In situ estimation of water quality parameters in freshwater aquaculture ponds using hyperspectral imaging system
In situ estimation of water quality parameters in freshwater aquaculture ponds using hyperspectral imaging system
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DOI：
10.1016/j.isprsjprs.2011.02.005
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发表时间：
2011-07
影响因子：
12.7
通讯作者：
A. Abd-Elrahman;Matthew D. Croxton;Roshan Pande-Chettri;G. Toor;Scot E. Smith;Jeffrey E. Hill
中科院分区：
文献类型：
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作者：
A. Abd-Elrahman;Matthew D. Croxton;Roshan Pande-Chettri;G. Toor;Scot E. Smith;Jeffrey E. Hill
了解水质参数对养殖观赏鱼的淡水水产养殖业务的可持续性是不可或缺的。在本研究中，我们的目的是评估移动地面高光谱（HS）成像传感器测定工作水产养殖池塘中叶绿素-a （Chl-a）浓度的能力，这些池塘代表了被操纵的、浅的、营养丰富的系统，并确定使用水下反射目标对叶绿素-a估计精度的影响。我们收集了水产养殖池塘的Chl-a测量值，范围为0.8 ~ 494μg/L。Chl-a测量值与HS图像反射率计算的两波段和三波段光谱指数有很强的相关性。利用10 cm深度水下目标捕获的光谱，两波段和三波段模型的决定系数（R2）分别为0.975和0.982。利用从水中捕获的光谱（无水下目标），两波段和三波段模型的r2值略低，分别为0.833和0.862。30 cm深度水下目标的数据与测量的叶绿素-a浓度的相关性最低，可能是由于水柱性质的变化和平台投射的阴影。总磷(P)和总氮(N)浓度与Chl-a浓度敏感的光谱指数建模显示出中等水平的相关性。去除模型异常值（N和P浓度最大的观测值）导致模型的决定系数显著增加（例如，使用三波段指数值的P模型从0.478增加到0.823），这突出了使用HS图像估计N和P浓度的可能性，以及需要更多的研究来模拟水产养殖水体系统中Chl-a与营养物质浓度之间的相互关系。
Knowledge of water quality parameters is integral to sustainability of freshwater aquaculture operations that raise ornamental fish. Our objective in this study is to evaluate the ability of a mobile, ground-based hyperspectral (HS) imaging sensor to determine chlorophyll-a (Chl-a) concentrations in working aquaculture ponds, which represent manipulated, shallow, nutrient-rich systems, and to determine the effect of using submerged reflectance targets on the accuracy of Chl-a estimation. We collected Chl-a measurements from aquaculture ponds ranging from 0.8 to 494μg/L. Chl-a measurements showed a strong correlation with two-band and three-band spectral indices computed from the HS image reflectance. Coefficient of determination (R2) values of 0.975 and 0.982 were obtained for the two- and three-band models, respectively, using spectra captured from the submerged target at 10 cm depth. Using spectra captured from water (no submerged targets), R2values were slightly lower at 0.833 and 0.862 for two- and three-band models. Data from the submerged target at 30 cm depth had the lowest correlation with measured chlorophyll-a concentrations, potentially due to variations in water column properties and shadows cast by the platform. Modeling total Phosphorous (P) and Nitrogen (N) concentrations of the collected samples with the spectral indices sensitive to Chl-a concentrations showed a moderate level of correlation. Removing a model outlier (observation with maximum N and P concentrations) led to a significant increase in the models’ coefficient of determination (e.g. from 0.478 to 0.823 for the P model using three-band index values), which highlighted the possibility of using HS imagery to estimate N and P concentrations and the need for more research to model the interrelationships between Chl-a and nutrient concentrations in aquaculture water systems.