 2016, Bali, Indonesia
Article
Generating mosaics of orthorectified remote sensing images is a challenging task because of the colorimetric differences between adjacent images introduced by land use, surface illumination, atmospheric conditions, and sensor. Most of the existing color correction methods involve pairwise techniques, which are limited when the collection of images...
Chapter
This section presents several sensitivity analysis methods to deal with spatial and/or temporal models. Focusing on the variance-based approach, solutions are proposed to perform global sensitivity analysis with functional inputs and outputs. Some of these solutions are illustrated on two industrial case studies: an environmental model for flood ri...
Article
In this paper, we investigate the use of the contribution to the sample mean plot (CSM plot) as a graphical tool for sensitivity analysis (SA) of computational models. We first provide an exact formula that links, for each uncertain model input X j , the CSM plot C j (·) with the first-order variance-based sensitivity index S j . We then build a ne...
Article
We demonstrate the use of sensitivity analysis to rank sources of uncertainty in models for economic appraisal of flood risk management policies, taking into account spatial scale issues. A methodology of multi-scale variance-based global sensitivity analysis is developed, and illustrated on the NOE model on the Orb River, France. The variability o...
Article
Full-text available
Urban sprawl monitoring is important for developing land management policies at various spatial scales. Segmentation and classification of satellite images allows obtaining polygons of impervious areas regularly over large areas, e.g. as has been implemented for the region Languedoc‐Roussillon in the south of France using 5 m RapidEye images. Start...
Article
Environmental models often involve complex dynamic and spatial inputs and outputs. This raises specific issues when performing uncertainty and sensitivity analyses (SA). Based on appli- cations in flood risk assessment and agro-ecology, we present current research to adapt the methods of variance-based SA to such models. After recalling the basic p...
Article
Cost-benefit analyses (CBA) of flood management plans usually require estimating expected annual flood damages on a study area and rely on a complex modelling chain, including hydrological, hydraulic and economic modelling, as well as geographic information system-based spatial analysis. As most model-based assessments, these CBA are fraught with u...
Article
Full-text available
This work aimed to prospect future space-borne LiDAR sensor capacities for global bathymetry over inland and coastal waters. The sensor performances were assessed using a methodology based on waveform simulation. A global representative simulated waveform database is first built from the Wa-LiD (Water LiDAR) waveform simulator and from distribution...
Conference Paper
Full-text available
Environmental models often involve complex dynamic and spatial inputs and outputs. This raises specific issues when performing uncertainty and sensitivity analyses (SA). Based on appli- cations in flood risk assessment and agro-ecology, we present current research to adapt the methods of variance-based SA to such models. After recalling the basic p...
Thesis
Full-text available
Variance-based global sensitivity analysis is used to study how the variability of the output of a numerical model can be apportioned to different sources of uncertainty in its inputs. It is an essential component of model building as it helps to identify model inputs that account for most of the model output variance. However, this approach is sel...
Article
Variance-based global sensitivity analysis (GSA) is used to study how the variance of the output of a model can be apportioned to different sources of uncertainty in its inputs. GSA is an essential component of model building as it helps to identify model inputs that account for most of the model output variance. However, this approach is seldom ap...
Article
Full-text available
In order to increase the reliability of flood damage assessment, we need to question the uncertainty associated with the whole flood risk modeling chain. Using a case study on the basin of the Orb River, France, we demonstrate how variance-based sensitivity analysis can be used to quantify uncertainty in flood damage maps at different spatial scale...
Thesis
L’analyse de sensibilité globale basée sur la variance permet de hiérarchiser les sources d’incertitude présentes dans un modèle numérique et d’identifier celles qui contribuent le plus à la variabilité de la sortie du modèle. Ce type d’analyse peine à se développer dans les sciences de la Terre et de l’Environnement, en partie à cause de la dimens...
Article
Full-text available
Variance-based Sobol' global sensitivity analysis (GSA) was initially designed for the study of models with scalar inputs and outputs, while many models in the environmental field are spatially explicit. As a result, GSA is not a common practise in environmental modelling. In this paper we describe a detailed case study where GSA is performed on a...
Article
LiDAR (Light Detection and Ranging) can be used as a ranging system using electromagnetic waves in the optical domain. LiDAR airborne or satellite sensors are promising techniques for river bathymetry and water surface altimetry considering its potential accuracy, its high spatial density and resolution. When considering physics of LiDAR, many fact...
Article
Full-text available
On s'intéresse ici aux diverses méthodes proposées pour évaluer la sensibilité d'une sortie de modèle Y = f(X1; ... ;Xk) à l'incertitude qui pèse sur un facteur d'entrée Xi distribué spatialement. On se restreint aux approches sans construction d'une surface de réponse (méta-modélisation). L'objet de ce document est : 1) de valider de manière empir...
Article
Full-text available
The variance-based Sobol' approach is one of the few global sensitivity analysis methods that is suitable for complex models with spatially distributed inputs. Yet it needs a large number of model runs to compute sensitivity indices: in the case of models where some inputs are 2D Gaussian random fields, it is of great importance to generate a relat...
Article
Full-text available
L'analyse coût-bénéfice basée sur la simulation des dommages évités permet d'obtenir des indicateurs synthétiques sur l'exposition d'un territoire aux inondations (les dommages moyens annualisés), ainsi que l'intérêt ou non de mener une politique de prévention des inondations (les dommages évités moyens annualisés, la valeur actuelle nette). Ces in...
Article
Cost-Benefit Analysis (CBA) is widely promoted as a tool for discussing efficiency of flood management policies. It gives rise to a global indicator, the Net Present Value (NPV), which allows the discussion of allocating regional, national, or supra-national fundings to local projects. Concerning flood management policies, CBA relies on damages avo...
Article
The variance-based Sobol' approach is one of the few global sensitivity analysis methods that is suitable for complex models with spatially distributed inputs. Yet it needs a large number of model runs to compute sensitivity indices: in the case of models where some inputs are 2D Gaussian random fields, it is of great importance to generate a relat...

Network

Cited By

Projects

Project (1)
Project
To develop an agri-environmental indicator for nitrogen losses in oil palm plantations. One of the objectives is to help identifying efficient combinations of management practices to reduce nitrogen losses. One of the challenges is to develop the indicator in a context of lack of available published knowledge.