Ladies of Landsat

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All are welcome! We are an affinity group working to make the field of #EarthObservation more #inclusive for #underrepresented scientists. #ScienceMastodon

We work alongside other groups like Sisters of SAR, Women of Waveforms and Dames of Drones. #EOChat #GISChat

Tune in here for updates on the #LoLManuscriptMonday series and new episodes of the #SceneFromAbove podcast.

Account run by @morganahcrowley & @flaviamendes

LoL Manuscript Mondayhttps://github.com/ladiesoflandsat/LOLManuscriptMonday
LinkedInhttps://www.linkedin.com/company/ladies-of-landsat

@sonaguliyeva et al. (2023) applied #machinelearning algorithms in mapping #croptypes using high resolution #Azersky imagery & demonstrated the value of #opensource platforms like #GoogleEarthEngine in the #classification of crops #LoLManuscriptMonday https://bit.ly/Guliyeva_2023

Shout out to Sona's co-authors & affiliated organizations #Azercosmos #NationalAviationAcademy #Azerbaijan #EOChat #GISChat

Maria Åsnes Moan et al. (2023) use bi-temporal airborne laser scanner data in #PRFSS to improve forest site index determination by detecting & excluding disturbed areas, which helps understand forest growth patterns & management planning. #CFSEFI #LoLManuscriptMonday https://bit.ly/Moan_2023

This research was done at the Petawawa Research Forest using data from the PRF remote sensing supersite! #PRFSS #CFSEFI #EOChat #GISChat

Detecting and excluding disturbed forest areas improves site index determination using bitemporal airborne laser scanner data

Abstract. Bitemporal airborne laser scanning (ALS) data are increasingly being used in forest management inventories for the determination of site index (SI). S

OUP Academic
A detailed report on the methodologies in this paper on the #decisiontree classifiers for mapping #forestcover & change was also published by Trix Estomata in 2018: https://bit.ly/Estomata_2018
Forest Cover and Change Classification Using ALOS PALSAR Mosaic Data and Decision Tree Classifiers

The “Climate-relevant Modernization of the National Forest Policy and Piloting of REDD Measures in the Philippines” project developed a remote sensing methodology for forest cover and forest cover change mapping. This methodology was applied to the

Mari Trix Estomata & Klaus Schmitt (2019) used #decisiontree classification & unbiased area estimation to map #forestcover extent & change in the #Philippines using #ALOS #PALSAR mosaic data in support of #REDD+ initiatives. #LoLManuscriptMonday http://bit.ly/Estomata_2019

Cheers to Estomata & Schmitt’s affiliation & supporting organizations for this #LoLManuscriptMonday feature: GIZ Philippines, the K&C Initiative of JAXA & the Forest Management Bureau of DENR Philippines #EOChat #GISChat 🛰️🌳🥳

Stritih et al. (2023) use #GEDI #lidar to characterize large-scale patterns in mountain forest structure in the European Alps & investigate the role of disturbance & recovery in forest state transitions using #Landsat-based maps. #LoLManuscriptMonday https://bit.ly/Stritih_2023
Alternative states in the structure of mountain forests across the Alps and the role of disturbance and recovery - Landscape Ecology

Context Structure is a central dimension of forest ecosystems that is closely linked to their capacity to provide ecosystem services. Drivers such as changing disturbance regimes are increasingly altering forest structure, but large-scale characterizations of forest structure and disturbance-mediated structural dynamics remain rare. Objectives Here, we characterize large-scale patterns in the horizontal and vertical structure of mountain forests and test for the presence of alternative structural states. We investigate factors determining the occurrence of structural states and the role of disturbance and recovery in transitions between states. Methods We used spaceborne lidar (GEDI) to characterize forest structure across the European Alps. We combined GEDI-derived structural metrics with Landsat-based disturbance maps and related structure to topography, climate, landscape configuration, and past disturbances. Results We found two alternative states of forest structure that emerged consistently across all forest types of the Alps: short, open-canopy forests (24%) and tall, closed-canopy forests (76%). In the absence of disturbance, open-canopy forests occurred at high elevations, forest edges, and warm, dry sites. Disturbances caused a transition to open-canopy conditions in approximately 50% of cases. Within 35 years after disturbance, 72% of forests recovered to a closed-canopy state, except in submediterranean forests, where recovery is slow and long-lasting transitions to open-canopy conditions are more likely. Conclusions As climate warming increases disturbances and causes thermophilization of vegetation, transitions to open-canopy conditions could become more likely in the future. Such restructuring could pose a challenge for forest management, as open-canopy forests have lower capacities for providing important ecosystem services.

SpringerLink

Ursa Kanjir et al. (2018) investigated the use of #opticalsatelliteimagery in #vesseldetection & the most common factors influencing the #accuracy of the methodologies by reviewing 119 papers published until 2017 #LoLManuscriptMonday https://bit.ly/Kanjir_2018

Cheers to Dr. Kanjir’s co-authors & affiliations, ZRC SAZU, European Joint Research Centre & the University of Ljublana for this awesome #LoLManuscriptMonday publication! 🛰️🚢#EOChat #GISChat

Very cool to see @LadiesOfLandsat here. Remote sensing, especially public domain sensing like Landsat, is going to be crucial for adapting to our climate future. https://mapstodon.space/@LadiesOfLandsat/109308575719751104
Ladies of Landsat (@[email protected])

Attached: 4 images Hi everyone! Here’s an #introduction to our organization, #LadiesOfLandsat. We are working to make the field of #remotesensing more inclusive for underrepresented scientists. 🛰 Our #ScienceMastodon page will be led by @[email protected] & @[email protected]. Our current efforts include a weekly #LoLManuscriptMonday series, co-hosting the #SceneFromAbove podcast with Sisters of SAR, blogging with #GeoAwesomeness, in-person events, & more! Thanks for connecting!

Mapstodon

📣Hey #LadiesofLandsat friends!

There is still time to join this amazing USGS study as a participant. #gischat #eochat

👉Email us at [email protected] to get more information!

Karen Joyce et al. (2022) quantify the lack of diversity on scientific editorial boards in remote sensing, thus leaving underrepresented scientists behind. They provide an action plan to improve inclusivity at all levels of publishing. #LoLManuscriptMonday https://bit.ly/Joyce2022

Cheers to Dr. Joyce's co-authors for this manuscript: Catherine Nakalembe, Cristina Gómez, @gopikasuresh, Kate Fickas, Meghan Halabisky, Michelle Kalamandeen & @morganahcrowley! #EOChat #GISChat

Discovering Inclusivity in Remote Sensing: Leaving No One Behind

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.

Frontiers
@MapScaping Also good to check in with @LadiesOfLandsat for suggestions.