Linking Net Assimilation With Multispectral Vegetation Classification To Understand Mesquitegrass Response To Fire

Masters Thesis

Abstract: Understanding vegetation dynamics across space and time has been a grand challenge in Earth sciences, but the induction of remote sensing products has made large-scale mapping of vegetation possible. We initially used Landsat satellites (30 m; eight-day return interval) to assess the Sawmill Fire of 2017 within the Santa Rita Experimental Range. Because of the spatial and temporal decoupling associated with this remote sensing product, important but smaller-scale disturbances may not be properly captured; this prompted the use of finer scaled data. As such, we used an unmanned aerial vehicle
(UAV) equipped with a five band Micasense RedEdge camera for derived land classification and scaling. Additionally, we measured leaf level net assimilated photosynthesis (ANET) to quantify plant function. We repeated the measurements in time at a control and burned site. Spectrally, changes in the Relative Normalized Burn Ratio (RNBR) between images using the Landsat products from before the fire and after the growing season showed barely any evidence of the fire because of its spatial scale, though there were significant impacts from the fire on vegetative form and ecosystem function. Classifications built from the multispectral camera showed an overall accuracy of 0.89. This study shows the need for fine-resolution data from newly available UAV systems for practical land management practices. Low altitude, fine resolution data, combined with ecophysiological datasets, can be used to quantify and follow tractable land cover changes not captured by our traditional, lower resolution remote sensing sensors and derived products. Following this change is especially important within the often-fragile environments of water-limited ecosystems. This study comes at a time of rapid ecosystem change at the local and global scale. Techniques within this study can aide land managers at all levels and can be applied broadly across similar ecosystems.

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Authors: Leland F Sutter JR

Associations: The University of Arizona

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