Centre dedocumentation
scientifique

Prediction 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)  

Public [article]  inRemote sensing of environment > Vol 237 (February 2020) . - 13 p. Titre : Prediction of plant diversity in grasslands using Sentinel-1 and -2 satellite image time series Type de document : Article/Communication Auteurs : Mathieu Fauvel, Auteur ; Maylis Lopes, Auteur ; Titouan Dubo, Auteur ; Justine Rivers-Moore, Auteur ; Pierre-Louis Frison , Auteur ; Nicolas Gross, Auteur ; Annie Ouin, Auteur Année de publication : 2020 Article en page(s) : 13 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] biodiversité végétale [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] Haute-Garonne (31) [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image RapidEye [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] indice de diversité [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] prairie [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] taxinomie Résumé : (auteur) The prediction of grasslands plant diversity using satellite image time series is considered in this article. Fifteen months of freely available Sentinel optical and radar data were used to predict taxonomic and functional diversity at the pixel scale (10 m × 10 m) over a large geographical extent (40,000 km2). 415 field measurements were collected in 83 grasslands to train and validate several statistical learning methods. The objective was to link the satellite spectro-temporal data to the plant diversity indices. Among the several diversity indices tested, Simpson and Shannon indices were best predicted with a coefficient of determination around 0.4 using a Random Forest predictor and Sentinel-2 data. The use of Sentinel-1 data was not found to improve significantly the prediction accuracy. Using the Random Forest algorithm and the Sentinel-2 time series, the prediction of the Simpson index was performed. The resulting map highlights the intra-parcel variability and demonstrates the capacity of satellite image time series to monitor grasslands plant taxonomic diversity from an ecological viewpoint. Numéro de notice : A2020-004 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2019.111536 date de publication en ligne : 26/11/2019 En ligne : https://doi.org/10.1016/j.rse.2019.111536 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94296 [article]

Combining GF-2 and RapidEye satellite data for mapping mangrove species using ensemble machine-learning methods / Liheng Peng in International Journal of Remote Sensing IJRS, vol 41 n° 3 (15 - 22 janvier 2020)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 41 n° 3 (15 - 22 janvier 2020) . - pp 813 - 838 Titre : Combining GF-2 and RapidEye satellite data for mapping mangrove species using ensemble machine-learning methods Type de document : Article/Communication Auteurs : Liheng Peng, Auteur ; Kai Liu, Auteur ; Jingjing Cao, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 813 - 838 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] boosting adapté [Termes descripteurs IGN] Chine, mer de [Termes descripteurs IGN] classification et arbre de régression [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] écosystème [Termes descripteurs IGN] extraction de la végétation [Termes descripteurs IGN] île [Termes descripteurs IGN] image Gaofen [Termes descripteurs IGN] image RapidEye [Termes descripteurs IGN] image satellite [Termes descripteurs IGN] mangrove [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] Rotation Forest classification Résumé : (auteur) Mangrove forests are important constitutions for sustainable development of coastal ecosystems, and they are often mapped and monitored with remote sensing approaches. Satellite images allow detailed studies of the distribution and composition of mangrove forests, and therefore facilitate the management and conservation of the ecosystems. The combination of multiple types of satellite images with different spatial and spectral resolutions is helpful in mangrove forests extraction and mangrove species discrimination as it reduces sampling workload and increases classification accuracies. In this study, the 1.0-m-resolution Gaofen-2 (GF-2) and the 5.0-m-resolution RapidEye-4 (RE-4) satellite images, acquired in February 2017 and November 2016 respectively, were used with ensemble machine-learning and object-oriented methods for mangroves mapping at both the community and species levels of the Qi’ao Island, Zhuhai, China. First, the mangroves on the island were segmented from the GF-2 image on a large scale, and then they were extracted combining with their digital elevation model (DEM) data. Second, the GF-2 image was further processed on a fine scale, in which object-oriented features from both the GF-2 and RE-4 images were extracted for each mangrove species. Third, it is followed by the mangrove species classification process which involves three ensemble machine-learning methods: the adaptive boosting (AdaBoost), the random forest (RF) and the rotation forest (RoF). These three methods employed a classification and regression tree (CART) as the base classifier. The results show that the overall accuracy (OA) of mangrove area extraction on the Qi’ao Island with the auxiliary data, DEM, achieves 98.76% (Kappa coefficient (κ) = 0.9289). The features extracted by the GF-2 and RE-4 images were shown to be beneficial for mangrove species discrimination. A maximum improvement in the OA of approximately 8% and a κκ of approximately 0.10 were achieved when employing RoF (OA = 92.01%, κ = 0.9016). Ensemble-learning methods can significantly improve the classification accuracy of CART, and the use of a bagging scheme (RF and RoF) is shown as a better way to map mangrove species than adaptive boosting (AdaBoost). In addition, RoF performed well in mangrove species classification but it was not as robust as the RF, whose average OA and κκ were 80.59% and 0.7608, respectively, while the RoF’s were 77.45% and 0.7214, respectively, in the 10-fold cross-validation. Numéro de notice : A2020-212 Thématique : IMAGERIE Nature : Article DOI : 10.1080/01431161.2019.1648907 date de publication en ligne : 30/07/2019 En ligne : https://doi.org/10.1080/01431161.2019.1648907 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94897 [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])  

