Canopy and surface fuels measurement using terrestrial lidar single-scan approach in the Mogollon Highlands of Arizona
Johnathan T. Tenny
A * , Temuulen Tsagaan Sankey A , Seth M. Munson B , Andrew J. Sánchez Meador C D and Scott J. Goetz A
A
B
C
D
Abstract
Fuel monitoring data are essential to evaluate wildfire risk, plan management activities and evaluate fuel treatment effects. Terrestrial light detection and ranging (lidar) is a field-based 3D scanning technology with great potential to reduce labor-intensive field measurements and provide new depths of vegetation structure data.
To facilitate the integration of terrestrial lidar into fuel monitoring programs, we developed a model, training process, and Python program that produces canopy fuel, surface fuel and terrain metrics commonly used in fire behavior and fire risk modeling.
We estimated canopy and surface fuel metrics from terrestrial lidar using a semi-empirical model incorporating physically based modeling of leaf area density and occlusion and a non-destructive model calibration process leveraging Bayesian regression. We compared lidar-derived fuel estimates with conventional fuel estimates across diverse conditions in semi-arid shrubland, woodland and forest in Arizona. We also compared estimates using single- and multiple-scan modes.
In single-scan mode, our lidar-derived fuel estimates were significantly related to conventional estimates of total canopy fuel load, maximum canopy bulk density, downed surface fuel load and standing surface fuel load.
Our methods provide opportunities to increase the scalability of fuel monitoring to better understand wildfire risk and treatment effectiveness.
Keywords: Arizona, canopy bulk density, canopy fuel load, foliage biomass, gap fraction, ground-based lidar, leaf area density, leaf area index, leaf mass per area, Mogollon Highlands, southwest US, specific leaf area, terrestrial laser scanning, t-lidar, plant area density, TLS, vertical profile, voxelmon.
References
Abril-Pla O, Andreani V, Carroll C, Dong L, Fonnesbeck CJ, Kochurov M, Kumar R, Lao J, Luhmann CC, Martin OA, Osthege M, Vieira R, Wiecki T, Zinkov R (2023) PyMC: a modern, and comprehensive probabilistic programming framework in Python. PeerJ Computer Science 9, e1516.
| Crossref | Google Scholar | PubMed |
Allen CD, Savage M, Falk DA, Suckling KF, Swetnam TW, Schulke T, Stacey PB, Morgan P, Hoffman M, Klingel JT (2002) Ecological restoration of southwestern ponderosa pine ecosystems: a broad perspective. Ecological Applications 12, 1418-1433.
| Crossref | Google Scholar |
Arkin J, Coops NC, Daniels LD, Plowright A (2023) Canopy and surface fuel estimations using RPAS and ground-based point clouds. Forestry 98, 15-28.
| Crossref | Google Scholar |
Balestra M, Marselis S, Sankey TT, Cabo C, Liang X, Mokroš M, Peng X, Singh A, Stereńczak K, Vega C, Vincent G, Hollaus M (2024) Lidar data fusion to improve forest attribute estimates: a review. Current Forestry Reports 10, 281-297.
| Crossref | Google Scholar |
Batchelor JL, Wilson TM, Olsen MJ, Ripple WJ (2022) New structural complexity metrics for forests from single terrestrial lidar scans. Remote Sensing 15, 145.
| Crossref | Google Scholar |
Batchelor JL, Hudak AT, Gould P, Moskal LM (2023) Terrestrial and airborne lidar to quantify shrub cover for Canada lynx (Lynx canadensis) habitat using machine learning. Remote Sensing 15, 4434.
| Crossref | Google Scholar |
Béland M, Widlowski J-L, Fournier RA, Côté J-F, Verstraete MM (2011) Estimating leaf area distribution in savanna trees from terrestrial LiDAR measurements. Agricultural and Forest Meteorology 151, 1252-1266.
| Crossref | Google Scholar |
Béland M, Baldocchi DD, Widlowski J-L, Fournier RA, Verstraete MM (2014) On seeing the wood from the leaves and the role of voxel size in determining leaf area distribution of forests with terrestrial LiDAR. Agricultural and Forest Meteorology 184, 82-97.
