A UAV and S2A data-based estimation of the initial biomass of green algae in the South Yellow Sea

Xu, Fuxiang; Gao, Zhiqiang; Jiang, Xiaoxu; Shang, Weitao; Ning, Jicai; Song, Debin; Ai, Jinquan

doi:10.1016/j.marpolbul.2018.01.061

Cited by 46 publications

(35 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Of the 13 papers reviewed, 10 focused on freshwater ecosystems [1,[18][19][20][21][22][23][24][25][26], two occurred in coastal or estuarine environments [15,27], and one in a polar desert landscape [28] (Table 1). Of these, five focused on identifying cyanobacterial blooms [1,20,21,23,28].…”

Section: Resultsmentioning

confidence: 99%

“…Of these, five focused on identifying cyanobacterial blooms [1,20,21,23,28]. Additionally, four studies used the visible spectrum to identify algal blooms [21,24,25,27], six incorporated the near-infrared (NIR) [1,19,20,23,26,28], and three utilized hyperspectral sensors [15,18,22]. Despite differences in targets and approaches, there were a number of general principles regarding technology (hardware, software), methodology (e.g., validation), and common challenges and barriers discussed further below.…”

Section: Resultsmentioning

confidence: 99%

“…Not only do UAVs reduce the need to conduct airborne studies in manned aircraft, they also provide several other benefits for remote sensing of complex and often difficult-to-reach algal bloom-affected areas. Of the 13 papers reviewed, 10 mentioned that UAVs are affordable in that they can be more efficient than airborne or satellite imagery acquisition in both time and money [1,15,18,20,21,[23][24][25]27,28], and can reduce in situ water quality sampling costs [29]. Furthermore, UAV-based algal bloom research can decrease the amount of fieldwork that is required to conduct water-quality measurements, and it can reduce the environmental and health consequences of traversing through protected, fragile, or toxic ecosystems [28].…”

Section: Benefits Of Uavs In Algal Bloom Researchmentioning

confidence: 99%

“…Furthermore, UAV-based algal bloom research can decrease the amount of fieldwork that is required to conduct water-quality measurements, and it can reduce the environmental and health consequences of traversing through protected, fragile, or toxic ecosystems [28]. Additionally, UAV-based spectral identification can increase the accuracy of detection of high biomass surface blooms due to increased temporal and spatial resolution and the ability to see what the naked eye cannot (via thermal imaging and hyperspectral classifications) [15,21,24,27,28]. Schedule-wise, UAV-based algal research can be more flexible than satellite or airborne missions because users can elect their own flight paths, spatial, spectral, and temporal resolutions, and revisit times, which is critical because spatial and temporal changes in phytoplankton biomass have high variability and may require revisit times as frequent as everyday [15,18,21,24,25,27,30].…”

Section: Benefits Of Uavs In Algal Bloom Researchmentioning

confidence: 99%

See 3 more Smart Citations

UAVs in Support of Algal Bloom Research: A Review of Current Applications and Future Opportunities

Kislik

Dronova

Kelly

2018

Drones

113

View full text Add to dashboard Cite

Algal blooms have become major public health and ecosystem vitality concerns globally. The prevalence of blooms has increased due to warming water and additional nutrient inputs into aquatic systems. In response, various remotely-sensed methods of detection, analysis, and forecasting have been developed. Satellite imaging has proven successful in the identification of various inland and coastal blooms at large spatial and temporal scales, and airborne platforms offer higher spatial and often spectral resolution at targeted temporal frequencies. Unmanned aerial vehicles (UAVs) have recently emerged as another tool for algal bloom detection, providing users with on-demand high spatial and temporal resolution at lower costs. However, due to the challenges of processing images of water, payload costs and limitations, and a lack of standardized methods, UAV-based algal bloom studies have not gained critical traction. This literature review explores the current state of this field, and highlights opportunities that could promote its growth. By understanding the technical parameters required to identify algal blooms with airborne platforms, and comparing these capabilities to current UAV technology, such knowledge will assist managers, researchers, and public health officials in utilizing UAVs to monitor and predict blooms at greater spatial and temporal precision, reducing exposure to potentially toxic events.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Benefits Of Uavs In Algal Bloom Researchmentioning

confidence: 99%

Section: Benefits Of Uavs In Algal Bloom Researchmentioning

confidence: 99%

See 2 more Smart Citations

UAVs in Support of Algal Bloom Research: A Review of Current Applications and Future Opportunities

Kislik

Dronova

Kelly

2018

Drones

113

View full text Add to dashboard Cite

show abstract

“…Additionally, [37] reported that the NGRDI was more effective than NDVI in extracting N fertilization. Furthermore, [38] revealed that NGRDI was effective in detecting green algae and biomass. Rasmussen et al [25], Smigaj et al [39], Shimada et al [40], and Li et al [41] also utilized the NGRDI to detect fertilizer or biomass in agricultural regions.…”

