waterquality : an open-source R package for the detection and quantification of cyanobacterial harmful algal blooms and water quality

Johansen, Richard; Reif, Molly; Emery, Erich; Nowosad, Jakub; Beck, Richard; Xu, Min; Liu, Hongxing.

doi:10.21079/11681/35053

Cited by 8 publications

(12 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PC contains a distinct spectral absorption feature centered on 620 nm, and Chl corresponds to 685 nm. To make the PC compatible with Sentinel-2 MSI, Johansen et al ( 2019) [43] simplified the empirical equation for the inversion of phycocyanin proposed by Wynne (2008) [44] with the ratio of the red band and the VRE5 band. We chose two indicators that reflect the chlorophyll concentration.…”

Section: Classification Parameter Selectionmentioning

confidence: 99%

Improving Satellite Retrieval of Coastal Aquaculture Pond by Adding Water Quality Parameters

et al. 2022

View full text Add to dashboard Cite

Coastal aquaculture is an important supply of animal proteins for human consumption, which is expanding globally. Meanwhile, extensive aquaculture may increase nutrient loadings and environmental concerns along the coast. Accurate information on aquaculture pond location is essential for coastal management. Traditional methods use morphological parameters to characterize the geometry of surface waters to differentiate artificially constructed conventional aquaculture ponds from other water bodies. However, there are other water bodies with similar morphology (e.g., saltworks, rice fields, and small reservoirs) that are difficult to distinguish from aquaculture ponds, causing a lot of omission/commissioning errors in areas with complex land-use types. Here, we develop an extraction method with shape and water quality parameters to map aquaculture ponds, including three steps: (1) Sharpen normalized difference water index to detect and binarize water pixels by the Otsu method; (2) Connect independent water pixels into water objects through the four-neighbor connectivity algorithm; and (3) Calculate the shape features and water quality features of water objects and input them into the classifier for supervised classification. We selected eight sites along the coast of China to evaluate the accuracy and generalization of our method in an environment with heterogeneous pond morphology and landscape. The results showed that six transfer characteristics including water quality characteristics improved the accuracy of distinguishing aquaculture ponds from salt pans, rice fields, and wetland parks, which typically had F1 scores > 85%. Our method significantly improved extraction efficiency on average, especially when aquaculture ponds are mixed with other morphological similar water bodies. Our identified area was in agreement with statistics data of 12 coastal provinces in China. In addition, our approach can effectively improve water objects when high-resolution remote sensing images are unavailable. This work was applied to open-source remote sensing imagery and has the potential to extract long-term series and large-scale aquaculture ponds globally.

show abstract

Section: Classification Parameter Selectionmentioning

confidence: 99%

Improving Satellite Retrieval of Coastal Aquaculture Pond by Adding Water Quality Parameters

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The main objective of the research was to expand the development of remote sensing tools used to estimate potential HAB indicators: (1) chlorophyll-a (chl-a), (2) phycocyanin, a proxy for cyanobacterial or blue-green algal biomass (BGA/PC), and (3) turbidity, focusing on small, inland waterbodies. An array of software-based tool options with decreasing complexity have been developed including the following: (1) an open-source R software package, waterquality (Johansen et al 2019;Johansen et al 2020), (2) an Environmental Systems Research Institute (ESRI) ArcGIS Pro desktop software toolbox (waterquality for ArcGIS Pro), and (3) an ESRI-based online web application, (Harmful Algal Bloom [HAB] Explorer). The primary focus of this document is the waterquality for ArcGIS Pro toolbox which was founded on the design and research established in the waterquality R software package (Johansen et al 2019;Johansen 2020) and builds on the extensive body of remote sensing research conducted by the USACE ERDC-EL and partners (Johansen et al 2019;Beck et al 2019;Johansen et al 2018;Reif 2011).…”

Section: Objectivementioning

confidence: 99%

“…Additionally, the most thorough way to interpret index values is through examining relationships between index values and associated in situ data values (from field-based sampling) using linear regression analysis (discussed in Chapter 6). More information about the algorithms and related output can be found in Beck et al (2016Beck et al ( , 2017Beck et al ( , and 2019 and Johansen et al (2019).…”

