Comparing GIS-Based and WUDAPT Approaches for Local Climate Zone Mapping: A Case Study in Denton County:

The Local Climate Zone (LCZ) classification scheme offers a standardized framework for characterizing the local thermal environment, revolutionizing urban climate studies by moving beyond the traditional urban-rural dichotomy. This method classifies the landscape into 10 built types and 7 land cover types to provide a better representation of the urban fabric and morphology. While machine learning approaches using satellite imagery have gained tremendous popularity in LCZ mapping, they often require high-quality training samples and can introduce uncertainties depending on model performance and data quality.

In this study, we applied a GIS-based approach to map LCZs in Denton County, TX at a 100-meter resolution. Utilizing 1-meter resolution land cover data and LIDAR-derived products, we extracted key indicators such as building height, building surface fraction, and impervious surface fraction, as outlined in the LCZ framework. Our approach highlights the benefits and trade-offs of using selective indicators to optimize the mapping process. This work contributes to a more comprehensive understanding of LCZ classification and further benefits urban climate research.

Assessment of Multivariate Drought Impacts on Agriculture in Central Chile for the Enhancement of Sustainable Adaptation:

Over the past decade, Chile has suffered under “Mega-drought” conditions placing significant pressure on agricultural production. In response to the crisis, the Chilean government has moved to promote sustainable agricultural practices to increase agricultural resilience in a changing climate. This research focuses on multivariate drought impacts on agriculture in Chile’s Región Metropolitana de Santiago and Región O’Higgins in order to spatially characterize priority areas for sustainable adaptation based on overall vulnerability to drought. To address this topic, I pose the following sub-questions: 1) How have meteorological conditions, streamflow levels, and vegetation conditions in agricultural areas changed from non-drought years (2000-2009) to drought years (2010-2020)? 2) Based on meteorological, streamflow, and vegetation condition factors, which agricultural areas are most susceptible to drought conditions and where should Chilean sustainable agricultural development be focused? The Palmer Drought Severity Index, the Standardized Streamflow Index, and the Vegetation Condition Index were used to calculate changes in meteorological, hydrological, and agricultural drought severity respectively from 2000-2020 derived from the Catchment Attributes and Meteorology for Large Scale Studies, Chile Dataset and from Landsat 5, Landsat 7 ETM+, and Landsat 8 satellites in the Google Earth Engine platform. Index values were compared by running a suitability analysis in ArcGIS Pro. Overall, the results suggest that high vulnerability agriculture is located primarily in the Región Metropolitana de Santiago and did not correspond to areas identified by the Chilean government as being in severe drought. This research will help improve our understanding of the potential of combining drought indices to determine agricultural vulnerability and inform the implementation of sustainable adaptation in Chile.

A Recipe For Health Disparity: Quality of Neighborhood Grocery Stores:

Access to fresh, healthy food is a crucial component of adopting and maintaining a healthy diet that may offset aging-related diseases such as Type 2 Diabetes. Supermarkets and grocery stores consistently serve as a mainstay for Americans’ main food source, particularly in urban settings. Classifying supermarkets and grocery stores based on factors such as cost and quality could be an important consideration in understanding health impacts across regions and within cities. This study offers classification of grocery stores and supermarkets across the United States based on the overall cost and quality of food. We performed network analysis within ArcGIS Pro to elaborate on access to these food sources by census tract in major urban areas. Regression models of many large urban areas across the United States showed positive associations between the presence of low-quality grocery stores and Type 2 Diabetes rates, whereas presence of higher-quality grocery stores was associated with lower Type 2 Diabetes rates. In many urban areas, these associations were still significant even after factoring in overall socioeconomic factors. Future analysis aims to further parse apart quality metrics, socioeconomic factors (i.e. income and vehicle access), as well as the impact of overall cost of these grocery stores and supermarkets. This study can offer insights into public health policy on reducing the incidence of aging-related diseases exacerbated by a poor diet and compounded by lack of access to higher quality, affordable food sources.

GIS and Remote sensing for Wildfire and Coastal Monitoring:

Dr. Bo Yang from UCSC will discuss the integration of multi-source remote sensing data, UAV mapping, and spatio-temporal modeling to monitor critical environmental changes. Covering applications from California wildfire tracking to coastal ecosystem assessment, this presentation highlights the use of drones, LiDAR, and high-resolution satellite data to enhance detection, prediction, and management of wildfire behavior and seagrass health. Discover how cutting-edge GIS tools and data fusion methods provide actionable insights, driving more effective environmental monitoring and resilience planning.

UC GIS Week

UC GIS Week 2023