Small (< 25 kg) aerial drones have expanded the remote sensing toolkit for disaster management activities. Here, we provide a critical review of drone-based remote sensing of natural hazard-related ...disasters to highlight research trends, biases, and expose new opportunities. We performed a systematic literature search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology, resulting in 635 relevant articles from which we derived statistics relating to geography, drone hardware, disaster management application, and drone remote sensing data type and analysis method. Key findings include a bias towards: (i) mass movement hazards (38%); (ii) small (< 1 km2) (76%) and rural (79%) study areas in high-income countries and territories (64%); (iii) image-based observations of features from the natural environment (77%); and (iv) support of mitigation-related vulnerability assessment and risk modeling (54%) and environmental recovery (23%). We recommend that future studies focus on: (i) earthquakes, floods, and cyclones and other windstorms due to higher loss of life and economic impacts; (ii) larger and urban study areas in low, lower-middle, and upper-middle income countries and territories to support vulnerable populations; (iii) under-demonstrated (and especially response-related) disaster management activities, which generally require observations of built features from urban environments; and (iv) data standards for integrating drone-based remote sensing with international disaster management methodologies.
•Of 635 studies, 87% performed mitigation- and recovery-related remote sensing.•Mass movements were the most commonly examined hazard type (38% of studies).•Study areas were small (76%), rural (79%), and high-income (64%).•We recommend more research on earthquakes, floods, and cyclones/windstorms.•More response-related studies in larger, urban, and lower-income areas are needed.
Geographic object-based image analysis (GEOBIA) is a remote sensing image analysis paradigm that defines and examines image-objects: groups of neighboring pixels that represent real-world geographic ...objects. Recent reviews have examined methodological considerations and highlighted how GEOBIA improves upon the 30+ year pixel-based approach, particularly for H-resolution imagery. However, the literature also exposes an opportunity to improve guidance on the application of GEOBIA for novice practitioners. In this paper, we describe the theoretical foundations of GEOBIA and provide a comprehensive overview of the methodological workflow, including: (i) software-specific approaches (open-source and commercial); (ii) best practices informed by research; and (iii) the current status of methodological research. Building on this foundation, we then review recent research on the convergence of GEOBIA with deep convolutional neural networks, which we suggest is a new form of GEOBIA. Specifically, we discuss general integrative approaches and offer recommendations for future research. Overall, this paper describes the past, present, and anticipated future of GEOBIA in a novice-accessible format, while providing innovation and depth to experienced practitioners.
Fugitive methane emissions from the oil and gas industry are targeted using leak detection and repair (LDAR) programs. Until recently, only a limited number of measurement standards have been ...permitted by most regulators, with emphasis on close-range methods (e.g. Method-21, optical gas imaging). Although close-range methods are essential for source identification, they can be labor-intensive. To improve LDAR efficiency, there has been a policy shift in Canada and the United States towards incorporating alternative technologies. However, the suitability of these technologies for LDAR remains unclear. In this paper, we systematically review and compare six technology classes for use in LDAR: handheld instruments, fixed sensors, mobile ground labs (MGLs), unmanned aerial vehicles (UAVs), aircraft, and satellites. These technologies encompass broad spatial and temporal scales of measurement. Minimum detection limits for technology classes range from <1 g h−1 for Method 21 instruments to 7.1 × 106 g h−1 for the GOSAT satellite, and uncertainties are poorly constrained. To leverage the diverse capabilities of these technologies, we introduce a hybrid screening-confirmation approach to LDAR called a comprehensive monitoring program. Here, a screening technology is used to rapidly tag high-emitting sites to direct close-range source identification. Currently, fixed sensors, MGLs, UAVs, and aircraft could be used as screening technologies, but their performances must be evaluated under a range of environmental and operational conditions to better constrain detection effectiveness. Methane-sensing satellites are improving rapidly and may soon be ready for facility-scale screening. We conclude with a speculative discussion of the future of LDAR, touching on integration, analytics, incentivization, and regulatory pathways.
