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Termes IGN > environnement > écologie > écosystème > biotope > milieu naturel > prairie
prairie
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Herbage, Prairie artificielle, Prairie naturelle, Prairie permanente, Prairie temporaire, Pré. Campagne. >> Pâturage, Écologie des prairies. >>Terme(s) spécifique(s) : Savane, Steppe, Pelouse. Equiv. LCSH : Grasslands, Meadows, Prairies. Voir aussi |
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Deep learning high resolution burned area mapping by transfer learning from Landsat-8 to PlanetScope / V.S. Martins in Remote sensing of environment, vol 280 (October 2022)
[article]
Titre : Deep learning high resolution burned area mapping by transfer learning from Landsat-8 to PlanetScope Type de document : Article/Communication Auteurs : V.S. Martins, Auteur ; D.P. Roy, Auteur ; H. Huang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 113203 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Afrique (géographie politique)
[Termes IGN] apprentissage profond
[Termes IGN] carte thématique
[Termes IGN] cartographie automatique
[Termes IGN] correction radiométrique
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] forêt tropicale
[Termes IGN] image Landsat-OLI
[Termes IGN] image PlanetScope
[Termes IGN] incendie
[Termes IGN] précision de la classification
[Termes IGN] régression
[Termes IGN] savaneRésumé : (auteur) High spatial resolution commercial satellite data provide new opportunities for terrestrial monitoring. The recent availability of near-daily 3 m observations provided by the PlanetScope constellation enables mapping of small and spatially fragmented burns that are not detected at coarser spatial resolution. This study demonstrates, for the first time, the potential for automated PlanetScope 3 m burned area mapping. The PlanetScope sensors have no onboard calibration or short-wave infrared bands, and have variable overpass times, making them challenging to use for large area, automated, burned area mapping. To help overcome these issues, a U-Net deep learning algorithm was developed to classify burned areas from two-date Planetscope 3 m image pairs acquired at the same location. The deep learning approach, unlike conventional burned area mapping algorithms, is applied to image spatial subsets and not to single pixels and so incorporates spatial as well as spectral information. Deep learning requires large amounts of training data. Consequently, transfer learning was undertaken using pre-existing Landsat-8 derived burned area reference data to train the U-Net that was then refined with a smaller set of PlanetScope training data. Results across Africa considering 659 PlanetScope radiometrically normalized image pairs sensed one day apart in 2019 are presented. The U-Net was first trained with different numbers of randomly selected 256 × 256 30 m pixel patches extracted from 92 pre-existing Landsat-8 burned area reference data sets defined for 2014 and 2015. The U-Net trained with 300,000 Landsat patches provided about 13% 30 m burn omission and commission errors with respect to 65,000 independent 30 m evaluation patches. The U-Net was then refined by training on 5,000 256 × 256 3 m patches extracted from independently interpreted PlanetScope burned area reference data. Qualitatively, the refined U-Net was able to more precisely delineate 3 m burn boundaries, including the interiors of unburned areas, and better classify “faint” burned areas indicative of low combustion completeness and/or sparse burns. The refined U-Net 3 m classification accuracy was assessed with respect to 20 independently interpreted PlanetScope burned area reference data sets, composed of 339.4 million 3 m pixels, with low 12.29% commission and 12.09% omission errors. The dependency of the U-Net classification accuracy on the burned area proportion within 3 m pixel 256 × 256 patches was also examined, and patches Numéro de notice : A2022-774 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.113203 Date de publication en ligne : 08/08/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113203 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101802
in Remote sensing of environment > vol 280 (October 2022) . - n° 113203[article]Mapping land-use intensity of grasslands in Germany with machine learning and Sentinel-2 time series / Maximilian Lange in Remote sensing of environment, vol 277 (August 2022)
[article]
Titre : Mapping land-use intensity of grasslands in Germany with machine learning and Sentinel-2 time series Type de document : Article/Communication Auteurs : Maximilian Lange, Auteur ; Hannes Feilhauer, Auteur ; Ingolf Kühn, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 112888 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Allemagne
