Details:

Journal Title:
Remote Sensing of Environment
Personal Author:
Ho, Jeff C. ; Stumpf, Richard P. ; Bridgeman, Thomas B. ; Michalak, Anna M.
Ho, Jeff C. ; Stumpf, Richard P. ; Bridgeman, Thomas B. ; Michalak, Anna M. Less -
NOAA Program & Office:
NOS (National Ocean Service)
Description:
Long-term records of phytoplankton blooms in freshwater lakes are necessary both for understanding basin scale changes to watersheds and for providing a key constraint for assessing processes driving blooms. However, due to the inherent constraints of in situ sampling and the short time period covered by modem space borne sensors, few long-term records exist. Historical data from sensors such as Landsat offer strong potential for creating new records of past blooms. Here, we use a novel evaluation procedure based on multiple metrics to assess algorithm suitability and robustness for generating long-term bloom records using Landsat 5 imagery. Evaluation metrics are based on bloom presence, spatial distribution, magnitude and timing, using both in situ Microcystis biovolume and remotely-sensed Cyanobacterial Index (CI) data from MERIS for 2002-2011. Applying this procedure for a test case focusing on Lake Erie's western basin, an algorithm based on a near infrared threshold with simple atmospheric correction through subtraction of the shortwave infrared band, combined with an additional "greenness" filter based on a hue threshold, performs best. Implementing this algorithm for 1984-2001 reveals the long-term trends in peak bloom magnitude prior to the start of the MERIS and MODIS record (2002-2015), and more than doubles the period of record that can be used to understand bloom occurrence and growth for this system. More broadly, we demonstrate that Landsat observations can be used to identify macro-scale features of blooms. For Lake Erie, the performance of the final Landsat algorithm is comparable to that of the MERIS CI algorithm, despite Landsat's broad spectral bands and long revisit time. (C) 2017 The Authors. Published by Elsevier Inc.
Source:
Remote Sensing of Environment, 191, 273-285.
DOI:
https://doi.org/10.1016/j.rse.2016.12.013
Document Type:
Journal Article
Rights Information:
CC BY
Compliance:
Submitted
Main Document Checksum:
[+]
urn:sha256:104f20fd4460ec1a32c269984b0eb42b025eedfedbcf5ed74c437b4e82b920c8
Download URL:
https://repository.library.noaa.gov/view/noaa/16056/noaa_16056_DS1.pdf
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