Putting Food on the Map: Automated Mapping of Community Gardens with High Resolution Aerial Imagery using an Object Based Approach in Google Earth Engine
Urban agriculture (UA), or growing and producing food within urban areas, is rising in popularity across the United States. There are social and environmental benefits from growing food within urban neighborhoods. UA presents the opportunity for food security in neighborhoods that do not have access to safe and healthy foods, which is disproportionately present in low-income communities. Growing food together also creates food literacy, community cohesion, and shared focus on achieving food security. Unfortunately, the benefits of UA are not accessible to everyone. The prices of food from UA at markets is often not affordable to low- income residents. Furthermore, as a form of greenspace, gardens raise surrounding rent prices, and have been shown to be correlated with gentrification. The socioeconomic dynamics associated with UA are complex and not studied well in part because UA is not well mapped in most U.S. cities. The goal of this study is to accurately map UA by exploiting the unique spatial pattern of UA in addition to spectral, structural, and temporal characteristics of UA vegetation. I used very high resolution aerial NAIP imagery from 2016, Sentinel-2 satellite imagery (2016-2020), and GEDI space lidar data (2019-2021) collected over the case study city of Portland, Oregon. I adopted a Geographic Object-Based Image Analysis (GEOBIA) approach by segmenting and classifying imagery using a Random Forest classifier. In an effort
to capture the UA pattern, I applied morphological operators to the classified image and compared my results to an open database on community gardens from the Portland Bureau of Parks and Recreation. The object-based image classification achieved a 79.6% accuracy and the morphological operations captured 66.9% of the area of Portland Bureau of Parks and Recreation Community Gardens. The detection rate at individual community gardens averaged 65.4% and ranged from 3.8% to 98.2%. Higher detection rates were found in gardens that had strong vegetation signals in garden beds intermixed with bare paths between them. Lower detection rates resulted from tree canopy covering or casting shade over the community gardens. In achieving a fully automated and accurate UA detection using open remote sensing data, this approach can be applied to studying the spatial distribution and dynamics of urban agriculture across the U.S