[Termes IGN] apprentissage automatique
[Termes IGN] bande spectrale
[Termes IGN] carte d'utilisation du sol
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] échantillonnage de données
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice de végétation
[Termes IGN] prairie
[Termes IGN] série temporelleRésumé : (auteur) Information on grassland land-use intensity (LUI) is crucial for understanding trends and dynamics in biodiversity, ecosystem functioning, earth system science and environmental monitoring. LUI is a major driver for numerous environmental processes and indicators, such as primary production, nitrogen deposition and resilience to climate extremes. However, large extent, high resolution data on grassland LUI is rare. New satellite generations, such as Copernicus Sentinel-2, enable a spatially comprehensive detection of the mainly subtle changes induced by land-use intensification by their fine spatial and temporal resolution. We developed a methodology quantifying key parameters of grassland LUI such as grazing intensity, mowing frequency and fertiliser application across Germany using Convolutional Neural Networks (CNN) on Sentinel-2 satellite data with 20 m × 20 m spatial resolution. Subsequently, these land-use components were used to calculate a continuous LUI index. Predictions of LUI and its components were validated using comprehensive in situ grassland management data. A feature contribution analysis using Shapley values substantiates the applicability of the methodology by revealing a high relevance of springtime satellite observations and spectral bands related to vegetation health and structure. We achieved an overall classification accuracy of up to 66% for grazing intensity, 68% for mowing, 85% for fertilisation and an r2 of 0.82 for subsequently depicting LUI. We evaluated the methodology's robustness with a spatial 3-fold cross-validation by training and predicting on geographically distinctly separated regions. Spatial transferability was assessed by delineating the models' area of applicability. The presented methodology enables a high resolution, large extent mapping of land-use intensity of grasslands. Numéro de notice : A2022-468 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.112888 Date de publication en ligne : 13/05/2022 En ligne : https://doi.org/10.1016/j.rse.2022.112888 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100805
in Remote sensing of environment > vol 277 (August 2022) . - n° 112888[article]Détection des prairies de fauche et estimation des périodes de fauche par télédétection / Emma Seneschal (2022)
Titre : Détection des prairies de fauche et estimation des périodes de fauche par télédétection Type de document : Mémoire Auteurs : Emma Seneschal, Auteur Editeur : Champs-sur-Marne : Ecole nationale des sciences géographiques ENSG Année de publication : 2022 Importance : 103 p. Format : 21 x 30 cm Note générale : bibliographie
Rapport de fin d'étude, cycle des Ingénieurs diplômés de l’ENSG 3ème année, Information Géographique, Analyse Spatiale et TélédétectionLangues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] Aves
[Termes IGN] Cantal (15)
[Termes IGN] cartographie thématique
[Termes IGN] classification dirigée
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] échantillonnage de données
[Termes IGN] habitat animal
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Jura (39)
[Termes IGN] Perceptron multicouche
[Termes IGN] prairie
[Termes IGN] régressionIndex. décimale : IGAST Mémoires du Master Information Géographique, Analyse Spatiale et Télédétection Résumé : (auteur) Ce travail s’inscrit dans le projet Parcelle qui vise à promouvoir le développement de la chaîne de traitement Iota2, développée par le CESBIO. Dans ce cadre-là, une collaboration s’est développée avec l’OFB qui a besoin de cartographier les prairies de fauche précoce dans le cadre de son «Observatoire National de l’Ecosystème Prairie de Fauche» (ONEPF). Le report des fauches est plébiscité depuis de nombreuses années face au déclin de l’avifaune prairiale. Des programmes agri-environnementaux incitent les agriculteurs à reporter les fenaisons jusqu’à mi-juillet. Les cartographies du suivi des prairies de fauche avec une récolte tardive constitueraient un outil de suivi des surfaces de l’habitat potentiellement favorable à la reproduction des oiseaux prairiaux en France. L’utilisation de la télédétection avec Iota2 permettrait une production annuelle plus rapide et moins coûteuse par rapport à des campagnes terrains et au processus actuel de production. Ce travail répond aux problématiques suivantes :
— Comment et avec quelle précision peut-on identifier et cartographier les prairies de fauche en France ?
— Est-il possible d’estimer la période de fauche et à quelle précision ?
