Song,K., Li,L., Li,S., Tedesco,L., Hall,B. and Li,L.(2012): Hyperspectral remote sensing of total phosphorus (TP) in three central Indiana water supply reservoirs. Water Air Soil Pollut., 223, 1481-1502.

『3つの中央インディアナ州利水ダムにおける全リン(TP)のハイパースペクトル・リモートセンシング』


Abstract
 The connection between nutrient input and algal blooms for inland water productivity is well known but bot the spatial pattern of water nutrient loading and algae concentration. Remote sensing provides an effective tool to monitor nutrient abundances via the association with algae concentration. Twenty-one field campaigns have been conducted with samples collected under a diverse range of algal bloom conditions for three central Indiana drinking water bodies, e.g., Eagle Creek Reservoir (ECR), Geist Reservoir (GR), and Morse Reservoir (MR) in 2005, 2006, and 2008, which are strongly influenced anthropogenic activities. Total phosphorus (TP) was estimated through hyperspectral remote sensing due to its close association with chlorophyll a (Chl-a), total suspended matter, Secchi disk transparency (SDT), and turbidity. Correlation analysis was performed to determine sensitive spectral variables for TP, Chl-a, and SDT. A hybrid model combining genetic algorithms and partial least square (GA-PLS) was established for remote estimation of TP, Chl-a, and SDT with selected sensitive spectral variables. The result indicates that TP has close association with diagnostic spectral variables with R2 ranging from 0.55 to 0.72. However, GA-PLS has better performance with an average R2 of 0.87 for aggregated dataset. GA-PLS was applied to the airborne imaging data (AISA) to map spatial distribution of TP, Chl-a, and SDT for MR and GR. The eutrophic status was evaluated with Carlson trophic state index using TP, Chl-a, and SDT maps derived from AISA images. Mapping results indicated that most MR belongs to mesotrophic (48.6%) and eutrophic (32.7%), while the situation was more severe for GR with 57.8% belongs to eutrophic class, and more than 40% to hypereutrophic class due to the high turbidity resulting from dredging practices.

Keywords: Chl-a; GA-PLS; Hyperspectral; SDT; Total phosphorus; Trophic state index』

1. Introduction
2. Material and methods
 2.1. Study area
 2.2. In situ data collection
 2.3. In situ spectra collection
 2.4. Airborne hyperspectral image
  2.4.1. Image acquisition
  2.4.2. Image preprocessing
 2.5. Laboratory analysis water quality parameters
 2.6. Modeling approaches
 2.7. Spectra processing and regressions
 2.8. GA-PLS model
  2.8.1. Genetic algorithm description
  2.8.2. Partial least square regression
  2.8.3. GA-PLS implementation
 2.9. Model assessment
 2.10. Water trophic assessment
3. Results and discussion
 3.1. Water quality characterization
 3.2. TP modeling with in situ data
  3.2.1. Correlation analysis
  3.2.2. Band ratio analysis
  3.2.3. GA-PLS models
 3.3. TP modeling with AISA image spectra
 3.4. TP modeling with in situ and image spectra
 3.5. Chl-a and SDT modeling with AISA image spectra
 3.6. Trophic status mapping
4. Conclusions
Acknowledgments
References 


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