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A continental‐scale assessment of density, size, distribution and historical trends of farm dams using deep learning convolutional neural networks

Malerba, Martino E, Wright, Nicholas and Macreadie, Peter I 2021, A continental‐scale assessment of density, size, distribution and historical trends of farm dams using deep learning convolutional neural networks, Remote sensing, vol. 13, no. 2, pp. 1-15, doi: 10.3390/rs13020319.

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Title A continental‐scale assessment of density, size, distribution and historical trends of farm dams using deep learning convolutional neural networks
Author(s) Malerba, Martino EORCID iD for Malerba, Martino E orcid.org/0000-0002-7480-4779
Wright, Nicholas
Macreadie, Peter IORCID iD for Macreadie, Peter I orcid.org/0000-0001-7362-0882
Journal name Remote sensing
Volume number 13
Issue number 2
Article ID 319
Start page 1
End page 15
Total pages 15
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2021-01-02
ISSN 2072-4292
Keyword(s) Science & Technology
Life Sciences & Biomedicine
Physical Sciences
Technology
Environmental Sciences
Geosciences, Multidisciplinary
Remote Sensing
Imaging Science & Photographic Technology
Environmental Sciences & Ecology
Geology
water security
artificial water bodies
GIS
deep learning classification models
Australian management policies
land-use change
surface water detection
Summary Farm dams are a ubiquitous limnological feature of agricultural landscapes worldwide. While their primary function is to capture and store water, they also have disproportionally large effects on biodiversity and biogeochemical cycling, with important relevance to several Sustainable Development Goals (SDGs). However, the abundance and distribution of farm dams is unknown in most parts of the world. Therefore, we used artificial intelligence and remote sensing data to address this critical global information gap. Specifically, we trained a deep learning convolutional neural network (CNN) on high-definition satellite images to detect farm dams and carry out the first continental-scale assessment on density, distribution and historical trends. We found that in Australia there are 1.765 million farm dams that occupy an area larger than Rhode Island (4678 km2) and store over 20 times more water than Sydney Harbour (10,990 GL). The State of New South Wales recorded the highest number of farm dams (654,983; 37% of the total) and Victoria the highest overall density (1.73 dams km−2). We also estimated that 202,119 farm dams (11.5%) remain omitted from any maps, especially in South Australia, Western Australia and the Northern Territory. Three decades of historical records revealed an ongoing decrease in the construction rate of farm dams, from >3% per annum before 2000, to ~1% after 2000, to <0.05% after 2010—except in the Australian Capital Territory where rates have remained relatively high. We also found systematic trends in construction design: farm dams built in 2015 are on average 50% larger in surface area and contain 66% more water than those built in 1989. To facilitate sharing information on sustainable farm dam management with authorities, scientists, managers and local communities, we developed AusDams.org—a free interactive portal to visualise and generate statistics on the physical, environmental and ecological impacts of farm dams.
Language eng
DOI 10.3390/rs13020319
Indigenous content off
Field of Research 0203 Classical Physics
0406 Physical Geography and Environmental Geoscience
0909 Geomatic Engineering
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30147653

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.