Remote estimation of water quality parameters of Himalayan lake (Kashmir) using Landsat 8 OLI imagery

Mushtaq, Fayma; Lala, Mili Ghosh Nee

doi:10.1080/10106049.2016.1140818

Cited by 62 publications

(31 citation statements)

References 39 publications

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“…The water quality parameters selected as eutrophic and pollution indices were the chlorophyll-a concentration ([Cla]) and water turbidity. Regression analyses were performed using atmospherically corrected true reflectance values of OLI bands downloaded from the USGS web page [12,39,40], and the time series of images were processed and analyzed to obtain the time series of each parameter. The images were processed with ENVI 4.8 remote sensing software (Harris Corporation.…”

Section: Methodology (A) Image Processingmentioning

confidence: 99%

“…Due to the MERIS sensor switch-off, there was a gap in the images available for water quality monitoring. The Landsat 8 satellite was launched on 11 February 2013, then the use of the OLI and Thermal Infrared Sensor (TIRS) sensors installed aboard Landsat 8 began [12,13]. Simultaneously, the EU Copernicus program for EO and monitoring started to expand.…”

Section: Introductionmentioning

confidence: 99%

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Monitoring Coastal Lagoon Water Quality through Remote Sensing: The Mar Menor as a Case Study

Erena¹,

Domínguez

Aguado‐Giménez³

et al. 2019

Water

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The Mar Menor is a hypersaline coastal lagoon located in the southeast of Spain. This fragile ecosystem is suffering several human pressures, such as nutrient and sediment inputs from agriculture and other activities and decreases in salinity. Therefore, the development of an operational system to monitor its evolution is crucial to know the cause-effect relationships and preserve the natural system. The evolution and variability of the turbidity and chlorophyll-a levels in the Mar Menor water body were studied here through the joint use of remote sensing techniques and in situ data. The research was undertaken using Operational Land Imager (OLI) images on Landsat 8 and two SPOT images, because cloudy weather prevented the use of OLI images alone. This provided the information needed to perform a time series analysis of the lagoon. We also analyzed the processes that occur in the salt lagoon, characterizing the different spatio-temporal patterns of biophysical parameters. Special attention was given to the role of turbidity and chlorophyll-a levels in the Mar Menor ecosystem with regard to the programs of integral management of this natural space that receives maximum environmental protection. The objective of the work has been fulfilled by answering the questions of the managers: when did the water quality in the Mar Menor begin to change? What is happening in the lagoon? Is remote sensing useful for monitoring the water quality in the Mar Menor? The answers to these questions have allowed the generation of a methodology and monitoring system to track the water quality in the Mar Menor in real-time and space. The tracking system using satellite images is open to the incorporation of images provided by new multispectral sensors.

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Section: Methodology (A) Image Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Monitoring Coastal Lagoon Water Quality through Remote Sensing: The Mar Menor as a Case Study

Erena¹,

Domínguez

Aguado‐Giménez³

et al. 2019

Water

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“…Hicks et al [62] suggest that ideal imagery for these types of studies should not be more than one day apart from the in-situ data collection. However, in most cases, this is not possible due to the temporal resolution of the platform or cloud coverage present in the imagery [3,6,26,60,61]. Furthermore, Barrett and Frazier [63] mention that water quality parameters can be directly influenced by rapidly changing environmental conditions in the study site, and as a result, the utility of predictive models developed from imagery that is generated days or weeks from the day of the in-situ sampling can be detrimentally impacted.…”

Section: Satellite Spatial Resolution Temporal Resolution (Day)mentioning

confidence: 99%

“…Considering the water quality results and the spatial distribution maps generated in this study, it makes sense that for fully mixed systems (such as the ponds used in this study), fewer sample stations led to more accurate models. The use of satellite remote sensing tools helps to expand the limited discrete sampling point coverage of traditional monitoring plans [3,6,26,59,60]. However, in addition to spatial resolution, two major drawbacks when using these tools are: (1) The longer revisiting time (temporal resolution) of these platforms and (2) cloud coverage limitations.…”

