Integrating Drone Imagery into High Resolution Satellite Remote Sensing Assessments of Estuarine Environments

Gray, Patrick C.; Ridge, Justin T.; Poulin, Sarah; Seymour, Alexander C.; Schwantes, Amanda M.; Swenson, Jennifer J.; Johnston, David W.

doi:10.3390/rs10081257

Cited by 86 publications

(68 citation statements)

References 60 publications

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“…They provide a low-cost, high-resolution replacement for, or complement to, satellite and airplane imagery, especially for environmental monitoring [15][16][17][44][45][46]. UAV data are increasingly being integrated with data from other platforms, such as satellites [47].…”

Section: Introductionmentioning

confidence: 99%

Standardized spectral and radiometric calibration of consumer cameras

Burggraaff¹,

Schmidt²,

Zamorano³

et al. 2019

Opt. Express

103

108

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Consumer cameras, particularly onboard smartphones and UAVs, are now commonly used as scientific instruments. However, their data processing pipelines are not optimized for quantitative radiometry and their calibration is more complex than that of scientific cameras. The lack of a standardized calibration methodology limits the interoperability between devices and, in the ever-changing market, ultimately the lifespan of projects using them. We present a standardized methodology and database (SPECTACLE) for spectral and radiometric calibrations of consumer cameras, including linearity, bias variations, read-out noise, dark current, ISO speed and gain, flat-field, and RGB spectral response. This includes golden standard ground-truth methods and do-it-yourself methods suitable for non-experts. Applying this methodology to seven popular cameras, we found high linearity in RAW but not JPEG data, inter-pixel gain variations >400% correlated with large-scale bias and read-out noise patterns, non-trivial ISO speed normalization functions, flat-field correction factors varying by up to 2.79 over the field of view, and both similarities and differences in spectral response. Moreover, these results differed wildly between camera models, highlighting the importance of standardization and a centralized database. accuracy of color measurements and their conversion to standard measures, such as the CIE 1931 XYZ and CIELAB color spaces, is limited by distortions in the observed colors [20] and differences in spectral response functions [21,[23][24][25].Extensive (spectro-)radiometric calibrations of consumer cameras are laborious and require specialized equipment and are thus not commonly performed [23,54,60]. A notable exception is the spectral and absolute radiometric calibration of a Raspberry Pi 3 V2 webcam by Pagnutti et al. [57], including calibrations of linearity, exposure stability, thermal and electronic noise, flat-field, and spectral response. Using this absolute radiometric calibration, digital values can be converted into SI units of radiance. However, the authors noted the need to characterize a large number of these cameras before the results could be applied in general. Moreover, certain calibrations are device-dependent and would need to be done separately on each device. Spectral and radiometric calibrations of seven cameras, including the Raspberry Pi, are given in [51]. These calibrations include dark current, flat-fielding, linearity, and spectral characterization. However, for the five digicams included in this work, JPEG data were used, severely limiting the quality and usefulness of these calibrations, as described above.Spectral characterizations are more commonly published since these are vital for quantitative color analysis. Using various methods, the spectral responses of several Canon [1,19,23,24,46,51,54,61], Nikon [1,13,23,24,54,[59][60][61][62], Olympus [23, 24, 51], Panasonic [46], SeaLife [13], Sigma [60], and Sony [1, 23, 46, 51, 61] digital cameras (digicams), as well as a number of smartpho...

