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On the Statistical Significance of Local Land Cover Measurements: A Comparative Analysis of Citizen Science and Remote Sensing Programs
  • Aidan Schneider
Aidan Schneider

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Accurate land cover data can provide powerful insight into characterizing the effects of climate change. Remote sensing satellites enable state-of-the-art land cover measurements, but data collected on the Earth's surface offers a new perspective on land cover characteristics through its more localized scope. Areas that may be generalized to a single pixel in a remote sensing satellite’s data products can be observed at a more granular level through on-site data collection strategies. Low-cost sensors, such as NASA’s Science and Technology Education for Land/Life Assessment (STELLA), make such granular data collection more cost-effective. Citizen Science programs, like the Global Learning and Observations to Benefit the Environment (GLOBE) Program, provide a blueprint for reliably scaling this type of on-site data collection. STELLA is an open-source platform that allows volunteers, like Citizen Scientists, to measure electromagnetic waves to calculate irradiance and temperature in a specific Area Of Interest (AOI). STELLA is cost-effective, as its kit can be assembled by any user with access to makerspace tools commonly found in educational institutions, like 3D printers and soldering irons. This work presents a comparative analysis of the land cover measurements recorded by the STELLA sensor and remote sensing satellites, such as LANDSAT9. Surface temperatures were recorded hourly using the STELLA sensor on four different types of land cover within a 500-square-meter area in Reno, Nevada. The results indicate a statistically significant discrepancy between measurements recorded by the STELLA sensor and LANDSAT, highlighting an untapped data trove in localized sensor measurements. Additionally, we present a data collection control flow for Citizen Science volunteers to record reliable STELLA sensor data. We demonstrate how such Citizen Science data can provide a valuable alternative perspective when compared to its state-of-the-art counterparts, rendering it a valuable tool for future studies. 
01 Jan 2024Submitted to ESS Open Archive
08 Jan 2024Published in ESS Open Archive