Spatiotemporal Deep-Learning-Based Algal Bloom Prediction for Lake Okeechobee Using Multisource Data Fusion

Tang, Youxi; Feng, Yingqi; Fung, Sasha; Xomchuk, Veronica Ruiz; Jiang, Mingshun; Moore, Tim; Beckler, Jordon S.

doi:10.1109/jstars.2022.3208620

Cited by 11 publications

(2 citation statements)

References 38 publications

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“…Water body extraction was carried out in [6], and permafrost areas were mapped in [7]. Many studies have been focused on mapping vegetation, i.e., estimating vegetation regions [8], monitoring vegetation growth [9] and vegetation changes [10], predicting the location of algal blooms [11], and discriminating between different macrophyte species [12], among other applications.…”

Section: Introductionmentioning

confidence: 99%

Mapping Invasive Aquatic Plants in Sentinel-2 Images Using Convolutional Neural Networks Trained With Spectral Indices

Rodriguez-Garlito

Paz

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Multispectral images collected by the European Space Agency (ESA)'s Sentinel-2 satellite offer a powerful resource for accurately and efficiently mapping areas affected by the distribution of invasive aquatic plants. In this work, we use different spectral indices to detect invasive aquatic plants in the Guadiana river, Spain. Our methodology uses a convolutional neural network (CNN) as the baseline classifier and trains it using spectral indices calculated using different Sentinel-2 band combinations. Specifically, we consider the following spectral indices: with two bands, we calculate the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference infrared index (NDII). With three bands, we calculate the red-green-blue (RGB) composite and the floating algae index (FAI). Finally, we also use four bands to calculate the bare soil index (BSI). In our results, we observed that CNNs can better map invasive aquatic plants in the considered case study when trained intelligently (using spectral indices) as compared to using all spectral bands provided by the Sentinel-2 instrument.

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Section: Introductionmentioning

confidence: 99%

Mapping Invasive Aquatic Plants in Sentinel-2 Images Using Convolutional Neural Networks Trained With Spectral Indices

Rodriguez-Garlito

Paz

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Chen et al, 2022). Tang et al created a multisource hybrid dataset for deep learning model training to predict harmful algal bloom events in Lake Okeechobee (Tang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

An early warning model for starfish disaster based on multi-sensor fusion

Liu

Hui

et al. 2023

Front. Mar. Sci.

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Starfish have a wide range of feeding habits, including starfish, sea urchins, sea cucumbers, corals, abalones, scallops, and many other marine organisms with economic or ecological value. The starfish outbreak in coastal areas will lead to severe economic losses in aquaculture and damage the ecological environment. However, the current monitoring methods are still artificial, time-consuming, and laborious. This study used an underwater observation platform with multiple sensors to observe the starfish outbreak in Weihai, Shandong Province. The platform could collect the temperature, salinity, depth, dissolved oxygen, conductivity, other water quality data, and underwater video data. Based on these data, the paper proposed an early warning model for starfish prevalence (EWSP) based on multi-sensor fusion. A deep learning-based object detection method extracts time-series information on the number of starfish from underwater video data. For the extracted starfish quantity information, the model uses the k-means clustering algorithm to divide the starfish prevalence level into four levels: no prevalence, mild prevalence, medium prevalence, and high prevalence. Correlation analysis concluded that the water quality factors most closely related to the starfish prevalence level are temperature and salinity. Therefore, the selected water quality factor and the number of historical starfish are inputted. The future starfish prevalence level of the starfish outbreak is used as an output to train the BP (back propagation) neural network to build EWSP based on multi-sensor fusion. Experiments show that the accuracy rate of this model is 97.26%, whose precision meets the needs of early warning for starfish outbreaks and has specific application feasibility.

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Comparative evaluation of performances of algae indices, pixel- and object-based machine learning algorithms in mapping floating algal blooms using Sentinel-2 imagery

Colkesen,

Ozturk,

Altuntas

2024

Stoch Environ Res Risk Assess

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One of the main threats to freshwater resources is pollution from anthropogenic activities such as rapid urbanization and excessive agricultural nutrient runoff. Remote sensing technologies have been effectively used in monitoring and mapping rapid changes in the marine environment and assessing the overall health of freshwater ecosystems. The main goal of this study is to comparatively evaluate the performance of index-based and classification-based approaches in mapping dense floating algal blooms observed in Lake Burdur using Sentinel-2 imagery. For index-based mapping, algae-specific indices, namely the Floating Algae Index (FAI), Adjusted Floating Algae Index, Surface Algal Blooms Index (SABI), and Algal Blooms Detection Index (ABDI), were used. At the same time, pixel- and object-based Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Long Short-Term Memory Network (LSTM) were utilized for classification-based algal mapping. For this purpose, seven Sentinel-2 images, selected through time series analysis performed on the Google Earth Engine platform, were used as the primary dataset in the application. The results show that high-density floating algae formations can be detected over 99% by both indices and classification-based approaches, whereas pixel-based classification is more successful in mapping low-density algal blooms. When two-class thematic maps representing water and floating algae classes were considered, the maps produced by index-based FAI using an appropriate threshold value and the classification-based RF algorithm reached an overall accuracy of over 99%. The highest algae density in the lake was observed on July 13, 2021, and was determined to be effective in ~ 45 km2 of the lake’s surface.

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Spatiotemporal Deep-Learning-Based Algal Bloom Prediction for Lake Okeechobee Using Multisource Data Fusion

Cited by 11 publications

References 38 publications

Mapping Invasive Aquatic Plants in Sentinel-2 Images Using Convolutional Neural Networks Trained With Spectral Indices

Mapping Invasive Aquatic Plants in Sentinel-2 Images Using Convolutional Neural Networks Trained With Spectral Indices

An early warning model for starfish disaster based on multi-sensor fusion

Comparative evaluation of performances of algae indices, pixel- and object-based machine learning algorithms in mapping floating algal blooms using Sentinel-2 imagery

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