Public [article]  inGeocarto international > vol 34 n° 10 [15/07/2019] . - pp 1123 - 1143 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 descripteurs IGN] Afrique du sud (état) [Termes descripteurs IGN] analyse discriminante [Termes descripteurs IGN] bande rouge [Termes descripteurs IGN] bande spectrale [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] Festuca (genre) [Termes descripteurs IGN] image RapidEye [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] paturage [Termes descripteurs IGN] prairie [Termes descripteurs IGN] répartition géographique Résumé : (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 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 [article]

Mapping leaf chlorophyll content from Sentinel-2 and RapidEye data in spruce stands using the invertible forest reflectance model / Roshanak Darvishzadeh in International journal of applied Earth observation and geoinformation, vol 79 (July 2019)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 79 (July 2019) . - pp 58-70 Titre : Mapping leaf chlorophyll content from Sentinel-2 and RapidEye data in spruce stands using the invertible forest reflectance model Type de document : Article/Communication Auteurs : Roshanak Darvishzadeh, Auteur ; Andrew K. Skidmore, Auteur ; Haidi Abdullah, Auteur ; Elias Cherenet, Auteur Année de publication : 2019 Article en page(s) : pp 58-70 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse multibande [Termes descripteurs IGN] bande rouge [Termes descripteurs IGN] bande spectrale [Termes descripteurs IGN] Bavière (Allemagne) [Termes descripteurs IGN] canopée [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] image RapidEye [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] modèle d'inversion [Termes descripteurs IGN] Picea abies [Termes descripteurs IGN] réflectance végétale [Termes descripteurs IGN] spectrophotométrie [Termes descripteurs IGN] teneur en chlorophylle des feuilles Résumé : (auteur) Leaf chlorophyll plays an essential role in controlling photosynthesis, physiological activities and forest health. In this study, the performance of Sentinel-2 and RapidEye satellite data and the Invertible Forest Reflectance Model (INFORM) radiative transfer model (RTM) for retrieving and mapping of leaf chlorophyll content in the Norway spruce (Picea abies) stands of a temperate forest was evaluated. Biochemical properties of leaf samples as well as stand structural characteristics were collected in two subsequent field campaigns during July 2015 and 2016 in the Bavarian Forest National Park (BFNP), Germany, parallel with the timing of the RapidEye and Sentinel-2 images. Leaf chlorophyll was measured both destructively and nondestructively using wet chemical spectrophotometry analysis and a hand-held chlorophyll content meter. The INFORM was utilised in the forward mode to generate two lookup tables (LUTs) in the spectral band settings of RapidEye and Sentinel-2 data using information obtained from the field campaigns. Before generating the LUTs, the sensitivity of the model input parameters to the spectral data from RapidEye and Sentinel-2 were examined. The canopy reflectance of the studied plots were obtained from the satellite images and used as input for the inversion of LUTs. The coefficient of determination (R2), root mean square errors (RMSE), and the normalised root mean square errors (NRMSE), between the retrieved and measured leaf chlorophyll, were then used to examine the attained results from RapidEye and Sentinel-2 data, respectively. The use of multiple solutions and spectral subsets for the inversion process were further investigated to enhance the retrieval accuracy of foliar chlorophyll. The result of the sensitivity analysis demonstrated that the simulated canopy reflectance of Sentinel-2 is sensitive to the alternation of all INFORM input parameters, while the simulated canopy reflectance from RapidEye did not show sensitivity to leaf water content variations. In general, there was agreement between the simulated and measured reflectance spectra from RapidEye and Sentinel-2, particularly in the visible and red-edge regions. However, examining the average absolute error from the simulated and measured reflectance revealed a large discrepancy in spectral bands around the near-infrared shoulder. The relationship between retrieved and measured leaf chlorophyll content from the Sentinel-2 data had a higher coefficient of determination with a higher NRMSE (NRMSE = 0.36 μg/cm2, R2 = 0.45) compared to those obtained using the RapidEye data (NRMSE = 0.31 μg/cm2 and R2 = 0.39). Using the mean of the ten best solutions (retrieved chlorophyll) the retrieval error for both Sentinel-2 and RapidEye data decreased (NRMSE = 0.34, NRMSE = 0.26, respectively), as compared to only selecting the single best solution. When the Sentinel-2 red edge bands were used as the spectral subset, the retrieval error of leaf chlorophyll decreased indicating the importance of red edge, as well as properly located spectral bands, for leaf chlorophyll estimation. The chlorophyll maps produced by the inversion of the two LUTs effectively represented the variation of foliar chlorophyll in BFNP and confirmed our earlier findings on the observed stress pattern caused by insect infestation. Our findings emphasise the importance of multispectral satellites which benefits from red edge spectral bands such as Sentinel-2 as well as RapidEye for regional mapping of vegetation foliar properties, particularly, chlorophyll using RTMs such as INFORM. Numéro de notice : A2019-460 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.03.003 date de publication en ligne : 08/03/2019 En ligne : https://doi.org/10.1016/j.jag.2019.03.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93577 [article]