Section: Satellite Spatial Resolution Temporal Resolution (Day)mentioning

confidence: 99%

Prediction of Optical and Non-Optical Water Quality Parameters in Oligotrophic and Eutrophic Aquatic Systems Using a Small Unmanned Aerial System

Arango

Nairn

2019

Drones

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The purpose of this study was to create different statistically reliable predictive algorithms for trophic state or water quality for optical (total suspended solids (TSS), Secchi disk depth (SDD), and chlorophyll-a (Chl-a)) and non-optical (total phosphorus (TP) and total nitrogen (TN)) water quality variables or indicators in an oligotrophic system (Grand River Dam Authority (GRDA) Duck Creek Nursery Ponds) and a eutrophic system (City of Commerce, Oklahoma, Wastewater Lagoons) using remote sensing images from a small unmanned aerial system (sUAS) equipped with a multispectral imaging sensor. To develop these algorithms, two sets of data were acquired: (1) In-situ water quality measurements and (2) the spectral reflectance values from sUAS imagery. Reflectance values for each band were extracted under three scenarios: (1) Value to point extraction, (2) average value extraction around the stations, and (3) point extraction using kriged surfaces. Results indicate that multiple variable linear regression models in the visible portion of the electromagnetic spectrum best describe the relationship between TSS (R2 = 0.99, p-value = <0.01), SDD (R2 = 0.88, p-value = <0.01), Chl-a (R2 = 0.85, p-value = <0.01), TP (R2 = 0.98, p-value = <0.01) and TN (R2 = 0.98, p-value = <0.01). In addition, this study concluded that ordinary kriging does not improve the fit between the different water quality parameters and reflectance values.

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“…High spatial resolution is necessary for Chla estimation by satellite remote sensing in relatively small lakes (<5 km 2 square). Chla estimation has been actively tested using Landsat 8 [4,5] and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) NASA's Terra satellite [6,7], which have spatial resolutions of 15-30 m. However, these satellites do not have adequate observation repeat cycles (16-day cycle) and observation wavelength (no observations near 700 nm, which is the wavelength advantageous for coastal Chla estimation). In contrast, the European Space Agency's (ESA) Sentinel-2 satellites [8] has a 10 m spatial resolution and 10 day temporal resolution (every five days with two satellites).…”

Section: Introductionmentioning

confidence: 99%

A Simple Red Tide Monitoring Method using Sentinel-2 Data for Sustainable Management of Brackish Lake Koyama-ike, Japan

Sakuno

Maeda²,

Mori

et al. 2019

Water

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We proposed and validated a method for monitoring red tides in the brackish Lake Koyama-ike, Japan, using Sentinel-2 Multispectral Instrument (MSI) data with a 10 m spatial resolution. To achieve this objective, we acquired 36 spectral reflectance/Chla data points in the field from 2012 to 2018. We obtained a high correlation of Chla (R2 = 0.83) using the proposed red tide model (RIKY = [MSI Band 5 – MSI Band 4]/[MSI Band 5 + MSI Band 4]) and field data. Based on our results, the proposed model was also validated using five Sentinel-2/Chla datasets from April to August 2017. Chla and red tide distribution characteristics estimated from Sentinel-2 data hardly appeared from April to July, and then spread rapidly throughout the lake (more than 70%) in August. Thus, Sentinel-2 data proved to be a very powerful tool in monitoring red tides in Lake Koyama-ike.

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Remote estimation of water quality parameters of Himalayan lake (Kashmir) using Landsat 8 OLI imagery

Cited by 62 publications

References 39 publications

Monitoring Coastal Lagoon Water Quality through Remote Sensing: The Mar Menor as a Case Study

Monitoring Coastal Lagoon Water Quality through Remote Sensing: The Mar Menor as a Case Study

Prediction of Optical and Non-Optical Water Quality Parameters in Oligotrophic and Eutrophic Aquatic Systems Using a Small Unmanned Aerial System

A Simple Red Tide Monitoring Method using Sentinel-2 Data for Sustainable Management of Brackish Lake Koyama-ike, Japan

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