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Characterizing the calibration domain of remote sensing models using convex hulls / Jean-Pierre Renaud in International journal of applied Earth observation and geoinformation, vol 112 (August 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 112 (August 2022) . - n° 102939 Titre : Characterizing the calibration domain of remote sensing models using convex hulls Type de document : Article/Communication Auteurs : Jean-Pierre Renaud , Auteur ; Ankit Sagar , Auteur ; Pierre Barbillon, Auteur ; Olivier Bouriaud , Auteur ; Christine Deleuze, Auteur ; Cédric Vega , Auteur Année de publication : 2022 Projets : DEEPSURF / Pironon, Jacques, ARBRE / AgroParisTech (2007 -) Article en page(s) : n° 102939 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] données allométriques [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] échantillonnage [Termes IGN] erreur systématique [Termes IGN] étalonnage de modèle [Termes IGN] étalonnage des données [Termes IGN] extrapolation [Termes IGN] placette d'échantillonnage Résumé : (auteur) The ever-increasing availability of remote sensing data allows production of forest attributes maps, which are usually made using model-based approaches. These map products are sensitive to various bias sources, including model extrapolation. To identify, over a case study forest, the proportion of extrapolated predictions, we used a convex hull method applied to the auxiliary data space of an airborne laser scanning (ALS) flight. The impact of different sampling efforts was also evaluated. This was done by iteratively thinning a set of 487 systematic plots using nested sub-grids allowing to divide the sample by two at each level. The analysis were conducted for all alternative samples and evaluated against 56 independent validation plots. Residuals of the extrapolated validation plots were computed and examined as a function of their distance to the model calibration domain. Extrapolation was also characterized for the pixels of the area of interest (AOI) to upscale at population level. Results showed that the proportion of extrapolated pixels greatly reduced with an increasing sampling effort. It reached a plateau (ca. 20% extrapolation) with a sampling intensity of ca. 250-calibration plots. This contrasts with results on model’s root mean squared error (RMSE), which reached a plateau at a much lower sampling intensity. This result emphasizes the fact that with a low sampling effort, extrapolation risk remains high, even at a relatively low RMSE. For all attributes examined (i.e., stand density, basal area, and quadratic mean diameter) estimations were generally found to be biased for validation plots that were extrapolated. The method allows an easy identification of map pixels that are out of the calibration domain, making it an interesting tool to evaluate model transferability over an area of interest (AOI). It could also serve to compare “competing” models at a variable selection phase. From a model calibration perspective, it could serve a posteriori, to evaluate areas (in the auxiliary space) that merit further sampling efforts to improve model reliability. Numéro de notice : A2022-581 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET/IMAGERIE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2022.102939 Date de publication en ligne : 28/07/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102939 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101341 [article]

Integrating a forward feature selection algorithm, random forest, and cellular automata to extrapolate urban growth in the Tehran-Karaj region of Iran / Hossein Shafizadeh-Moghadam in Computers, Environment and Urban Systems, vol 87 (May 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 87 (May 2021) . - n° 101595 Titre : Integrating a forward feature selection algorithm, random forest, and cellular automata to extrapolate urban growth in the Tehran-Karaj region of Iran Type de document : Article/Communication Auteurs : Hossein Shafizadeh-Moghadam, Auteur ; Masoud Minaei, Auteur ; Robert Gilmore Pontius Jr, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 101595 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] automate cellulaire [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] croissance urbaine [Termes IGN] extrapolation [Termes IGN] image Landsat [Termes IGN] modèle de simulation [Termes IGN] occupation du sol [Termes IGN] Téhéran [Termes IGN] utilisation du sol Résumé : (auteur) This paper couples a Forward Feature Selection algorithm with Random Forest (FFS-RF) to create a transition index map, which then guides the spatial allocation for the extrapolation of urban growth using a Cellular Automata model. We used Landsat imagery to generate land cover maps at the years 1998, 2008, and 2018 for the Tehran-Karaj Region (TKR) in Iran. The FFS-RF considered the independent variables of slope, altitude, and distances from urban, crop, greenery, barren, and roads. The FFS-RF revealed temporal non-stationary of drivers from 1998–2008 to 2008–2018. The FFS-RF detected that altitude and distance from greenery were the most important drivers of urban growth during 1998–2008, then distances from crop and barren were the most important drivers during 2008–2018. We used the Total Operating Characteristic to evaluate the transition index maps. Validation during 2008–2018 showed that FFS-RF produced a transition index map that had predictive power no better than an allocation of urban growth near existing urban. Simulation to 2060 extrapolated that Tehran, Karaj, and their adjacent cities will interconnect spatially to form a gigantic city-region. Numéro de notice : A2021-274 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101595 Date de publication en ligne : 16/02/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101595 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97357 [article]

FOSTER - An R package for forest structure extrapolation / Martin Queinnec in Plos one, vol 16 n° 1 (January 2021)  

