Trophic classification and ecosystem checking of lakes using remotely sensed information

Baban, Serwan Mj

doi:10.1080/02626669609491560

Cited by 40 publications

(9 citation statements)

References 14 publications

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“…The index has since then been widely accepted owing to its calculation simplicity and ability to communicate between researchers, government agencies, and local community residents [24]. The use of remote sensing for lake trophic index formulation is based on the fact that the consequences of eutrophication and an increase in productivity will be associated with a change in the optical properties of the water mass [25].…”

Section: Introductionmentioning

confidence: 99%

Trophic State Index derivation through the remote sensing of Case-2 water bodies in the Mediterranean region

Papoutsa¹,

Akylas²,

Hadjimitsis³

2014

Open Geosciences

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Abstract:The main goal of this study is the derivation of Carlson's Trophic State Index (TSI) through the remote sensing of four different Case-2 waters in the Mediterranean region such as Cyprus and Greece. TSI SD is derived through extensive field ground campaign of Secchi Disk Depth measurements for the Asprokremmos Dam, located in Paphos District in Cyprus; Alyki Salt Lake, located in Larnaca District in Cyprus; and in Karla Lake, located in Volos District in Greece; and finally to three coastal water areas in the Limassol coastal area. Several regression models have been applied in order to develop the best regression model between the TSI SD and in-band reflectance values for Landsat TM/ETM derived from spectroradiometric measurements using a GER-1500 field spectroradiometer over the main case study area in Asprokremmos Dam in Cyprus. Finally, we apply several regression models for Asprokremmos Dam for retrieving the suitable Landsat TM/ETM band or band combinations (obtained from field spectroradiometric measurements) in which TSI SD can be determined. Indeed, the best regression model has been obtained by correlating 'TSI Versus Band2/Band3', with R 2 =0.89. All field TSI SD and in-band reflectance values from the four different water bodies have been used to develop the best fitted model for the established T SI SD Versus Band2/Band3 model. We find that the exponential regression model provides the best fitted equation over the four different water bodies. Keywords

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Section: Introductionmentioning

confidence: 99%

Trophic State Index derivation through the remote sensing of Case-2 water bodies in the Mediterranean region

Papoutsa¹,

Akylas²,

Hadjimitsis³

2014

Open Geosciences

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“…Therefore, Chla is commonly used to assess the trophic level of lakes [1][2][3], and also to measure the water quality [4,5].…”

Section: Introductionmentioning

confidence: 99%

Selecting the Best Band Ratio to Estimate Chlorophyll-a Concentration in a Tropical Freshwater Lake Using Sentinel 2A Images from a Case Study of Lake Ba Be (Northern Vietnam)

Thảo

Koike

et al. 2017

IJGI

129

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This study aims to develop a method to estimate chlorophyll-a concentration (Chla) in tropical freshwater lake waters using in situ data of Chla, water reflectance, and concurrent Sentinel 2A MSI imagery (S2A) over Lake Ba Be, a Ramsar site and the largest natural freshwater lake in Vietnam. Data from 30 surveyed sampling sites over the lake water in June 2016 and May 2017 demonstrated the appropriateness of S2A green-red band ratio (band 3 versus band 4) for estimating Chla. This was shown through a strong correlation of corresponded field measured reflectance ratio with Chla by an exponential curve (r 2 = 0.68; the mean standard error of the estimates corresponding to 5% of the mean value of in situ Chla). The small error between in situ Chla, and estimated Chla from S2A acquired concurrently, confirmed the S2A green-red band ratio as the most suitable option for monitoring Chla in Lake Ba Be water. Resultant Chla distribution maps over time described a partially-seasonal pattern and also displayed the spatial dynamic of Chla in the lake. This allows a better understanding of the lake's limnological processes to be developed and provides an insight into the factors that affect lake water quality. The results also confirmed the potential of S2A to be used as a free tool for lake monitoring and research due to high spatial resolution data (10 m pixel size).

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“…The concentration of chlorophyll-a provides direct measurements of the growth of the algae in aquatic environments [57]. Three different types of algorithms were used to compute the chlorophyll-a content: chlorophyll-a OC3M (ocean chlorophyll three-band algorithm for MODIS [58]), chlorophyll-a GSM (Garver-Siegel-Maritorena [59]) and chlorophyll-a GIOP (Generalized Inherent Optical Property [60]).…”

Section: Chlorophyll-a (Mg/m 3 )mentioning

confidence: 99%

Mapping and Forecasting Onsets of Harmful Algal Blooms Using MODIS Data over Coastal Waters Surrounding Charlotte County, Florida

et al. 2018

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Over the past two decades, persistent occurrences of harmful algal blooms (HAB; Karenia brevis) have been reported in Charlotte County, southwestern Florida. We developed data-driven models that rely on spatiotemporal remote sensing and field data to identify factors controlling HAB propagation, provide a same-day distribution (nowcasting), and forecast their occurrences up to three days in advance. We constructed multivariate regression models using historical HAB occurrences (213 events reported from January 2010 to October 2017) compiled by the Florida Fish and Wildlife Conservation Commission and validated the models against a subset (20%) of the historical events. The models were designed to capture the onset of the HABs instead of those that developed days earlier and continued thereafter. A prototype of an early warning system was developed through a threefold exercise. The first step involved the automatic downloading and processing of daily Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua products using SeaDAS ocean color processing software to extract temporal and spatial variations of remote sensing-based variables over the study area. The second step involved the development of a multivariate regression model for same-day mapping of HABs and similar subsequent models for forecasting HAB occurrences one, two, and three days in advance. Eleven remote sensing variables and two non-remote sensing variables were used as inputs for the generated models. In the third and final step, model outputs (same-day and forecasted distribution of HABs) were posted automatically on a web map. Our findings include: (1) the variables most indicative of the timing of bloom propagation are bathymetry, euphotic depth, wind direction, sea surface temperature (SST), ocean chlorophyll three-band algorithm for MODIS [chlorophyll-a OC3M] and distance from the river mouth, and (2) the model predictions were 90% successful for same-day mapping and 65%, 72% and 71% for the one-, two- and three-day advance predictions, respectively. The adopted methodologies are reliable at a local scale, dependent on readily available remote sensing data, and cost-effective and thus could potentially be used to map and forecast algal bloom occurrences in data-scarce regions.

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Trophic classification and ecosystem checking of lakes using remotely sensed information

Cited by 40 publications

References 14 publications

Trophic State Index derivation through the remote sensing of Case-2 water bodies in the Mediterranean region

Trophic State Index derivation through the remote sensing of Case-2 water bodies in the Mediterranean region

Selecting the Best Band Ratio to Estimate Chlorophyll-a Concentration in a Tropical Freshwater Lake Using Sentinel 2A Images from a Case Study of Lake Ba Be (Northern Vietnam)

Mapping and Forecasting Onsets of Harmful Algal Blooms Using MODIS Data over Coastal Waters Surrounding Charlotte County, Florida

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