Large lake gauging using fractional imagery. (1st February 2019)
- Record Type:
- Journal Article
- Title:
- Large lake gauging using fractional imagery. (1st February 2019)
- Main Title:
- Large lake gauging using fractional imagery
- Authors:
- Park, Edward
Lewis, Quinn W.
Sanwlani, Nivedita - Abstract:
- Abstract: Large floodplain lakes provide riparian habitat, are sediment and nutrient sinks, help control flow connectivity and flooding along rivers, and are both used by humans and strongly impacted by human activity. However, water level in many remote large floodplain lakes, especially in developing countries, is often monitored inconsistently or not at all. In this study, a novel method for estimating large lake water level using passive, optical remote sensing data combined with any digital elevation model (DEM) is presented. The method obtains water level estimates at 30 m 2 resolution using Landsat, in this case in conjunction with SRTM elevation data, nested within a 240 m 2 grid "fishnet". A probabilistic mean of elevation values for all water-designated pixels (between 5% and 95% filled within each grid) produces lake water levels often accurate to within ±50 cm of gauged reference data on Lake Curuai in the Amazon River and Tonle Sap Lake along the Mekong River. The method is relatively insensitive to cloud cover, especially as lake size increases. This study is the first to use solely passive optical remote sensing data for water level estimation and thus could be used to produce accurate, long-term estimations of water level in many large lakes globally. The use of optical sensors to obtain lake water level is both an important complement and potential alternative to methods that use active sensors. Highlights: A novel method to estimate lake water level usingAbstract: Large floodplain lakes provide riparian habitat, are sediment and nutrient sinks, help control flow connectivity and flooding along rivers, and are both used by humans and strongly impacted by human activity. However, water level in many remote large floodplain lakes, especially in developing countries, is often monitored inconsistently or not at all. In this study, a novel method for estimating large lake water level using passive, optical remote sensing data combined with any digital elevation model (DEM) is presented. The method obtains water level estimates at 30 m 2 resolution using Landsat, in this case in conjunction with SRTM elevation data, nested within a 240 m 2 grid "fishnet". A probabilistic mean of elevation values for all water-designated pixels (between 5% and 95% filled within each grid) produces lake water levels often accurate to within ±50 cm of gauged reference data on Lake Curuai in the Amazon River and Tonle Sap Lake along the Mekong River. The method is relatively insensitive to cloud cover, especially as lake size increases. This study is the first to use solely passive optical remote sensing data for water level estimation and thus could be used to produce accurate, long-term estimations of water level in many large lakes globally. The use of optical sensors to obtain lake water level is both an important complement and potential alternative to methods that use active sensors. Highlights: A novel method to estimate lake water level using optical imagery is presented. The method is validated successfully with stage data for two large floodplain lakes.. The simple method enables calculation of relatively accurate water levels for large floodplain lakes over long periods. … (more)
- Is Part Of:
- Journal of environmental management. Volume 231(2019)
- Journal:
- Journal of environmental management
- Issue:
- Volume 231(2019)
- Issue Display:
- Volume 231, Issue 2019 (2019)
- Year:
- 2019
- Volume:
- 231
- Issue:
- 2019
- Issue Sort Value:
- 2019-0231-2019-0000
- Page Start:
- 687
- Page End:
- 693
- Publication Date:
- 2019-02-01
- Subjects:
- Lakes -- Water level -- Floodplains -- Rivers -- Optical remote sensing
Environmental policy -- Periodicals
Environmental management -- Periodicals
Environment -- Periodicals
Ecology -- Periodicals
363.705 - Journal URLs:
- http://www.sciencedirect.com/science/journal/03014797 ↗
http://www.elsevier.com/journals ↗
http://www.idealibrary.com ↗
http://firstsearch.oclc.org ↗ - DOI:
- 10.1016/j.jenvman.2018.10.044 ↗
- Languages:
- English
- ISSNs:
- 0301-4797
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4979.383000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 12402.xml