Hyperspectral Remote Sensing of Total Phosphorus (TP) in Three Central Indiana Water Supply Reservoirs |
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Authors: | Kaishan Song Lin Li Shuai Li Lenore Tedesco Bob Hall Linhai Li |
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Institution: | (1) Department of Earth Sciences, Indiana University–Purdue University, Indianapolis, IN, USA;(2) Northeast Institute of Geography and Agricultural Ecology, CAS, Changchun, China;(3) Center for Earth and Environmental Sciences, Indiana University–Purdue University, Indianapolis, IN, USA |
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Abstract: | The connection between nutrient input and algal blooms for inland water productivity is well known but not 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 R
2 ranging from 0.55 to 0.72. However, GA-PLS has better performance with an average R
2 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. |
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