Sarah Smith-Tripp

I am an interdisciplinary ecosystem scientist interested in risk and resilience research. My PhD uses remote-sensing techniques, including satellite imagery and lidar (light detection and ranging) to model forest recovery (or lack of recovery) after large and high severity wildfire. I am to provide forest managers with accessible and accurate tools so they can take proactive steps to protect people and forests. Starting in 2025, I am seeking a private or government position working in policy development and scenario forecasting.

I am enthusiastic about science communication and am always looking for ways to engage the population. Please reach out if you are interested in a talk or research demonstration on drones, satellites, or forest science. I am also proficient in numerous statistical approaches and am always happy for #StatzChatz

news

Apr 1, 2023	I won the forestry 3MT for my talk titled “When a fire burns half a million hectares – how do we know what’s rising from the ashes?.” Check out the link and let me know your thoughts!

selected publications

Landsat assessment of variable spectral recovery linked to post-fire forest structure in dry sub-boreal forests

Smith-Tripp, Sarah M., Coops, Nicholas C., Mulverhill, Christopher, White, Joanne C., and Axelson, Jodi

ISPRS Journal of Photogrammetry and Remote Sensing Feb 2024

doi

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Forest disturbances such as wildfires can dramatically alter forest structure and composition, increasing the likelihood of ecosystem changes. Up-to-date and accurate measures of post-disturbance forest recovery in managed forests are critical, particularly for silvicultural planning. Measuring the live and dead vegetation post-fire is challenging because areas impacted by wildfire may be remote, difficult to access, and/or dangerous to survey. The difficulties of post-fire monitoring are compounded by the global increase in the frequency and severity of disturbances, as expansion of disturbed areas also increases the number and size of areas requiring post-disturbance monitoring. Methods that safely, efficiently, and extensively differentiate silviculturally beneficial coniferous growth from barren ground or deciduous shrubs are necessary to inform post-fire forest management. Satellite imagery can detect burn patterns, but monitoring changes in forest structure post fire is challenging due to complex vegetation responses. To overcome this challenge, this study combines post-disturbance spectral trajectory measures from a time series of historical Landsat imagery with field and remotely piloted aircraft (RPA) lidar (light detection and ranging) data to examine vegetation recovery of lodgepole pine (Pinus contorta) dominated sub-boreal forests after high-severity fires in 2006 in central British Columbia, Canada. Distinct spectral recovery trajectories were identified using data-clustering from a combination of seven Landsat spectral indices, with trajectories varying by recovery magnitude and rate. The forest structure associated with each distinct trajectory of spectral recovery was analyzed using 430 ha of spatially explicit forest structure measures (e.g., basal area, stem counts) and composition (e.g., percent coniferous) derived from 26 coincident field plots and high density RPA lidar (>200 points/m2) data. By comparing spectral trajectories to forest structure measures, we found the most spatially abundant cluster of spectral recovery coincided with a basal area of 0.62 m2/ha, high stem densities (>5000 stems/ha) and a high abundance of coniferous trees (>95 % coniferous). Around 10 % of the landscape was associated with relatively high abundance of deciduous vegetation (>20 %) in addition to very high conifer stem densities (>8000 stems/ha). By identifying the structural characteristics associated with unique Landsat spectral trajectories, we highlight the combined value of RPA lidar data and satellite image time series in providing a detailed and spatially explicit characterization of post-fire recovery relevant to managed forests.