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Evaluation de variables limnologiques grâce à des images Landsat / Danielle Teixeira Alves Da Silva in Géomatique expert, n° 118 (septembre - octobre 2017)
[article]
Titre : Evaluation de variables limnologiques grâce à des images Landsat Type de document : Article/Communication Auteurs : Danielle Teixeira Alves Da Silva, Auteur ; Aziz Serradj, Auteur ; Aline do Vale Figueiredo, Auteur ; Vanessa Becker, Auteur Année de publication : 2017 Article en page(s) : pp 30 - 39 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse spatiale
[Termes IGN] Brésil
[Termes IGN] carte thématique
[Termes IGN] chlorophylle
[Termes IGN] eaux continentales
[Termes IGN] écologie
[Termes IGN] image Landsat
[Termes IGN] limnologie
[Termes IGN] ressources aquatiques
[Termes IGN] teneur en chlorophylle des feuilles
[Termes IGN] zone semi-arideRésumé : (auteur) Utilisation des images Landsat pour estimer la concentration de la chlorophylle-a et de la transparence de l'eau sur un territoire semi-aride du Nord-est brésilien. Numéro de notice : A2017-586 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86728
in Géomatique expert > n° 118 (septembre - octobre 2017) . - pp 30 - 39[article]Exemplaires(2)
Code-barres Cote Support Localisation Section Disponibilité 265-2017051 RAB Revue Centre de documentation En réserve L003 Disponible IFN-001-P001984 PER Revue Nogent-sur-Vernisson Salle périodiques Exclu du prêt Forest change detection in incomplete satellite images with deep neural networks / Salman H. Khan in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)
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[article]
Titre : Forest change detection in incomplete satellite images with deep neural networks Type de document : Article/Communication Auteurs : Salman H. Khan, Auteur ; Xuming He, Auteur ; Fatih Porikli, Auteur ; Mohammed Bennamoun, Auteur Année de publication : 2017 Article en page(s) : pp 5407 - 5423 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] analyse multirésolution
[Termes IGN] apprentissage profond
[Termes IGN] détection de changement
[Termes IGN] forêt
[Termes IGN] réflectance de surface
[Termes IGN] réseau neuronal artificiel
[Termes IGN] retouche
[Termes IGN] surveillance de la végétationRésumé : (Auteur) Land cover change monitoring is an important task from the perspective of regional resource monitoring, disaster management, land development, and environmental planning. In this paper, we analyze imagery data from remote sensing satellites to detect forest cover changes over a period of 29 years (1987-2015). Since the original data are severely incomplete and contaminated with artifacts, we first devise a spatiotemporal inpainting mechanism to recover the missing surface reflectance information. The spatial filling process makes use of the available data of the nearby temporal instances followed by a sparse encoding-based reconstruction. We formulate the change detection task as a region classification problem. We build a multiresolution profile (MRP) of the target area and generate a candidate set of bounding-box proposals that enclose potential change regions. In contrast to existing methods that use handcrafted features, we automatically learn region representations using a deep neural network in a data-driven fashion. Based on these highly discriminative representations, we determine forest changes and predict their onset and offset timings by labeling the candidate set of proposals. Our approach achieves the state-of-the-art average patch classification rate of 91.6% (an improvement of ~16%) and the mean onset/offset prediction error of 4.9 months (an error reduction of five months) compared with a strong baseline. We also qualitatively analyze the detected changes in the unlabeled image regions, which demonstrate that the proposed forest change detection approach is scalable to new regions. Numéro de notice : A2017-663 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2707528 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2707528 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87105
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 9 (September 2017) . - pp 5407 - 5423[article]A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform / Bangqian Chen in ISPRS Journal of photogrammetry and remote sensing, vol 131 (September 2017)
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[article]
Titre : A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform Type de document : Article/Communication Auteurs : Bangqian Chen, Auteur ; Xiangming Xiao, Auteur ; Lianghao Pan, Auteur ; Russell Doughty, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 104 - 120 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] carte forestière
[Termes IGN] Chine
[Termes IGN] Google Earth Engine
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Landsat-OLI
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] mangrove
