Overview: This study takes a broad-scale look at the cooling effect and climatic amelioration of the California (CA) urban forests using remote sensing imagery and a cutting-aged web-based spatial data analysis tool Google Earth Engine. We will develop metrics to characterize cooling effect of urban forests in all California cities, its recent trends (~1990-present) & equitability of distribution within the cities. The key motivation of this project is to help cities better understand vulnerability of their population to emerging thermal hazards, select and design their greening initiatives and other climatic amelioration strategies, and make such initiatives more equitable.
Motivation: Urban forests provide various climatic, ecological, social, and public health benefits to society that become increasingly critical as global population shifts to cities. One of their critical values is the capacity to cool ambient microclimates, which makes urban forests a highly attractive pathway for nature-based climate solutions in cities, e.g., via restoration and greening programs. However, this potential is threatened by the loss, fragmentation, and stress of urban vegetation, further exacerbated by the inequity of urban forest distribution, management controversies related to water scarcity and fire risk, and ongoing urban heating and ambient climate warming. This study is asking the following questions: 1) How does the cooling effect of CA urban forests and other forms of green space vary in space and time across all state’s cities? 2) Is the distribution of cooling benefits equitable among different population groups, particularly vulnerable communities? And 3) How has this potential been changing in recent decades, and which cities and neighborhoods are at particularly high risk of losing these benefits in the near future?
Student research activities and skills: Student involved in this study will collaborate with Dr. Iryna Dronova (ESPM faculty member) to investigate these questions by developing novel analyses and metrics in Google Earth Engine. This software can be run in a Google Chrome web browser, so no specialized computing and hardware resources will be required for this phase of the analysis. At the early stage of the project we will work on learning Earth Engine and developing the key skills and practices to move to the project-specific analyses efficiently, as well as cover the basics of the key urban forestry concepts and California-specific issues relevant to the project.
We will then work to assess the cooling effect of CA urban forests using established techniques measuring gradients of atmospheric and surface temperature in relation to distribution of the urban forest elements and summarizing them across space and time. These analyses will use open-access, publicly available remote sensing, geospatial, and socio-economic datasets, and open-access computational tools that make such analyses feasible and scalable across neighborhood, city, and state levels of analysis.
NOT REQUIRED: No prior knowledge of Earth Engine is required (though welcome if you have it) – this will be a great opportunity to learn this tool and advance your skills in remote sensing, spatial analysis and “big geographic data”! It is also not required to have experience in urban forestry, only basic experience in plant and/or ecosystem ecology.
PRIOR EXPERIENES AND SKILLS: For this project, it is helpful to have 1) an introductory-level experience with geographic information systems (GIS), and 2) a basic acquaintance with introductory statistics – such as basic summary statistical measures, linear regression, statistical graphs such as histogram, boxplot, scatter plot, and similar concepts.
Main expectations. The students participating in this study will be expected to work 6-9 hours/week and regularly meet with the professor (every 1-2 weeks) to discuss the study, data, methods and analysis outcomes. The students will be also expected to maintain a basic shared document and help organized other project documents such as codes & data files to help keep track of the study’s key findings in a collaborative way. The work and meetings can be performed both remotely and on campus.
The students participating in this study will be expected to work 6-9 hours/week, regularly meet with the professor (every 1-2 weeks) to help learn the analysis skills, develop urban forest analyses and discuss the study, data, methods and research outcomes. The students will be also expected to maintain a basic shared document and any related documents such as data files and codes to help keep track of the study’s key findings in a collaborative way. The work and meetings can be performed both remotely and on campus.
Skills important to the project:
1. An introductory-level experience with geographic information systems (GIS)
2. A basic acquaintance with introductory statistics – such as basic summary statistical measures, linear regression, statistical graphs such as histogram, boxplot, scatter plot, and similar concepts