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A generalized space-time OBIA classification scheme to map sugarcane areas at regional scale, using Landsat images time-series and the random forest algorithm / Ana Claudia Dos Santos Luciano in International journal of applied Earth observation and geoinformation, vol 80 (August 2019)
[article]
Titre : A generalized space-time OBIA classification scheme to map sugarcane areas at regional scale, using Landsat images time-series and the random forest algorithm Type de document : Article/Communication Auteurs : Ana Claudia Dos Santos Luciano, Auteur ; Michelle Cristina Araújo Picoli, Auteur ; Jansle Vieira Rocha, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 127-136 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse spatio-temporelle
[Termes IGN] apprentissage automatique
[Termes IGN] Brésil
[Termes IGN] carte agricole
[Termes IGN] classification dirigée
[Termes IGN] classification orientée objet
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] extraction de données
[Termes IGN] image à haute résolution
[Termes IGN] image Landsat
[Termes IGN] production agricole
[Termes IGN] Saccharum officinarum
[Termes IGN] série temporelle
[Termes IGN] surface cultivée
[Termes IGN] zone d'intérêtRésumé : (auteur) The monitoring of sugarcane areas is important for sustainable planning and management of the sugarcane industry in Brazil. We developed an operational Object-Based Image Analysis (OBIA) classification scheme, with generalized space-time classifier, for mapping sugarcane areas at the regional scale in São Paulo State (SP). Binary random forest (RF) classification models were calibrated using multi-temporal data from Landsat images, at 10 sites located across SP. Space and time generalization were tested and compared for three approaches: a local calibration and application; a cross-site spatial generalization test with the RF model calibrated on a site and applied on other sites; and a unique space–time classifier calibrated with all sites together on years 2009–2014 and applied to the entire SP region on 2015. The local RF models Dice Coefficient (DC) accuracies at sites 1 to 8 were between 0.83 and 0.92 with an average of 0.89. The cross-site classification accuracy showed an average DC of 0.85, and the unique RF model had a DC of 0.89 when compared with a reference map of 2015. The results demonstrated a good relationship between sugarcane prediction and the reference map for each municipality in SP, with R² = 0.99 and only 5.8% error for the total sugarcane area in SP, and compared with the area inventory from the Brazilian Institute of Geography and Statistics, with R² = 0.95 and –1% error for the total sugarcane area in SP. The final unique RF model allowed monitoring sugarcane plantations at the regional scale on independent year, with efficiency, low-cost, limited resources and a precision approximating that of a photointerpretation. Numéro de notice : A2019-470 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.04.013 Date de publication en ligne : 25/04/2019 En ligne : https://doi.org/10.1016/j.jag.2019.04.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93612
in International journal of applied Earth observation and geoinformation > vol 80 (August 2019) . - pp 127-136[article]Evaluating the potential of the red edge channel for C3 (Festuca spp.) grass discrimination using Sentinel-2 and Rapid Eye satellite image data / Charles Otunga in Geocarto international, vol 34 n° 10 ([15/07/2019])
[article]
Titre : Evaluating the potential of the red edge channel for C3 (Festuca spp.) grass discrimination using Sentinel-2 and Rapid Eye satellite image data Type de document : Article/Communication Auteurs : Charles Otunga, Auteur ; John Odindi, Auteur ; Onisimo Mutanga, Auteur ; Clément Adjorlolo, Auteur Année de publication : 2019 Article en page(s) : pp 1123 - 1143 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Afrique du sud (état)
[Termes IGN] analyse discriminante
[Termes IGN] bande rouge
[Termes IGN] bande spectrale
[Termes IGN] carte de la végétation
[Termes IGN] Festuca (genre)
[Termes IGN] image RapidEye
[Termes IGN] image Sentinel-MSI
[Termes IGN] paturage
[Termes IGN] prairie