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] série temporelleRésumé : (auteur) Due to rapid losses of mangrove forests caused by anthropogenic disturbances and climate change, accurate and contemporary maps of mangrove forests are needed to understand how mangrove ecosystems are changing and establish plans for sustainable management. In this study, a new classification algorithm was developed using the biophysical characteristics of mangrove forests in China. More specifically, these forests were mapped by identifying: (1) greenness, canopy coverage, and tidal inundation from time series Landsat data, and (2) elevation, slope, and intersection-with-sea criterion. The annual mean Normalized Difference Vegetation Index (NDVI) was found to be a key variable in determining the classification thresholds of greenness, canopy coverage, and tidal inundation of mangrove forests, which are greatly affected by tide dynamics. In addition, the integration of Sentinel-1A VH band and modified Normalized Difference Water Index (mNDWI) shows great potential in identifying yearlong tidal and fresh water bodies, which is related to mangrove forests. This algorithm was developed using 6 typical Regions of Interest (ROIs) as algorithm training and was run on the Google Earth Engine (GEE) cloud computing platform to process 1941 Landsat images (25 Path/Row) and 586 Sentinel-1A images circa 2015. The resultant mangrove forest map of China at 30 m spatial resolution has an overall/users/producer’s accuracy greater than 95% when validated with ground reference data. In 2015, China’s mangrove forests had a total area of 20,303 ha, about 92% of which was in the Guangxi Zhuang Autonomous Region, Guangdong, and Hainan Provinces. This study has demonstrated the potential of using the GEE platform, time series Landsat and Sentine-1A SAR images to identify and map mangrove forests along the coastal zones. The resultant mangrove forest maps are likely to be useful for the sustainable management and ecological assessments of mangrove forests in China. Numéro de notice : A2017-419 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.07.011 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.07.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86313
in ISPRS Journal of photogrammetry and remote sensing > vol 131 (September 2017) . - pp 104 - 120[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2017091 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017093 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017092 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Spatiotemporal analyses of urban vegetation structural attributes using multitemporal Landsat TM data and field measurements / Zhibin Ren in Annals of Forest Science, vol 74 n° 3 (September 2017)
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[article]
Titre : Spatiotemporal analyses of urban vegetation structural attributes using multitemporal Landsat TM data and field measurements Type de document : Article/Communication Auteurs : Zhibin Ren, Auteur ; Ruiliang Pu, Auteur ; Haifeng Zheng, Auteur ; et al., Auteur Année de publication : 2017 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse spatio-temporelle
[Termes IGN] analyse structurale
[Termes IGN] attribut
[Termes IGN] données de terrain
[Termes IGN] données dendrométriques
[Termes IGN] écosystème urbain
[Termes IGN] flore urbaine
[Termes IGN] image Landsat-TM
[Termes IGN] indice foliaire
[Termes IGN] surveillance de la végétationRésumé : (Auteur)
Key message : We conducted spatiotemporal analyses of urban vegetation structural attributes using multitemporal Landsat TM data and field measurements. We showed that multitemporal TM data has the potential of rapidly estimating urban vegetation structural attributes including LAI, CC, and BA at an urban landscape level.
Context : Urban vegetation structural properties/attributes are closely linked to their ecological functions and thus directly affect urban ecosystem process such as energy, water, and gas exchange. Understanding spatiotemporal dynamics of urban vegetation structures is important for sustaining urban ecosystem service and improving the urban environment.
Aims : The purposes of this study were to evaluate the potential of estimating urban vegetation structural attributes from multitemporal Landsat TM imagery and to analyze spatiotemporal changes of the urban structural attributes.
Methods : We first collected three scenes of TM images acquired in 1997, 2004, and 2010 and conducted a field survey to collect urban vegetation structural data (including crown closure (CC), tree height (H), leaf area index (LAI), basal area (BA), stem density (SD), diameter at breast height (DBH), etc.). We then calculated and normalized NDVI maps from the multitemporal TM images. Finally, spatiotemporal urban vegetation structural maps were created using NDVI-based urban vegetation structure predictive models.