| Crossref | Google Scholar |
Belmonte A, Sankey T, Biederman J, Bradford J, Goetz S, Kolb T (2021) UAV-based estimate of snow cover dynamics: optimizing semi-arid forest structure for snow persistence. Remote Sensing 13, 1036.
| Crossref | Google Scholar |
Bester MS, Maxwell AE, Nealey I, Gallagher MR, Skowronski NS, McNeil BE (2023) Synthetic forest stands and point clouds for model selection and feature space comparison. Remote Sensing 15, 4407.
| Crossref | Google Scholar |
Burke M, Driscoll A, Heft-Neal S, Xue J, Burney J, Wara M (2021) The changing risk and burden of wildfire in the United States. Proceedings of the National Academy of Sciences of the United States of America 118, e2011048118.
| Crossref | Google Scholar | PubMed |
Caster J, Sankey TTs, Sankey JB, Bowker MA, Buscombe D, Duniway MC, Barger N, Faist A, Joyal T (2021) Biocrust and the soil surface: influence of climate, disturbance, and biocrust recovery on soil surface roughness. Geoderma 403, 115369.
| Crossref | Google Scholar |
Clary WP, Tiedemann AR (1987) Fuelwood potential in large-tree Quercus gambelii stands. Western Journal of Applied Forestry 2, 87-90.
| Crossref | Google Scholar |
Covington WW, Moore MM (1994) Southwestern ponderosa forest structure: changes since Euro-American settlement. Journal of Forestry 92, 39-47.
| Crossref | Google Scholar |
Donager JJ, Sankey TTs, Sankey JB, Sanchez Meador AJ, Springer AE, Bailey JD (2018) Examining forest structure with terrestrial lidar: suggestions and novel techniques based on comparisons between scanners and forest treatments. Earth and Space Science 5, 753-776.
| Crossref | Google Scholar |
Donager JJ, Sánchez Meador AJ, Blackburn RC (2021) Adjudicating perspectives on forest structure: how do airborne, terrestrial, and mobile lidar-derived estimates compare? Remote Sensing 13, 2297.
| Crossref | Google Scholar |
Ellis TM, Bowman DMJS, Jain P, Flannigan MD, Williamson GJ (2022) Global increase in wildfire risk due to climate-driven declines in fuel moisture. Global Change Biology 28, 1544-1559.
| Crossref | Google Scholar | PubMed |
Ex SA, Smith FW, Keyser TL, Rebain SA (2016) Estimating canopy bulk density and canopy base height for interior western US conifer stands. Forest Science 62, 690-697.
| Crossref | Google Scholar |
Finney MA (2006) An overview of FlamMap fire modeling capabilities. In ‘Fuels Management – How to Measure Success: Conference Proceedings’. 28-30 March 2006; Portland, OR. Proceedings RMRS-P-41. (Eds PL Andrews, BW Butler, Comps) pp. 213–220. (USDA Forest Service, Rocky Mountain Research Station: Fort Collins, CO)
Fleischner TL, Floyd ML, Rack J, Hanna D, Blevins K, Christman B, Holycross AT (2024) The Mogollon Highlands ecoregion of the American southwest: a neglected center of ecological diversity. Natural Areas Journal 44, 104-119.
| Crossref | Google Scholar |
Fulé PZ, Barrett MP, Cocke AE, Crouse JE, Roccaforte JP, Normandin DP, Covington WW, Moore MM, Heinlein TA, Stoddard MT, Rodman KC (2023) Fire regimes over a 1070-m elevational gradient, San Francisco Peaks/Dook’o’oosłííd, Arizona, USA. Fire Ecology 19(1),.
| Crossref | Google Scholar |
García M, Danson FM, Riaño D, Chuvieco E, Ramirez FA, Bandugula V (2011) Terrestrial laser scanning to estimate plot-level forest canopy fuel properties. International Journal of Applied Earth Observation and Geoinformation 13, 636-645.
| Crossref | Google Scholar |
Gill AM, Stephens SL, Cary GJ (2013) The worldwide ‘wildfire’ problem. Ecological Applications 23, 438-454.
| Crossref | Google Scholar | PubMed |
Grau E, Durrieu S, Fournier R, Gastellu-Etchegorry J-P, Yin T (2017) Estimation of 3D vegetation density with terrestrial laser scanning data using voxels. A sensitivity analysis of influencing parameters. Remote Sensing of Environment 191, 373-388.
