Student Research Reports

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Urban development is altering land cover globally, impacting ecosystems, biodiversity, and climates. Our study investigates how satellite imagery and remote sensing can identify patterns of urban development and analyze related environmental changes. We used initial field observations from the GLOBE Observer App, where citizen scientists collect high-resolution, ground-level, using their smartphone cameras. Each observation included geotagged photographs taken in standardized directions. This data revealed that existing land cover classifications varied in accuracy, prompting our investigation into their value and in future research. This led to our primary research question: How effectively can remote sensing detect urban expansion and its impacts on surrounding communities? Using urban cities like Chicago, Atlanta, Los Angeles, Detroit, and Austin, we studied impacts such as green space gains/losses, heat island formations, and nighttime brightness changes. To do this, we utilized Landsat and Sentinel-2 imagery, NDVI, ECOSTRESS, and VIIRS data. Integrating multiple remote sensing datasets, we trained a machine learning model that classified geographic zones into three categories of development: regenerative, unequal, and stable. This classification differentiated areas that experienced positive environmental and urban recovery, harmful development, and minimal change. Our findings demonstrate that remote sensing can reveal patterns of urban growth and their impact on communities. Moving forward, the zone classifications could be used as a valuable resource for urban planners and local communities. By identifying areas at risk of environmental degradation, intervention could be more targeted and effective. This framework could also be extended to other cities to support global comparisons of urban development. Keywords: Urbanization, Satellite, Regeneration, LandCover, RemoteSensing