Submitted by Lynn Huntsinger on
California is home to thousands of small-scale diversified farms that not only provide food but also serve as refuges for native flora and fauna. Diversified farming practices on these farms increase ecosystem functioning. In the past 30 years, small-scale farms are increasingly operated by underrepresented and socially-disadvantaged farms, particularly Latinx immigrants and Southeast Asian refugees. While these diversified farms may be resilient to ecologically stressors, these farmers may also face socio-economic pressures. Therefore, considering the increasing number and severity of droughts in California, it is important to understand socio-ecological effects drought on the spatio-temporal dynamics of small-scale farms in the landscape.
We will explore the temporal and spatial shifts of small-scale diversified farms during the last major droughts in the last 30 years in California. To do this, we will use remote sensing technologies on the platform of Google Earth Engine. Drawing from historical Landsat images, we will classify diversified farms versus monocultural farms and examine how the numbers/areas of each type of farming practice vary with the major droughts in California in the last 30 years.
The student will work with Wenjing Xu and Aidee Guzman in the Huntsinger lab.
Undergraduate students will be mostly involved in Landsat image classification (programming on Earth Engine, field validation, classification evaluation) and basic spatial analysis (calculating areas of land use type over years). This is an excellent opportunity for student interested in gaining experience in applying geospatial technologies to solve real world questions.
Past experience in geospatial programming. Understand basics of remote sensing classification and spatial analysis. Interest in landscape ecology and agroecology. Driver’s license. Past experience in field work and Earth Engine (JavaScript) is good but not required.