Section: Discussionmentioning

confidence: 99%

Detection of Aquatic Plants Using Multispectral UAV Imagery and Vegetation Index

Song

Park

2020

Remote Sensing

View full text Add to dashboard Cite

In this study, aquatic plants in a small reservoir were detected using multispectral UAV (Unmanned Aerial Vehicle) imagery and various vegetation indices. A Firefly UAV, which has both fixed-wing and rotary-wing flight modes, was flown over the study site four times. A RedEdge camera was mounted on the UAV to acquire multispectral images. These images were used to analyze the NDVI (Normalized Difference Vegetation Index), ENDVI (Enhance Normalized Difference Vegetation Index), NDREI (Normalized Difference RedEdge Index), NGRDI (Normalized Green-Red Difference Index), and GNDVI (Green Normalized Difference Vegetation Index). As for multispectral characteristics, waterside plants showed the highest reflectance in Rnir, while floating plants had a higher reflectance in Rre. During the hottest season (on 25 June), the vegetation indices were the highest, and the habitat expanded near the edge of the reservoir. Among the vegetation indices, NDVI was the highest and NGRDI was the lowest. In particular, NGRDI had a higher value on the water surface and was not useful for detecting aquatic plants. NDVI and GNDVI, which showed the clearest difference between aquatic plants and water surface, were determined to be the most effective vegetation indices for detecting aquatic plants. Accordingly, the vegetation indices using multispectral UAV imagery turned out to be effective for detecting aquatic plants. A further study will be accompanied by a field survey in order to acquire and analyze more accurate imagery information.

show abstract

Commercially available unoccupied aerial systems for monitoring harmful algal blooms: A comparative study

Fernandez-Figueroa

Wilson

Rogers

2021

Limnology & Ocean Methods

View full text Add to dashboard Cite

Reliable remote sensing platforms and methods for monitoring phytoplankton are needed for mitigating the detrimental impacts of cyanobacterial harmful algal blooms on small inland waterbodies. Commercial unoccupied aerial systems (UASs) present an affordable high-resolution solution for the rapid assessment of cyanobacterial abundance in small aquatic systems by recording the reflectance of photosynthetic pigments found in all phytoplankton (i.e., chlorophyll a [Chl a]) and those related to cyanobacteria (i.e., phycocyanin). This study evaluates the performance of four sensors, including visible light spectra (red, green, blue -[RGB]) sensors on the Phantom 4 and Phantom 4 Professional platforms, the MAPIR Survey3W modified multispectral (i.e., near-infrared, green, blue) sensor, and the Parrott Sequoia multispectral (i.e., green, red, near-infrared, red-edge) sensor for estimating cyanobacterial abundance. The performance of each sensor was determined by comparing 26 vegetation indices to Chl a and phycocyanin measurements of 54 ponds that varied in size and productivity. Vegetation indices that included the red and near-infrared wavelengths generated from Parrot Sequoia aerial images provided the best Chl a (i.e., Normalized Difference Vegetation Index, r 2 = 0.79, p < 0.0001) and phycocyanin (i.e., Green Normalized Difference Vegetation Index, r 2 = 0.64, p < 0.0001) estimates. The RGB sensors were moderately effective for estimating Chl a, whereas the MAPIR Survey3W generated poor estimates of both pigments due to differences in recorded wavelengths. Results suggest commercial multiband multispectral UAS sensors provide a low-cost, plug-and-play alternative for managers and researchers interested in integrating remote sensing tools for quantitatively estimating phytoplankton abundance in small inland systems.Small inland waterbodies are ubiquitous and provide important ecosystems services, including, but not limited to, drinking water supply, food production, aquatic recreation, and climate regulation (Biggs et al. 2017). Blooms dominated by cyanobacterial harmful algal blooms (CyanoHABs) are becoming more frequent and intense as a result of climate change and cultural eutrophication (Huisman et al. 2018). CyanoHABs impair freshwater systems through the production of toxins (i.e., cyanotoxins) linked to illness and fatalities in livestock, pets, and humans, as well as off-flavor compounds (e.g., geosmin and 2-methylisoborneol) that impart a musty scent and flavor on drinking water and farm-raised fish leading to significant economic losses to aquaculture and drinking water industries (Merel et al. 2013;Tucker and Schrader 2020). CyanoHABs are traditionally monitored via cell counts or by measuring the concentration of photosynthetic pigments found in all phytoplankton (i.e., chlorophyll a [Chl a]) relative to accessory pigments abundant in cyanobacteria (i.e., phycocyanin). However, traditional methods are constrained by processing times, training, cost, and may not be representative of the entire waterbody (...

show abstract

A UAV and S2A data-based estimation of the initial biomass of green algae in the South Yellow Sea

Cited by 46 publications

References 32 publications

UAVs in Support of Algal Bloom Research: A Review of Current Applications and Future Opportunities

UAVs in Support of Algal Bloom Research: A Review of Current Applications and Future Opportunities

Detection of Aquatic Plants Using Multispectral UAV Imagery and Vegetation Index

Commercially available unoccupied aerial systems for monitoring harmful algal blooms: A comparative study

Contact Info

Product

Resources

About