Section: Outputsmentioning

confidence: 99%

“…However, limited resources for traditional field-based monitoring of numerous lakes and reservoirs covering vast geographic areas often leads to reactional responses to harmful algal bloom (HAB) outbreaks. Satellite remote sensing methodologies using HAB indicators is a good low-cost option to traditional methods and has been proven to maximize and complement current field-based approaches while providing a synoptic view of water quality (Beck et al 2016;Beck et al 2017;Beck et al 2019;Johansen et al 2019;Mishra et al 2019;Stumpf and Tomlinson 2007;Wang et al 2020;Xu et al 2019;Reif 2011). To assist USACE water quality management, we developed an Environmental Systems Research Institute (ESRI) ArcGIS Pro desktop software toolbox (waterquality for ArcGIS Pro) founded on the design and research established in the waterquality R software package (Johansen et al 2019;Johansen 2020).…”

Section: Unit Conversion Factorsmentioning

confidence: 99%

“…Traditional field-based water sampling methods are relied upon for water quality monitoring and are a critical component of USACE water quality management plans. Advances in remote sensing technology serve to maximize and complement current field-based approaches and to improve monitoring water quality indicators of HABs (Beck et al 2016;Beck et al 2017;Beck et al 2019;Johansen et al 2019;Mishra et al 2019;Stumpf and Tomlinson 2007;Wang et al 2020;Xu et al 2019). Specifically, spatial and temporal variations of water quality may not be systematically captured across an entire waterbody using traditional monitoring methods.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

waterquality for ArcGIS Pro Toolbox : user's guide

Saltus¹,

Reif²,

Johansen³

2022

Self Cite

View full text Add to dashboard Cite

Monitoring water quality of small inland lakes and reservoirs is a critical component of the US Army Corps of Engineers (USACE) water quality management plans. However, limited resources for traditional field-based monitoring of numerous lakes and reservoirs covering vast geographic areas often leads to reactional responses to harmful algal bloom (HAB) outbreaks. Satellite remote sensing methodologies using HAB indicators is a good low-cost option to traditional methods and has been proven to maximize and complement current field-based approaches while providing a synoptic view of water quality (Beck et al. 2016; Beck et al. 2017; Beck et al. 2019; Johansen et al. 2019; Mishra et al. 2019; Stumpf and Tomlinson 2007; Wang et al. 2020; Xu et al. 2019; Reif 2011). To assist USACE water quality management, we developed an Environmental Systems Research Institute (ESRI) ArcGIS Pro desktop software toolbox (waterquality for ArcGIS Pro) founded on the design and research established in the waterquality R software package (Johansen et al. 2019; Johansen 2020). The toolbox enables the detection, monitoring, and quantification of HAB indicators (chlorophyll-a, phycocyanin, and turbidity) using Sentinel-2 satellite imagery. Four tools are available: (1) automating the download of Sentinel-2 Level-2A imagery, (2) creating stacked image with options for cloud and non-water features masks, (3) applying water quality algorithms to generate relative estimations of one to three water quality parameters (chlorophyll-a, phycocyanin, and turbidity), and (4) creating linear regression graphs and statistics comparing in situ data (from field-based water sampling) to relative estimation data. This document serves as a user’s guide for the waterquality for ArcGIS Pro toolbox and includes instructions on toolbox installation and descriptions of each tool’s inputs, outputs, and troubleshooting guidance.

show abstract

Predicting water quality variability in a Mediterranean hypereutrophic monomictic reservoir using Sentinel 2 MSI: the importance of considering model functional form

Abbas

Alameddine

2023

Environ Monit Assess

View full text Add to dashboard Cite

waterquality : an open-source R package for the detection and quantification of cyanobacterial harmful algal blooms and water quality

Cited by 8 publications

References 22 publications

Improving Satellite Retrieval of Coastal Aquaculture Pond by Adding Water Quality Parameters

Improving Satellite Retrieval of Coastal Aquaculture Pond by Adding Water Quality Parameters

waterquality for ArcGIS Pro Toolbox : user's guide

Predicting water quality variability in a Mediterranean hypereutrophic monomictic reservoir using Sentinel 2 MSI: the importance of considering model functional form

Contact Info

Product

Resources

About