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Remote Sensing for Ecology and ConservationA Handbook of Techniques$
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Ned Horning, Julie A. Robinson, Eleanor J. Sterling, Woody Turner, and Sacha Spector

Print publication date: 2010

Print ISBN-13: 9780199219940

Published to Oxford Scholarship Online: November 2020

DOI: 10.1093/oso/9780199219940.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 20 January 2022

Marine and coastal environments

Marine and coastal environments

(p.140) 6 Marine and coastal environments
Remote Sensing for Ecology and Conservation

Ned Horning

Julie A. Robinson

Eleanor J. Sterling

Woody Turner

Sacha Spector

Oxford University Press

New remote sensing challenges arise from the addition of the water column to the remote sensing signal. At the same time, new opportunities for use of remotely sensed data are possible in the marine environment. Marine environments can have organisms in such great abundance that they are readily monitored using remote sensing. From measuring ocean productivity, to harmful algal blooms (HABs), to fisheries management, remote sensing is a key component of many efforts to manage and conserve marine ecosystems. For example, the small giant clam, Tridacna maxima, is endangered in some areas of the Pacific, and because of commercial harvest pressure is listed in Appendix II of the Convention on the International Trade of Endangered Species (CITES, meaning they are not yet threatened by extinction but could become so if their trade is not tightly regulated). Andréfouët et al. (2005a) used field observations and remotely sensed data to study the productivity of the clam fishery in tiny (22.2 km2, including a 9.9 km2 lagoon) Fangatau Atoll (Eastern Tuamotu, French Polynesia). The fishery was under pressure due to the large (4 ton per year) export of clams to Tahiti. Remotely sensed data included a mosaic of aerial photographs (1.5 m resolution), a digital photograph taken from the International Space Station (red, green, blue, 5.6 m resolution), and Landsat TM imagery (30 m resolution). The authors classified each image of key lagoon habitats, using maximum likelihood supervised classification, with each image classified independently. They estimated the population size for the entire lagoon by multiplying the mean clam density in each habitat (from field data) by the total area of each habitat (in the maps made from the remotely sensed data). Amazingly, an estimated 23.65 ± 5.33 million clams (mean ± 95 percent confidence interval) inhabited the 4.05 km2 area of suitable habitat in the lagoon. The high spatial resolution data (1.5 m aerial and 5.6 m astronaut photography data) both gave equivalent estimates of the biomass with good estimates of accuracy, but the Landsat 30 m data overestimated the population.

Keywords:   airborne photography, bathymetry, climate change, depth correction, ecological model, filter, giant clam, heat stress, ice, kelp

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