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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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 131 (September 2017) . - pp 104 - 120 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 temporelle Résumé : (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 [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

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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 131 (September 2017) . - pp 65 - 76 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 hydrique Résumé : (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 [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

Improving 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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 131 (September 2017) . - pp 77 - 91 Titre : Improving the prediction of African savanna vegetation variables using time series of MODIS products Type de document : Article/Communication Auteurs : Miriam Tsalyuk, Auteur ; Maggi Kelly, Auteur ; Wayne M. Getz, Auteur Année de publication : 2017 Article en page(s) : pp 77 - 91 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Végétation [Termes IGN] Afrique (géographie physique) [Termes IGN] biomasse forestière [Termes IGN] dégradation de la flore [Termes IGN] Enhanced vegetation index [Termes IGN] image Terra-MODIS [Termes IGN] Leaf Area Index [Termes IGN] Namibie [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] prédiction [Termes IGN] savane [Termes IGN] variation Résumé : (Auteur) African savanna vegetation is subject to extensive degradation as a result of rapid climate and land use change. To better understand these changes detailed assessment of vegetation structure is needed across an extensive spatial scale and at a fine temporal resolution. Applying remote sensing techniques to savanna vegetation is challenging due to sparse cover, high background soil signal, and difficulty to differentiate between spectral signals of bare soil and dry vegetation. In this paper, we attempt to resolve these challenges by analyzing time series of four MODIS Vegetation Products (VPs): Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR) for Etosha National Park, a semiarid savanna in north-central Namibia. We create models to predict the density, cover, and biomass of the main savanna vegetation forms: grass, shrubs, and trees. To calibrate remote sensing data we developed an extensive and relatively rapid field methodology and measured herbaceous and woody vegetation during both the dry and wet seasons. We compared the efficacy of the four MODIS-derived VPs in predicting vegetation field measured variables. We then compared the optimal time span of VP time series to predict ground-measured vegetation. We found that Multiyear Partial Least Square Regression (PLSR) models were superior to single year or single date models. Our results show that NDVI-based PLSR models yield robust prediction of tree density (R2 = 0.79, relative Root Mean Square Error, rRMSE = 1.9%) and tree cover (R2 = 0.78, rRMSE = 0.3%). EVI provided the best model for shrub density (R2 = 0.82) and shrub cover (R2 = 0.83), but was only marginally superior over models based on other VPs. FPAR was the best predictor of vegetation biomass of trees (R2 = 0.76), shrubs (R2 = 0.83), and grass (R2 = 0.91). Finally, we addressed an enduring challenge in the remote sensing of semiarid vegetation by examining the transferability of predictive models through space and time. Our results show that models created in the wetter part of Etosha could accurately predict trees’ and shrubs’ variables in the drier part of the reserve and vice versa. Moreover, our results demonstrate that models created for vegetation variables in the dry season of 2011 could be successfully applied to predict vegetation in the wet season of 2012. We conclude that extensive field data combined with multiyear time series of MODIS vegetation products can produce robust predictive models for multiple vegetation forms in the African savanna. These methods advance the monitoring of savanna vegetation dynamics and contribute to improved management and conservation of these valuable ecosystems. Numéro de notice : A2017-537 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.07.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.07.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86575 [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

Reconstruction of time-varying tidal flat topography using optical remote sensing imageries / Kuo-Hsin Tseng in ISPRS Journal of photogrammetry and remote sensing, vol 131 (September 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 131 (September 2017) . - pp 92 - 103 Titre : Reconstruction of time-varying tidal flat topography using optical remote sensing imageries Type de document : Article/Communication Auteurs : Kuo-Hsin Tseng, Auteur ; Chung-Yen Kuo, Auteur ; Tang-Huang Lin, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 92 - 103 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] données marégraphiques [Termes IGN] estran [Termes IGN] image Landsat-ETM+ [Termes IGN] image Landsat-OLI [Termes IGN] image Landsat-TM [Termes IGN] marée océanique [Termes IGN] modèle numérique de terrain [Termes IGN] Normalized Difference Water Index [Termes IGN] Taïwan [Termes IGN] variation temporelle Résumé : (Auteur) Tidal flats (TFs) occupy approximately 7% of the total coastal shelf areas worldwide. However, TFs are unavailable in most global digital elevation models (DEMs) due to water-impermeable nature of existing remote sensing approaches (e.g., radar used for WorldDEM™ and Shuttle Radar Topography Mission DEM and optical stereo-pairs used for ASTER Global Digital Elevation Map Version 2). However, this problem can be circumvented using remote sensing imageries to observe land exposure at different tidal heights during each revisit. This work exploits Landsat-4/-5/-7/-8 Thematic Mapper (TM)/Enhanced TM Plus/Operational Land Imager imageries to reconstruct topography of a TF, namely, Hsiang-Shan Wetland in Taiwan, to unveil its formation and temporal changes since the 1980s. We first classify water areas by applying modified normalized difference water index to each Landsat image and normalize chances of water exposure to create an inundation probability map. This map is then scaled by tidal amplitudes extracted from DTU10 tide model to convert the probabilities into actual elevations. After building DEM at intertidal zone, a water level-area curve is established, and accuracy of DEM is validated by sea level (SL) at the timing of each Landsat snapshot. A 22-year (1992–2013) dataset composed of 227 Landsat scenes are analyzed and compared with tide gauge data. Root-mean-square differences of SL reaches 48 cm with a correlation coefficient of 0.93, indicating that the present technique is useful for constructing accurate coastal DEMs, and that products can be utilized for estimating instant SL. This study shows the possibility of exploring evolution of intertidal zones using an archive of optical remote sensing imageries. The technique developed in the present study potentially helps in quantifying SL from the start of optical remote sensing era. Numéro de notice : A2017-538 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.07.008 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.07.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86576 [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