Public [article]  inPlos one > vol 16 n° 1 (January 2021) . - n° 0244846 Titre : FOSTER - An R package for forest structure extrapolation Type de document : Article/Communication Auteurs : Martin Queinnec, Auteur ; Piotr Tompalski, Auteur ; Douglas K. Bolton, Auteur ; Nicholas C. Coops, Auteur Année de publication : 2021 Article en page(s) : n° 0244846 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] autocorrélation spatiale [Termes IGN] classification barycentrique [Termes IGN] Colombie-Britannique (Canada) [Termes IGN] données localisées 3D [Termes IGN] extrapolation [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] R (langage) [Termes IGN] structure d'un peuplement forestier [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) The uptake of technologies such as airborne laser scanning (ALS) and more recently digital aerial photogrammetry (DAP) enable the characterization of 3-dimensional (3D) forest structure. These forest structural attributes are widely applied in the development of modern enhanced forest inventories. As an alternative to extensive ALS or DAP based forest inventories, regional forest attribute maps can be built from relationships between ALS or DAP and wall-to-wall satellite data products. To date, a number of different approaches exist, with varying code implementations using different programming environments and tailored to specific needs. With the motivation for open, simple and modern software, we present FOSTER (Forest Structure Extrapolation in R), a versatile and computationally efficient framework for modeling and imputation of 3D forest attributes. FOSTER derives spectral trends in remote sensing time series, implements a structurally guided sampling approach to sample these often spatially auto correlated datasets, to then allow a modelling approach (currently k-NN imputation) to extrapolate these 3D forest structure measures. The k-NN imputation approach that FOSTER implements has a number of benefits over conventional regression based approaches including lower bias and reduced over fitting. This paper provides an overview of the general framework followed by a demonstration of the performance and outputs of FOSTER. Two ALS-derived variables, the 95th percentile of first returns height (elev_p95) and canopy cover above mean height (cover), were imputed over a research forest in British Columbia, Canada with relative RMSE of 18.5% and 11.4% and relative bias of -0.6% and 1.4% respectively. The processing sequence developed within FOSTER represents an innovative and versatile framework that should be useful to researchers and managers alike looking to make forest management decisions over entire forest estates. Numéro de notice : A2021-306 Affiliation des auteurs : non IGN Thématique : FORET/INFORMATIQUE/MATHEMATIQUE Nature : Article DOI : 10.1371/journal.pone.0244846 Date de publication en ligne : 28/01/2021 En ligne : https://doi.org/10.1371/journal.pone.0244846 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97656 [article]

Interpretive tools for 3D structural geological modelling, part 2: surface design from sparse spatial data / K.B. Sprague in Geoinformatica, vol 9 n° 1 (March - May 2005)  

Public [article]  inGeoinformatica > vol 9 n° 1 (March - May 2005) . - pp 5 - 32 Titre : Interpretive tools for 3D structural geological modelling, part 2: surface design from sparse spatial data Type de document : Article/Communication Auteurs : K.B. Sprague, Auteur ; E.A. DE Kemp, Auteur Année de publication : 2005 Article en page(s) : pp 5 - 32 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] Arctique, océan [Termes IGN] Canada [Termes IGN] courbe de Bézier [Termes IGN] données clairsemées [Termes IGN] extrapolation [Termes IGN] géologie locale [Termes IGN] interprétation automatique [Termes IGN] mine [Termes IGN] modèle géologique [Termes IGN] modélisation 3D [Termes IGN] modélisation géologique volumique (MGV) Résumé : (Auteur) We present software tools and methods applicable to the geological modelling of sparse spatial and structural data within a 3-D digital environment. Free-form surfaces derived from section-style control frames and constrained by field-based structural measurements are employed as partially automated design aids intended to speed up and streamline the 3-D geological model building process. Some design degrees of freedom such as NURBS tension (or weights), knot sequencing and tying surface features are also discussed with examples drawn from spatial and structural data collected in Baffin Island by the Geological Survey of Canada and nearmine exploration data from Canadian mines. Interpolation of field-based structural measurements along the boundary of an unknown surface is also demonstrated. This work is potentially relevant to regional mappers and others dealing with sparse spatial and structural data, and/or conceptual surface modelling. Numéro de notice : A2005-072 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-004-5620-8 En ligne : https://doi.org/10.1007/s10707-004-5620-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27210 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
057-05011	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Numerical recipes / William H. Press (1988)

Public Titre : Numerical recipes : the art of scientific computing Type de document : Monographie Auteurs : William H. Press, Auteur ; Brian P. Flannery, Auteur ; Saul A. Teukolsky, Auteur ; William T. Vetterling, Auteur Editeur : Cambridge [Royaume-Uni] : Cambridge University Press Année de publication : 1988 Importance : 818 p. Format : 17 x 24 cm ISBN/ISSN/EAN : 978-0-521-30811-3 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Analyse numérique [Termes IGN] algèbre linéaire [Termes IGN] corrélation linéaire [Termes IGN] distribution, loi de [Termes IGN] équation différentielle [Termes IGN] extrapolation [Termes IGN] Fortran [Termes IGN] intégrale [Termes IGN] interpolation [Termes IGN] Pascal [Termes IGN] problème des valeurs limites [Termes IGN] transformation de Fourier [Termes IGN] valeur propre [Termes IGN] vecteur propre Numéro de notice : 57844 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Monographie Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=60293

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
57844-02	DEP-RECG	Livre	Marne-la-Vallée	Dépôt en unité	Exclu du prêt