Ilan Valencius

Ilan Valencius

PhD student in the Earth and Environmental Science Department at Boston College.

Posts by Collection

portfolio

publications

Sampling forests with terrestrial laser scanning

Published in Annals of Botany, 2021

Terrestrial laser scanners (TLSs) have successfully captured various properties of individual trees and have potential to further increase the quality and efficiency of forest surveys.

Recommended citation: Boucher, Peter B., Ian Paynter, David A. Orwig, Ilan Valencius, and Crystal Schaaf. (2021). "Sampling forests with terrestrial laster scanning" Annals of Botany. 128(6), 689-708 http://ivalencius.github.io/files/mcab073.pdf

talks

Weekly river monitoring of rivers by Sentinel-2 and Planet Labs satellites reveals sub-seasonal patterns from the Tropics to the Arctic

Published:

Improving our understanding of watershed response to disturbance is essential, given widespread mining and agriculture in riparian zones, as well as the increasing prevalence of floods and fire due to climate change. Remote observation of rivers by satellites has recently produced novel insights into river response trajectories, especially focusing on suspended sediment transport, which can be an indicator of upstream watershed health. Yet detection of subtle or fine-scale patterns and changes in remains difficult, given the spatial, temporal, and/or spectral limitations of long-standing satellite missions. New public and private satellite missions offer the potential for weekly to sub-daily earth observation at spatial resolution of 10 meters or better. Capitalizing on these advancements, we have developed a suite of algorithms for the Sentinel-2 Multispectral Instrument and Planet Labs satellites, trained on tens of thousands of groundtruth samples from diverse settings in the United States, Canada, and Greenland. We find that these algorithms perform comparably to state-of-the-art algorithms for Landsat satellites, with approximately 70% relative error for individual measurements. Calibration for individual rivers improves algorithm performance: for Planet Labs observations of the Yellowstone River in Montana, USA, we achieve a roughly 50% improvement in relative error across different sensors, similar to Landsat-based, river-specific calibrations. The improved spatial and temporal resolution offered by these satellites allows for sediment transport estimates on rivers as narrow as 10–20 m wide; facilitates sediment source attribution; generates longitudinal and tributary estimates on seasonal and event timescales; and enables the near-continuous monitoring of perturbed watersheds.

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

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