Geography & Environment
http://hdl.handle.net/10211.3/141093
2024-03-28T17:09:31ZUsing participatory mapping for community-based marine spatial planning in St. George's Caye, Belize
http://hdl.handle.net/10211.3/214083
Using participatory mapping for community-based marine spatial planning in St. George's Caye, Belize
Abdel-Raheem, Salma Tharwat
As the environmental toll of growing human populations continues to increase, coastal and island nations are hard-pressed to effectively manage their natural resources for sociopolitical, economic, and cultural benefits. Marine Spatial Planning is a holistic approach to effective natural resource management that integrates local knowledge and participation at a variety of spatial scales. Engagement through participatory mapping efforts and facilitated by geographic information systems, is an effective means to communicate local objectives and concerns. We present here a case study of participatory mapping efforts by residents of Saint George’s Caye, Belize. Residents participated in guided focus group sessions on three different occasions between January and October of 2018 to map their concerns and planning objectives at two distinct spatial scales. Of the nearly 200 residents on the Caye, approximately 120 participated in the mapping sessions and provided collective feedback on 4 different threats and associated risks, 2 conservation priorities, and 4 natural resource uses. Resulting maps will be used by the local Village Council to create a comprehensive plan for the Caye for future legislative protections. This work exemplifies the need for local engagement at various spatial scales to ensure effective and holistic marine spatial planning and natural resource management.
2019-01-01T00:00:00ZA remote sensing analysis of pitch canker in Bishop Pines at Point Reyes National Seashore
http://hdl.handle.net/10211.3/214079
A remote sensing analysis of pitch canker in Bishop Pines at Point Reyes National Seashore
Xiong, Kang
Pitch canker is a fungal disease that threatens the health of Bishop Pine (Pinus muricata D. Don) trees at Point Reyes National Seashore (PRNS). Understanding spatial patterns of tree mortality is important for land management decisions that aim to protect healthy forests. Remote sensing methods have proven to be successful in mapping and monitoring forest health. Using two different types of imagery at fine spatial resolution (WorldView-2 at 2 m pixel size and unmanned aircraft system (UAS) imagery at 0.05 m pixel size), three images (from 2013, 2017 and 2018) were classified with object-based image analysis (OBIA). A GIS change detection method was employed to quantify changes in pitch canker severity (PCS) from 2013 to 2017. Overall accuracies of 63.8% and 81% were achieved for the 2017 and 2018 classified images, respectively. In contrast to previous findings, the red-edge (RE) band was found to be a poor predictor of pitch canker severity; however, the addition of multiple variables in a stepwise multiple linear regression model increased the overall accuracy (81%) and coefficient of determination value (0.3562) for the 2018 imagery. We found that pitch canker severity in Bishop Pine trees increased between years 2013 and 2017 in over 32 ha. An additional 55 ha of Bishop Pine forest was identified in the 2017 classified image as having shown an increase in pitch canker severity. This study presents novel methods for successfully detecting and classifying pitch canker severity in forest ecosystems.
2019-01-01T00:00:00ZMainstreaming sustainable landscapes in the East Bay Municipal Utility District
http://hdl.handle.net/10211.3/214072
Mainstreaming sustainable landscapes in the East Bay Municipal Utility District
Woodard, Jessica Anne
Water suppliers in arid and Mediterranean regions of the United States have
invested heavily in incentive programs to replace water-thirsty lawn with climateappropriate
plants and landscapes. These “sustainable landscapes” can thrive on a
fraction of the water required by lawns and reap water savings that increase with time,
representing an important opportunity for water conservation. Yet even the most
successful lawn conversion program cannot expect to replace the largest irrigated crop in
the United States with rebated lawn conversions alone. Underlying the design of and
heavy investment in lawn conversion programs is an ambitious end-goal: to transform the
landscaping market away from lawns, and mainstream sustainable landscapes.
Using a market transformation framework, this study investigates the geographic
variation of reported attitudes (aesthetic preference and willingness to replace lawn) and
lawn conversion rates, as indicators of landscape transformation, across the East Bay
Municipal Utility District (EBMUD). The difference between 2014 and 2017 customer
acceptance and lawn conversion rates show growth during drought years, especially in
cities with higher rebates participation rates. This research provides a method evaluate
landscape transformation indicators to develop strategies to hasten the adoption of
region-wide sustainable landscaping.
2019-01-01T00:00:00ZRemote sensing and computer vision algorithms at scale : defense and humanitarian uses
http://hdl.handle.net/10211.3/214064
Remote sensing and computer vision algorithms at scale : defense and humanitarian uses
Wenzler, Logan J.
The objective of this thesis is to understand how cloud computing and artificial
intelligence can be applied to vast amounts of remotely sensed data to better understand
macro-level trends for humanitarian and defense issues. Computer vision algorithms and
data were provided by Orbital Insight, Inc., a geospatial analytics company based in Palo
Alto, CA.
Specific projects were curated, data was acquired, and analysis was applied to
three use cases: “Patterns of life for The Battle of Marawi”, “Indications and Warnings
using multi-class aircraft detections”, and “Camp Fire land cover analysis”. The use cases
show how with imagery ingestion pipelines, cloud computing, and computer vision
algorithms, a massive quantity of data can be analyzed in a relatively short amount of
time. Without these workflows and new technologies, analysis of large amounts of data
would prove to be less efficient and resource heavy. The events show the benefits users
of spatial data would have to gain a better understanding of humanitarian and defense
issues. Algorithms used to derive insights from thousands of imagery scenes consisted of
a car detection algorithm, multi-class aircraft algorithm, and a land cover classification
algorithm. Additionally, the thesis briefly explores the use of geolocation data to
supplement computer-vision algorithm data. The thesis shows on a high level, through
examples, how users could use the technologies to analyze data more efficiently. This
analysis can be incorporated into high-level humanitarian or defense decisions. Future
work regarding this field should seek to evaluate the algorithm performance on a more
granular level. Researchers should also build different algorithms on open-source
imagery to allow for more users to benefit from the efficiency computer vision provides.
2019-01-01T00:00:00Z