Les séries temporelles denses, multi-spectrales et à haute résolution des satellites S1 & S2 ont été retenues pour l’étude des gestions des prairies (fauche, pâture et mixte). Les comportements des prairies selon leur mode de gestion ont été analysés grâce aux profils spectro-temporels des parcelles (bandes et indices spectraux issus de S2). Iota2 a été utilisé pour classifier avec Random Forest ou Deep Learning les prairies selon leur type de gestion. Plusieurs configurations ont été testées : calcul d’indices spectraux, ajout d’informations dérivées de MNT, augmentation de données, modification de l’architecture du réseau de neurones profonds, etc. Les cartographies prédictives des prairies de fauche ont été générées pour les années 2019 et 2021 respectivement sur les zones géographiques Jura-Mâconais et du Cantal. De meilleurs résultats ont été obtenus avec les échantillons d’apprentissage des sites du Jura et de Mâcon (F-score de 0.96 pour les parcelles de fauche). Les nouvelles fonctionnalités de Iota2 ont permis d’estimer la période de fauche par régression (avec un MultiLayerPerceptron). Les premiers résultats réalisés avec les séries temporelles S2 semblent prometteurs (R2 supérieurs à 0.5 et bonnes précisions). Ainsi, Iota2 est un outil performant qui permet la production rapide et qualitative de cartes de suivi des gestions prairiales en intégrant la télédétection. Iota2 pourrait être intégrée dans le processus de l’ONEPF.Note de contenu : Introduction
1- Avifaune et prairie
2- Prairies et télédétection
3- Données
4- Détection des prairies de fauche
5- Détection des périodes de fauche
ConclusionNuméro de notice : 24021 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Mémoire de fin d'études IT Organisme de stage : CESBIO Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101819 Documents numériques
en open access
Détection des prairies... - pdf auteur -Adobe Acrobat PDF Monitoring forest-savanna dynamics in the Guineo-Congolian transition area of the centre region of Cameroon / Le Bienfaiteur Sagang Takougoum (2022)
Titre : Monitoring forest-savanna dynamics in the Guineo-Congolian transition area of the centre region of Cameroon Type de document : Thèse/HDR Auteurs : Le Bienfaiteur Sagang Takougoum, Auteur ; Bonaventure Sonké, Directeur de thèse ; Nicolas Barbier, Directeur de thèse Editeur : Yaoundé : Université de Yaoundé Année de publication : 2022 Importance : 166 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse pour obtenir le grade de Docteur de l'Université de Yaoundé 1, Spécialité Botanique-EcologieLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse aérienne
[Termes IGN] Cameroun
[Termes IGN] carte d'utilisation du sol
[Termes IGN] carte de la végétation
[Termes IGN] classification dirigée
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] données de terrain
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] dynamique de la végétation
[Termes IGN] écotone
[Termes IGN] flore locale
[Termes IGN] forêt
[Termes IGN] Google Earth Engine
[Termes IGN] image Landsat
[Termes IGN] image SPOT 6
[Termes IGN] image SPOT 7
[Termes IGN] incendie de forêt
[Termes IGN] modèle statistique
[Termes IGN] savane
[Termes IGN] surveillance forestièreIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) Understanding the effects of global change (combining anthropic and climatic pressures) on biome distribution needs innovative approaches allowing to address the large spatial scales involved and the scarcity of available ground data. Characterizing vegetation dynamics at landscape to regional scale requires both a high level of spatial detail (resolution), generally obtained through precise field measurements, and a sufficient coverage of the land surface (extent) provided by satellite images. The difficulty usually lies between these two scales as both signal saturation from satellite data and ground sampling limitations contribute to inaccurate extrapolations. Airborne laser scanning (ALS) data has revolutionized the trade-off between spatial detail and landscape coverage as it gives accurate information of the vegetation’s structure over large areas which can be used to calibrate satellite data. Also recent satellite data of improved spectral and spatial resolutions (Sentinel 2) allow for detailed characterizations of compositional gradients in the vegetation, notably in terms of the abundance of broad functional/optical plant types. Another major obstacle comes from the lack of a temporal perspective on dynamics and disturbances. Growing satellite imagery archives over several decades (45 years; Landsat) and available computing facilities such as Google Earth Engine (GEE) provide new possibilities to track long term successional trajectories and detect significant disturbances (i.e. fire) at a fine spatial detail (30m) and relate them to the current structure and composition of the vegetation. With these game changing tools our objective was to track long-term dynamics of forest-savanna ecotone in the Guineo-Congolian transition area of the Central Region of Cameroon with induced changes in the vegetatio structure and composition within two contrasted scenarios of anthropogenic pressures: 1) the Nachtigal area which is targeted for the dam construction and subject to intense agricultural activities and 2) the Mpem et Djim National Park (MDNP) which has no management plan. The maximum likelihood classification of the Spot 6/7 image aided with the information from the canopy height derived from ALS data discriminated the vegetation types within the Nachtigal area with good accuracy (96.5%). Using field plots data in upscaling aboveground biomass (AGB) form field plots estimates to the satellite estimates with model-based approaches lead to a systematic overestimation in AGB density estimates and a root mean squared prediction error (RMSPE) of up to 65 Mg.ha−1 (90%), whereas calibration with ALS data (AGBALS) lead to low bias and a drop of ~30% in RMSPE (down to 43 Mg.ha−1, 58%) with little effect of the satellite sensor used. However, these results also confirm that, whatever the spectral indices used and attention paid to sensor quality and pre-processing, the signal is not sufficient to warrant accurate pixel wise predictions, because of large relative RMSPE, especially above (200–250 Mg.ha−1). The design-based approach, for which average AGB density values were attributed to mapped land cover classes, proved to be a simple and reliable alternative (for landscape to region level estimations), when trained with dense ALS samples. AGB and species diversity measured within 74 field inventory plots (distributed along a savanna to forest successional gradient) were higher for the vegetation located in the MDNP compared to their pairs in the Nachtigal area. The automated unsupervised long-term (45 years) land cover change monitoring from Landsat image archives based on GEE captured a consistent and regular pattern of forest progression into savanna at an average rate of 1% (ca. 6 km².year-1). No fire occurrence was captured for savanna that transited to forest within five years of monitoring. Distinct assemblages of spectral species are apparent in forest vegetation which is consistent with the age of transition. As forest gets older AGBALS recovers at a rate of 4.3 Mg.ha-1.year-1 in young forest stands ( Note de contenu : Chapter 1. Generalities
1.1 Introduction
1.2 Literature Review
Chapter 2. Material And Methods
2.1 Material
2.2 Methods
Chapter 3. Results And Discussion
3.1 Results
3.2 Discussion
Chapter 4. Conclusion And Perspectives
4.1 Conclusion
4.2 PerspectivesNuméro de notice : 26820 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Thèse étrangère Note de thèse : Thèse de doctorat : Botanique-Ecologie : Yaoundé : 2022 Organisme de stage : Institut de Recherche pour le Développement IRD nature-HAL : Thèse DOI : sans Date de publication en ligne : 13/04/2022 En ligne : https://hal.inrae.fr/tel-03528875/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100465 Monitoring grassland dynamics by exploiting multi-modal satellite image time series / Anatol Garioud (2022)
Titre : Monitoring grassland dynamics by exploiting multi-modal satellite image time series Titre original : Suivi de la dynamique des prairies permanentes par analyse des séries temporelles multi-modales Type de document : Thèse/HDR Auteurs : Anatol Garioud , Auteur ; Clément Mallet , Directeur de thèse ; Silvia Valero, Directeur de thèse Editeur : Champs-sur-Marne [France] : Université Gustave Eiffel Année de publication : 2022 Importance : 194 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse présentée et soutenue en vue de l'obtention du Doctorat de l'Université Gustave Eiffel, Spécialité Sciences et Technologies de l'Information GéographiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] analyse multivariée
[Termes IGN] apprentissage profond
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] données auxiliaires
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Mâcon
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] prairie
[Termes IGN] régression
[Termes IGN] série temporelle
[Termes IGN] seuillage d'image
[Termes IGN] superpixel
[Termes IGN] surveillance agricole
