Submitting Campus

Prescott

Student Status

Undergraduate

Class

Undergraduate Student Works

Advisor Name

Dr. Ronny Schroeder

Abstract/Description

Vegetation health can be assessed using the Normalized Difference Vegetation Index (NDVI), which can be collected from a variety of multispectral sensor types, each offering different spatial resolutions. Validating the datasets of these sensors is crucial to determine how accurately the platforms portray information, especially when using them for environmental monitoring. Two multispectral datasets collected over the same area were used for this analysis. Landsat, a joint satellite mission between USGS and NASA, provided moderate-resolution imagery suitable for vegetation assessment. The MicaSense sensor, developed by EagleNXT for mounting on Unmanned Aircraft Systems (UAS), supplied high-resolution imagery that enabled finer-scale vegetation detail.

NDVI values derived from both datasets were compared to examine spatial correspondence between the satellite and UAS-based vegetation indices. We applied the coefficient of determination (R²) to assess the percentage of spatial variability in the Landsat data explained by the UAS-based data. This comparison evaluates the reliability of the platforms and helps determine how effectively they can be used to monitor vegetation across differing spatial scales. Using the Getis-Ord Gi* statistic, we applied a hotspot analysis to the high-resolution MicaSense NDVI data to detect statistically significant MicaSense vegetation clusters, supporting the detection of bald eagle nesting and foraging, demonstrating its value for habitat prediction.

Document Type

Poster

Publication/Presentation Date

2026

Location

Denver, CO

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