Template scripts on Google Earth Engine to detect green tides on the foreshores of beaches using Landsat archive

This github data repository gives access to template scripts to detect and monitor green tides on the foreshores of beaches using Landsat archive. The template provide examples for beaches in Northern Brittany, France. Two templates show how to estimate the green tide surfaces using Google Earth Engine Javascript. One template focuses on retrieving spectral signatures for certain point of interest on one image and export them in a chart

Spotting Green Tides over Brittany from Space: Three Decades of Monitoring with Landsat Imagery

International audienceGreen tides of macroalgae have been negatively affecting the coasts of Brittany, France, for at least five decades, caused by excessive nitrogen inputs from the farming sector. Regular areal estimates of green tide surfaces are publicly available but only from 2002 onwards. Using free and openly accessible Landsat satellite imagery archives over 35 years (1984–2019), this study explores the potential of remote sensing for detection and long-term monitoring of green macroalgae blooms. By using a Google Earth Engine (GEE) script, we were able to detect and quantify green tide surfaces using the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) at four highly affected beaches in Northern Brittany. Mean green tide coverage was derived and analyzed from 1984 to 2019, at both monthly and annual scales. Our results show important interannual and seasonal fluctuations in estimated macroalgae cover. In terms of trends over time, green tide events did not show a decrease in extent at three out of four studied sites. The observed decrease in nitrogen concentrations for the rivers draining the study sites has not resulted in a reduction of green tide extents

Dataset on green tide surfaces over Brittany using Landsat imagery for 35 years of monitoring

This dataset details all data used for a manuscript submission entitled "Spotting green tides over Brittany from space: three decades of monitoring with Landsat imagery". It presents data derived from Earth observation detection on the macroalgae surface on four studied sites in Brittany, France. These estimates were made using Landsat 5 and 8 satellite imagery, using the Google Earth Engine environment. Spectral signatures of natural features found on the study sites (sand, water and algae) are also presented. Additional datasets include 1) green macroalgae surface estimates made by an external source, CEVA (French Algae Technology and Innovation Center) and derived from aerial photography. This data was used for comparison with our results 2) nitrogen concentrations for four water stations close to the study sites. Nitrogen is considered the main physico-chemical factor controlling algae growth

Advancing Floating Macroplastic Detection from Space Using Experimental Hyperspectral Imagery

Airborne and spaceborne remote sensing (RS) collecting hyperspectral imagery provides unprecedented opportunities for the detection and monitoring of floating riverine and marine plastic debris. However, a major challenge in the application of RS techniques is the lack of a fundamental understanding of spectral signatures of water-borne plastic debris. Recent work has emphasised the case for open-access hyperspectral reflectance reference libraries of commonly used polymer items. In this paper, we present and analyse a high-resolution hyperspectral image database of a unique mix of 40 virgin macroplastic items and vegetation. Our double camera setup covered the visible to shortwave infrared (VIS-SWIR) range from 400 to 1700 nm in a darkroom experiment with controlled illumination. The cameras scanned the samples floating in water and captured high-resolution images in 336 spectral bands. Using the resulting reflectance spectra of 1.89 million pixels in linear discriminant analyses (LDA), we determined the importance of each spectral band for discriminating between water and mixed floating debris, and vegetation and plastics. The absorption peaks of plastics (1215 nm, 1410 nm) and vegetation (710 nm, 1450 nm) are associated with high LDA weights. We then compared Sentinel-2 and Worldview-3 satellite bands with these outcomes and identified 12 satellite bands to overlap with important wavelengths for discrimination between the classes. Lastly, the Normalised Vegetation Difference Index (NDVI) and Floating Debris Index (FDI) were calculated to determine why they work, and how they could potentially be improved. These findings could be used to enhance existing efforts in monitoring macroplastic pollution, as well as form a baseline for the design of future multispectral RS systems. View Full-Tex

Hyperspectral plastics dataset supplementary to the paper ‘Advancing floating plastic detection from space using hyperspectral imagery’

This database is the supplementary material of Tasseron et al., (2021): Advancing floating plastic detection from space using hyperspectral imagery

Direct and Indirect River Plastic Detection from Space

Plastic pollution is a threat to both human health and ecosys-tems. Quantification efforts often rely on observations in the field, which can be costly and labour-intensive. Satellite remote sensing offers an opportunity for upscaling plastic detection in time and space, as it enables covering large areas and using historical images. To date, several approaches have been successful for the detection of marine plastics, but detecting riverine plastics with satellite imagery are lacking. In this paper, we explore the use of satellite imagery for direct and indirect detection of floating plastics in rivers, ultimately calculating river surface plastic densities. The direct detection was done using Worldview-3 imagery and enables to detect large objects ( 1.2 m of size). The indirect approach uses plastic entrapped in water hyacinths as a proxy for plastic density at the river surface. Sentinel-1 imagery allowed to estimate water hyacinth coverage, which we combined with field data to estimate entrapment ratio and plastic density within hyacinths. Items as small as 2.7 cm could be detected with field data. The indirect approach detects a thousand more plastic items (2.1 × 104 items km-2) than the direct approach (5.8 × 101 items km-2), a likely result of the larger range of item size detectable with this method

Rivers as Plastic Reservoirs

Land-based plastic waste, carried to the sea through rivers, is considered a main source of marine plastic pollution. However, most plastics that leak into the environment never make it into the ocean. Only a small fraction of plastics that are found in the terrestrial and aquatic compartments of river systems are emitted, and the vast majority can be retained for years, decades, and potentially centuries. In this perspective paper we introduce the concept of river systems as plastic reservoirs. Under normal conditions, hydrometeorological variables (such as wind, runoff and river discharge) mobilize, transport and deposit plastics within different river compartments (e.g., riverbanks, floodplains, lakes, estuaries). The emptying of these plastic reservoirs primarily occurs under extreme hydrological conditions (e.g., storms, floods). In this paper we specifically focus on the retention mechanisms within different river compartments, and their effect on the fate of the plastics that are accumulated on various timescales. We aim to introduce the concept of rivers as (long-term) sinks for plastic pollution, and provide suggestions for future research directions

Hyperspectral dataset and associated MATLAB scripts supplementary to the paper 'Towards Robust River Plastic Detection: Combining Lab and Field-based Hyperspectral Imagery'

This database is the supplementary material of Tasseron et al., (2022): 'Towards Robust River Plastic Detection: Combining Lab and Field-based Hyperspectral Imagery' [Submitted and currently under review], preprint available online at https://doi.org/10.31223/X5RW7V. The dataset contains raw images, MATLAB scripts used for training classifier algorithms, trained pipelines, required toolboxes and labelled training datasets used in subsequent analyses

Template scripts on Google Earth Engine to detect green tides on the foreshores of beaches using Landsat archive

This github data repository gives access to template scripts to detect and monitor green tides on the foreshores of beaches using Landsat archive. The template provide examples for beaches in Northern Brittany, France. Two templates show how to estimate the green tide surfaces using Google Earth Engine Javascript. One template focuses on retrieving spectral signatures for certain point of interest on one image and export them in a chart. <br