Riverine litter monitoring from multispectral fine pixel satellite images SCOPUS

Title
Riverine litter monitoring from multispectral fine pixel satellite images
Author(s)
Garaba, Shungudzemwoyo P.; Park, Young Je
KIOST Author(s)
Park, Young Je(박영제)
Alternative Author(s)
박영제
Publication Year
2024-04
Abstract
Mismanaged litter or debris in aquatic systems can pose threats to water quality and blue economic activities. Monitoring strategies like remote sensing can complement and support the gathering of relevant descriptors useful for understanding challenges related to leakage litter. We present a robust technique for detecting and quantifying floating riverine litter, a soup of natural and anthropogenic materials. Spectral information of GeoEye, PlanetScope and Skysat fine resolution satellite imagery was statistically transformed into spatial anomalies correlated to fractional-pixel riverine litter abundance. Algorithm development also involved techniques that accounted for variation in satellite data characteristics. The detected fractional abundance was converted into a unit surface area in a region-of-interest. Intercomparison of derived area coverage from matching images captured by the various unique sensors in the PlanetScope and Skysat constellation were consistent (R² = 0.98). Likewise, the litter surface area derived manually had a very strong linear relationship to the algorithm estimates (R² > 0.99). The prospect of time series observations over several years at sub-daily to near daily intervals were also demonstrated using the cloud-free PlanetScope and Skysat imagery. Transferability as well as easy adaptation of the algorithm was further showcased by application over water bodies in Guatemala and Slovakia. © 2023 The Author(s)
ISSN
2666-7657
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/44933
DOI
10.1016/j.envadv.2023.100451
Bibliographic Citation
Environmental Advances, v.15, 2024
Publisher
Elsevier
Keywords
Band difference anomaly; Riverine litter detection and quantification, PlanetScope - SkySat, GeoEye, multispectral fine pixel satellite images
Type
Article
Language
English
Document Type
Article
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