Merge branch 'main' into homepage-layout

README.md

@@ -283,3 +283,32 @@ tags:
 Add the contents of your blog post after this preable, using the appropriate components.
 
 Commit this and any included images to a new branch (we recommend using the same format as the blob post file name).  Request a review from one of the website admins and enable auto merge.
+
+## Updating Open Opportunities
+1. Open [_data/opportunities.yml](_data/opportunities.yml)
+2. Entries must have the following structure:
+```
+- project: {project_name}
+  title: {position title}
+  description: {multiline string describing project and position}
+  required: {optional list of required skills}
+  desired: {optional list of desired skills}
+  link: {optional link to project webpage}
+```
+
+For example:
+```
+- project: Radio Telemetry Tracker
+  title: Lead
+  description: |
+    This is a multiline project description.
+
+    This is still part of the position description, and includes a blank line between the previous line.
+  desired:
+  - Desired skill 1
+  - Desired skill 2
+  required:
+  - required skill 1
+  - required skill 2
+  link: /radio-collar-tracker
+```

_bibliography/publications.bib

@@ -1,3 +1,56 @@
+@Article{WallaceGurungKastner_JCGI_2024,
+  author    = {Wallace, Ronan and Gurung, Yungdrung Tsewang and Kastner, Ryan},
+  journal   = {Journal of Critical Global Issues},
+  title     = {Relocating Lubra Village and Visualizing Himalayan Flood Damages with Remote Sensing},
+  year      = {2024},
+  issn      = {2997-0083},
+  month     = feb,
+  number    = {1},
+  volume    = {1},
+  abstract  = {As weather patterns change worldwide, isolated communities impacted by climate change go unnoticed and we need community-driven solutions. In Himalayan Mustang, Nepal, indigenous Lubra Village faces threats of increasing flash flooding. After every flood, residual muddy sediment hardens across the riverbed like concrete, causing the riverbed elevation to rise. As elevation increases, sediment encroaches on Lubra’s agricultural fields and homes, magnifying flood vulnerability. In the last monsoon season alone, the Lubra community witnessed floods swallowing several agricultural fields and damaging two homes. One solution considers relocating the village to a new location entirely. However, relocation poses a challenging task, as eight centuries of ancestry, heritage, and nuanced cultural complexities exist in both aspects of communal opinion and civil engineering. To investigate this issue further, we utilize remote sensing technologies such as drones and satellite imagery to create unique, highly detailed 3D visualizations and 2D maps to document climate-related impacts in Lubra Village. We also investigate quantifying riverbed elevation trends with digital elevation models to address how the riverbed elevation changes overtime. In tandem, we conduct oral interviews with members of Lubra to understand how flooding and droughts affect their ways of life, allowing us to contextualize these models. Pairing visualized data with personal accounts, we provide an informative story that depicts Himalayan climate change on a local level for supporting Lubra in informing local policy and requesting relief aid.},
+  doi       = {10.62895/2997-0083.1006},
+  publisher = {School for International Training},
+}
+
+@MastersThesis{Crutchfield2023,
+  author   = {Christopher L. Crutchfield},
+  school   = {University of California San Diego},
+  title    = {{S}pot, an {A}lgorithm for {L}ow-{R}esolution, {L}ow-{C}ontrast, {M}oving {O}bject-{T}racking with a {N}on-{S}tationary {C}amera},
+  year     = {2023},
+  month    = jun,
+  abstract = {The ability to track moving objects in a video stream is helpful for many applications,
+from pedestrian and vehicle tracking in a city to animal tracking for ecology and conservation.
+This write-up introduces Spot, an algorithm for moving object tracking in low-resolution, low-
+contrast videos. This write-up will discuss two motivating examples to guide the development of
