Marja Haagsma
Marja Haagsma
PhD candidate, Biological and Ecological Engineering, Oregon State University
Verified email at
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Advancing ecohydrology in the 21st century: A convergence of opportunities
AJ Guswa, D Tetzlaff, JS Selker, DE Carlyle‐Moses, EW Boyer, M Bruen, ...
Ecohydrology, e2208, 2020
Field trials to detect drainage pipe networks using thermal and RGB data from unmanned aircraft
CB Kratt, DK Woo, KN Johnson, M Haagsma, P Kumar, J Selker, S Tyler
Agricultural Water Management 229, 105895, 2020
Remote sensing of ploidy level in quaking aspen (Populus tremuloides Michx.)
B Blonder, BJ Graae, B Greer, M Haagsma, K Helsen, RE Kapás, H Pai, ...
Journal of Ecology 108 (1), 175-188, 2020
HyperRail: Modular, 3D printed, 1–100 m, programmable, and low-cost linear motion control system for imaging and sensor suites
JML Alcala, M Haagsma, CJ Udell, JS Selker
HardwareX 6, e00081, 2019
Hyperspectral imagery of Pinus strobiformis infected with fungal pathogen
M Haagsma, G Page, JS Johnson
Is more data better? A comparison of multi-and hyperspectral imaging in phenotyping.
M Haagsma, G Page, J Johnson, C Still, K Waring, R Sniezko, J Selker
EGU General Assembly Conference Abstracts, 10673, 2020
Using Hyperspectral Imagery to Detect an Invasive Fungal Pathogen and Symptom Severity in Pinus strobiformis Seedlings of Different Genotypes
M Haagsma, GFM Page, JS Johnson, C Still, KM Waring, RA Sniezko, ...
Remote Sensing 12 (24), 4041, 2020
Objective Identification of Diseased Plants from Hyperspectral Images Using Machine Learning
M Haagsma, GFM Page, JS Johnson, CJ Still, KM Waring, R Sniezko, ...
AGUFM 2019, B31K-2418, 2019
Using Hyperspectral Imagery to Detect an Invasive Fungal Pathogen and Determine Symptom Severity in Pinus Strobiformis Seedlings
M Haagsma, GFM Page, JS Johnson, CJ Still, JS Selker
AGUFM 2018, B33K-2828, 2018
Identification of Tile Drain Networks using Thermal and RGB Data from Unmanned Aircraft
S Tyler, CB Kratt, DK Woo, KB Johnson, M Haagsma, C Sladek, P Kumar, ...
AGUFM 2018, NS43C-0853, 2018
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