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Section: Introductionmentioning

confidence: 99%

Standardized spectral and radiometric calibration of consumer cameras

Burggraaff¹,

Schmidt²,

Zamorano³

et al. 2019

Opt. Express

103

108

View full text Add to dashboard Cite

show abstract

“…While wetland classes were accurately characterised, the accuracy of the extent of wetland classification was not identified in this study. Machine learning imputation and Support Vector Machine [272] supervised classification learning methods for land cover and wetland class, form, and type have average accuracies of 80% and 79% respectively, and range from 72%-99% (random forest) (e.g., References [35,36,38,52,75,113,155,203,271]) and 73%-90% (Support Vector Machine) (e.g., References [49,61,75,91,99,101,116]). These exceed the proposed minimum accuracy requirements in regions such as Alberta Canada but require that training data capture the full variability of each class identified by the classifier [273,274].…”

Section: Wetland Extent For Baseline Inventory and Long-term Monitoringmentioning

confidence: 99%

Remote Sensing of Boreal Wetlands 1: Data Use for Policy and Management

et al. 2020

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Wetlands have and continue to undergo rapid environmental and anthropogenic modification and change to their extent, condition, and therefore, ecosystem services. In this first part of a two-part review, we provide decision-makers with an overview on the use of remote sensing technologies for the ‘wise use of wetlands’, following Ramsar Convention protocols. The objectives of this review are to provide: (1) a synthesis of the history of remote sensing of wetlands, (2) a feasibility study to quantify the accuracy of remotely sensed data products when compared with field data based on 286 comparisons found in the literature from 209 articles, (3) recommendations for best approaches based on case studies, and (4) a decision tree to assist users and policymakers at numerous governmental levels and industrial agencies to identify optimal remote sensing approaches based on needs, feasibility, and cost. We argue that in order for remote sensing approaches to be adopted by wetland scientists, land-use managers, and policymakers, there is a need for greater understanding of the use of remote sensing for wetland inventory, condition, and underlying processes at scales relevant for management and policy decisions. The literature review focuses on boreal wetlands primarily from a Canadian perspective, but the results are broadly applicable to policymakers and wetland scientists globally, providing knowledge on how to best incorporate remotely sensed data into their monitoring and measurement procedures. This is the first review quantifying the accuracy and feasibility of remotely sensed data and data combinations needed for monitoring and assessment. These include, baseline classification for wetland inventory, monitoring through time, and prediction of ecosystem processes from individual wetlands to a national scale.

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“…The majority of these research studies deploy UAS to collect various types of information on inanimate components of marine systems (e.g., beach topography -see Seymour et al, 2018), vegetation (e.g., coastal wetlands -see Gray et al, 2018a) or animals (e.g., sea turtle biology, ecology, and density -see Sykora-Bodie et al, 2017;Gray et al, 2018b;Rees et al, 2018). The information generated can be used in a variety of pure or applied ecological or environmental studies, including coastal management and protected species population assessments, and can contribute to our understanding of how coastlines evolve (Seymour et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Applying Unoccupied Aircraft Systems to Study Human Behavior in Marine Science and Conservation Programs

et al. 2019

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The declining costs of Unoccupied Aircraft Systems (UAS, aka drones), their ease of use, and their ability to collect high resolution data from a variety of sensors has resulted in an explosion of applications across the globe. Scientists working in the marine environment are increasingly using UAS to study a variety of topics, from counting wildlife populations in remote locations to estimating the effects of storms and sea level rise on shorelines. UAS also provide transformative potential to study the ways in which humans interact with and affect marine and coastal ecosystems, but doing so presents unique ethical and legal challenges. Human subjects have property rights that must be respected and they have rights to privacy, as well as expectations of privacy and security that may extend beyond actual legal rights. Using two case studies to illustrate these challenges, we outline the legal and regulatory landscapes that scientists confront when people are their primary study subjects, and conclude with an initial set of legal best practices to guide researchers in their efforts to study human interactions with natural resources in the marine environment.

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Integrating Drone Imagery into High Resolution Satellite Remote Sensing Assessments of Estuarine Environments

Cited by 86 publications

References 60 publications

Standardized spectral and radiometric calibration of consumer cameras

Standardized spectral and radiometric calibration of consumer cameras

Remote Sensing of Boreal Wetlands 1: Data Use for Policy and Management

Applying Unoccupied Aircraft Systems to Study Human Behavior in Marine Science and Conservation Programs

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