Remote chlorophyll- a retrieval in eutrophic inland waters by concentration classification Taihu Lake case study

2008 ◽  
Author(s):  
Cong Du ◽  
Shixin Wang ◽  
Yi Zhou ◽  
Fuli Yan
Keyword(s):  
Chlorophyll A ◽  
Taihu Lake ◽  
Inland Waters

Scattering spectrum properties and their relationship to biogeochemical parameters: a case study in Taihu Lake

Limnology ◽  
2011 ◽  
Vol 13 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Chang-Chun Huang ◽  
Yun-Mei Li ◽  
Qiao Wang ◽  
De-Yong Sun ◽  
Cheng-Feng Le ◽  
...  
Keyword(s):  
Taihu Lake ◽  
Scattering Spectrum ◽  
Biogeochemical Parameters

Are seasonal lakes as productive as permanent lakes? A case study from Ireland

2010 ◽  
Vol 67 (8) ◽  
pp. 1291-1302 ◽  
Author(s):  
Helder Cunha Pereira ◽  
Norman Allott ◽  
Catherine Coxon
Keyword(s):  
Seasonal Variation ◽  
Linear Regression ◽  
Chlorophyll A ◽  
Algal Biomass ◽  
P Limitation ◽  
Underlying Causes ◽  
Western Ireland ◽  
Log Linear ◽  
First Time

This paper compares, for the first time, nutrient levels and chlorophyll a measured in a set of seasonal lakes with those reported for permanent lakes in the literature. Twenty-two turloughs (karstic seasonal lakes) in western Ireland were sampled monthly from the onset of flooding (October) until they dried out (6 to 9 months). The turloughs showed similar levels of nutrients and chlorophyll a to those reported for Irish and international lakes. Chlorophyll a peaked between November and February in the majority of turloughs, sometimes with values higher than those measured in mesotrophic lakes in summer. A significant log-linear regression was found between total phosphorus and chlorophyll a, which suggests P limitation of algal biomass in the majority of the turloughs. The regression characteristics were not significantly different than those described in similar studies of permanent lakes. Patterns in seasonal variation of nutrients are also presented, their underlying causes being discussed in relation to their transport within catchments. Our results show that despite being predominantly winter phenomena, turloughs can be as productive as permanent lakes.

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.

Effects of agricultural production on phosphorus losses from paddy soils: a case study in the Taihu Lake Region of China

2005 ◽  
Vol 13 (1) ◽  
pp. 25-33 ◽  
Author(s):  
Huanchao Zhang ◽  
Fuliang Cao ◽  
Shengzuo Fang ◽  
Gaiping Wang ◽  
Hongai Zhang ◽  
...  
Keyword(s):  
Agricultural Production ◽  
Taihu Lake ◽  
Paddy Soils ◽  
Lake Region ◽  
Phosphorus Losses

scholarly journals Estimation of chlorophyll-a concentration in productive turbid waters using a Hyperspectral Imager for the Coastal Ocean—the Azov Sea case study

2011 ◽  
Vol 6 (2) ◽  
pp. 024023 ◽  
Author(s):  
Anatoly A Gitelson ◽  
Bo-Cai Gao ◽  
Rong-Rong Li ◽  
Sergey Berdnikov ◽  
Vladislav Saprygin
Keyword(s):  
Chlorophyll A ◽  
Coastal Ocean ◽  
Chlorophyll A Concentration ◽  
Turbid Waters ◽  
Azov Sea

scholarly journals The Identification of Fishing Ground Area with MODIS Satellite Image (Case Study: South Coast of West Java)

2018 ◽  
Author(s):  
Ketut Wikantika
Keyword(s):  
Chlorophyll A ◽  
Satellite Image ◽  
South Coast ◽  
Data Conversion ◽  
Fishing Ground ◽  
West Java ◽  
Water Region ◽  
Computation Data

According to UNCLOS, Indonesian marine territorial covers an area equal to around 2.8 million square kilometers inner archipelagic seas. Though the Indonesian water region is very wide, the resource within it is not yet been exploited optimally. Indonesia still has problems that have to be copped with, including identification of marine fishing ground areas. This report proposes a technology to make the fish-catching be more efficient and effective with the help of MODIS satellite image in term of Surface Temperature and chlorophyll-a computation. Data conversion from digital number to Water Brightness Temperature are performed. The determination of potential fishing ground area were conducted based on temperature and chlorophyll-a parameters which serve as an indicator of upwelling and observations were carried out on parameters which show this phenomenon. Based on the result, during May 2004 the upwelling process were not happened yet, and it seems to occur in June 2004. It showes by the decreasing of water temperature in South Coast of West Java particularly between the border of West Java and Central of Java. This phenomenon acts as an indicator for the raising of primer productivity and will takes about one month after upwelling to the bloom of phytoplankton.

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