John Dees

University of California, Berkeley, Energy and Resources Group, Graduate Student

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Approaches to Teaching the Works of Karen Tei Yamashita--Image (map) from

This CORE deposit contains one of three figures (maps) in contributor essay "Tropic of Orang... more This CORE deposit contains one of three figures (maps) in contributor essay "Tropic of Orange as Palimpsest: A Literary Cartographic Approach" by Anastasia Lin and John Dees included in the forthcoming MLA volume Approaches to Teaching the Works of Karen Tei Yamashita, co-edited by Ruth Y. Hsu and Pamela Thoma. Figure 1 is a map of Arcangel's and Rafaela's roots and routes to promote discussion of both transnationalism and globalism. Caption: Figure 1. Map by John Dees in ArcGIS 9.3.

Political Affiliation and Rooftop Solar Adoption in New York and Texas

2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC), 2018

Renewable energy is often presented as a partisan issue. However, solar incentives have made roof... more Renewable energy is often presented as a partisan issue. However, solar incentives have made rooftop PV attractive to many consumers, regardless of political affiliation. This paper quantifies the role of political affiliation in rooftop PV adoption for the Democratic-leaning state of New York and Republican-leaning state of Texas. High resolution rooftop PV installation data from Google Project Sunroof is combined with election data from the Harvard Election Data Archive. We applied a bootstrapped LOWESS model to fit local linear relationships between rooftop PV adoption and both voting behavior and household income. In doing so, we find that Republican-majority communities are installing rooftop PV at equal or greater rates than Democraticmajority communities. While Republican politicians may be less likely than Democrats to support renewable energy, this partisan division is not found at the voter level.

Microplastics and Their Degradation Products in Surface Waters: A Missing Piece of the Global Carbon Cycle Puzzle

ACS ES&T Water, 2020

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OBIA and Data Fusion Techniques for Classification and Delineation of Evergreen and Deciduous Tree Canopy

Remote sensing technology has revolutionized the management of land use, urban environments, and ... more Remote sensing technology has revolutionized the management of land use, urban environments, and natural resources. Massive datasets from satellite imagery, aerial imagery platforms, LiDAR (Light Detection and Ranging), and a variety of other sources are now either publically or commercially available for environmental analysis. Each of these data types offers unique opportunities and challenges for environmental research. Traditional methods of land cover analysis have focused on pixel-level data from a variety of multispectral sensors (pixels store reflectance values of various wavelengths of the electromagnetic spectrum—red, green, blue, infrared, etc.). A more recent thread of research in remote sensing employs Object-Based Image Analysis (OBIA) methods whereby software and user-generated algorithms work in tandem to separate imagery into meaningful objects; OBIA attempts to mimic the object-distinguishing capabilities of human sight. OBIA software such as eCognition Developer has the capability to fuse multiple data types as context into object analysis. This data fusion capability represents a powerful tool for producing high-accuracy classifications. This project proposes to use OBIA and data fusion to delineate evergreen and deciduous tree canopy within Hall County, Georgia. Two sets of high-resolution (seasonal leaf-on and leaf-off aerial) imagery will be used along with elevation datasets generated from LiDAR data (normalized Digital Surface Model). An algorithm (eCognition ruleset) will be written which utilizes the specific “fingerprint” (height, spectral characteristics, and change detection in seasonal imagery) of each tree canopy type to correctly classify land cover within the imagery

Panel 3, C: Steinbeck’s Monster: 1930s to Present

Part of Panel 3: Mapping History and Identity in American Literature. Steinbeck’s Monster: 1930s ... more

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