Of Course We Fly Unmanned—We’re Women!

Joyce, Karen E.; Anderson, Karen; Bartolo, Renée E.

doi:10.3390/drones5010021

Cited by 36 publications

(20 citation statements)

References 28 publications

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“…Photogrammetry products derived from overlapping aerial images acquired with unoccupied aerial vehicles (UAVs, often referred to as ‘drones’ Joyce et al., 2021 ) could greatly improve the quantification of AGB in non‐forest ecosystems. This is true for (a) direct cases at local scales and (b) indirectly through enabling improvements in the calibration and validation of biomass products obtained from satellite observations over larger extents.…”

Section: Introductionmentioning

confidence: 99%

Global application of an unoccupied aerial vehicle photogrammetry protocol for predicting aboveground biomass in non‐forest ecosystems

Cunliffe

Anderson

Boschetti

et al. 2021

Remote Sens Ecol Conserv

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Section: Introductionmentioning

confidence: 99%

Global application of an unoccupied aerial vehicle photogrammetry protocol for predicting aboveground biomass in non‐forest ecosystems

Cunliffe

Anderson

Boschetti

et al. 2021

Remote Sens Ecol Conserv

Self Cite

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“…These could be by using social media platforms and professional networks such as the Ladies of Landsat, Sisters of SAR, Women in Copernicus, IEEE GRSS IDEA, Women+ in Geospatial, and other networks (Crowley et al, 2021a;Crowley et al, 2021b;Riedler et al, 2021;Stéphenne et al, 2021;Vizireanu et al, 2021) that aim to promote and support women in remote sensing. Actively nominating women for grants/awards, or by creating 'special issues' within a journal to amplify their work can also help to promote and support their research (Amon 2017;Van Oosten et al, 2017;Joyce et al, 2021).…”

Section: It's Time To Do Bettermentioning

confidence: 99%

Discovering Inclusivity in Remote Sensing: Leaving No One Behind

Joyce

Nakalembe

Gómez

et al. 2022

Front. Remote Sens.

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Innovative and beneficial science stems from diverse teams and authorships that are inclusive of many perspectives. In this paper, we explore the status of inclusivity in remote sensing academic publishing, using an audit of peer-reviewed journal editorial board composition. Our findings demonstrate diversity deficiency in gender and country of residence, limiting the majority of editors to men residing in four countries. We also examine the many challenges underrepresented communities within our field face, such as implicit bias, harsher reviews, and fewer citations. We assert that in the field of remote sensing, the gatekeepers are not representative of the global society and this lack of representation restricts what research is valued and published, and ultimately who becomes successful. We present an action plan to help make the field of remote sensing more diverse and inclusive and urge every individual to consider their role as editor, author, reviewer, or reader. We believe that each of us have a choice to continue to align with a journal/institution/society that is representative of the dynamic state of our field and its people, ensuring that no one is left behind while discovering all the fascinating possibilities in remote sensing.

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“…Unoccupied Aerial Vehicles (UAV) [11] provide ultra-high resolution imagery facilitating individual crown assessment and informing more efficient implementation of costly management actions like culling or spraying repellent [2]. Many previous UAV-based forest health monitoring (FHM) studies, including temporal change surveys, have been conducted using multispectral and hyperspectral sensors [12].…”

Section: Introductionmentioning

confidence: 99%

Mapping Boreal Forest Spruce Beetle Health Status at the Individual Crown Scale Using Fused Spectral and Structural Data

Cessna

Alonzo

Foster

et al. 2021

Forests

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The frequency and severity of spruce bark beetle outbreaks are increasing in boreal forests leading to widespread tree mortality and fuel conditions promoting extreme wildfire. Detection of beetle infestation is a forest health monitoring (FHM) priority but is hampered by the challenges of detecting early stage (“green”) attack from the air. There is indication that green stage might be detected from vertical gradients of spectral data or from shortwave infrared information distributed within a single crown. To evaluate the efficacy of discriminating “non-infested”, “green”, and “dead” health statuses at the landscape scale in Alaska, USA, this study conducted spectral and structural fusion of data from: (1) Unoccupied aerial vehicle (UAV) multispectral (6 cm) + structure from motion point clouds (~700 pts m−2); and (2) Goddard Lidar Hyperspectral Thermal (G-LiHT) hyperspectral (400 to 1000 nm, 0.5 m) + SWIR-band lidar (~32 pts m−2). We achieved 78% accuracy for all three health statuses using spectral + structural fusion from either UAV or G-LiHT and 97% accuracy for non-infested/dead using G-LiHT. We confirm that UAV 3D spectral (e.g., greenness above versus below median height in crown) and lidar apparent reflectance metrics (e.g., mean reflectance at 99th percentile height in crown), are of high value, perhaps capturing the vertical gradient of needle degradation. In most classification exercises, UAV accuracy was lower than G-LiHT indicating that collecting ultra-high spatial resolution data might be less important than high spectral resolution information. While the value of passive optical spectral information was largely confined to the discrimination of non-infested versus dead crowns, G-LiHT hyperspectral band selection (~400, 675, 755, and 940 nm) could inform future FHM mission planning regarding optimal wavelengths for this task. Interestingly, the selected regions mostly did not align with the band designations for our UAV multispectral data but do correspond to, e.g., Sentinel-2 red edge bands, suggesting a path forward for moderate scale bark beetle detection when paired with suitable structural data.

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Of Course We Fly Unmanned—We’re Women!

Abstract: Striving to achieve a diverse and inclusive workplace has become a major goal for many organisations around the world [...]

Cited by 36 publications

References 28 publications

Global application of an unoccupied aerial vehicle photogrammetry protocol for predicting aboveground biomass in non‐forest ecosystems

Global application of an unoccupied aerial vehicle photogrammetry protocol for predicting aboveground biomass in non‐forest ecosystems

Discovering Inclusivity in Remote Sensing: Leaving No One Behind

Mapping Boreal Forest Spruce Beetle Health Status at the Individual Crown Scale Using Fused Spectral and Structural Data

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