[Termes IGN] répartition géographiqueRésumé : (auteur) Integrating the Red Edge channel in satellite sensors is valuable for plant species discrimination. Sentinel-2 MSI and Rapid Eye are some of the new generation satellite sensors that are characterized by finer spatial and spectral resolution, including the red edge band. The aim of this study was to evaluate the potential of the red edge band of Sentinel-2 and Rapid Eye, for mapping festuca C3 grass using discriminant analysis and maximum likelihood classification algorithms. Spectral bands, vegetation indices and spectral bands plus vegetation indices were analysed. Results show that the integration of the red edge band improved the festuca C3 grass mapping accuracy by 5.95 and 4.76% for Sentinel-2 and Rapid Eye when the red edge bands were included and excluded in the analysis, respectively. The results demonstrate that the use of sensors with strategically positioned red edge bands, could offer information that is critical for the sustainable rangeland management. Numéro de notice : A2019-301 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1474274 Date de publication en ligne : 24/05/2018 En ligne : https://doi.org/10.1080/10106049.2018.1474274 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93221
in Geocarto international > vol 34 n° 10 [15/07/2019] . - pp 1123 - 1143[article]Using LiDAR-modified topographic wetness index, terrain attributes with leaf area index to improve a single-tree growth model in south-eastern Finland / Cheikh Mohamedou in Forestry, an international journal of forest research, vol 92 n° 3 (July 2019)
[article]
Titre : Using LiDAR-modified topographic wetness index, terrain attributes with leaf area index to improve a single-tree growth model in south-eastern Finland Type de document : Article/Communication Auteurs : Cheikh Mohamedou, Auteur ; Lauri Korhonen, Auteur ; Kalle Eerikäinen, Auteur ; Timo Tokola, Auteur Année de publication : 2019 Article en page(s) : pp 253 - 263 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] croissance des arbres
[Termes IGN] diamètre des arbres
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] erreur systématique
[Termes IGN] Finlande
[Termes IGN] humidité du sol
[Termes IGN] indice d'humidité
[Termes IGN] Leaf Area Index
[Termes IGN] modèle de croissance végétale
[Termes IGN] Perceptron multicoucheRésumé : (Auteur) Tree growth information is crucial in forest management and planning. Terrain-derived attributes such as the topographic wetness index (TWI), in addition to leaf area index (LAI) are closely related to tree growth, but are not commonly used in empirical growth models. In this study, we examined if modified TWI and LAI estimated from airborne light detection and ranging (LiDAR) data could be used to improve the predictions of a national single-tree diameter growth model. Altogether 1118 sample trees were selected within 197 subjectively placed plots in randomly selected forest stands in south-eastern Finland. Linear mixed effect (LME) and multilayer perceptron models were used to model the bias of 5-year growth predictions of the model and thus ultimately improve its predictions. The root mean square error (RMSE) of the national model was 0.604 cm. LME modelling reduced this value to 0.404 cm and MLP to 0.568 cm. The predictors included in the best-performing LME model were modified TWI, LAI estimated from LiDAR intensities, and elevation. Without an LAI estimate, the best RMSE was 0.436 cm. When applied as such, original and modified TWIs produced similar accuracy. We conclude that both TWI and LAI obtained from LiDAR data improve the diameter growth predictions of the national model. Numéro de notice : A2019-293 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1093/forestry/cpz010 Date de publication en ligne : 28/02/2019 En ligne : https://doi.org/10.1093/forestry/cpz010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93184
in Forestry, an international journal of forest research > vol 92 n° 3 (July 2019) . - pp 253 - 263[article]Feasibility study of vegetation indices derived from Sentinel-2 and PlanetScope satellite images for validating the LAI biophysical parameter to monitoring development stages of winter wheat / Radoslaw Gurdak in Geoinformation issues, Vol 10 n°1 (2018)
[article]
Titre : Feasibility study of vegetation indices derived from Sentinel-2 and PlanetScope satellite images for validating the LAI biophysical parameter to monitoring development stages of winter wheat Type de document : Article/Communication Auteurs : Radoslaw Gurdak, Auteur ; Patryk Grzybowski, Auteur Année de publication : 2019 Article en page(s) : pp 27 - 35 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] blé (céréale)
[Termes IGN] Enhanced vegetation index
[Termes IGN] étude de faisabilité
[Termes IGN] image PlanetScope
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice de végétation
[Termes IGN] Leaf Area Index