Results : The results show that NDVI can be used as a predictor for some selected urban vegetation structural attributes (i.e., CC, LAI, and BA), but not for the other attributes (i.e., H, SD, and DBH) that are well predicted by NDVI in natural vegetation. The results also indicate that urban vegetation structural attributes (i.e., CC, LAI, and BA) in the study area decreased sharply from 1997 to 2004 but increased slightly from 2004 to 2010. The CC, LAI, and BA class distributions were all skewed toward low values in 1997 and 2004. Moreover, LAI, CC, and BA of urban vegetation all present a decreasing trend from suburban areas to urban central areas.
Conclusion : The experimental results demonstrate that Landsat TM imagery could provide a fast and cost-effective method to obtain a spatiotemporal 30-m resolution urban vegetation structural dataset (including CC, LAI, and BA).Numéro de notice : A2017-353 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article DOI : 10.1007/s13595-017-0654-x Date de publication en ligne : 05/07/2017 En ligne : https://doi.org/10.1007/s13595-017-0654-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85719
in Annals of Forest Science > vol 74 n° 3 (September 2017)[article]Unsupervised domain adaptation for early detection of drought stress in hyperspectral images / P. Schmitter in ISPRS Journal of photogrammetry and remote sensing, vol 131 (September 2017)
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[article]
Titre : Unsupervised domain adaptation for early detection of drought stress in hyperspectral images Type de document : Article/Communication Auteurs : P. Schmitter, Auteur ; J. Steinrucken, Auteur ; C. Römer, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 65 - 76 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] détection automatique
[Termes IGN] image hyperspectrale
[Termes IGN] stress hydriqueRésumé : (Auteur) Hyperspectral images can be used to uncover physiological processes in plants if interpreted properly. Machine Learning methods such as Support Vector Machines (SVM) and Random Forests have been applied to estimate development of biomass and detect and predict plant diseases and drought stress. One basic requirement of machine learning implies, that training and testing is done in the same domain and the same distribution. Different genotypes, environmental conditions, illumination and sensors violate this requirement in most practical circumstances. Here, we present an approach, which enables the detection of physiological processes by transferring the prior knowledge within an existing model into a related target domain, where no label information is available. We propose a two-step transformation of the target features, which enables a direct application of an existing model. The transformation is evaluated by an objective function including additional prior knowledge about classification and physiological processes in plants. We have applied the approach to three sets of hyperspectral images, which were acquired with different plant species in different environments observed with different sensors. It is shown, that a classification model, derived on one of the sets, delivers satisfying classification results on the transformed features of the other data sets. Furthermore, in all cases early non-invasive detection of drought stress was possible. Numéro de notice : A2017-536 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.07.003 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.07.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86574
in ISPRS Journal of photogrammetry and remote sensing > vol 131 (September 2017) . - pp 65 - 76[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2017091 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017093 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017092 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Change detection using Landsat time series: A review of frequencies, preprocessing, algorithms, and applications / Zhe Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)
PermalinkEvaluation of seasonal variations of remotely sensed leaf area index over five evergreen coniferous forests / Rong Wang in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)
PermalinkA higher order conditional random field model for simultaneous classification of land cover and land use / Lena Albert in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)
PermalinkModeling canopy reflectance over sloping terrain based on path length correction / Gaofei Yin in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)
PermalinkPotential application of remote sensing in monitoring ecosystem services of forests, mangroves and urban areas / Ram Avtar in Geocarto international, vol 32 n° 8 (August 2017)
PermalinkA relative evaluation of random forests for land cover mapping in an urban area / Di Shi in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 8 (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)
PermalinkSimultaneous estimation of leaf area index, fraction of absorbed photosynthetically active radiation, and surface albedo from multiple-satellite data / Han Ma in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)
PermalinkUsing Landsat time series for characterizing forest disturbance dynamics in the coupled human and natural systems of Central Europe / Cornelius Senf 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)
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