MABI: A novel Mixed Algal Blooms Index based on color space transformation

Harmful algal blooms (HABs) pose serious threats to coastal economies and ecosystems, yet effective monitoring remains challenging due to diverse bloom types and complex environmental conditions. This paper proposes a Mixed Algal Blooms Index (MABI) that uses a new color space to improve HABs detect...

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Vydané v:Marine pollution bulletin Ročník 210; s. 117321
Hlavní autori: Ou, Zirui, Li, Xing, Jin, Fangyuqing, Peng, Shuai, Liu, Wei, Li, ErZhu, Zhang, Lianpeng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England Elsevier Ltd 01.01.2025
Predmet:
algal blooms
Algorithms
CIE color space
Color
data collection
Ecosystem
Environmental Monitoring - methods
Harmful Algal Bloom
Harmful algal blooms (HABs)
Internet
Landsat
marine pollution
Mixed algal blooms
Otsu algorithm
poisonous algae
Satellite Imagery
Sentinel-2 imagery
species
Mixed algal blooms
Sentinel-2 imagery
Harmful algal blooms (HABs)
CIE color space
Otsu algorithm
ISSN:0025-326X, 1879-3363, 1879-3363
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Popis
Shrnutí:Harmful algal blooms (HABs) pose serious threats to coastal economies and ecosystems, yet effective monitoring remains challenging due to diverse bloom types and complex environmental conditions. This paper proposes a Mixed Algal Blooms Index (MABI) that uses a new color space to improve HABs detection. By employing Sentinel-2's near-infrared, short-wave infrared, and green bands to calculate tristimulus values—replacing traditional RGB bands—MABI significantly enhances the distinction between algal blooms and water. And an improved grid-based Otsu automatic threshold segmentation algorithm is utilized to extract algal blooms. The inter-class distance is employed as an indicator to compare 14 commonly used algal blooms indices. Validation across nine global sites, covering coastal and inland areas, shows MABI's robustness, with an overall accuracy of 0.98 and a Kappa coefficient of 0.95. Compared to traditional algal bloom indices, the proposed MABI shows notable advantages in detecting blooms, effectively identifying both mixed blooms from multiple algae species and single-species blooms. We also verified the effectiveness of MABI with Landsat-8, and the combination of Landsat and Sentinel-2 imagery is expected to enhance its capability to monitor the full lifecycle of algal blooms. While MABI shows some resistance to thin clouds and shadows, its detection accuracy can still be affected in optically complex waters. Therefore, careful threshold selection or combining with other indices is recommended for comprehensive assessment. This study utilized Google Earth Engine (GEE) for data acquisition, processing, algorithm development, and validation, offering an efficient and reliable tool for accurately monitoring HABs with wide-ranging applications. •Proposes MABI for HABs detection using Sentinel-2 bands in a new color space•Enhances distinction between algal blooms and water via tristimulus values•Validates MABI across nine global sites with high accuracy and Kappa coefficient•Outperforms traditional indices in detecting mixed and single-species blooms•Effective for monitoring algal blooms with Landsat-8 integration potential
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0025-326X
1879-3363
1879-3363
DOI:10.1016/j.marpolbul.2024.117321