Multi-sensor vehicle systems have been implemented in large-scale field programs to detect, attribute, and estimate emissions rates of methane (CH
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) and other compounds from oil and gas wells and ...facilities. Most vehicle systems use passive sensing; they must be positioned downwind of sources to detect emissions. A major deployment challenge is predicting the best measurement locations and driving routes to sample infrastructure. Here, we present and validate a methodology that incorporates high-resolution weather forecast and geospatial data to predict measurement locations and optimize driving routes. The methodology estimates the downwind road intersection point (DRIP) of theoretical CH
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plumes emitted from each well or facility. DRIPs serve as waypoints for Dijkstra's shortest path algorithm to determine the optimal driving route. We present a case study to demonstrate the methodology for planning and executing a vehicle-based concentration mapping survey of 50 oil and gas wells near Pecos, Texas. Validation was performed by comparing DRIPs with 174 CH
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plumes measured by vehicle surveys of oil and gas wells and facilities in Alberta, Canada. Results indicate median Manhattan distances of 145.8 m between DRIPs and plume midpoints and 160.3 m between DRIPs and peak plume enhancements. A total of 46 (26%) of the plume segments overlapped DRIPs. Locational errors of DRIPs are related to misattributions of emissions sources and discrepancies between modeled and instantaneous wind direction measured when the vehicle intersects plumes. Although the development of the methodology was motivated by CH
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emissions from oil and gas facilities, it should be applicable to other types of point source air emissions from known facilities.
Implications: This paper presents and validates a method that addresses the challenge of measuring industrial emissions from public roads. The method can increase the effectiveness and efficiency of targeted vehicle-based emissions surveys where the locations of potential sources are known. We believe the method has broad application in addition to the upstream oil and gas context it was designed for.
Residential natural gas meter set assemblies (MSAs) emit methane (CH4), but reported emissions factors vary. To test existing emissions factors, we quantified CH4 emissions from 37 residential MSAs ...in Calgary, Alberta, Canada. A notable difference with previous studies is the targeted measurement of regulator vents in this study, which were measured with a static chamber, while fugitives were measured with a modified hi-flow sampler. Emissions were dominated by pressure regulator vents (emissions factor = 1.18 g CH4/h/MSA), but 7 fugitives were found (emissions factor = 0.018 g CH4/h/MSA). Six regulator vents were emitting at notably higher rates (≥ 1.79 g CH4/h/MSA). The total empirical emissions factor was 1.20 g CH4/h/MSA (95 % CI, 1.03 to 1.37 g/h/MSA). This is ∼7 times higher than the emissions factor for residential MSAs used in the U.S. EPA's Greenhouse Gas Inventory, which may not include emissions from regulator vents. Upscaling to annual CH4 emissions in Calgary indicates 3234.6 t CH4/yr (95 % CI, 2776.4 t to 3692.9 t CH4/yr) could be emitted from MSAs. This is equivalent to 4.1 % (95 % CI, 3.5 % to 4.7 %) of total city-level CH4 emissions as estimated with satellite data. Results suggest residential MSA emissions may be under-estimated and further study isolating root causes of regulator vent emissions is required to guide mitigation and improve emissions modeling.
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•Regulator vents on residential meters may emit methane by design but are understudied.•We fit close-range measurement methods to vent and fugitive emissions on the meters.•All regulator vents were emitting methane and dominated total meter emissions.•The total meter emissions factor derived from our measurements was 1.20 g CH4/h.•Understanding the mechanisms behind regulator emissions could accelerate mitigation.
Abstract
Small aerial drones are used in a growing number of commercial applications. However, drones cannot fly in all weather, which impacts their reliability for time-sensitive operations. The ...magnitude and global variability of weather impact is poorly understood. We explore weather-limited drone flyability (the proportion of time drones can fly safely) by comparing historical wind speed, temperature, and precipitation data to manufacturer-reported thresholds of common commercial and weather-resistant drones with a computer simulation. We show that global flyability is highest in warm and dry continental regions and lowest over oceans and at high latitudes. Median global flyability for common drones is low: 5.7 h/day or 2.0 h/day if restricted to daylight hours. Weather-resistant drones have higher flyability (20.4 and 12.3 h/day, respectively). While these estimates do not consider all weather conditions, results suggest that improvements to weather resistance can increase flyability. An inverse analysis for major population centres shows the largest flyability gains for common drones can be achieved by increasing maximum wind speed and precipitation thresholds from 10 to 15 m/s and 0–1 mm/h, respectively.
