Landsat remote sensing of chlorophyll <i>a</i> concentrations in central North Island lakes of New Zealand

Allan, Mathew Grant; Hamilton, David P.; Hicks, Brendan J.; Brabyn, Lars

doi:10.1080/01431161003645840

Cited by 76 publications

(40 citation statements)

References 33 publications

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“…A remote sensing approach yields comprehensive and cost effective estimates of water clarity compared to on-the-ground point measurements [13]. Temporal and spatial patterns of water clarity may be more accurately captured when using whole-lake imagery rather than point measurements [14]. Furthermore, water clarity measurements can be reconstructed using the decadal-scale database of historical and available satellite imagery [13].…”

Section: Introductionmentioning

confidence: 99%

Reconstructing Turbidity in a Glacially Influenced Lake Using the Landsat TM and ETM+ Surface Reflectance Climate Data Record Archive, Lake Clark, Alaska

et al. 2015

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Lake Clark is an important nursery lake for sockeye salmon (Oncorhynchus nerka) in the headwaters of Bristol Bay, Alaska, the most productive wild salmon fishery in the world. Reductions in water clarity within Alaska lake systems as a result of increased glacial runoff have been shown to reduce salmon production via reduced abundance of zooplankton and macroinvertebrates. In this study, we reconstruct long-term, lake-wide water clarity for Lake Clark using the Landsat TM and ETM+ surface reflectance products and in situ water clarity data collected between 2009 and 2013. Analysis of a Landsat scene acquired in 2009, coincident with in situ measurements in the lake, and uncertainty analysis with four scenes acquired within two weeks of field data collection showed that Band 3 surface reflectance was the best indicator of turbidity (r 2 = 0.55, RMSE << 0.01). We then processed 151 (98 partial-and 53 whole-lake) Landsat scenes using this relation and detected no significant long-term trend in mean turbidity for Lake Clark This study demonstrates the utility of hindcasting turbidity in a glacially influenced lake using the Landsat surface reflectance products. It may also help land and resource managers reconstruct turbidity records for lakes that lack in situ monitoring, and may be useful in predicting future water clarity conditions based on projected climate scenarios.

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Section: Introductionmentioning

confidence: 99%

Reconstructing Turbidity in a Glacially Influenced Lake Using the Landsat TM and ETM+ Surface Reflectance Climate Data Record Archive, Lake Clark, Alaska

et al. 2015

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“…The strength of using remote sensing for water quality analysis is its ability to tool to map turbidity and suspended particulate matter concentration at surface in turbid coastal waters [7]. The strength of using remote sensing for water quality analysis is its ability to capture synoptic data of a whole study area to produce continuous surface data, often showing detailed spatial variability in water quality [8]. While a number of remote sensing studies have been devoted to retrieving total suspended solids (TSS), studies on retrieving turbidity have been limited [7].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variation of Turbidity Based on Landsat 8 OLI in Cam Ranh Bay and Thuy Trieu Lagoon, Vietnam

Quang

Sasaki

Higa

et al. 2017

Water

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Abstract:In recent years, seagrass beds in Cam Ranh Bay and Thuy Trieu Lagoon have declined from 800 to 550 hectares, resulting insignificantly reducing the number of fish catch. This phenomenon is due to the effect of the degradation of water environment. Turbidity is one of the most important water quality parameters directly related to underwater light penetration which affects the primary productivity. This study aims to investigate spatiotemporal variation of turbidity in the waters with major factors affecting its patterns using remote sensing data. An algorithm for turbidity retrieval was developed based on the correlation between in situ measurements and a red band of Landsat 8 OLI with R 2 = 0.84 (p < 0.05). Simulating WAves Nearshore (SWAN) model was used to compute bed shear stress, a major factor affecting turbidity in shallow waters. In addition, the relationships between turbidity and rainfall, and bed shear stress induced by wind were analyzed. It was found that: (1) In the dry season, turbidity was low at the middle of the bay while it was high in shallow waters nearby coastlines. Resuspension of bed sediment was a major factor controlling turbidity during time with no rainfall. (2) In the rainy season or for a short time after rainfall in the dry season, turbidity was high due to a large amount of runoff entering into the study area.

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“…Radiometric correction of the thermal band involves first converting values to spectral radiance using Equation (1) and then converting spectral radiance to surface temperature using Equation (6). This conversion requires two calibration constants: K 1 and K 2 [40].…”

Section: 22mentioning

confidence: 99%

Automated Method for Monitoring Water Quality Using Landsat Imagery

Barrett

Frazier

2016

Water

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Regular monitoring of water quality is increasingly necessary to keep pace with rapid environmental change and protect human health and well-being. Remote sensing has been suggested as a potential solution for monitoring certain water quality parameters without the need for in situ sampling, but universal methods and tools are lacking. While many studies have developed predictive relationships between remotely sensed surface reflectance and water parameters, these relationships are often unique to a particular geographic region and have little applicability in other areas. In order to remotely monitor water quality, these relationships must be developed on a region by region basis. This paper presents an automated method for processing remotely sensed images from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) and extracting corrected reflectance measurements around known sample locations to allow rapid development of predictive water quality relationships to improve remote monitoring. Using open Python scripting, this study (1) provides an openly accessible and simple method for processing publicly available remote sensing data; and (2) allows determination of relationships between sampled water quality parameters and reflectance values to ultimately allow predictive monitoring. The method is demonstrated through a case study of the Ozark/Ouchita-Appalachian ecoregion in eastern Oklahoma using data collected for the Beneficial Use Monitoring Program (BUMP).

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Landsat remote sensing of chlorophyll a concentrations in central North Island lakes of New Zealand

Cited by 76 publications

References 33 publications

Reconstructing Turbidity in a Glacially Influenced Lake Using the Landsat TM and ETM+ Surface Reflectance Climate Data Record Archive, Lake Clark, Alaska

Reconstructing Turbidity in a Glacially Influenced Lake Using the Landsat TM and ETM+ Surface Reflectance Climate Data Record Archive, Lake Clark, Alaska

Spatiotemporal Variation of Turbidity Based on Landsat 8 OLI in Cam Ranh Bay and Thuy Trieu Lagoon, Vietnam

Automated Method for Monitoring Water Quality Using Landsat Imagery

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