Remote sensing and geostatistics in urban water-resource monitoring: a review
Zhixin Liu
A , Jiayi Xu A , Mingzhe Liu B * , Zhengtong Yin C , Xuan Liu D , Lirong Yin E * and Wenfeng Zheng F *
+ Author Affiliations
- Author Affiliations
A School of Life Science, Shaoxing University, Shaoxing, Zhejiang, 312000, PR China.
B School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, 325000, PR China.
C College of Resource and Environment Engineering, Guizhou University, Guiyang, Guizhou, 550025, PR China.
D School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
E Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA.
F School of Automation, University of Electronic Science and Technology of China, Chengdu, 610054, PR China.
Handling Editor: Wan Zhanhong
Marine and Freshwater Research 74(10) 747-765 https://doi.org/10.1071/MF22167
Submitted: 23 August 2022 Accepted: 6 February 2023 Published: 14 March 2023
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing
Abstract
Context: At present, many cities are facing severe water-resources problems caused by urbanisation. With the development of remote sensing and geostatistics, they have been widely used in urban water-resource monitoring.
Aims: To review and summarise the application of remote sensing and geostatistics in monitoring urban water resources and prospect for their furtherdevelopment.
Methods: First, bibliometrics was used to analyse the existing literature in this field. We then discuss the use of remote sensing and geostatistics to improve urban water-resources monitoring capacity, focusing on the classification of technologies and equipment and their applications in urban surface-water and urban groundwater monitoring. Finally, a look at the future research direction is taken.
Conclusions: In the past decade, the relevant research has shown an upward trend. The use of remote sensing and geostatistics can improve the city’s water-resource monitoring capacity, thereby promoting better use of water resources in cities.
Implications: In the future, with the development and addition of deep learning, remote-sensing and geographic-analysis systems can be used to conduct remote-sensing monitoring and data analysis on urban water resources more accurately, intelligently, and quickly, and improve the status of urban water resources.
Keywords: catchment management, conservation, ecohydrology, environmental monitoring, geostatistics, GI, monitoring, remote sensing, urban water resource.
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