+Spot-satellite-based surveillance of vehicles in cityscapes and animal tracking using drones for
+ecological purposes.
+Spot uses image processing techniques to generate a pipeline to track moving objects
+frame-to-frame. It then leverages Bayesian Filtering techniques to use the frame-to-frame motion to track individual identity between consecutive frames.
+Each stage of Spot’s pipeline–both image processing and the Bayesian Filtering portions
+of the pipeline–introduces many parameters. To determine which parameters are ideal for a
+particular dataset, a design space exploration tool, dubbed Sherlock, is used to choose the optimal
+parameters. As part of this, we evaluate multiple possible objective functions and demonstrate
+the importance of selecting an appropriate one.
+Spot is competitive with other modern, moving object-tracking algorithms on cityscape
+data, outperforming others in some of the metrics presented. For tracking animals from drone
+footage, Spot demonstrated an ability to track wildlife at a similar rate to its performance in
+some cityscape videos.},
+  file     = {:Crutchfield2023 - Spot, an Algorithm for Low Resolution, Low Contrast, Moving Object Tracking with a Non Stationary Camera.pdf:PDF},
+  url      = {https://escholarship.org/uc/item/14j7c3qc},
+}
+
+@MastersThesis{Hicks_2023,
+  author    = {Hicks, Stanley Dillon},
+  school    = {UC San Diego},
+  title     = {Remote Sensing of Mangroves using Machine Learning based on Satellite and Aerial Imagery},
+  year      = {2023},
+  address   = {La Jolla, California},
+  abstract  = {Mangrove forests are critical to mitigating climate change and provide many essential benefits to their ecosystems and local environments but are under threat due to deforestation. However, monitoring mangroves through remote sensing can help pinpoint and alleviate the causes of their deforestation. Machine learning can be used with remotely sensed low-resolution satellite or high-resolution aerial imagery to automatically create mangrove extent maps with higher accuracy and frequency than previously possible. This study explores and offers recommendations for two practical scenarios. In the first practical scenario, where only low-resolution hyperspectral satellite imagery is acquired, we implemented several classical machine learning models and applied these results to data acquired in the Clarendon parish of Jamaica. We found that utilizing extensive feature engineering and hyperspectral bands can result in strong performance for mangrove extent classification, with an accuracy of 93% for our extremely randomized trees model. In the second practical scenario, we explored when there is full coverage of both low-resolution satellite and high-resolution aerial imagery over a survey area. We created a hybrid model which fuses low-resolution pixels and high-resolution imagery, achieving an accuracy of 97% when applied to a dataset based in Baja California Sur, Mexico, offering another high-performance method to automatically create mangrove extent maps if both high- and low-resolution imagery is available. Overall, the methods tested over these two scenarios provide stakeholders flexibility in data and methods used to achieve accurate, automatic mangrove extent measurement, enabling more frequent mangrove monitoring and further enabling the protection of these important ecosystems.},
+  language  = {eng},
+  publisher = {University of California, San Diego},
+  url       = {https://escholarship.org/uc/item/4pf2f7tr},
+}
+
 @Article{bresnehan_cyronak_brewin_etal_csr_2022,
   author   = {Philip Bresnahan and Tyler Cyronak and Robert J.W. Brewin and Andreas Andersson and Taylor Wirth and Todd Martz and Travis Courtney and Nathan Hui and Ryan Kastner and Andrew Stern and Todd McGrain and Danica Reinicke and Jon Richard and Katherine Hammond and Shannon Waters},
   journal  = {Continental Shelf Research},
@@ -93,6 +146,18 @@ @InProceedings{perry_tiwari_balaji_reuns_2021
   issn      = {2155-6814},
 }
 
