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Remote Sens. 2019, 11(3), 317; https://doi.org/10.3390/rs11030317

Kd(PAR) and a Depth Based Model to Estimate the Height of Submerged Aquatic Vegetation in an Oligotrophic Reservoir: A Case Study at Nova Avanhandava

1
Department of Cartography—Presidente Prudente, São Paulo State University (UNESP), São Paulo, SP 19160-900, Brazil
2
Department of Geography, University of Georgia (UGA), Athens, GA 30609, USA
3
Department of Environmental Engineering—São José dos Campos, São Paulo State University (UNESP), São Paulo, SP 12245-000, Brazil
4
Federal Institute of Education, Science and Technology of Pará State (IFPA), Castanhal, PA 68740-970, Brazil
*
Author to whom correspondence should be addressed.
Received: 11 December 2018 / Revised: 25 January 2019 / Accepted: 29 January 2019 / Published: 5 February 2019
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Abstract

Submerged aquatic vegetation (SAV) carry out important biological functions in freshwater systems, however, uncontrolled growth can lead to many negative ecologic and economic impacts. Radiation availability is the primary limiting factor for SAV and it is a function of water transparency measured by Kd(PAR) (downwelling attenuation coefficient of Photosynthetically Active Radiation) and depth. The aim of this study was to develop a Kd(PAR) and depth based model to estimate the height of submerged aquatic vegetation in a tropical oligotrophic reservoir. This work proposed a new graphical model to represent the SAV height in relation to Kd(PAR) and depth. Based on the visual analysis of the model, it was possible to establish a set of Boolean rules to classify the SAV height and identify regions where SAV can grow with greater or lesser vigor. Kd(PAR) was estimated using a model based on satellite data. Overall, the occurrence and height of SAV were directly influenced by the Kd(PAR), depending on the depth. This study highlights the importance of optical parameters in examining the occurrence and status of SAV in Brazilian Reservoirs. It was concluded that the digital model and its graphical representation overcomes the limitations found by other researchers to understand the SAV behavior in relation to those independent variables: depth and Kd(PAR). View Full-Text
Keywords: remote sensing; water quality; inland waters; Boolean classification; echosounder data remote sensing; water quality; inland waters; Boolean classification; echosounder data
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Rotta, L.H.; Mishra, D.R.; Alcântara, E.; Imai, N.; Watanabe, F.; Rodrigues, T. Kd(PAR) and a Depth Based Model to Estimate the Height of Submerged Aquatic Vegetation in an Oligotrophic Reservoir: A Case Study at Nova Avanhandava. Remote Sens. 2019, 11, 317.

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