Centre de documentation
scientifique

Monitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms / Lien T.H. Pham in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 128 (June 2017) . - pp 86 - 97 Titre : Monitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms Type de document : Article/Communication Auteurs : Lien T.H. Pham, Auteur Année de publication : 2017 Article en page(s) : pp 86 - 97 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse d'image orientée objet [Termes IGN] analyse diachronique [Termes IGN] analyse spectrale [Termes IGN] apprentissage automatique [Termes IGN] biomasse forestière [Termes IGN] carte thématique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] détection de changement [Termes IGN] image SPOT 4 [Termes IGN] image SPOT 5 [Termes IGN] mangrove [Termes IGN] surveillance de la végétation [Termes IGN] teneur en carbone [Termes IGN] texture d'image [Termes IGN] Viet Nam Résumé : (Auteur) Mangrove forests are well-known for their provision of ecosystem services and capacity to reduce carbon dioxide concentrations in the atmosphere. Mapping and quantifying mangrove biomass is useful for the effective management of these forests and maximizing their ecosystem service performance. The objectives of this research were to model, map, and analyse the biomass change between 2000 and 2011 of mangrove forests in the Cangio region in Vietnam. SPOT 4 and 5 images were used in conjunction with object-based image analysis and machine learning algorithms. The study area included natural and planted mangroves of diverse species. After image preparation, three different mangrove associations were identified using two levels of image segmentation followed by a Support Vector Machine classifier and a range of spectral, texture and GIS information for classification. The overall classification accuracy for the 2000 and 2011 images were 77.1% and 82.9%, respectively. Random Forest regression algorithms were then used for modelling and mapping biomass. The model that integrated spectral, vegetation association type, texture, and vegetation indices obtained the highest accuracy (R2adj = 0.73). Among the different variables, vegetation association type was the most important variable identified by the Random Forest model. Based on the biomass maps generated from the Random Forest, total biomass in the Cangio mangrove forest increased by 820,136 tons over this period, although this change varied between the three different mangrove associations. Numéro de notice : A2017-332 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.03.013 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.03.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85485 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2017061	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2017063	DEP-EXM	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2017062	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Effect of training class label noise on classification performances for land cover mapping with satellite image time series / Charlotte Pelletier in Remote sensing, vol 9 n° 2 (February 2017)  

Public [article]  inRemote sensing > vol 9 n° 2 (February 2017) . - pp 1 - 24 Titre : Effect of training class label noise on classification performances for land cover mapping with satellite image time series Type de document : Article/Communication Auteurs : Charlotte Pelletier, Auteur ; Silvia Valero, Auteur ; Jordi Inglada, Auteur ; Nicolas Champion , Auteur ; Claire Marais-Sicre, Auteur ; Gérard Dedieu, Auteur Année de publication : 2017 Projets : 1-Pas de projet / Article en page(s) : pp 1 - 24 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] base de données d'occupation du sol [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] image Landsat-8 [Termes IGN] image SPOT 4 [Termes IGN] série temporelle Résumé : (auteur) Supervised classification systems used for land cover mapping require accurate reference databases. These reference data come generally from different sources such as field measurements, thematic maps, or aerial photographs. Due to misregistration, update delay, or land cover complexity, they may contain class label noise, i.e., a wrong label assignment. This study aims at evaluating the impact of mislabeled training data on classification performances for land cover mapping. Particularly, it addresses the random and systematic label noise problem for the classification of high resolution satellite image time series. Experiments are carried out on synthetic and real datasets with two traditional classifiers: Support Vector Machines (SVM) and Random Forests (RF). A synthetic dataset has been designed for this study, simulating vegetation profiles over one year. The real dataset is composed of Landsat-8 and SPOT-4 images acquired during one year in the south of France. The results show that both classifiers are little influenced for low random noise levels up to 25%–30%, but their performances drop down for higher noise levels. Different classification configurations are tested by increasing the number of classes, using different input feature vectors, and changing the number of training instances. Algorithm complexities are also analyzed. The RF classifier achieves high robustness to random and systematic label noise for all the tested configurations; whereas the SVM classifier is more sensitive to the kernel choice and to the input feature vectors. Finally, this work reveals that the cross-validation procedure is impacted by the presence of class label noise. Numéro de notice : A2017-896 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : doi.org/10.3390/rs9020173 Date de publication en ligne : 18/02/2017 En ligne : https://doi.org/10.3390/rs9020173 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91880 [article]

Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas / Charlotte Pelletier in Remote sensing of environment, vol 187 (15 December 2016)  

Public [article]  inRemote sensing of environment > vol 187 (15 December 2016) . - pp 156 - 168 Titre : Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas Type de document : Article/Communication Auteurs : Charlotte Pelletier, Auteur ; Silvia Valero, Auteur ; Jordi Inglada, Auteur ; Nicolas Champion , Auteur ; Gérard Dedieu, Auteur Année de publication : 2016 Article en page(s) : pp 156 - 168 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] caractérisation [Termes IGN] carte d'occupation du sol [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] France (administrative) [Termes IGN] image Landsat-8 [Termes IGN] image Sentinel [Termes IGN] image SPOT 4 [Termes IGN] méthode robuste [Termes IGN] précision de la classification [Termes IGN] série temporelle Résumé : (Auteur) New remote sensing sensors will acquire High spectral, spatial and temporal Resolution Satellite Image Time Series (HR-SITS). These new data are of great interest to map land cover thanks to the combination of the three high resolutions that will allow a depiction of scene dynamics. However, their efficient exploitation involves new challenges, especially for adapting traditional classification schemes to data complexity. More specifically, it requires: (1) to determine which classifier algorithms can handle the amount and the variability of data; (2) to evaluate the stability of classifier parameters; (3) to select the best feature set used as input data in order to find the good trade-off between classification accuracy and computational time; and (4) to establish the classifier accuracy over large areas. This work aims at studying these different issues, and more especially at demonstrating the ability of state-of-the-art classifiers, such as Random Forests (RF) or Support Vector Machines (SVM), to classify HR-SITS. For this purpose, several studies are carried out by using SPOT-4 and Landsat-8 HR-SITS in the south of France. Firstly, the choice of the classifier is discussed by comparing RF and SVM algorithms on HR-SITS. Both classifiers show their ability to tackle the classification problem with an Overall Accuracy (OA) of 83.3 % for RF and 77.1 % for SVM. But RF have some advantages such as a small training time, and an easy parameterization. Secondly, the stability of RF parameters is appraised. RF parameters appear to cause little influence on the classification accuracy, about 1% OA difference between the worst and the best parameter configuration. Thirdly, different input data – composed of spectral bands with or without spectral and/or temporal features – are proposed in order to enhance the characterization of land cover. The addition of features improves the classification accuracy, but the gain in OA is weak compared with the increase in the computational cost. Eventually, the classifier accuracy is assessed on a larger area where the landscape variabilities affect the classification performances. Numéro de notice : A2016--109 Affiliation des auteurs : IGN+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2016.10.010 Date de publication en ligne : 15/10/2016 En ligne : http://doi.org/10.1016/j.rse.2016.10.010 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84726 [article]

Earth observation-based multi-scale impact assessment of internally displaced person (IDP) camps on wood resources in Zalingei, Darfur / Kristin Spröhnle in Geocarto international, vol 31 n° 5 - 6 (May - June 2016)  

Public [article]  inGeocarto international > vol 31 n° 5 - 6 (May - June 2016) . - pp 575 - 595 Titre : Earth observation-based multi-scale impact assessment of internally displaced person (IDP) camps on wood resources in Zalingei, Darfur Type de document : Article/Communication Auteurs : Kristin Spröhnle, Auteur ; Olaf Kranz, Auteur ; Elisabeth Schoepfer, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 575 - 595 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse diachronique [Termes IGN] Darfour [Termes IGN] déboisement [Termes IGN] image Ikonos [Termes IGN] image Quickbird [Termes IGN] image SPOT 4 [Termes IGN] impact sur l'environnement [Termes IGN] migration humaine [Termes IGN] Soudan Résumé : (Auteur) This study describes the development of a semi-automatic object-based image analysis approach for the detection and quantification of deforestation in Zalingei, Darfur, in consequence of the increasing concentration of refugees or internally displaced persons (IDPs) in the region. The classification workflow is based on a multi-scale approach, ranging from the analysis of high resolution SPOT-4 to very high resolution IKONOS and QuickBird satellite imagery between 2003 and 2008. The overall accuracy rates for the classification of the SPOT 4 data ranged from 92% up to 95%, while those for the QuickBird and IKONOS classification have shown values of 88 and 87%, respectively. The resulting trends in woody vegetation cover were compared with the development of the local population and the variability of precipitation. The results show that the strong increase in human population in the Zalingei IDP camps can be associated with considerable decrease in woody vegetation in the camp vicinity. Numéro de notice : A2016-171 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1062053 Date de publication en ligne : 03/08/2015 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2015.1062053 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80512 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2016031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

