MyJournals Home  

RSS FeedsRemote Sensing, Vol. 10, Pages 1273: Colour Classification of 1486 Lakes across a Wide Range of Optical Water Types (Remote Sensing)


15 august 2018 10:01:21

Remote Sensing, Vol. 10, Pages 1273: Colour Classification of 1486 Lakes across a Wide Range of Optical Water Types (Remote Sensing)

Remote sensing by satellite-borne sensors presents a significant opportunity to enhance the spatio-temporal coverage of environmental monitoring programmes for lakes, but the estimation of classic water quality attributes from inland water bodies has not reached operational status due to the difficulty of discerning the spectral signatures of optically active water constituents. Determination of water colour, as perceived by the human eye, does not require knowledge of inherent optical properties and therefore represents a generally applicable remotely-sensed water quality attribute. In this paper, we implemented a recent algorithm for the retrieval of colour parameters (hue angle, dominant wavelength) and derived a new correction for colour purity to account for the spectral bandpass of the Landsat 8 Operational Land Imager (OLI). We used this algorithm to calculate water colour on almost 45,000 observations over four years from 1486 lakes from a diverse range of optical water types in New Zealand. We show that the most prevalent lake colours are yellow-orange and blue, respectively, while green observations are comparatively rare. About 40% of the study lakes show transitions between colours at a range of time scales, including seasonal. A preliminary exploratory analysis suggests that both geo-physical and anthropogenic factors, such as catchment land use, provide environmental control of lake colour and are promising avenues for future analysis. Digg Facebook Google StumbleUpon Twitter
34 viewsCategory: Geology, Physics
Remote Sensing, Vol. 10, Pages 1274: A Performance Evaluation of a Geo-Spatial Image Processing Service Based on Open Source PaaS Cloud Computing Using Cloud Foundry on OpenStack (Remote Sensing)
Remote Sensing, Vol. 10, Pages 1272: Mapping Damage-Affected Areas after Natural Hazard Events Using Sentinel-1 Coherence Time Series (Remote Sensing)
blog comments powered by Disqus
The latest issues of all your favorite science journals on one page


Register | Retrieve



Use these buttons to bookmark us: Digg Facebook Google StumbleUpon Twitter

Valid HTML 4.01 Transitional
Copyright © 2008 - 2019 Indigonet Services B.V.. Contact: Tim Hulsen. Read here our privacy notice.
Other websites of Indigonet Services B.V.: Nieuws Vacatures News Tweets Travel Photos Nachrichten Indigonet Finances Leer Mandarijn