+@Misc{qi_ucsd_2021,
+  author    = {Qi, Katherine L.},
+  title     = {Mangroves from the Sky: Comparing Remote Sensing Methods for Regional Analyses in Baja California Sur},
+  year      = {2021},
+  abstract  = {Consequences of global warming are causing mangrove migration from tropical habitats towards temperate zones. Forests at limits and transition zones are important to monitor for promoting local management and conservation efforts. The advancement of remote sensing technology in the past decade has allowed more insight into these habitats at large scales, and recent studies using satellite imagery have succeeded in creating baselines for global mangrove extent. However, the high surveying range comes with a cost of reduced resolution, causing gaps in areas with high fragmentation or low canopy height, such as in dwarf mangrove habitats. By using drones, we were able to conduct detailed analyses of canopy height distribution for dwarf mangroves in Baja California Sur. This new model provides a focused approach at analyzing parameters that contribute to the multidimensionality of mangrove forests with primarily remote sensing data. Additionally, improved biomass models were constructed with the drone data and compared against satellite data. Due to its inaccuracies in approximated mangrove extent and canopy height, satellite imagery significantly underestimates above ground biomass and carbon measurements in this region, and potentially dwarf mangroves in general. The pairing of satellite and drone imagery allows for a more robust view of mangrove ecosystems, which is critical in understanding their poleward movement with respect to climate change.},
+  address   = {La Jolla, California},
+  booktitle = {Mangroves from the Sky: Comparing Remote Sensing Methods for Regional Analyses in Baja California Sur},
+  language  = {eng},
+  publisher = {University of California, San Diego},
+  url       = {https://escholarship.org/uc/item/8fm8j2fh},
+}
+
 @InProceedings{tueller_maddukuri_paxson_et_al_oceans_2021,
   author    = {Peter Tueller and Raghav Maddukuri and Patrick Paxson and Vivaswat Suresh and Arjun Ashok and Madison Bland and Ronan Wallace and Julia Guerrero and Brice Semmens and Ryan Kastner},
   booktitle = {OCEANS 2021 MTS/IEEE SAN DIEGO},
@@ -312,18 +377,6 @@ @InProceedings{santos_barnes_lo_et_al_ieee_mass_2014
   url       = {https://ieeexplore.ieee.org/document/7035779},
 }
 
-@Misc{qi_ucsd_2021,
-  author    = {Qi, Katherine L.},
-  title     = {Mangroves from the Sky: Comparing Remote Sensing Methods for Regional Analyses in Baja California Sur},
-  year      = {2021},
-  abstract  = {Consequences of global warming are causing mangrove migration from tropical habitats towards temperate zones. Forests at limits and transition zones are important to monitor for promoting local management and conservation efforts. The advancement of remote sensing technology in the past decade has allowed more insight into these habitats at large scales, and recent studies using satellite imagery have succeeded in creating baselines for global mangrove extent. However, the high surveying range comes with a cost of reduced resolution, causing gaps in areas with high fragmentation or low canopy height, such as in dwarf mangrove habitats. By using drones, we were able to conduct detailed analyses of canopy height distribution for dwarf mangroves in Baja California Sur. This new model provides a focused approach at analyzing parameters that contribute to the multidimensionality of mangrove forests with primarily remote sensing data. Additionally, improved biomass models were constructed with the drone data and compared against satellite data. Due to its inaccuracies in approximated mangrove extent and canopy height, satellite imagery significantly underestimates above ground biomass and carbon measurements in this region, and potentially dwarf mangroves in general. The pairing of satellite and drone imagery allows for a more robust view of mangrove ecosystems, which is critical in understanding their poleward movement with respect to climate change.},
-  address   = {La Jolla, California},
-  booktitle = {Mangroves from the Sky: Comparing Remote Sensing Methods for Regional Analyses in Baja California Sur},
-  language  = {eng},
-  publisher = {University of California, San Diego},
-  url       = {https://escholarship.org/uc/item/8fm8j2fh},
-}
-
 @Comment{jabref-meta: databaseType:bibtex;}
 
 @Comment{jabref-meta: keypatterndefault:[authors3]_[journal:abbr:regex("[a-z\\&]","")][booktitle:abbr:regex("[a-z\\&]","")]_[year];}
@@ -335,4 +388,4 @@ @Comment{jabref-meta:
 pages[normalize_page_numbers]
 ;}
 
-@Comment{jabref-meta: saveOrderConfig:specified;year;true;author;false;title;true;}
+@Comment{jabref-meta: saveOrderConfig:specified;year;true;author;false;title;true;}