Challenges in grassland mowing event detection with multimodal Sentinel images / Anatol Garioud (2019)  

Public Titre : Challenges in grassland mowing event detection with multimodal Sentinel images Type de document : Article/Communication Auteurs : Anatol Garioud, Auteur ; Sébastien Giordano , Auteur ; Silvia Valero, Auteur ; Clément Mallet , Auteur Congrès : MultiTemp 2019, 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (5 - 7 août 2019; Shanghai, Chine), Commanditaire Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN Année de publication : 2019 Importance : pp 1 - 4 Format : 21 x 30 cm Note générale : version auteur dans HAL https://hal.archives-ouvertes.fr/hal-02387167 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] détection d'événement [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image RapidEye [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] image TerraSAR-X [Termes descripteurs IGN] méthode robuste [Termes descripteurs IGN] nébulosité [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] Perceptron multicouche [Termes descripteurs IGN] prairie [Termes descripteurs IGN] régression [Termes descripteurs IGN] réseau neuronal récurrent [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] surveillance de la végétation Résumé : (auteur) Permanent Grasslands (PG) are heterogeneous environments with high spatial and temporal dynamics, subject to increasing environmental challenges. This study aims to identify requirements, key constraining factors and solutions for robust and complete detection of Mowing Events. Remote sensing is a powerful tool to monitor and investigate Near-Real-Time and seasonally PG cover. Here, pros and cons of Sentinel-2 (S2) and Sentinel-1 (S1) time series exploitation for Mowing Events (MowEve) detection are analysed. A deep-based approach is proposed to obtain consistent and homogeneous biophysical parameter times series for MowEve detection. Recurrent Neural Networks are proposed as regression strategy allowing the synergistic integration of optical and Synthetic Aperture Radar data to reconstruct dense NDVI times series. Experimental results corroborates the interest of deriving consistent and homogeneous series of biophysical parameters for subsequent MowEve detection. Numéro de notice : C2019-028 Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/Multi-Temp.2019.8866914 date de publication en ligne : 29/11/2019 En ligne : https://doi.org/10.1109/Multi-Temp.2019.8866914 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94538

Multi-scale assessment of invasive plant species diversity using Pléiades 1A, RapidEye and Landsat-8 data / Siddhartha Khare in Geocarto international, vol 33 n° 7 (July 2018)  

Permalink

A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning / Rasmus M. Houborg in ISPRS Journal of photogrammetry and remote sensing, vol 135 (January 2018)  

Permalink

Application of topo-edaphic factors and remotely sensed vegetation indices to enhance biomass estimation in a heterogeneous landscape in the Eastern Arc mountains of Tanzania / Mercy Ojoyi in Geocarto international, vol 31 n° 1 - 2 (January - February 2016)  

Permalink

Automatic orthorectification of high-resolution optical satellite images using vector roads / Aleš Marsetič in IEEE Transactions on geoscience and remote sensing, vol 53 n° 11 (November 2015)  

Permalink

Testing the reliability and stability of the internal accuracy assessment of random forest for classifying tree defoliation levels using different validation methods / Samuel Adelabu in Geocarto international, vol 30 n° 7 - 8 (August - September 2015)  

Permalink

Estimation de paramètres forestiers par données Lidar aéroporté et imagerie satellitaire RapidEye : étude de sensibilité / Jean-Matthieu Monnet in Revue Française de Photogrammétrie et de Télédétection, n° 211 - 212 (juillet - décembre 2015)

Permalink

Combining RapidEye and lidar satellite imagery for mapping of mining and mine reclamation / Aaron E. Maxwell in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 2 (February 2014)  

Permalink

Hierarchical extraction of landslides from multiresolution remotely sensed optical images / Camille Kurtz in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)

Permalink

Contribution of texture and red-edge band for vegetated areas detection and identification / Arnaud Le Bris (juillet 2013)  

Permalink

Détection et identification de zones de végétation arborée et viticole : utilisation d’images satellite RapidEye et de données BDOrtho / Arnaud Le Bris (2013)

Permalink