Recent Highlights

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Forest Patch Geometry and Climate Regulate the Impact of Forest Thinning on Snowpack in the Southwest United States 

Since 2017, we been monitoring snowpack for the Salt River Project, a water and power utility that serves millions of people in the Phoenix area to improve forecasts of how much water will be available from winter snow.  Recently, we’ve been studying how forest thinning—which is commonly used in northern Arizona to reduce wildfire risk and improve forest health—affects snowpack in northern Arizona. To do this, we used a detailed snow model at one-meter resolution, supported by extensive field surveys and drone flights that measured snow depth, forest structure, and surface temperatures. We found that forest thinning generally allows more snow to reach the ground instead of getting caught in the tree canopy and lost to the atmosphere, which increases overall snowmelt. But not all thinning strategies work the same way. Approaches that create openings while still providing shade—such as long, east–west oriented gaps—tend to produce more concentrated snowmelt and keep snow on the ground longer. This pattern is especially valuable for maximizing water supply and potentially reducing vegetation stress during the dry period between snowmelt and the start of the summer monsoon season. 

Broxton, P. et al. 2025. Ecohydrology  https://doi.org/10.1002/eco.70111.

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Quantifying Forest Structural and Functional Responses to Fire Severity Using Multi-Source Remotely Sensed Data

After the 2020 Bighorn Fire in Arizona’s Santa Catalina Mountains, we studied how the forest is recovering using satellite images and airplane-based laser mapping. We found that grasslands and open areas greened up faster than dense forests, which lost more trees and regrew slowly. The land often looked green again before the forest structure was truly rebuilt, showing that both vegetation greenness and forest structure must be tracked to understand recovery and plan for future wildfire resilience.

Lee, K. et al. 2025. Geographies. https://doi.org/10.3390/geographies5030030

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Using Fine Resolution Remotely Sensed Data-Derived Land Cover to Inform Dryland State and Transition Models 

Natural Resource Managers often use state and transition models (STMs) to organize and communicate ecological information.  It is often used to understand the condition of lands managed. STM vegetative states (an important component) are developed using field assessments.  We wanted to repeat one such field assessment completed on the Santa Rita Experimental Range (SRER) using high-resolution (1 m) hyperspectral imagery. We found that our analysis revealed areas that were actually of another ecostate, areas that were classified differently or missed in the field mapping.  High resolution, remote sensing provided a more comprehensive assessment.  This type of analysis would be extremely helpful to natural resource managers determining the state of lands managed and the states lands are transitioning into.

van der Leeuw, E. et al. 2024. Rangeland Ecology & Management https://doi.org/10.1016/j.rama.2024.06.003