Estimation of photosynthetic efficiency for monitoring seagrass health using neural networks

H. Ressom, S. K. Fyfe, S. Srirangam, M. T. Musavi, Padma Natarajan

Research output: Contribution to conferencePaperpeer-review

Abstract

Photosynthetic efficiency is a measure of plant stress that can be used very effectively to monitor the health of marine plants like seagrasses. However in situ measurements of the photosynthetic efficiency of seagrasses are time consuming and expensive. In this paper, neural network-based models are developed to estimate photosynthetic efficiency from field measured spectral reflectance data. Variable selection based on correlation analysis and dimension reduction based on principal component analysis are used for data preprocessing. The significance of the proposed neural network-based approach is that it can model the unknown non-linear relationship between photosynthetic efficiency and spectral reflectance measurements without requiring any prior knowledge of their inherent relationship. The goal is to develop a reliable neural network-based model, which can be extended for application to remotely sensed spectral reflectance data thereby enabling aerial or satellite monitoring of seagrass health.

Original languageEnglish (US)
Pages232-237
Number of pages6
StatePublished - Dec 1 2004
Externally publishedYes
EventProceedings of the IASTED International Conference on Neural Networks and Computational Intelligence - Grindelwald, Switzerland
Duration: Feb 23 2004Feb 25 2004

Other

OtherProceedings of the IASTED International Conference on Neural Networks and Computational Intelligence
CountrySwitzerland
CityGrindelwald
Period2/23/042/25/04

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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