Merge branch 'main' into pages/113-stereo-diving-rig-missing

Gemfile.lock

@@ -1,6 +1,6 @@
 GIT
   remote: https://github.com/UCSD-E4E/jekyll-resize
-  revision: 94608bc31238abb979097d5695053921772ac940
+  revision: 8770c21518120d54120ef7cc8d7402597d34fee1
   specs:
     jekyll-resize (1.3.1)
       jekyll (> 3.3, < 5.0)
@@ -94,7 +94,7 @@ GEM
     racc (1.7.3)
     rake (13.2.1)
     rb-fsevent (0.11.2)
-    rb-inotify (0.10.1)
+    rb-inotify (0.11.1)
       ffi (~> 1.0)
     rexml (3.2.8)
       strscan (>= 3.0.9)

_bibliography/publications.bib

@@ -25,6 +25,18 @@ @MastersThesis{Crutchfield2023
   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},
@@ -120,6 +132,18 @@ @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},
@@ -339,18 +363,6 @@ @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},
-}
-
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