| Crossref | Google Scholar |
Grier CC, Elliott KJ, McCullough DG (1992) Biomass distribution and productivity of Pinus edulis—Juniperus monosperma woodlands of north-central Arizona. Forest Ecology and Management 50, 331-350.
| Crossref | Google Scholar |
Gupta V, Reinke K, Jones S, Wallace L, Holden L (2015) Assessing metrics for estimating fire induced change in the forest understorey structure using terrestrial laser scanning. Remote Sensing 7, 8180-8201.
| Crossref | Google Scholar |
Heinsch FA, Andrews PL (2010) ‘BehavePlus Fire Modeling System, Version 5.0: design and features.’ (USDA Forest Service, Rocky Mountain Research Station: Ft. Collins, CO) 10.2737/RMRS-GTR-249
Hood SM, Varner JM, Jain TB, Kane JM (2022) A framework for quantifying forest wildfire hazard and fuel treatment effectiveness from stands to landscapes. Fire Ecology 18, 33.
| Crossref | Google Scholar |
Hudak AT, Kato A, Bright BC, Loudermilk EL, Hawley C, Restaino JC, Ottmar RD, Prata GA, Cabo C, Prichard SJ, Rowell EM, Weise DR (2020) Towards spatially explicit quantification of pre- and postfire fuels and fuel consumption from traditional and point cloud measurements. Forest Science 66, 428-442.
| Crossref | Google Scholar |
Hughes TF, Latt CR, Tappeiner JC, Newton M (1987) Biomass and leaf-area estimates for varnishleaf ceanothus, deerbrush, and whiteleaf manzanita. Western Journal of Applied Forestry 2, 124-128.
| Crossref | Google Scholar |
Humagain K, Portillo-Quintero C, Cox R, Cain J (2017) Mapping tree density in forests of the southwestern USA using Landsat 8 data. Forests 8, 287.
| Crossref | Google Scholar |
Jones MW, Abatzoglou JT, Veraverbeke S, Andela N, Lasslop G, Forkel M, Smith AJP, Burton C, Betts RA, Werf GR, Sitch S, Canadell JG, Santín C, Kolden C, Doerr SH, Le Quéré C (2022) Global and regional trends and drivers of fire under climate change. Reviews of Geophysics 60, e2020RG000726.
| Crossref | Google Scholar |
Kaye JP, Hart SC, Fulé PZ, Covington WW, Moore MM, Kaye MW (2005) Initial carbon, nitrogen, and phosphorus fluxes following ponderosa pine restoration treatments. Ecological Applications 15, 1581-1593.
| Crossref | Google Scholar |
Keyser T, Smith FW (2010) Influence of crown biomass estimators and distribution on canopy fuel characteristics in ponderosa pine stands of the Black Hills. Forest Science 56, 156-165.
| Crossref | Google Scholar |
Krisanski S, Taskhiri MS, Gonzalez Aracil S, Herries D, Muneri A, Gurung MB, Montgomery J, Turner P, 4677 (2021) Forest Structural Complexity Tool—An open source, fully-automated tool for measuring forest point clouds. Remote Sensing 13,.
| Crossref | Google Scholar |
Loudermilk EL, Hiers JK, O’Brien JJ, Mitchell RJ, Singhania A, Fernandez JC, Cropper WP, Slatton KC (2009) Ground-based LIDAR: a novel approach to quantify fine-scale fuelbed characteristics. International Journal of Wildland Fire 18, 676.
| Crossref | Google Scholar |
Loudermilk EL, Pokswinski S, Hawley CM, Maxwell A, Gallagher MR, Skowronski NS, Hudak AT, Hoffman C, Hiers JK (2023) Terrestrial laser scan metrics predict surface vegetation biomass and consumption in a frequently burned southeastern US ecosystem. Fire 6, 151.
| Crossref | Google Scholar |
Lutes DC, Keane RE, Caratti JF, Key CH, Benson NC, Sutherland S, Gangi LJ (2006) FIREMON: Fire Effects Monitoring and Inventory System. (USDAorest Service, Rocky Mountain Research Station: Ft Collins, CO) 10.2737/RMRS-GTR-164.
