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Andy Hardy
Researcher at Aberystwyth University
Publications - 34
Citations - 1394
Andy Hardy is an academic researcher from Aberystwyth University. The author has contributed to research in topics: Mangrove & Flood myth. The author has an hindex of 12, co-authored 29 publications receiving 842 citations. Previous affiliations of Andy Hardy include Ifakara Health Institute & Newcastle University.
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Journal ArticleDOI
The Global Mangrove Watch—A New 2010 Global Baseline of Mangrove Extent
Peter Bunting,Ake Rosenqvist,Richard Lucas,Lisa-Maria Rebelo,Lammert Hilarides,Nathan Thomas,Andy Hardy,Takuya Itoh,Masanobu Shimada,C. Max Finlayson +9 more
TL;DR: This study presents a new global baseline of mangrove extent for 2010 and has been released as the first output of the Global Mangrove Watch (GMW) initiative, the first study to apply a globally consistent and automated method for mapping mangroves.
Journal ArticleDOI
Distribution and drivers of global mangrove forest change, 1996–2010
TL;DR: The primary driver of anthropogenic mangrove loss was found to be the conversion ofMangrove to aquaculture/agriculture, although substantial advance of mangroves was also evident in many regions.
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Daily discharge estimation at ungauged river sites using remote sensing
Stephen Birkinshaw,Phillip Moore,Chris Kilsby,Greg O'Donnell,Andy Hardy,Andy Hardy,Phillipa A. M. Berry +6 more
TL;DR: In this paper, a methodology is developed to estimate daily river discharge at an ungauged site using remote sensing data using ERS-2 and ENVISAT satellite altimetry.
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Using low-cost drones to map malaria vector habitats
TL;DR: The drone-based surveys carried out in this study provide a low-cost and flexible solution to mapping water bodies for operational dissemination of LSM initiatives in mosquito vector-borne disease elimination campaigns.
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Mapping Mangrove Extent and Change: A Globally Applicable Approach
TL;DR: This study demonstrates a globally applicable method for monitoring mangrove forest extent at high spatial resolution using a novel map-to-image change method that makes fewer assumptions of the data, is less sensitive to variation between scenes due to environmental factors and is able to automatically identify a change threshold.