Steep rock slopes present key opportunities and challenges within Earth science applications. Due to partial or complete inaccessibility, high-precision surveys of these high-relief landscapes remain ...a challenge. Direct georeferencing (DG) of unoccupied aerial vehicles (UAVs) with advanced onboard GNSS receivers presents opportunities to generate high-resolution 3D datasets without ground-based access to the study area. However, recent research has revealed large vertical errors using DG that may prove problematic in near-vertical terrain. To address these concerns, we examined more than 75 photogrammetric UAV-datasets with various imaging angles (nadir, oblique, and combinations) and ground control scenarios, including DG, along a steep slope exposure. Results demonstrate that mean errors in DG scenarios are up to 0.12 m higher than datasets using integrated georeferencing with well-distributed GCPs. Inclusion of GCPs greatly reduced mean error values but had limited influence on precision (<0.01 m) for any given imaging strategy. Use of multiple image angles resulted in the highest precisions, regardless of georeferencing strategy. These findings have implications for applications requiring the highest precision and accuracy (e.g., geotechnical engineering, hazard mitigation and mapping, and geomorphic change detection), which should consider using ground control whenever possible. However, for applications less concerned with absolute accuracy, our results show that DG datasets provide strong internal consistency and relative accuracy that may be suitable for high precision measurements within a model, without use of ground control.
Abstract
Satellite observations have been used to measure methane (CH
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) emissions from the oil and gas (O&G) industry, particularly by revealing previously undocumented, very large emission events ...and basin-level emission estimates. However, most satellite systems use passive remote sensing to retrieve CH
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mixing ratios, which is sensitive to sunlight, earth surface properties, and atmospheric conditions. Accordingly, the reliability of satellites for routine CH
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emissions monitoring varies across the globe. To better understand the potentials and limitations of routine monitoring of CH
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emissions with satellites, we investigated the global observational coverage of the TROPOMI instrument onboard the Sentinel-5P satellite—the only satellite system currently with daily global coverage. A 0.1° × 0.1° gridded global map that indicates the average number of days with valid observations from TROPOMI for 2019–2021 was generated by following the measurement retrieval quality-assurance threshold (≥ 0.5). We found TROPOMI had promising observational coverage over dryland regions (maximum: 58.6%) but limited coverage over tropical regions and high latitudes (minimum: 0%). Cloud cover and solar zenith angle were the primary factors affecting observational coverage at high latitudes, while aerosol optical thickness was the primary factor over dryland regions. To further assess the country-level reliability of satellites for detecting and quantifying CH
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emissions from the onshore O&G sector, we extracted the average annual TROPOMI observational coverage (TOC) over onshore O&G infrastructure for 160 countries. Seven of the top-10 O&G-producing countries had an average annual TOC < 10% (< 36 days per year), which indicates the limited ability to routinely identify large emissions events, track their duration, and quantify emissions rates using inverse modelling. We further assessed the potential performance of the latter by combining TOC and the uncertainties from the global O&G inventory. Results indicate that the accuracy of emissions quantifications of onshore O&G sources using TROPOMI data and inverse modeling will be higher in countries located in dryland and mid-latitude regions and lower in tropical and high-latitude regions. Therefore, current passive-sensing satellites have low potential for frequent monitoring of large methane emissions from O&G sectors in countries located in tropical and high latitudes (e.g., Canada, Russia, Brazil, Norway, and Venezuela). Alternative methods should be considered for routine emissions monitoring in these regions.
For more than four decades remote sensing images have been used to document and understand the evolution of aeolian sand dunes. Early studies focused on mapping and classifying dunes. Recent advances ...in sensor technology and software have allowed investigators to move towards quantitative investigation of dune form evolution and pattern development. These advances have taken place alongside progress in numerical models, which are capable of simulating the multitude of dune patterns observed in nature. The potential to integrate remote sensing (RS), spatial analysis (SA), and modeling to predict the future changes of real-world dune systems is steadily becoming a reality. Here we present a comprehensive review of significant recent advances involving RS and SA. Our objective is to demonstrate the capacity of these technologies to provide new insight on three important research domains: (1) dune activity, (2) dune patterns and hierarchies, and (3) extra-terrestrial dunes. We outline how several recent advances have capitalized on the improved spatial and spectral resolution of RS data, the availability of topographic data, and new SA methods and software. We also discuss some of the key research challenges and opportunities in the application of RS and SA dune field, including: the integration of RS data with field-based measurements of vegetation cover, structure, and aeolian transport rate in order to develop predictive models of dune field activity; expanding the observational evidence of dune form evolution at temporal and spatial scales that can be used to validate and refine simulation models; the development and application of objective and reproducible SA methods for characterizing dune field pattern; and, expanding efforts to quantify three-dimensional topographic changes of dune fields in order to develop improved understanding of spatio-temporal patterns of erosion and deposition. Overall, our review indicates a progressive evolution in the way sand dunes are studied: whereas traditional field studies of airflow and sand transport can clarify event-based process–form interactions, investigators are realizing a synoptic perspective is required to address the response of dune systems to major forcings. The integration and evolution of the technologies discussed in this review are likely to form a foundation for future advances in aeolian study.