L. Johnson

High-resolution landscape-scale biomass mapping using a spatiotemporal patchwork of LiDAR coverages

S. Stehman, G. Domke, M. Mahoney, 2022, Int. J. Appl. Earth Obs. Geoinformation.

Mapping Two Decades of New York State Forest Aboveground Biomass Change Using Remote Sensing

M. Mahdianpari, B. Salehi, C. Beier, 2022, Remote. Sens..

A COMPARATIVE ANALYSIS OF PIXEL-BASED AND OBJECT-BASED APPROACHES FOR FOREST ABOVE-GROUND BIOMASS ESTIMATION USING RANDOM FOREST MODEL

M. Mahdianpari, B. Salehi, C. Beier, 2022, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

Classification and mapping of low-statured shrubland cover types in post-agricultural landscapes of the US Northeast

C. Beier, Michael Mahoney, L. Johnson, 2022, International Journal of Remote Sensing.

Decision tree-based machine learning models for above-ground biomass estimation using multi-source remote sensing data and object-based image analysis

M. Mahdianpari, B. Salehi, H. Tamiminia, 2022 .

Mapping historical forest biomass for stock-change assessments at parcel to landscape scales

L. Johnson, Colin M. Beier, Michael J. Mahoney, 2023, Forest Ecology and Management.

EVALUATING PIXEL-BASED AND OBJECT-BASED APPROACHES FOR FOREST ABOVE-GROUND BIOMASS ESTIMATION USING A COMBINATION OF OPTICAL, SAR, AND AN EXTREME GRADIENT BOOSTING MODEL

M. Mahdianpari, B. Salehi, C. Beier, 2022, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

Ground-Truthing Forest Change Detection Algorithms in Working Forests of the US Northeast

Stephen R. Logan, C. Beier, E. Bevilacqua, 2022, Journal of Forestry.

Assessing the performance of spatial cross-validation approaches for models of spatially structured data

M. Mahoney, Hannah Frick, L. Johnson, 2023, 2303.07334.