An assessment of image features and random forest for land cover mapping over large areas using high resolution Satellite Image Time Series / Charlotte Pelletier (2016)  

Public Titre : An assessment of image features and random forest for land cover mapping over large areas using high resolution Satellite Image Time Series Type de document : Article/Communication Auteurs : Charlotte Pelletier, Auteur ; Silvia Valero, Auteur ; Jordi Inglada, Auteur ; Gérard Dedieu, Auteur ; Nicolas Champion , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2016 Conférence : IGARSS 2016, International Geoscience And Remote Sensing Symposium 10/07/2016 15/07/2016 Pékin Chine Proceedings IEEE Importance : pp 3338 - 3341 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte d'occupation du sol [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] image à haute résolution [Termes IGN] image Landsat-8 [Termes IGN] image SPOT 4 [Termes IGN] série temporelle Résumé : (auteur) New high resolution Satellite Image Time Series (SITS) are becoming crucial to land cover mapping over large areas. Their high temporal resolution will allow to better depict scene dynamics. However, it will also increase the amount of data to process. The classification of these data involves therefore new challenges such as: (1) selecting the best feature set to use as input data, (2) dealing with data variability coming from landscape diversity, and (3) establishing the robustness of existing classifiers over large areas. This work aims at addressing these questions through three different studies. Experimental results are obtained by using SPOT-4 and Landsat-8 SITS. Numéro de notice : C2016-034 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS.2016.7729863 Date de publication en ligne : 03/11/2016 En ligne : https://doi.org/10.1109/IGARSS.2016.7729863 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91791

Distinctive order based self-similarity descriptor for multi-sensor remote sensing image matching / Amin Sedaghat in ISPRS Journal of photogrammetry and remote sensing, vol 108 (October 2015)  

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Spatial modelling of wildland fire danger for risk analysis and conflict resolution in Malaysia : linking Fire Danger Rating Systems (FDRS) with Wildfire Threat Rating Systems (WTRS) / H. Assilzadeh in Geocarto international, vol 27 n° 4 (July 2012)  

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Trois IDG à l'épreuve de Xynthia / Françoise de Blomac in SIG la lettre, n° 115 (mars 2010)

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The role of the integration of remote sensing and GIS in land use/land cover analysis after an earthquake / C. Aydoner in International Journal of Remote Sensing IJRS, vol 30 n° 7 (April 2009)  

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Spécification et développement d'une méthode intégrée de cartographie du combustible à partir d'images téléacquises (dans le cadre du projet Fireparadox) / G. El-Amine (2009)

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Suivi par télédétection de la dynamique des milieux savanicoles et forestiers gabonais : exemple de la forêt classée de la Mondah et du parc national de la Lope / Marcellin Nziengui in Photo interprétation, vol 44 n° 2 (Septembre 2008)

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Interprétation visuelle des images satellitaires (Landsat TM, SPOT 4, IRS et Ikonos) et mouvements de masse : cas d'étude au Liban / C. Abdallah in Photo interprétation, vol 42 n° 1 (Mars 2006)

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Cartographie du couvert végétal des iles Loyauté (Nouvelle-Calédonie) à partir des données SPOT 4 / P. Dumas in Photo interprétation, vol 41 n° 2 (Juin 2005)

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Estimation and monitoring of bare soil/vegetation ratio with SPOT vegetation and HRVIR / Grégoire Mercier in IEEE Transactions on geoscience and remote sensing, vol 43 n° 2 (February 2005)  

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Accuracy of DTM generated from Spot 4 and Spot 5 HRS data / I. Ewiak (2004)  

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