This paper evaluates the accuracy of shoreline positions obtained from the infrared (IR) bands of Landsat 7, Landsat 8, and Sentinel-2 imagery on natural beaches. A workflow for sub-pixel shoreline extraction, already tested on seawalls, is used. The present work analyzes the behavior of that workflow and resultant shorelines on a micro-tidal (<20 cm) sandy beach and makes a comparison with other more accurate sets of shorelines. These other sets were obtained using differential GNSS surveys and terrestrial photogrammetry techniques through the C-Pro monitoring system. 21 sub-pixel shorelines and their respective high-precision lines served for the evaluation. The results prove that NIR bands can easily confuse the shoreline with whitewater, whereas SWIR bands are more reliable in this respect. Moreover, it verifies that shorelines obtained from bands 11 and 12 of Sentinel-2 are very similar to those obtained with bands 6 and 7 of Landsat 8 (−0.75 ± 2.5 m; negative sign indicates landward bias). The variability of the brightness in the terrestrial zone influences shoreline detection: brighter zones cause a small landward bias. A relation between the swell and shoreline accuracy is found, mainly identified in images obtained from Landsat 8 and Sentinel-2. On natural beaches, the mean shoreline error varies with the type of image used. After analyzing the whole set of shorelines detected from Landsat 7, we conclude that the mean horizontal error is 4.63 m (±6.55 m) and 5.50 m (±4.86 m), respectively, for high and low gain images. For the Landsat 8 and Sentinel-2 shorelines, the mean error reaches 3.06 m (±5.79 m).

Industrial emissions play a major role in the global methane budget. The Permian basin is thought to be responsible for almost half of the methane emissions from all U.S. oil- and gas-producing regions, but little is known about individual contributors, a prerequisite for mitigation. We use a new class of satellite measurements acquired during several days in 2019 and 2020 to perform the first regional-scale and high-resolution survey of methane sources in the Permian. We find an unexpectedly large number of extreme point sources (37 plumes with emission rates >500 kg hour−1), which account for a range between 31 and 53% of the estimated emissions in the sampled area. Our analysis reveals that new facilities are major emitters in the area, often due to inefficient flaring operations (20% of detections). These results put current practices into question and are relevant to guide emission reduction efforts.

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Abstract. The detection of methane emissions from industrial activities can help enable effective climate change mitigation strategies. These industrial emissions, such as from oil and gas (O&amp;G) extraction and coal mining, typically occur as large plumes of highly concentrated gas. Different satellite missions have recently shown the potential to map such methane plumes from space. In this work, we report on the promising potential of the WorldView-3 (WV-3) satellite mission for methane mapping. This relies on its unique very high spatial resolution (up to 3.7 m) data in the shortwave infrared part of the spectrum, which is complemented by a good spectral sampling of the methane absorption feature at 2300 nm and a high signal to noise ratio. The proposed retrieval methodology is based on the calculation of methane concentration enhancements from pixel-wise estimates of methane transmittance at WV-3 SWIR band 7 (2235–2285 nm), which is positioned at a highly-sensitive methane absorption region. A sensitivity analysis based on end-to-end simulations has helped to understand retrieval errors and detection limits. The results have shown the good performance of WV-3 for methane mapping, especially over bright and homogeneous areas. The potential of WV-3 for methane mapping has been further tested with real data, which has led to the detection of 26 independent point emissions over different methane hotspot regions, such as O&amp;G extraction fields in Algeria and Turkmenistan, and the Shanxi coal mining region in China. In particular, the detection of very small leaks (< 100 kg h−1) from oil pipelines in Turkmenistan shows the unique capability of WV-3 for mapping industrial methane emissions from space. The mission includes pointing capabilities that allow for a daily revisit over these oil pipelines or other critical infrastructure.

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Coastal video monitoring has been proven to be a valuable shore-based remote-sensing technique to study coastal processes, as it offers the possibility of high-frequency, continuous and autonomous observations of the coastal area. However, the installation of a video systems infrastructure requires economical and technical efforts, along with being often limited by logistical constraints. This study presents methodological approaches to exploit “surfcam” internet streamed images for quantitative scientific studies. Two different methodologies to collect the required ground control points (GCPs), both during fieldwork and using web tools freely available are presented, in order to establish a rigorous geometric connection between terrestrial and image spaces. The application of an image projector tool allowed the estimation of the unknown camera parameters necessary to georectify the online streamed images. Three photogrammetric procedures are shown, distinct both in the design of the computational steps and in number of GCPs available to solve the spatial resection system. Results showed the feasibility of the methodologies to generate accurate rectified planar images, with the best horizontal projection accuracy of 1.3 m compatible with that required for a quantitative analysis of coastal processes. The presented methodologies can turn “surfcam” infrastructures and any online streaming beach cam, into fully remote shore-based observational systems, fostering the use of these freely available images for the study of nearshore morphodynamics.