『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