An Appraisal of the Potential of Landsat 8 in Estimating Chlorophyll-a, Ammonium Concentrations and Other Water Quality Indicators

Markogianni, Vassiliki; Kalivas, Dionissios; Petropoulos, George P.; Dimitriou, Elias

doi:10.3390/rs10071018

Cited by 52 publications

(45 citation statements)

References 66 publications

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“…Combining in situ and RS data for water-quality monitoring allows a holistic analysis that can provide alternative management scenarios [243]. Coastal and ocean monitoring are challenging due to multiple sources that modify physico-biological processes [244].…”

Section: Water Qualitymentioning

confidence: 99%

Contribution of Remote Sensing Technologies to a Holistic Coastal and Marine Environmental Management Framework: A Review

et al. 2020

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Coastal and marine management require the evaluation of multiple environmental threats and issues. However, there are gaps in the necessary data and poor access or dissemination of existing data in many countries around the world. This research identifies how remote sensing can contribute to filling these gaps so that environmental agencies, such as the United Nations Environmental Programme, European Environmental Agency, and International Union for Conservation of Nature, can better implement environmental directives in a cost-effective manner. Remote sensing (RS) techniques generally allow for uniform data collection, with common acquisition and reporting methods, across large areas. Furthermore, these datasets are sometimes open-source, mainly when governments finance satellite missions. Some of these data can be used in holistic, coastal and marine environmental management frameworks, such as the DAPSI(W)R(M) framework (Drivers–Activities–Pressures–State changes–Impacts (on Welfare)–Responses (as Measures), an updated version of Drivers–Pressures–State–Impact–Responses. The framework is a useful and holistic problem-structuring framework that can be used to assess the causes, consequences, and responses to change in the marine environment. Six broad classifications of remote data collection technologies are reviewed for their potential contribution to integrated marine management, including Satellite-based Remote Sensing, Aerial Remote Sensing, Unmanned Aerial Vehicles, Unmanned Surface Vehicles, Unmanned Underwater Vehicles, and Static Sensors. A significant outcome of this study is practical inputs into each component of the DAPSI(W)R(M) framework. The RS applications are not expected to be all-inclusive; rather, they provide insight into the current use of the framework as a foundation for developing further holistic resource technologies for management strategies in the future. A significant outcome of this research will deliver practical insights for integrated coastal and marine management and demonstrate the usefulness of RS to support the implementation of environmental goals, descriptors, targets, and policies, such as the Water Framework Directive, Marine Strategy Framework Directive, Ocean Health Index, and United Nations Sustainable Development Goals. Additionally, the opportunities and challenges of these technologies are discussed.

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Section: Water Qualitymentioning

confidence: 99%

Contribution of Remote Sensing Technologies to a Holistic Coastal and Marine Environmental Management Framework: A Review

et al. 2020

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“…Such systems also do not require accuracy of edge detection, therefore use of freely available moderate resolution remote sensing is appropriate for most purposes. Trophic status indicators include chlorophyll-a [52,308], turbidity (Secchi disk depth), total phosphorus [216], and coloured dissolved organic carbon or matter [28,221]. Sass et al [222] found that red and red/blue wavelengths from Landsat 4/5 and 7 were the most accurate indicators of trophic status, including accuracies of approximately 80% (chlorophyll-a), 90% (turbidity), and (-)70% (Secchi disk depth).…”

Section: Remote Sensing Of Wetland Biogeochemistrymentioning

confidence: 99%

Remote Sensing of Boreal Wetlands 1: Data Use for Policy and Management

et al. 2020

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Wetlands have and continue to undergo rapid environmental and anthropogenic modification and change to their extent, condition, and therefore, ecosystem services. In this first part of a two-part review, we provide decision-makers with an overview on the use of remote sensing technologies for the ‘wise use of wetlands’, following Ramsar Convention protocols. The objectives of this review are to provide: (1) a synthesis of the history of remote sensing of wetlands, (2) a feasibility study to quantify the accuracy of remotely sensed data products when compared with field data based on 286 comparisons found in the literature from 209 articles, (3) recommendations for best approaches based on case studies, and (4) a decision tree to assist users and policymakers at numerous governmental levels and industrial agencies to identify optimal remote sensing approaches based on needs, feasibility, and cost. We argue that in order for remote sensing approaches to be adopted by wetland scientists, land-use managers, and policymakers, there is a need for greater understanding of the use of remote sensing for wetland inventory, condition, and underlying processes at scales relevant for management and policy decisions. The literature review focuses on boreal wetlands primarily from a Canadian perspective, but the results are broadly applicable to policymakers and wetland scientists globally, providing knowledge on how to best incorporate remotely sensed data into their monitoring and measurement procedures. This is the first review quantifying the accuracy and feasibility of remotely sensed data and data combinations needed for monitoring and assessment. These include, baseline classification for wetland inventory, monitoring through time, and prediction of ecosystem processes from individual wetlands to a national scale.

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“…Locations of the two study areas are shown in color composite images (R6G3B2) in Figure 3. The flooding reference images were obtained from the corresponding Landsat 8 OLI images using the modified normalized difference water index (mNDWI) [37,38]. The resolution of the flooding reference images was 30 m. The flooding fraction images were obtained by aggregating the corresponding flooding reference images.…”

Section: Study Materialsmentioning

confidence: 99%

Remote Sensing of Wetland Flooding at a Sub-Pixel Scale Based on Random Forests and Spatial Attraction Models

Chen

et al. 2019

Remote Sensing

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Wetland flooding is significant for the flora and fauna of wetlands. High temporal resolution remote sensing images are widely used for the timely mapping of wetland flooding but have a limitation of their relatively low spatial resolutions. In this study, a novel method based on random forests and spatial attraction models (RFSAM) was proposed to improve the accuracy of sub-pixel mapping of wetland flooding (SMWF) using remote sensing images. A random forests-based SMWF algorithm (RM-SMWF) was developed firstly, and a comprehensive complexity index of a mixed pixel was formulated. Then the RFSAM-SMWF method was developed. Landsat 8 Operational Land Imager (OLI) images of two wetlands of international importance included in the Ramsar List were used to evaluate RFSAM-SMWF against three other SMWF methods, and it consistently achieved more accurate sub-pixel mapping results in terms of visual and quantitative assessments in the two wetlands. The effects of the number of trees in random forests and the complexity threshold on the mapping accuracy of RFSAM-SMWF were also discussed. The results of this study improve the mapping accuracy of wetland flooding from medium-low spatial resolution remote sensing images and therefore benefit the environmental studies of wetlands.

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An Appraisal of the Potential of Landsat 8 in Estimating Chlorophyll-a, Ammonium Concentrations and Other Water Quality Indicators

Cited by 52 publications

References 66 publications

Contribution of Remote Sensing Technologies to a Holistic Coastal and Marine Environmental Management Framework: A Review

Contribution of Remote Sensing Technologies to a Holistic Coastal and Marine Environmental Management Framework: A Review

Remote Sensing of Boreal Wetlands 1: Data Use for Policy and Management

Remote Sensing of Wetland Flooding at a Sub-Pixel Scale Based on Random Forests and Spatial Attraction Models

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