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

Abstract

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.

A High-Resolution Global Map of Giant Kelp (Macrocystis pyrifera) Forests and Intertidal Green Algae (Ulvophyceae) with Sentinel-2 Imagery

“…Therefore, the central areas of forests should provide optimal material to build a global map of giant kelp. (c) The use of Unmanned Aerial Vehicles (UAV) for coastal habitat mapping is a simple, cost-effective and reliable technology [39] that has been successfully used to map and validate intertidal biogenic reefs [40], saltmarsh biomass [41], and algal blooms [42]. Recent surveys to detect macroalgae in temperate coastlines have shown that RGB (additive primary colors-red, green, and blue-model) and multispectral cameras mounted on UAVs produce accurate imagery able to detect water turbidity and a range of taxonomical groups of algae in surface or shallow water, with the exception of spectrally similar species [18,43].…”

Use of Drones in Fishery Science

“…), improved tracking of planktonic food and harmful algal blooms (Kislik et al. ), and fluid lensing to see through water and catalog subsurface habitat (Chirayath and Earle ) are examples of novel postprocessing techniques that can provide greater insight into critical questions in fisheries research.…”

“…Based on the evolution of processing methods and future possibilities, postcollection data can be integrated into other frameworks or processed into brand-new data sets. Behavioral analysis and fine-scale movement patterns (Raoult et al 2018;Rieucau et al 2018), improved tracking of planktonic food and harmful algal blooms (Kislik et al 2018), and fluid lensing to see through water and catalog subsurface habitat (Chirayath and Earle 2016) are examples of novel postprocessing techniques that can provide greater insight into critical questions in fisheries research.…”

A Method for Chlorophyll-a and Suspended Solids Prediction through Remote Sensing and Machine Learning

“…Although Landsat is more widely used, the European Sentinel-2 satellite has promising characteristics: it has higher spatial resolution than Landsat 8 and also spectral bands in the region called the red edge, which is of great interest for water and vegetation studies.Remote sensing serves as a powerful technique for monitoring environmental and seasonal changes, and its ability to remotely monitor water resources has increased in recent decades because of the quality and availability of satellite imagery data [10]. Even so, the analysis of small water bodies may not be adequate due to the medium image resolution of the most typical commercial satellites [8,11].The combination of aerial image data obtained by Unmanned Aerial Vehicles (UAVs) together with modern pattern recognition systems is promising due to many factors: it has the potential to capture more details due to its higher spatial resolution; it can allow shorter revisit time, ensuring the possibility of constant and dynamic monitoring; it enables monitoring of hard-to-reach areas; it has an affordable cost compared to usual water collection and analysis methodologies; and finally, image acquisition is not affected by cloud cover, which may be a major limitation in the use of satellite imagery [8,12,13].Another aspect to be considered in modeling water physicochemical variables through remote sensing is that the spatial distribution and time evolution of these parameters in ecosystems can be very complex and nonlinear [2] and that spatial and temporal trends can be difficult to understand at larger scales [11].Due to the complex nature of aquatic environments, approaches involving learning algorithms are being used to improve the accuracy and reliability of predictive models generated by empirical methods [7,[14][15][16][17]. Those learning algorithms, often called learners, can overcome traditional regression modeling difficulties to model non-linear relations between dependent and independent variables [18].…”

“…The combination of aerial image data obtained by Unmanned Aerial Vehicles (UAVs) together with modern pattern recognition systems is promising due to many factors: it has the potential to capture more details due to its higher spatial resolution; it can allow shorter revisit time, ensuring the possibility of constant and dynamic monitoring; it enables monitoring of hard-to-reach areas; it has an affordable cost compared to usual water collection and analysis methodologies; and finally, image acquisition is not affected by cloud cover, which may be a major limitation in the use of satellite imagery [8,12,13].…”