Project Description: 

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

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 apply Object-based image
classification on the Google Earth Engine platform. 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.

Undergraduate's Role: 

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.

Undergraduate's Qualifications: 

Past experience in geospatial programming and remote sensing. Interest in landscape ecology
and agroecology. Driver’s license. Past experience in field work and Earth Engine (JavaScript) is
good but not required.

On Campus
3-6 hours