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Assessing the ability of hybrid poplar for in-situ phytoextraction of cadmium by using UAV-photogrammetry and 3D flow simulator.

Authors :
Capolupo, Alessandra
Nasta, Paolo
Palladino, Mario
Cervelli, Elena
Boccia, Lorenzo
Romano, Nunzio
Source :
International Journal of Remote Sensing. Aug2018, Vol. 39 Issue 15/16, p5175-5194. 20p. 1 Map.
Publication Year :
2018

Abstract

The purpose of this study is to evaluate the capability of hybrid poplar (Populus deltoides × Populus nigra) to reduce cadmium (Cd) concentrations in an experimental site of Campania Region (southern Italy) subjected to illegal deposit of industrial and household waste. We propose to evaluate the efficiency of poplar for Cd phytoextraction by coupling the use of a process-based, distributed hydrological model (HydroGeoSphere, HGS) with photogrammetric images acquired by Unmanned Aerial Vehicle (UAV). This scenario-based approach exploits in-situ measurements so as to be able to reproduce reliable near-real-world processes. The original bare soil (BS; unplanted reference location) is used as benchmark and compared to the situation where poplar trees are planted (PP) for bioremediation purposes. The 'virtual' positions of poplars were chosen by considering the expected Cd accumulation areas that are correlated to topographic indices retrieved from the high-resolution (0.03 × 0.03 m) digital elevation model (DEM) generated by UAV photogrammetric photos. Transfer and accumulation of Cd in the poplars were described by a timevariant sink term featuring the HGS transport equation. The numerical simulations show that poplar trees are able to reduce Cd concentrations by 15%, 36%, and 64% in spring, summer, and autumn, respectively. Coupling an advanced 3D hydrological model with a high-resolution DEM generated by UAV-photogrammetry seems a promising and viable approach for assessing the efficiency of phytoremediation techniques. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01431161
Volume :
39
Issue :
15/16
Database :
Academic Search Index
Journal :
International Journal of Remote Sensing
Publication Type :
Academic Journal
Accession number :
131332737
Full Text :
https://doi.org/10.1080/01431161.2017.1422876