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Open AccessArticle

A Continental-Scale Assessment of Density, Size, Distribution and Historical Trends of Farm Dams Using Deep Learning Convolutional Neural Networks

1
Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, Melbourne, VIC 3125, Australia
2
Sustainability and Biosecurity, Department of Primary Industries and Regional Development, 3 Baron-Hay Court, South Perth, WA 6151, Australia
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(2), 319; https://doi.org/10.3390/rs13020319
Received: 11 December 2020 / Revised: 9 January 2021 / Accepted: 13 January 2021 / Published: 18 January 2021
(This article belongs to the Special Issue Applications of Remote Sensing in Limnology)
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. View Full-Text
Keywords: water security; artificial water bodies; GIS; deep learning classification models; Australian management policies; land-use change; surface water detection water security; artificial water bodies; GIS; deep learning classification models; Australian management policies; land-use change; surface water detection
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MDPI and ACS Style

Malerba, M.E.; Wright, N.; Macreadie, P.I. A Continental-Scale Assessment of Density, Size, Distribution and Historical Trends of Farm Dams Using Deep Learning Convolutional Neural Networks. Remote Sens. 2021, 13, 319. https://doi.org/10.3390/rs13020319

AMA Style

Malerba ME, Wright N, Macreadie PI. A Continental-Scale Assessment of Density, Size, Distribution and Historical Trends of Farm Dams Using Deep Learning Convolutional Neural Networks. Remote Sensing. 2021; 13(2):319. https://doi.org/10.3390/rs13020319

Chicago/Turabian Style

Malerba, Martino E.; Wright, Nicholas; 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 Sens. 13, no. 2: 319. https://doi.org/10.3390/rs13020319

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