[Termes IGN] Soil Adjusted Vegetation IndexRésumé : (auteur) The main objective of the presented work is to assess applicability of vegetation indices derived from non-commercial and commercial satellites for monitoring development stages of winter wheat. Two types of data were used in the study: Sentinel-2 and PlanetScope images. Various vegetation indices were derived from these data and correlated with ground measured LAI values. The results of the study revealed that there is a good relationship between satellite based indices – Normalized Difference Vegetation Index – NDVI, Enhanced Vegetation Index – EVI, Soil Adjusted Vegetation Index – SAVI and ground based LAI, but strength of this relation depends on the phase of crop development. Sentinel-2 and PlanetScope data are suitable for estimating LAI with high accuracy and their precision for LAI determination is very similar. Depending on availability, they can be used interchangeably. The highest correlation between ground measured LAI and vegetation indices for Sentinel-2 appeared SAVI – r = 0.862 (phase: early tillering) and for PlanetScope NDVI – r = 0.667 (phase: ripening). Compatibility of average LAI values derived from PlanetScope and Sentinel-2 images are 33.21% and 10.63%. Numéro de notice : A2018-647 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans En ligne : http://www.igik.edu.pl/en/a/Geoinformation-Issues-Vol-10-No-1-2018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93657
in Geoinformation issues > Vol 10 n°1 (2018) . - pp 27 - 35[article]Stem-leaf segmentation and phenotypic trait extraction of individual maize using terrestrial LiDAR data / Shichao Jin in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)
[article]
Titre : Stem-leaf segmentation and phenotypic trait extraction of individual maize using terrestrial LiDAR data Type de document : Article/Communication Auteurs : Shichao Jin, Auteur ; Yanjun Su, Auteur ; Fangfang Wu, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 1336 - 1346 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] maïs (céréale)
[Termes IGN] phénologie
[Termes IGN] segmentation en régionsRésumé : (Auteur) Accurate and high throughput extraction of crop phenotypic traits, as a crucial step of molecular breeding, is of great importance for yield increasing. However, automatic stem-leaf segmentation as a prerequisite of many precise phenotypic trait extractions is still a big challenge. Current works focus on the study of the 2-D image-based segmentation, which are sensitive to illumination and occlusion. Light detection and ranging (LiDAR) can obtain accurate 3-D information with its active laser scanning and strong penetration ability, which breaks through phenotyping from 2-D to 3-D. However, few researches have addressed the problem of the LiDAR-based stem-leaf segmentation. In this paper, we proposed a median normalized-vector growth (MNVG) algorithm, which can segment stem and leaf with four steps, i.e., preprocessing, stem growth, leaf growth, and postprocessing. The MNVG method was tested by 30 maize samples with different heights, compactness, leaf numbers, and densities from three growing stages. Moreover, phenotypic traits at leaf, stem, and individual levels were extracted with the truly segmented instances. The mean accuracy of segmentation at point level in terms of the recall, precision, F-score, and overall accuracy were 0.92, 0.93, 0.92, and 0.93, respectively. The accuracy of phenotypic trait extraction in leaf, stem, and individual levels ranged from 0.81 to 0.95, 0.64 to 0.97, and 0.96 to 1, respectively. To our knowledge, this paper proposed the first LiDAR-based stem-leaf segmentation and phenotypic trait extraction method in agriculture field, which may contribute to the study of LiDAR-based plant phonemics and precise agriculture. Numéro de notice : A2019-114 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2866056 Date de publication en ligne : 19/09/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2866056 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92454
in IEEE Transactions on geoscience and remote sensing > vol 57 n° 3 (March 2019) . - pp 1336 - 1346[article]PermalinkEvaluation of time-series SAR and optical images for the study of winter land-use / Julien Denize (2019)PermalinkPermalinkPotentialités de l’imagerie couleur embarquée pour la détection et la cartographie des maladies fongiques de la vigne / Florent Abdelghafour (2019)PermalinkPermalinkTime-space tradeoff in deep learning models for crop classification on satellite multi-spectral image time series / Vivien Sainte Fare Garnot (2019)PermalinkAssessing the structural differences between tropical forest types using Terrestrial Laser Scanning / Mathieu Decuyper in Forest ecology and management, vol 429 (1 December 2018)PermalinkDrought sensitiveness on forest growth in peninsular Spain and the Balearic Islands / Marina Peña-Gallardo in Forests, vol 9 n° 9 (September 2018)PermalinkEstimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data / P. Kumar in Geocarto international, vol 33 n° 9 (September 2018)PermalinkExtracting leaf area index using viewing geometry effects : A new perspective on high-resolution