[Termes IGN] ToulouseIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) The vast grassland surfaces as well as the growing recognition of the ecosystem services thez provide have revealed urgent needs for their conservation and sutainable management. Despite the acknowledged importance of grassland management practices, there are currently no large-scale efforts reporting on their frequency and nature. Satellite remote sensing time series appear to be a suitable tool for efficient grassland monitoring and allow synoptic and regular analysis. The research conducted in this PhD aims to develop methods for the detection of grassland management practices from complementary optical and SAR multivariate time series. Advances in deep learning are employed to regress multivariate SAR time series and contextual knowledge towards optical NDVI. Resulting gap-free time series are used to efficiently explore methods aiming to detect vegetation status changes related to management practices on grasslands. Note de contenu : INTRODUCTION
1. Grasslands and remote sensing: context, diversity and challenges
1.1 Definition, extent and importance of grasslands
1.2 Earth observation from space: principles and applications over grasslands
1.3 Problem statement and objectives
1.4 Outline of the manuscript
2. Study areas and datasets
2.1 Study areas
2.2 Satellite data
2.3 Reference and ancillary datasets
2.4 Feature derived from sentinel images for grassland monitoring
2.5 Description of the feature engineering steps
2.6 Exploring the relationships between derived satellite features
2.7 Concluding remarks
HIGH-TEMPORAL SAMPLED TIME-SERIES
3. Sentinels regression for vegetation monitoring
3.1 Monitoring vegetation through optical-SAR synergy
3.2 Retrieving missing data in optical time series
3.3 SenRVM: a deep learning-based regression framework
3.4 Concluding remarks
4. Outcomes of the SenRVM approach
4.1 Experimental design for training and evaluating SenRVM models
4.2 Assessment of SenRVM predictions
4.3 Empirical analysis of the SenRVM results
4.4 Generalization capabilities of single-class grassland SenRVM models
4.5 Further post-processing of SenRVM results
4.6 Concluding remarks
MONITORING GRASSLANDS
5. Detecting and quantifying grassland management practices
5.1 Challenges and related work
5.2 The proposed methodology
5.3 Description of validation data
5.4 Experimental setup
5.5 Assessment of the proposed method
5.6 Potential outcomes
5.7 Concluding remarks
GENERAL CONCLUSION
6. Conclusion and perspectives
6.1 Summary
6.2 PerspectivesNuméro de notice : 26831 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Sciences et Technologies de l'Information Géographique : Gustave Eiffel : 2022 Organisme de stage : LASTIG (IGN) nature-HAL : Thèse DOI : sans En ligne : https://theses.hal.science/tel-03843683 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100728 Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 26831-01 THESE Livre Centre de documentation Thèses Disponible Superpixel-based regional-scale grassland community classification using genetic programming with Sentinel-1 SAR and Sentinel-2 multispectral images / Zhenjiang Wu in Remote sensing, vol 13 n° 20 (October-2 2021)PermalinkSpectral reflectance estimation of UAS multispectral imagery using satellite cross-calibration method / Saket Gowravaram in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 10 (October 2021)PermalinkPotentialité des données satellitaires Sentinel-2 pour la cartographie de l’impact des feux de végétation en Afrique tropicale : application au Togo / Yawo Konko in Bois et forêts des tropiques, n° 347 ([02/04/2021])PermalinkDétection des zones de dégradation et de régénération de la couverture végétale dans le sud du Sénégal à travers l'analyse des tendances de séries temporelles MODIS NDVI et des changements d'occupation des sols à partir d'images LANDSAT / Boubacar Solly in Revue Française de Photogrammétrie et de Télédétection, n° 223 (mars - décembre 2021)PermalinkAnalyse de la dynamique d’embroussaillement des pelouses calcaires par traitement d’images / Théo Mesure (2021)PermalinkAssessing the interest of a multi-modal gap-filling strategy for monitoring changes in grassland parcels / Anatol Garioud (2021)PermalinkPermalinkTopographic, edaphic and climate influences on aspen (Populus tremuloides) drought stress on an intermountain bunchgrass prairie / Andrew Neary in Forest ecology and management, vol 479 ([01/01/2021])PermalinkMapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data / Yaotong Cai in International journal of applied Earth observation and geoinformation, vol 92 (October 2020)PermalinkCombining optical and radar satellite image time series to map natural vegetation: savannas as an example / Maylis Lopes in Remote sensing in ecology and conservation, vol 6 n° 3 (September 2020)PermalinkTemporal Validation of Four LAI Products over Grasslands in the Northeastern Tibetan Plateau / Gaofei Yin in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 4 (April 2020)PermalinkPrediction of plant diversity in grasslands using Sentinel-1 