Quantification of sawgrass marsh aboveground biomass in the coastal Everglades using object-based ensemble analysis and Landsat data

Large-scale biomass quantification of sawgrass (Cladium jamaicense) marsh is critical to understand the carbon and energy cycle in the Florida Everglades. There is also a need to monitor biomass changes in the coastal Everglades due to continuing sea level rise. Previous research in biomass estimati...

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Veröffentlicht in:	Remote sensing of environment Jg. 204; S. 366 - 379
Hauptverfasser:	Zhang, Caiyun, Denka, Sara, Cooper, Hannah, Mishra, Deepak R.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Elsevier Inc 01.01.2018 Elsevier BV
Schlagworte:	aboveground biomass Algorithms Artificial intelligence Artificial neural networks Biomass carbon Carbon cycle Cladium Cladium mariscus subsp. jamaicense Coastal marshes coasts Correlation coefficient Correlation coefficients Data processing energy Ensemble analysis for biomass prediction Florida harvest date Landsat Landsat satellites Learning algorithms Learning theory Machine learning Machine learning regression algorithms marshes Mathematical models Modelling Neural networks Object-based biomass modeling Pixels prediction Regression analysis Remote sensing sea level Sea level rise Studies Support vector machines uncertainty Florida United States > US Machine learning regression algorithms Object-based biomass modeling Ensemble analysis for biomass prediction Coastal marshes
ISSN:	0034-4257, 1879-0704
Online-Zugang:	Volltext
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