Marcozzi AA, Johnson JV, Parsons RA, Flanary SJ, Seielstad CA, Downs JZ (2023) Application of lidar-derived fuel cells to wildfire modeling at laboratory scale. Fire 6, 394.
| Crossref | Google Scholar |
Martin-Ducup O, Dupuy J-L, Soma M, Guerra-Hernandez J, Marino E, Fernandes PM, Just A, Corbera J, Toutchkov M, Sorribas C, Bock J, Piboule A, Pirotti F, Pimont F (2025) Unlocking the potential of airborne LiDAR for direct assessment of fuel bulk density and load distributions for wildfire hazard mapping. Agricultural and Forest Meteorology 362, 110341.
| Crossref | Google Scholar |
McClaran MP, McMurtry CR, Archer SR (2013) A tool for estimating impacts of woody encroachment in arid grasslands: allometric equations for biomass, carbon and nitrogen content in Prosopis velutina. Journal of Arid Environments 88, 39-42.
| Crossref | Google Scholar |
Molina-Valero JA, Martínez-Calvo A, Ginzo Villamayor MJ, Novo Pérez MA, Álvarez-González JG, Montes F, Pérez-Cruzado C (2022) Operationalizing the use of TLS in forest inventories: the R package FORTLS. Environmental Modelling & Software 150, 105337.
| Crossref | Google Scholar |
Mueller SE, Thode AE, Margolis EQ, Yocom LL, Young JD, Iniguez JM (2020) Climate relationships with increasing wildfire in the southwestern US from 1984 to 2015. Forest Ecology and Management 460, 117861.
| Crossref | Google Scholar |
Murphy MC, Loudermilk EL, Pokswinski S, Williams B, Link E, Lienesch L, Douglas L, Skowronski N, Gallagher M, Maxwell A, Snitker G, Hawley C, Wallace D, Payne I, Yurkiewicz T, Sanchez Meador AJ, Anderson C, Jackson JM, Parsons R, Floca M, Nealey I, Altintas I, Hiers JK, Wallace J (2024) Terrestrial 3D laser scanning for ecosystem and fire effects monitoring. (USDA Forest Service, Southern Research Station: Asheville, NC) 10.2737/SRS-GTR-277.
Newnham GJ, Armston JD, Calders K, Disney MI, Lovell JL, Schaaf CB, Strahler AH, Danson FM (2015) Terrestrial laser scanning for plot-scale forest measurement. Current Forestry Reports 1, 239-251.
| Crossref | Google Scholar |
Nguyen V-T, Fournier RA, Côté J-F, Pimont F (2022) Estimation of vertical plant area density from single return terrestrial laser scanning point clouds acquired in forest environments. Remote Sensing of Environment 279, 113115.
| Crossref | Google Scholar |
O’Donnell FC, Donager J, Sankey T, Masek Lopez S, Springer AE (2021) Vegetation structure controls on snow and soil moisture in restored ponderosa pine forests. Hydrological Processes 35, e14432.
| Crossref | Google Scholar |
Ottmar RD, Vihnanek RE, Wright CS, Seymour GB (2007a) Stereo photo series for quantifying natural fuels Volume IX: oak/juniper in southern Arizona and New Mexico. (USDA Forest Service, Pacific Northwest Research Station: Portland, OR) 10.2737/PNW-GTR-714
Ottmar RD, Vihnanek RE, Wright CS (2007b) Stereo photo series for quantifying natural fuels Volume X: sagebrush with grass and ponderosa pine–juniper types in central Montana. (USDA Forest Service, Pacific Northwest Research Station: Portland, OR) 10.2737/PNW-GTR-719.
Parks SA, Abatzoglou JT (2020) Warmer and drier fire seasons contribute to increases in area burned at high severity in western US forests from 1985‐2017. Geophysical Research Letters 47, e2020GL089858.
| Crossref | Google Scholar |
Pimont F, Dupuy J-L, Rigolot E, Prat V, Piboule A (2015) Estimating leaf bulk density distribution in a tree canopy using terrestrial lidar and a straightforward calibration procedure. Remote Sensing 7, 7995-8018.
| Crossref | Google Scholar |
Pimont F, Soma M, Dupuy J-L (2019) Accounting for wood, foliage properties, and laser effective footprint in estimations of leaf area density from multiview-LiDAR data. Remote Sensing 11, 1580.
| Crossref | Google Scholar |
PRISM Climate Group (2021) PRISM Long-Term Average Climate Dataset Norm91m [Dataset]. Oregon State University. Available at https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_Norm91m [verified 7 March 2025]
Reinhardt E, Crookston NL (2003) The fire and fuels extension to the forest vegetation simulator. (USDA Forest Service, Rocky Mountain Research Station: Ft. Collins, CO) 10.2737/RMRS-GTR-116.