and -2 satellite image time series / Mathieu Fauvel in Remote sensing of environment, Vol 237 (February 2020)PermalinkOn the joint exploitation of optical and SAR satellite imagery for grassland monitoring / Anatol Garioud (2020)PermalinkChange detection work-flow for mapping changes from arable lands to permanent grasslands with advanced boosting methods / Jiří Šandera in Geodetski vestnik, vol 63 n° 3 (September - November 2019)PermalinkLand-cover change in the Wulagai grassland, Inner Mongolia of China between 1986 and 2014 analysed using multi-temporal Landsat images / Temulun Tangud in Geocarto international, vol 34 n° 11 ([15/08/2019])PermalinkEvaluating 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])PermalinkA novel method for separating woody and herbaceous time series / Qiang Zhou in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 7 (July 2019)PermalinkChallenges in grassland mowing event detection with multimodal Sentinel images / Anatol Garioud (2019)PermalinkJoint analysis of SAR and optical satellite images time series for grassland event detection / Anatol Garioud (2019)PermalinkMicrowave indices from active and passive sensors for remote sensing applications / Emanuele Santi (2019)PermalinkPermalinkEstimating forest standing biomass in savanna woodlands as an indicator of forest productivity using the new generation WorldView-2 sensor / Timothy Dube in Geocarto international, vol 33 n° 2 (February 2018)PermalinkEstimation of forest aboveground biomass from HJ1B imagery using a canopy reflectance model and a forest growth model / Xinyun Wang in Geocarto international, vol 33 n° 2 (February 2018)PermalinkPermalinkMapping grassland management intensity using Sentinel-2 satellite data / Marijke Elisabeth Bekkema in GI Forum, vol 2018 n° 1 ([01/01/2018])PermalinkSuivi écologique des prairies semi-naturelles : analyse statistique de séries temporelles denses d’images satellite à haute résolution spatiale / Maylis Lopes (2018)PermalinkRemote sensing of species diversity using Landsat 8 spectral variables / Sabelo Madonsela in ISPRS Journal of photogrammetry and remote sensing, vol 133 (November 2017)PermalinkImproving the prediction of African savanna vegetation variables using time series of MODIS products / Miriam Tsalyuk in ISPRS Journal of photogrammetry and remote sensing, vol 131 (September 2017)PermalinkReducing classification error of grassland overgrowth by combing low-density lidar acquisitions and optical remote sensing data / Timo P Pitkänen in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)PermalinkRetrieving grassland canopy water content by considering the information from neighboring pixels / Binbin He in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 8 (August 2017)PermalinkChange detection in forests and savannas using statistical analysis based on geographical objects / Lucilia Rezende Leite in Boletim de Ciências Geodésicas, vol 23 n° 2 (abr - jun 2017)PermalinkPermalinkLes prairies de l’estuaire de la Loire : étude de la dynamique de la végétation de 1982 à 2014 / Mathieu Le Dez in Mappemonde, n° 119 (janvier 2017)PermalinkExposure-related forest-steppe: A diverse landscape type determined by topography and climate / Martin Hais in Journal of Arid Environments, vol 135 (December 2016)PermalinkPlant community mycorrhization in temperate forests and grasslands: relations with edaphic properties and plant diversity / Maret Gerz in Journal of vegetation science, vol 27 n° 1 (January 2016)PermalinkUAS Experiences in Africa / Marius Schrôder in GIM international, vol 29 n° 12 (December 2015)PermalinkLand cover changes assessment using object-based image analysis in the Binah River watershed (Togo and Benin) / Hèou Maléki Badjana in Earth and space science, vol 2 n° 10 (October 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)PermalinkUnderstanding the effects of ALS pulse density for metric retrieval across diverse forest types / Phil Wilkes in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 8 (August 2015)PermalinkSavannah woody structure modelling and mapping using multi-frequency (X-, C- and L-band) Synthetic Aperture Radar data / Laven Naidoo in ISPRS Journal of photogrammetry and remote sensing, vol 105 (July 2015)PermalinkExploring life forms for linking orthopteran assemblage and grassland plant community / Rocco Labadessa in Hacquetia, vol 14 n° 1 (June 2015)PermalinkSurfaces d'intérêts écologiques : mise à jour / Emmanuelle Raulin in La Vendée agricole, vol 2015 n° 12 (20 mars 2015)PermalinkEmploying ground and satellite-based QuickBird data and Random forest to discriminate five tree species in a Southern African Woodland / Samuel Adelabu in Geocarto international, vol 30 n° 3 - 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