Centre dedocumentation
scientifique

Discrimination and classification of mangrove forests using EO-1 Hyperion data : a case study of Indian Sundarbans / Tanumi Kumar in Geocarto international, vol 34 n° 4 (April 2019)  

Public [article]  inGeocarto international > vol 34 n° 4 (April 2019) . - pp 415 - 442 Titre : Discrimination and classification of mangrove forests using EO-1 Hyperion data : a case study of Indian Sundarbans Type de document : Article/Communication Auteurs : Tanumi Kumar, Auteur ; Abhishek Mandal, Auteur ; Dibyendu Dutta, Auteur ; R. Nagaraja, Auteur ; Vinay Kumar Dadhwal, Auteur Année de publication : 2019 Article en page(s) : pp 415 - 442 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse discriminante [Termes descripteurs IGN] Avicennia marina [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] classification Spectral angle mapper [Termes descripteurs IGN] image EO1-Hyperion [Termes descripteurs IGN] Inde [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] mangrove [Termes descripteurs IGN] palétuvier Résumé : (Auteur) In remote sensing the identification accuracy of mangroves is greatly influenced by terrestrial vegetation. This paper deals with the use of specific vegetation indices for extracting mangrove forests using Earth Observing-1 Hyperion image over a portion of Indian Sundarbans, followed by classification of mangroves into floristic composition classes. Five vegetation indices (three new and two published), namely Mangrove Probability Vegetation Index, Normalized Difference Wetland Vegetation Index, Shortwave Infrared Absorption Index, Normalized Difference Infrared Index and Atmospherically Corrected Vegetation Index were used in decision tree algorithm to develop the mangrove mask. Then, three full-pixel classifiers, namely Minimum Distance, Spectral Angle Mapper and Support Vector Machine (SVM) were evaluated on the data within the mask. SVM performed better than the other two classifiers with an overall precision of 99.08%. The methodology presented here may be applied in different mangrove areas for producing community zonation maps at finer levels. Numéro de notice : A2019-452 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1408699 date de publication en ligne : 11/12/2017 En ligne : https://doi.org/10.1080/10106049.2017.1408699 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92839 [article]

Estimation of aboveground biomass and carbon in a tropical rain forest in Gabon using remote sensing and GPS data / Kalifa Goïta in Geocarto international, vol 34 n° 3 (March 2019)  

Public [article]  inGeocarto international > vol 34 n° 3 (March 2019) . - pp 243 - 259 Titre : Estimation of aboveground biomass and carbon in a tropical rain forest in Gabon using remote sensing and GPS data Type de document : Article/Communication Auteurs : Kalifa Goïta, Auteur ; Jacques Mouloungou, Auteur ; Goze Bertin Bénié, Auteur Année de publication : 2019 Article en page(s) : pp 243 - 259 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] forêt tropicale [Termes descripteurs IGN] Gabon [Termes descripteurs IGN] hauteur des arbres [Termes descripteurs IGN] image Landsat-ETM+ [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] inventaire forestier local [Termes descripteurs IGN] Libreville (Gabon) [Termes descripteurs IGN] mangrove [Termes descripteurs IGN] MNS SRTM [Termes descripteurs IGN] puits de carbone Résumé : (Auteur) The knowledge of biomass stocks in tropical forests is critical for climate change and ecosystem services studies. This research was conducted in a tropical rain forest located near the city of Libreville (the capital of Gabon), in the Akanda Peninsula. The forest cover was stratified in terms of mature, secondary and mangrove forests using Landsat-ETM data. A field inventory was conducted to measure the required basic forest parameters and estimate the aboveground biomass (AGB) and carbon over the different forest classes. The Shuttle Radar Topography Mission (SRTM) data were used in combination with ground-based GPS measurements to derive forest heights. Finally, the relationships between the estimated heights and AGB were established and validated. Highest biomass stocks were found in the mature stands (223 ± 37 MgC/ha), followed by the secondary forests (116 ± 17 MgC/ha) and finally the mangrove forests (36 ± 19 MgC/ha). Strong relationships were found between AGB and forest heights (R2 > 0.85). Numéro de notice : A2019-450 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1386720 date de publication en ligne : 06/02/2018 En ligne : https://doi.org/10.1080/10106049.2017.1386720 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92838 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2019021	SL	Revue	Centre de documentation	Revues en salle	Disponible

Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery / Jose Alan A. Castillo in ISPRS Journal of photogrammetry and remote sensing, vol 134 (December 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 134 (December 2017) . - pp 70 - 85 Titre : Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery Type de document : Article/Communication Auteurs : Jose Alan A. Castillo, Auteur ; Armando A. Apan, Auteur ; Tek N. Maraseni, Auteur ; Severino G. Salmo, Auteur Année de publication : 2017 Article en page(s) : pp 70 - 85 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] carte d'utilisation du sol [Termes descripteurs IGN] déboisement [Termes descripteurs IGN] estimation statistique [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] mangrove [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] Philippines [Termes descripteurs IGN] régression linéaire [Termes descripteurs IGN] rétrodiffusion [Termes descripteurs IGN] variable biophysique (végétation) Résumé : (Auteur) The recent launch of the Sentinel-1 (SAR) and Sentinel-2 (multispectral) missions offers a new opportunity for land-based biomass mapping and monitoring especially in the tropics where deforestation is highest. Yet, unlike in agriculture and inland land uses, the use of Sentinel imagery has not been evaluated for biomass retrieval in mangrove forest and the non-forest land uses that replaced mangroves. In this study, we evaluated the ability of Sentinel imagery for the retrieval and predictive mapping of above-ground biomass of mangroves and their replacement land uses. We used Sentinel SAR and multispectral imagery to develop biomass prediction models through the conventional linear regression and novel Machine Learning algorithms. We developed models each from SAR raw polarisation backscatter data, multispectral bands, vegetation indices, and canopy biophysical variables. The results show that the model based on biophysical variable Leaf Area Index (LAI) derived from Sentinel-2 was more accurate in predicting the overall above-ground biomass. In contrast, the model which utilised optical bands had the lowest accuracy. However, the SAR-based model was more accurate in predicting the biomass in the usually deficient to low vegetation cover non-forest replacement land uses such as abandoned aquaculture pond, cleared mangrove and abandoned salt pond. These models had 0.82–0.83 correlation/agreement of observed and predicted value, and root mean square error of 27.8–28.5 Mg ha−1. Among the Sentinel-2 multispectral bands, the red and red edge bands (bands 4, 5 and 7), combined with elevation data, were the best variable set combination for biomass prediction. The red edge-based Inverted Red-Edge Chlorophyll Index had the highest prediction accuracy among the vegetation indices. Overall, Sentinel-1 SAR and Sentinel-2 multispectral imagery can provide satisfactory results in the retrieval and predictive mapping of the above-ground biomass of mangroves and the replacement non-forest land uses, especially with the inclusion of elevation data. The study demonstrates encouraging results in biomass mapping of mangroves and other coastal land uses in the tropics using the freely accessible and relatively high-resolution Sentinel imagery. Numéro de notice : A2017-730 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.10.016 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.10.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88428 [article]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2017121	RAB	Revue	Centre de documentation	En réserve 3L	Disponible
081-2017122	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt
081-2017123	DEP-EXM	Revue	Saint-Mandé	Dépôt en unité	Exclu du prêt

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 descripteurs IGN] analyse diachronique [Termes descripteurs IGN] carte forestière [Termes descripteurs IGN] Chine [Termes descripteurs IGN] Google Earth Engine [Termes descripteurs IGN] image Landsat-ETM+ [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] mangrove [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs 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 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]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2017091	RAB	Revue	Centre de documentation	En réserve 3L	Disponible
081-2017093	DEP-EXM	Revue	MATIS	Dépôt en unité	Exclu du prêt
081-2017092	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Potential 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)  

Public [article]  inGeocarto international > vol 32 n° 8 (August 2017) . - pp 874 - 885 Titre : Potential application of remote sensing in monitoring ecosystem services of forests, mangroves and urban areas Type de document : Article/Communication Auteurs : Ram Avtar, Auteur ; Pankaj Kumar, Auteur ; Akiko Oono, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 874 - 885 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] aide à la décision [Termes descripteurs IGN] forêt [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] image optique [Termes descripteurs IGN] image radar [Termes descripteurs IGN] image satellite [Termes descripteurs IGN] mangrove [Termes descripteurs IGN] service écosystémique [Termes descripteurs IGN] surveillance écologique [Termes descripteurs IGN] zone urbaine Résumé : (Auteur) The application of remote sensing (RS) techniques to monitor ecosystem services has increased in recent years. Nevertheless, the potential application of RS to monitor some of ecosystem services is still challenging. The paper reviews the applications of RS to monitor ecosystem services of forests, mangroves and urban areas. Satellite data provide substantial information about dynamics of environmental changes over time from local to global scale. These information are useful data sources for the people who are involved in the on-going evaluation and decision-making process to manage ecosystem. Many recent research papers on the topic were reviewed to find new applications and limitations of RS for monitoring ecosystem services. Advanced RS techniques have high potential to monitor ecosystem services with the advancement of sensors ranging from aerial photography to high and medium resolution optical RS and from hyperspectral RS to microwave RS. Numéro de notice : A2017-456 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1206974 date de publication en ligne : 15/08/2016 En ligne : http://dx.doi.org/10.1080/10106049.2016.1206974 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86381 [article]

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)  

Permalink

A cyber-enabled spatial decision support system to inventory mangroves in Mozambique: coupling scientific workflows and cloud computing / Wenwu Tang in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)  

Permalink

Télédétection pour l'observation des surfaces continentales, Volume 5. Observation des surfaces continentales par télédétection 3 / Nicolas Baghdadi (2017)

Permalink

Evaluation par imagerie satellitaire de la dynamique spatiale du parc marin des mangroves de la république Démocratique du Congo entre 2006 et 2015 / B.M. Kalambay in Afrique Science, vol 12 n° 5 (septembre - octobre 2016)

Permalink

Above- and belowground tree biomass models for three mangrove species in Tanzania: a nonlinear mixed effects modelling approach / Marco Andrew Njana in Annals of Forest Science [en ligne], vol 73 n° 2 (June 2016)  

Permalink

Mangrove forest characterization in Southeast Côte d’Ivoire / Isimemen Osemwegie in Open journal of forestry, vol 6 n° 3 (February 2016)  

Permalink

High-resolution forest canopy height estimation in an African blue carbon ecosystem / David Lagomasino in Remote sensing in ecology and conservation, vol 1 n° 1 (October 2015)    

Permalink

Les mangroves écosystèmes sous haute protection / Anne Konitz in Rivages, le magazine du conservatoire du littoral, n° 85 (automne 2015) 

Permalink

Mangrove tree crown delineation from high-resolution imagery / Muditha K. Heenkenda in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 6 (June 2015)

Permalink

Geospatial analysis of land-use change processes in a densely populated coastal city: the case of Port Harcourt, south-east Nigeria / Glory O. Enaruvbe in Geocarto international, vol 30 n° 3 - 4 (March - April 2015)  

Permalink