Hyperspectral Data Applied in Monitoring and Evaluating the Water Trophic State of Keqin Lake, Zhalong Wetland
Received date: 2011-02-18
Request revised date: 2011-04-06
Online published: 2012-02-20
Copyright
The approach of field reflectance spectra was used to monitor and evaluate the trophic state in Keqin Lake, Zhalong wetland, China, based on 30 in situ water sampling data, together with the analysis of algorithms of chlorophyll-a, SD, TP and CODMn. The field reflectance hyperspectral was measured with ASD Fieldspec spectrometer. Furthermore, with the TSIM trophic state index model, the trophic state of Keqin Lake was monitored and evaluated. The results showed thatThe single band is significantly correlated with the concentration of chlorophyll-a, it is higher than that gained with other models. On the basis of analyzing the water reflectance spectral characteristics, the sensitive wave band of water quality parameters is diagnosed with the methods of differential spectroscopy and statistical analysis, the sensitive wave bands of TN, TP, SD and CODMn are 733 nm, 765 nm, 782 nm, and 680 nm, respectively. To compared with multi parameter TSIM, single parameter TSIM is limited. So the assessment of trophic state was performed by multi-parameter TSIM. Keqin Lake is in mesotrophic state, and needs to be prevented from further worsen.
Key words: Keqin Lake; hyperspectral; water quality parameter; eutrophication
ZHANG Nan-nan , ZANG Shu-ying . Hyperspectral Data Applied in Monitoring and Evaluating the Water Trophic State of Keqin Lake, Zhalong Wetland[J]. SCIENTIA GEOGRAPHICA SINICA, 2012 , 32(2) : 232 -237 . DOI: 10.13249/j.cnki.sgs.2012.02.232
Fig .1 Location of study area and the sampling sites图1 研究区及采样点示意图 |
Fig .2 Correlative analyses between Chlorophyll-a concentration and reflectance, unitary and derivative图2 原始光谱、归一化光谱、一阶微分光谱与叶绿素a浓度的相关性 |
Table 1 The estimated model of Chl-a concentration表1 叶绿素a浓度估测模型 |
线性拟合方程 (n=22) | R2 | |
---|---|---|
R703nm | Y=19.8375x-12.587 | 0.892 |
R696nm/R503nm | Y=13.6449x-6.7936 | 0.885 |
R(λ574)' | Y=16534.3x+6.6368 | 0.889 |
Table 2 The verification of the Chl-a concentration estimated model表2 叶绿素a浓度估测模型验证 |
验证方程 (n=8) | R2 | |
---|---|---|
R703nm | Y=1.0469x+0.18 | 0.771 |
R696nm/R503nm | Y=1.011x+0.2984 | 0.770 |
R(λ574)' | Y=1.0797x-0.2756 | 0.753 |
Fig .3 Correlation between first derivative reflectance and water quality parameters图3 一阶微分光谱反射率与各水质参数相关性 |
Table 3 Regression analysis of water quality parameters and first derivative reflectanc表3 水质参数与一阶微分反射率的拟合模型 |
线性拟合方程 | R2 | sig. |
---|---|---|
SD=-24875x(782)+37.4832 | 0.73 | ** |
COD=11241.4x(680)+5.4406 | 0.663 | ** |
TN=221.032x(733)+0.4032 | 0.758 | ** |
TP=164.383x(765)+0.0748 | 0.745 | ** |
注: **通过置信水平0.01的系数检验。 |
Fig .4 Modeling validation with estimated data and measured data of water quality parameters图4 水质参数高光谱估测数据和实测数据模型验证 |
Fig. 5 The assessments of water quality with parameters and average TSIM图5 各水质参数及TSIM平均值的富营养化状态评价结果 |
Fig .6 Modeling validation with estimated data and measured data of water quality parameters图6 各参数水质高光谱实测数据与估测数据验证 |
The authors have declared that no competing interests exist.
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