unmanned aerial system photography / Lukas Roth in ISPRS Journal of photogrammetry and remote sensing, vol 141 (July 2018)PermalinkAssessment of Sentinel-1A data for rice crop classification using random forests and support vector machines / Nguyen-Thanh Son in Geocarto international, vol 33 n° 6 (June 2018)PermalinkCartographie pour la réflexion sur un périmètre à irriguer dans le Sud Kivu / Anne Girardin in XYZ, n° 155 (juin - août 2018)PermalinkLive fuel moisture content (LFMC) time series for multiple sites and species in the French Mediterranean area since 1996 / N. Martin-St Paul in Annals of Forest Science, vol 75 n° 2 (June 2018)PermalinkClose-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform / Mohd Shahrimie Mohd Asaari in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)PermalinkMapping spatial variability of foliar nitrogen in coffee (Coffea arabica L.) plantations with multispectral Sentinel-2 MSI data / Abel Chemura in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)PermalinkOptimal management of larch (Larix olgensis A. Henry) plantations in Northeast China when timber production and carbon stock are considered / Wei Peng in Annals of Forest Science, vol 75 n° 1 (March 2018)PermalinkDétection de changement par imagerie radar sur les zones naturelles et agricoles en milieu tropical / Jérôme Lebreton (2018)PermalinkPermalinkPermalinkSentinel-2 data analysis and comparison with UAV multispectral images for precision viticulture / Frederica Nonni in GI Forum, vol 2018 n° 1 ([01/01/2018])PermalinkPermalinkSuivi des cultures dans le périmètre du Loukkos-Maroc : Apport de la télédétection radar et optique / Siham Acharki (2018)PermalinkTowards assessment of cork production through National Forest Inventories / Maria Pasalodos-Tato in Forestry, an international journal of forest research, vol 91 n° 1 (January 2018)PermalinkUnderstanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications / Amanda Veloso in Remote sensing of environment, vol 199 (15 September 2017)PermalinkCrown bulk density and fuel moisture dynamics in Pinus pinaster stands are neither modified by thinning nor captured by the Forest Fire Weather Index / Marc Soler Martin in Annals of Forest Science, vol 74 n° 3 (September 2017)PermalinkUsing landsat surface reflectance data as a reference target for multiswath hyperspectral data collected over mixed agricultural rangeland areas / Cooper McCann in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)PermalinkA graph-based approach to detect spatiotemporal dynamics in satellite image time series / Fabio Guttler in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)PermalinkDeveloping detailed age-specific thematic maps for coffee (Coffea arabica L.) in heterogeneous agricultural landscapes using random forests applied on Landsat 8 multispectral sensor / Abel Chemura in Geocarto international, vol 32 n° 7 (July 2017)PermalinkForêtdavenir / Jérémy Abgrall in Forêt entreprise, n° 235 (juillet - août 2017)PermalinkSuperresolution for UAV images via adaptive multiple sparse representation and its application to 3-D reconstruction / Muhammad Haris in IEEE Transactions on geoscience and remote sensing, vol 55 n° 7 (July 2017)PermalinkVers un observatoire agro-environnemental des territoires : Un système décisionnel multi-échelle pour le bassin de la Charente / Françoise Vernier in Revue internationale de géomatique, vol 27 n° 3 (juillet-septembre 2017)PermalinkWREP : A wavelet-based technique for extracting the red edge position from reflectance spectra for estimating leaf and canopy chlorophyll contents of cereal crops / Dong Li in ISPRS Journal of photogrammetry and remote sensing, vol 129 (July 2017)PermalinkViCTOr : paysage virtuel pour explorer les dynamiques de la VIticulture et de la Consommation en TouRaine / Etienne Delay in Cybergeo, European journal of geography, n° 2017 ([01/06/2017])PermalinkCaractériser l'agriculture périurbaine pour mieux l'intégrer à la planification urbaine : propositions méthodologiques / Esther Sanz Sanz in Espace géographique, vol 46 n° 2 (avril - juin 2017)PermalinkA comparison of two downscaling procedures to increase the spatial resolution of mapping actual evapotranspiration / Milad Mahour in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)PermalinkUAS, sensors, and data processing in agroforestry: a review towards practical applications / Luis Padua in International Journal of Remote Sensing IJRS, vol 38 n° 8-10 (April 2017)PermalinkAgricultural cropland mapping using black-and-white aerial photography, Object-Based Image Analysis and Random Forests / M.F.A. Vogels in International journal of applied Earth observation and geoinformation, vol 54 (February 2017)PermalinkPermalinkRaft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features / Wang Min in ISPRS Journal of photogrammetry and remote sensing, vol 123 (January 2017)PermalinkTélédétection pour l'observation des surfaces continentales, Volume 3. Observation des surfaces continentales par télédétection 1 / Nicolas Baghdadi (2017)PermalinkCartographie de la dynamique de terroirs villageois à l’aide d’un drone dans les aires protégées de la République démocratique du Congo / Jean Semeki Ngabinzeke in Bois et forêts des tropiques, n° 330 (4e trimestre 2016)PermalinkThe driving forces of landscape change in Europe: A systematic review of the evidence / Tobias Plieninger in Land use policy, vol 57 (30 November 2016)PermalinkDisaggregation of remotely sensed soil moisture in heterogeneous landscapes using holistic structure-based models / Subit Chakrabarti in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)PermalinkSoil moisture retrieval in agricultural fields using adaptive model-based polarimetric decomposition of SAR data / Lian He in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)PermalinkApport des images THRS pour la catégorisation des agro-systèmes complexes à Mayotte / Rafaël Molina in Géomatique expert, n° 111 (juillet- août 2016)PermalinkAssessment and validation of evapotranspiration using SEBAL algorithm and Lysimeter data of IARI agricultural farm, India / Anju Bala in Geocarto international, vol 31 n° 7 - 8 (July - August 2016)PermalinkEvaluating the productivity of four main tree species in Germany under climate change with static reduced models / Martin Gutsch in Annals of Forest Science, vol 73 n° 2 (June 2016)PermalinkIntegrating risk preferences in forest harvest scheduling / Kyle J. Eyvindson in Annals of Forest Science, vol 73 n° 2 (June 2016)PermalinkMonitoring of water stress in wheat using multispectral indices derived from Landsat-TM / Nitika Dangwal in Geocarto international, vol 31 n° 5 - 6 (May - June 2016)PermalinkMovement analysis of free-grazing domestic ducks in Poyang Lake, China: a disease connection / Dian J. Prosser in International journal of geographical information science IJGIS, vol 30 n° 5-6 (May - June 2016)PermalinkQuantifying the completeness of and correspondence between two historical maps: a case study from nineteenth-century Palestine / Gad Schaffer in Cartography and Geographic Information Science, Vol 43 n° 2 (April - May 2016)PermalinkTemporal MODIS data for identification of wheat crop using noise clustering soft classification approach / Priyadarshi Upadhyay in Geocarto international, vol 31 n° 3 - 4 (March - April 2016)PermalinkAssessment of the cover changes and the soil loss potential in European forestland: First approach to derive indicators to capture the ecological impacts on soil-related forest ecosystems / P. Borrelli in Ecological indicators, vol 60 (January 2016)PermalinkAutomatisation des processus de création d’atlas dynamique et interactif développé pour internet / Victoire Marlet (2016)PermalinkEffects of water and heat on growth of winter wheat in the North China Plain / Hongyan Wang in Geocarto international, vol 31 n° 1 - 2 (January - February 2016)PermalinkPermalinkPlus de 50 ans de productions agricoles franciliennes : une terre de grandes cultures aux portes de Paris / Sylvie Bernadet (2016)PermalinkPointwise approach for texture analysis and characterization from very high resolution remote sensing images / Minh-Tan Pham (2016)PermalinkAutomated annual cropland mapping using knowledge-based temporal features / François Waldner in ISPRS Journal of photogrammetry and remote sensing, vol 110 (December 2015)PermalinkModernization of the Nova Scotia coordinate referencing system through active control technology / Jason Bond in Geomatica, vol 69 n° 4 (December 2015)PermalinkCombining leaf physiology, hyperspectral imaging and partial least squares-regression (PLS-R) for grapevine water status assessment / Tal Rapaport in ISPRS Journal of photogrammetry and remote sensing, vol 109 (November 2015)PermalinkQuantitative evaluation of volunteered geographic information paradigms: social location-based services case study / B. Lipej in Survey review, vol 47 n° 344 (September 2015)PermalinkTélédétection pour l'agriculture de précision par caméra hyperspectrale miniature / D. Constantin in Géomatique suisse, vol 113 n° 9 (septembre 2015)PermalinkUtilisation des technologies géospatiales pour l'évaluation des transformations spatiales dues aux pressions anthropiques dans le canton Afféma (Sud-est ivoirien) / Armand Kangah in Photo interprétation, European journal of applied remote sensing, vol 51 n° 3 (septembre 2015)PermalinkRegional dynamics of terrestrial vegetation productivity and climate feedbacks for territory of Ukraine / Dmytro Movchan in International journal of geographical information science IJGIS, vol 29 n° 8 (August 2015)PermalinkUsing high-resolution, multispectral imagery to assess the effect of soil properties on vegetation reflectance at an abandoned feedlot / Prosper Gbolo in Geocarto international, vol 30 n° 7 - 8 (August - September 2015)PermalinkFollow the herd / Corry Brennan in GEO: Geoconnexion international, vol 14 n° 7 (July 2015)PermalinkImpact of diurnal variation in vegetation water content on radar backscatter from maize during water stress / Tim Van Emmerik in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)PermalinkUtilisation des données des capteurs MODIS et SPOT-VGT pour l'analyse de la dynamique des feux dans deux territoires (réserve protégée et unités pastorales) au Ferlo (Sénégal) / Mamadou Adama Sarr in Photo interprétation, European journal of applied remote sensing, vol 51 n° 2 (juin 2015)PermalinkCirconscrire les gisements de biomasse-énergie pour protéger l'alimentation et la biodiversité : le défi intenable / Yves Poinsot in VertigO, vol 15 n° 1 (mai 2015)PermalinkMultispectral sensor spectral resolution simulations for generation of hyperspectral vegetation indices from Hyperion data / Prabir Das in Geocarto international, vol 30 n° 5 - 6 (May - July 2015)PermalinkLes séries de végétation de la vallée d’Ascu (Corse) : typologie et cartographie au 1:25 000 / Pauline Delbosc in Ecologia mediterranea, vol 41 n° 1 (2015)PermalinkDu champ à l'argent, tout passe par l'écran / Françoise de Blomac in DécryptaGéo le mag, n° 165 (mars 2015)PermalinkLes journées de la recherche 2015 à l'IGN / Anonyme in Géomatique expert, n° 103 (mars - avril 2015)PermalinkLe RPG, un référentiel ? / Françoise de Blomac in DécryptaGéo le mag, n° 165 (mars 2015)PermalinkFully polarimetric synthetic aperture radar (SAR) processing for crop type identification / Gang Hong in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 2 (February 2015)PermalinkTemporal decorrelation in L-, C-, and X-band satellite radar interferometry for pasture on drained cs / Yu Morishita in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)PermalinkUsing geographically weighted regression kriging for crop yield mapping in West Africa / Muhammad Imran in International journal of geographical information science IJGIS, vol 29 n° 2 (February 2015)PermalinkPermalinkUne approche cartographique pour relancer la sylviculture du châtaignier dans les Cévennes / Jean-Michel Boissier in Revue forestière française, vol 66 n° 6 (novembre - décembre 2014)PermalinkAssessment of crop foliar nitrogen using a novel dual-wavelength laser system and implications for conducting laser-based plant physiology / Jan U.H. Eitel in ISPRS Journal of photogrammetry and remote sensing, vol 97 (November 2014)PermalinkEstimating leaf chlorophyll of barley at different growth stages using spectral indices to reduce soil background and canopy structure effects / Kiyun Yu in ISPRS Journal of photogrammetry and remote sensing, vol 97 (November 2014)PermalinkModélisation spatiale des températures dans le vignoble des coteaux du Layon / Cyril Bonnefoy in Revue internationale de géomatique, vol 24 n° 3 (septembre - novembre 2014)PermalinkAlley coppice—a new system with ancient roots / Christopher D. Morhart in Annals of Forest Science, vol 71 n° 5 (July - August 2014)PermalinkL'agroforesterie : la réconciliation de l'agriculteur et du forestier / Anonyme in Forêts de France, n° 574 (Juin 2014)PermalinkCrop type classification by simultaneous use of satellite images of different resolutions / Mark W. Liu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 2 (June 2014)PermalinkDetecting winter wheat phenology with SPOT-VEGETATION data in the North China Plain / Linlin Lu in Geocarto international, vol 29 n° 3 - 4 (June - July 2014)PermalinkLaboratory measurements of plant drying: Implications to estimate moisture content from radiative transfer models in two temperate species / Sara Jurdao in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 5 (May 2014)PermalinkAnalyse des indicateurs énergétiques des entreprises agricoles. Une approche Spatial OLAP / Sandro Bimonte in Revue internationale de géomatique, vol 24 n° 1 (mars – mai 2014)PermalinkTread lightly / Penelope Richardson in GEO: Geoconnexion international, vol 13 n° 3 (march 2014)PermalinkUn vaste champ d'applications / Françoise de Blomac in DécryptaGéo le mag, n° 155 (01/03/2014)PermalinkMapping the human footprint from satellite measurements in Japan / Fan Yang in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)PermalinkRegards sur la forêt / Andrée Corvol (2014)PermalinkPermalinkParcel-level identification of crop types using different classification algorithms and multi-resolution imagery in southeastern Turkey / Ugur Alganci in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 11 (November 2013)Permalink