Nayani Ilangakoon
Cited by
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Reimagine fire science for the anthropocene
JK Shuman, JK Balch, RT Barnes, PE Higuera, CI Roos, DW Schwilk, ...
PNAS nexus 1 (3), pgac115, 2022
Harnessing the NEON data revolution to advance open environmental science with a diverse and data‐capable community
RC Nagy, JK Balch, EK Bissell, ME Cattau, NF Glenn, BS Halpern, ...
Ecosphere 12 (12), e03833, 2021
Empirical methods for remote sensing of nitrogen in drylands may lead to unreliable interpretation of ecosystem function
H Dashti, NF Glenn, S Ustin, JJ Mitchell, Y Qi, NT Ilangakoon, AN Flores, ...
IEEE transactions on Geoscience and Remote Sensing 57 (6), 3993-4004, 2019
Constraining plant functional types in a semi-arid ecosystem with waveform lidar
NT Ilangakoon, NF Glenn, H Dashti, TH Painter, TD Mikesell, LP Spaete, ...
Remote Sensing of Environment 209, 497-509, 2018
Regional scale dryland vegetation classification with an integrated lidar-hyperspectral approach
H Dashti, A Poley, N F. Glenn, N Ilangakoon, L Spaete, D Roberts, ...
Remote Sensing 11 (18), 2141, 2019
Airborne and spaceborne lidar reveal trends and patterns of functional diversity in a semi-arid ecosystem
N Ilangakoon, NF Glenn, FD Schneider, H Dashti, S Hancock, L Spaete, ...
Frontiers in Remote Sensing 2, 743320, 2021
Estimating leaf area index by bayesian linear regression using terrestrial Lidar, LAI-2200 plant canopy analyzer, and landsat tm spectral indices
NT Ilangakoon, PV Gorsevski, A Simic Milas
Canadian Journal of Remote Sensing 41 (4), 315-333, 2015
Ten simple rules for working with high resolution remote sensing data
AL Mahood, MB Joseph, AI Spiers, MJ Koontz, N Ilangakoon, KK Solvik, ...
Peer Community Journal 3, 2023
Performance of the ecosystem demography model (EDv2. 2) in simulating gross primary production capacity and activity in a dryland study area
H Dashti, K Pandit, NF Glenn, DJ Shinneman, GN Flerchinger, AT Hudak, ...
Agricultural and Forest Meteorology 297, 108270, 2021
2014 Lidar-Derived 1m Digital Elevation Model Data Set for Reynolds Creek Experimental Watershed, Southwestern Idaho
N Ilangakoon, NF Glenn, LP Spaete, H Dashti, A Li
Shrubland Species Cover, Biometric, Carbon and Nitrogen Data, Southern Idaho, 2014
NF Glenn, LP Spaete, R Shrestha, A Li, N Ilangakoon, J Mitchell, SL Ustin, ...
Shrubland Species Cover, Biometric, Carbon and Nitrogen Data, Southern Idaho, 2014. ORNL DAAC, Oak Ridge, Tennessee, USA
NF Glenn, LP Spaete, R Shrestha, A Li, N Ilangakoon, J Mitchell, SL Ustin, ...
Relationship between leaf area index (LAI) estimated by terrestrial LiDAR and remotely sensed vegetation indices as a proxy to forest carbon sequestration
NT Ilangakoon
Bowling Green State University, 2014
Cyberinfrastructure deployments on public research clouds enable accessible Environmental Data Science education
TL McIntosh, E Verleye, JK Balch, ME Cattau, NT Ilangakoon, N Korinek, ...
Practice and Experience in Advanced Research Computing, 367-373, 2023
Integrating Hyperspectral and LiDAR Data in the Study of Vegetation
JJ Mitchell, NF Glenn, KM Dahlin, N Ilangakoon, H Dashti, MC Maloney
Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for …, 2018
Spatial patterns of vegetation biomass and soil organic carbon acquired from airborne lidar and hyperspectral imagery at Reynolds Creek Critical Zone Observatory
RM Will, A Li, NF Glenn, SG Benner, L Spaete, NT Ilangakoon
AGU Fall Meeting Abstracts 2015, B53C-0571, 2015
Local factors mediate climate and land use effects on global fire regimes (2003-2020)
A Mahood, T Tuff, M Cattau, V Iglesias, T Hanson, J Stephens, ...
Scaling plant functional types from field to satellite to assess forest resilience and state change
CH Amaral, J Shastri, NT Ilangakoon, T McIntosh, M Cattau, N Korinek, ...
AGU23, 2023
Multi-scale Machine Learning for Predicting Prescribed Burn Effects on Ecosystem Services
J Gammie, C Amaral, NT Ilangakoon, A LoPresti, LE Dee, C Monteleoni, ...
AGU23, 2023
Postfire recovery of western US conifer forests (1984-2017) using space-borne lidar data
N Ilangakoon, JK Balch, RC Nagy, V Iglesias
bioRxiv, 2023.02. 18.528502, 2023
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