Cloud Detection by Luminance and Inter-band Parallax Analysis for Pushbroom Satellite Imagers

Dagobert, Tristan; Gioi, Rafael Grompone von; Franchis, Carlo de; Morel, Jean‐Michel; Hessel, Charles

doi:10.5201/ipol.2020.271

Cited by 8 publications

(5 citation statements)

References 21 publications

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“…We collected L1C Sentinel-2 time series corresponding to the areas of interest, preferably considering time series longer than six months. We first coregistered all the images of a time series using the method by Hessel et al We also applyied a cloud detection algorithm, such as the one proposed by Dagobert et al, to estimate the cloud cover. All images with more than 15% of the pixels covered by clouds are discarded.…”

Section: Practical Methane Emission Trackingmentioning

confidence: 99%

Global Tracking and Quantification of Oil and Gas Methane Emissions from Recurrent Sentinel-2 Imagery

Ehret

Truchis

Mazzolini

et al. 2022

Environ. Sci. Technol.

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Methane (CH4) emission estimates from top-down studies over oil and gas basins have revealed systematic underestimation of CH4 emissions in current national inventories. Sparse but extremely large amounts of CH4 from oil and gas production activities have been detected across the globe, resulting in a significant increase of the overall oil and gas contribution. However, attribution to specific facilities remains a major challenge unless high-spatial-resolution images provide sufficient granularity within the oil and gas basin. In this paper, we monitor known oil and gas infrastructures across the globe using recurrent Sentinel-2 imagery to detect and quantify more than 1200 CH4 emissions. In combination with emission estimates from airborne and Sentinel-5P measurements, we demonstrate the robustness of the fit to a power law from 0.1 /h to 600 /h. We conclude here that the prevalence of ultraemitters (>25 /h) detected globally by Sentinel-5P directly relates to emission occurrences below its detection threshold in the range >2 /h, which correspond to large emitters covered by Sentinel-2. We also verified that this relation is also valid at a more local scale for two specific countries, namely, Algeria and Turkmenistan, and the Permian basin in the United States.

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Section: Practical Methane Emission Trackingmentioning

confidence: 99%

Global Tracking and Quantification of Oil and Gas Methane Emissions from Recurrent Sentinel-2 Imagery

Ehret

Truchis

Mazzolini

et al. 2022

Environ. Sci. Technol.

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“…The size of this area is large enough so that the background estimation is not impacted too much by the presence of methane in the input images. We collected the L1C Sentinel-2 time series corresponding to this location and, using a cloud detection algorithm such as the one proposed by Dagobert et al [11], we removed all images with a cloud coverage of more than 15% of the image pixels. We then manually detected the plume shown in this section.…”

Section: Methodsmentioning

confidence: 99%

Automatic Methane Plume Quantification Using Sentinel-2 Time Series

Ehret

Truchis²,

Mazzolini³

et al. 2022

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

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Methane emissions monitoring is essential to control methane pollution. In this paper, we propose an automatic practical methodology using time series to estimate the quantity of methane in a given plume using a multispectral satellite like Sentinel-2. Sentinel-2 proposes a low revisit time, a good spatial resolution and a low acquisition cost. Contrary to previous methods, the proposed approach does not require a manual selection of an optimal reference image. We compared its performance on an oil-and-gas site in Kazakhstan. This is the first step toward an automatic global monitoring system for methane plume detection and quantification with these satellites.

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“…In order to apply our method we first remove the useless images containing clouds or that are too blurry (Anger et al, 2019). Detecting clouds in images from pushbroom satellites can be achieved by exploiting the parallax effect on the different channels (Dagobert et al, 2019a). However, this method is not adapted to our input images as PlanetScope satellites do not use pushbroom sensors.…”

Section: Our Methodsmentioning

confidence: 99%

Stockpile Monitoring Using Linear Shape-From-Shading on Planetscope Imagery

d'Autume

Perry²,

Morel

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The storage and management of stockpiles of materials is a fundamental process in large scale activities such as mining, civil engineering, and in the management of waste landfill sites. Following the evolution of stocks has always been important, and advancements in remote sensing technology are not only facilitating this, but also making it possible in near real-time. Nowadays, this monitoring appears to be performed almost exclusively using UAV based techniques. This paper proposes to apply a simple Shape-from-Shading method on low resolution satellite images provided by PlanetScope to monitor the evolution of the volume of stockpiles. The proposed Shape-from-Shading formulation makes it possible to handle occluding objects in the scene. The loss in accuracy due to the low resolution of PlanetScope images is well compensated by the daily revisit frequency and by the fact that spaceborne acquisitions require no human supervision. With satellites it is also easy to follow simultaneously several stocking sites all over the world. We test our method on two coal storage sites and demonstrate that the stockpiles are well detected.

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Cloud Detection by Luminance and Inter-band Parallax Analysis for Pushbroom Satellite Imagers

Cited by 8 publications

References 21 publications

Global Tracking and Quantification of Oil and Gas Methane Emissions from Recurrent Sentinel-2 Imagery

Global Tracking and Quantification of Oil and Gas Methane Emissions from Recurrent Sentinel-2 Imagery

Automatic Methane Plume Quantification Using Sentinel-2 Time Series

Stockpile Monitoring Using Linear Shape-From-Shading on Planetscope Imagery

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