Analysis of Hyperspectral Data for Coastal Bathymetry and Water Quality

2001 ◽  
Author(s):  
William Philpot
Keyword(s):  
Water Quality ◽  
Hyperspectral Data

scholarly journals Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae and Turbidity

2018 ◽  
Vol 15 (9) ◽  
pp. 1881 ◽  
Author(s):  
Sina Keller ◽  
Philipp Maier ◽  
Felix Riese ◽  
Stefan Norra ◽  
Andreas Holbach ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Chlorophyll A ◽  
Green Algae ◽  
Hyperspectral Data ◽  
Coefficient Of Determination ◽  
Inland Waters ◽  
Learning Approaches ◽  
River Elbe ◽  
Regression Framework

Inland waters are of great importance for scientists as well as authorities since they are essential ecosystems and well known for their biodiversity. When monitoring their respective water quality, in situ measurements of water quality parameters are spatially limited, costly and time-consuming. In this paper, we propose a combination of hyperspectral data and machine learning methods to estimate and therefore to monitor different parameters for water quality. In contrast to commonly-applied techniques such as band ratios, this approach is data-driven and does not rely on any domain knowledge. We focus on CDOM, chlorophyll a and turbidity as well as the concentrations of the two algae types, diatoms and green algae. In order to investigate the potential of our proposal, we rely on measured data, which we sampled with three different sensors on the river Elbe in Germany from 24 June–12 July 2017. The measurement setup with two probe sensors and a hyperspectral sensor is described in detail. To estimate the five mentioned variables, we present an appropriate regression framework involving ten machine learning models and two preprocessing methods. This allows the regression performance of each model and variable to be evaluated. The best performing model for each variable results in a coefficient of determination R 2 in the range of 89.9% to 94.6%. That clearly reveals the potential of the machine learning approaches with hyperspectral data. In further investigations, we focus on the generalization of the regression framework to prepare its application to different types of inland waters.

scholarly journals Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis

2021 ◽  
Vol 13 (22) ◽  
pp. 4643
Author(s):  
Jinhua Liu ◽  
Jianli Ding ◽  
Xiangyu Ge ◽  
Jingzhe Wang
Keyword(s):  
Water Quality ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Fractional Order ◽  
Total Nitrogen ◽  
Hyperspectral Data ◽  
Spectral Information ◽  
Gradient Boosting ◽  
Discrete Wavelet ◽  
Extreme Gradient Boosting

Controlling and managing surface source pollution depends on the rapid monitoring of total nitrogen in water. However, the complex factors affecting water quality (plant shading and suspended matter in water) make direct estimation extremely challenging. Considering the spectral response mechanisms of emergent plants, we coupled discrete wavelet transform (DWT) and fractional order discretization (FOD) techniques with three machine learning models (random forest (RF), bagging algorithm (bagging), and eXtreme Gradient Boosting (XGBoost)) to mine this potential spectral information. A total of 567 models were developed, and airborne hyperspectral data processed with various DWT scales and FOD techniques were compared. The effective information in the hyperspectral reflectance data were better emphasized after DWT processing. After DWT processing the original spectrum (OR), its sensitivity to TN in water was maximally improved by 0.22, and the correlation between FOD and TN in water was optimally increased by 0.57. The transformed spectral information enhanced the TN model accuracy, especially for FOD after DWT. For RF, 82% of the model R2 values improved by 0.02~0.72 compared to the model using FOD spectra; 78.8% of the bagging values improved by 0.01~0.53 and 65.0% of the XGBoost values improved by 0.01~0.64. The XGBoost model with DWT coupled with grey relation analysis (GRA) yielded the best estimation accuracy, with the highest precision of R2 = 0.91 for L6. In conclusion, appropriately scaled DWT analysis can substantially improve the accuracy of extracting TN from UAV hyperspectral images. These outcomes may facilitate the further development of accurate water quality monitoring in sophisticated global waters from drone or satellite hyperspectral data.

scholarly journals Hyper-spectral Remote Sensing of Water Quality Parameters in Lakes: A Case Study of Hyderabad City, Telangana State, India

2017 ◽  
Vol 8 (2) ◽  
pp. 5
Author(s):  
V. Hema Sailaja ◽  
P. Suman Babu ◽  
M. Anji Reddy
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll A ◽  
Secchi Depth ◽  
Quality Parameters ◽  
Water Quality Modeling ◽  
Hyperspectral Data ◽  
Water Quality Parameters ◽  
Model Versus

This paper is a research work intended to present a comprehensive water quality modeling for predicting three water quality parameters (Chlorophyll (a), Turbidity and Secchi Depth) in typical Inland lake environments (Hussain sagar and Umda sagar) using Hyperspectral Remote sensing technique. They are estimated through regression models by combining the field Spectro-radiometer reflectance values with concurrent in situ ground data (Analytical) collected in the study area and correlated and validated with the available Hyperspectral data (Hyperion).  A total of 180 in situ water sample and 900 spectral signatures were analysed during campaigns from 2010 to 2014 study period. The mean values of Chlorophyll-a varied between 6.983mgL<sup>-1</sup> and 24.858mgL<sup>-1</sup>, Turbidity varied between 16.583mgL<sup>-1</sup> and 48.867mgL<sup>-1</sup> and Secchi depth varied between 0.104mgL<sup>-1</sup> and 0.375mgL<sup>-1</sup> over the study period considering the two lakes during pre and post monsoon seasons. The band ratios of the reflected spectra at R670/R710, R710/R740 and R710/R550 are used for the development of the mathematical model of chlorophyll-a, Turbidity and Secchi depth respectively. The trained sets of the pixels extracted from the hyperspectral data for pure spectra are processed for preparing the water quality distribution maps. When subjected to multi-variant statistical tests of significance, the models have yielded satisfactory R<sup>2</sup> values. The model versus in situ analysis results demonstrated R<sup>2</sup>= 0.81% for Chlorophyll-a, R<sup>2</sup>= 0.81%  for Turbidity and R<sup>2</sup>= 0.78% for Secchi depth correlation and that of model versus satellite data exhibited R<sup>2</sup>= 0.60% for Chlorophyll-a, R<sup>2</sup>= 0.66% for Turbidity and R<sup>2</sup>= 0.65 %  for Secchi depth mean efficiency.

scholarly journals Water Quality Assessment of River Ganga and Chilika Lagoon using AVIRIS-NG Hyperspectral Data

2019 ◽  
Vol 116 (7) ◽  
pp. 1172 ◽  
Author(s):  
S. Chander ◽  
Ashwin Gujrati ◽  
K. Abdul Hakeem ◽  
Vaibhav Garg ◽  
Annie Maria Issac ◽  
...  
Keyword(s):  
Water Quality ◽  
Quality Assessment ◽  
Water Quality Assessment ◽  
Hyperspectral Data ◽  
Chilika Lagoon ◽  
River Ganga
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