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Aboveground biomass estimates of tropical mangrove forest using Sentinel-1 SAR coherence data : The superiority of deep learning over a semi-empirical model / S.M. Ghosh in Computers & geosciences, vol 150 (May 2021)  

Public [article]  inComputers & geosciences > vol 150 (May 2021) . - n° 104737 Titre : Aboveground biomass estimates of tropical mangrove forest using Sentinel-1 SAR coherence data : The superiority of deep learning over a semi-empirical model Type de document : Article/Communication Auteurs : S.M. Ghosh, Auteur ; M.D. Behera, Auteur Année de publication : 2021 Article en page(s) : n° 104737 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] bande C [Termes IGN] biomasse aérienne [Termes IGN] classification par réseau neuronal [Termes IGN] forêt tropicale [Termes IGN] image Sentinel-SAR [Termes IGN] Inde [Termes IGN] mangrove [Termes IGN] R (langage) Résumé : (auteur) The availability of advanced Machine Learning algorithms has made the estimation process of biophysical parameters more efficient. However, the efficiency of those methods seldom compared with the efficiency of already established semi-empirical procedures. Aboveground biomass (AGB) of mangrove forests is a crucial biophysical parameter as it is positively correlated to the carbon stocks and fluxes. The free availability of Sentinel-1 C-band SAR data and machine learning algorithms hold promises in estimating AGB of tropical mangrove forests. We reported high AGB (70 t/ha to 666 t/ha) using 185 field quadrats of 0.04ha each from Bhitarkanika Wildlife Sanctuary, located on the eastern Indian coast that could be attributed to species composition. The AGB maps generated using Interferometric Water Cloud Model (IWCM) and Deep Learning models were different from each other as they rely on different variables. IWCM was more dependent, especially on ground and vegetation components of coherence, while canopy height acted as the most crucial variable in the Deep Learning model. However, the negligible variations in Deep Learning-based AGB maps can be attributed to interpreting the importance of coherence and VH backscatter. Due to low canopy penetration power of C-band SAR, high temporal decorrelation resulting from longer time gap between interferometric image pairs, and high spatial heterogeneity of mangrove forests, IWCM found as an unsuitable method for AGB estimation. Interestingly, a Deep Learning algorithm could translate the exact relationship between predictor variables and mangrove AGB in Bhitarkanika Wildlife Sanctuary. The AGB estimation studies in mangrove forests using Sentinel data should focus more on using machine learning algorithms like Deep Learning rather than semi-empirical models. Numéro de notice : A2021-941 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2021.104737 En ligne : https://doi.org/10.1016/j.cageo.2021.104737 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99751 [article]

Forest canopy height estimation using satellite laser altimetry : a case study in the Western Ghats, India / S.M. Ghosh in Applied geomatics, vol 9 n° 3 (September 2017)  

Public [article]  inApplied geomatics > vol 9 n° 3 (September 2017) . - pp 159 - 166 Titre : Forest canopy height estimation using satellite laser altimetry : a case study in the Western Ghats, India Type de document : Article/Communication Auteurs : S.M. Ghosh, Auteur ; M. Dev Behera, Auteur Année de publication : 2017 Article en page(s) : pp 159 - 166 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] altimétrie satellitaire par laser [Termes IGN] données altimétriques [Termes IGN] données ICEsat [Termes IGN] données laser [Termes IGN] données localisées 3D [Termes IGN] forêt tropicale [Termes IGN] Ghats occidentaux [Termes IGN] hauteur des arbres [Termes IGN] Inde [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] pente Résumé : (Auteur) Canopy height is a crucial metric required to quantify the aboveground plant biomass accurately. The study explores the data derived using Light Detection and Ranging (LiDAR) technology from GeoScience Laser Altimeter System (GLAS) aboard Ice, Cloud, and Land Elevation satellite (ICESat) to derive canopy height estimate equations in the tropical forests of the Western Ghats, India. The interpretation of LiDAR waveforms for the purpose of estimating canopy heights is not straightforward, especially over sloping terrain where vegetation and ground are found at comparable heights. Canopy height models are developed using GLAS waveform extent and terrain index, derived from ASTER digital elevation, to counter the effect of topographic relief effects in canopy height estimates over steep terrain. The model was applied to calculate tree heights for whole of the Western Ghats. Results showed that the model can estimate tree heights within the specified height range with an accuracy of more than 90% while using percent overestimation/underestimation method of validation. This shows the effectiveness of the model, especially over steep slopes, also revealing that the models were able to successfully account for the pulse broadening effect. The study highlights the development of a LiDAR-based canopy height model for tropical forest and its ability to yield better canopy height estimates especially over steep slopes. Numéro de notice : A2017-597 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1007/s12518-017-0190-2 En ligne : https://doi.org/10.1007/s12518-017-0190-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86815 [article]