Romme WH, Allen CD, Bailey JD, Baker WL, Bestelmeyer BT, Brown PM, Eisenhart KS, Floyd ML, Huffman DW, Jacobs BF, Miller RF, Muldavin EH, Swetnam TW, Tausch RJ, Weisberg PJ (2009) Historical and modern disturbance regimes, stand structures, and landscape dynamics in piñon–juniper vegetation of the western United States. Rangeland Ecology & Management 62, 203-222.
| Crossref | Google Scholar |
Rowell E, Loudermilk EL, Seielstad C, O’Brien JJ (2016) Using simulated 3D surface fuelbeds and terrestrial laser scan data to develop inputs to fire behavior models. Canadian Journal of Remote Sensing 42, 443-459.
| Crossref | Google Scholar |
Rowell E, Loudermilk EL, Hawley C, Pokswinski S, Seielstad C, Queen Ll, O’Brien JJ, Hudak AT, Goodrick S, Hiers JK (2020) Coupling terrestrial laser scanning with 3D fuel biomass sampling for advancing wildland fuels characterization. Forest Ecology and Management 462, 117945.
| Crossref | Google Scholar |
Sancho-Knapik D, Escudero A, Mediavilla S, Scoffoni C, Zailaa J, Cavender-Bares J, Álvarez-Arenas TG, Molins A, Alonso-Forn D, Ferrio JP, Peguero-Pina JJ, Gil-Pelegrín E (2021) Deciduous and evergreen oaks show contrasting adaptive responses in leaf mass per area across environments. The New Phytologist 230, 521-534.
| Crossref | Google Scholar | PubMed |
Sankey T, Shrestha R, Sankey JB, Hardegree S, Strand E (2013) Lidar-derived estimate and uncertainty of carbon sink in successional phases of woody encroachment. Journal of Geophysical Research: Biogeosciences 118, 1144-1155.
| Crossref | Google Scholar |
Scott JH, Burgan RE (2005) Standard fire behavior fuel models: a comprehensive set for use with Rothermel’s surface fire spread model. (USDA Forest Service, Rocky Mountain Research Station: Ft. Collins, CO) 10.2737/RMRS-GTR-153.
Shokirov S, Jucker T, Levick SR, Manning AD, Bonnet T, Yebra M, Youngentob KN (2023) Habitat highs and lows: using terrestrial and UAV LiDAR for modelling avian species richness and abundance in a restored woodland. Remote Sensing of Environment 285, 113326.
| Crossref | Google Scholar |
Sikkink PG, Keane RE (2008) A comparison of five sampling techniques to estimate surface fuel loading in montane forests. International Journal of Wildland Fire 17, 363.
| Crossref | Google Scholar |
Silva VS da, Silva CA, Mohan M, Cardil A, Rex FE, Loureiro GH, Almeida DRA de, Broadbent EN, Gorgens EB, Dalla Corte AP, Silva EA, Valbuena R, Klauberg C (2020) Combined impact of sample size and modeling approaches for predicting stem volume in Eucalyptus spp. forest plantations using field and LiDAR data. Remote Sensing 12, 1438.
| Crossref | Google Scholar |
Singleton MP, Thode AE, Sánchez Meador AJ, Iniguez JM (2019) Increasing trends in high-severity fire in the southwestern USA from 1984 to 2015. Forest Ecology and Management 433, 709-719.
| Crossref | Google Scholar |
Skowronski NS, Clark KL, Duveneck M, Hom J (2011) Three-dimensional canopy fuel loading predicted using upward and downward sensing LiDAR systems. Remote Sensing of Environment 115, 703-714.
| Crossref | Google Scholar |
Soma M, Pimont F, Durrieu S, Dupuy J-L (2018) Enhanced measurements of leaf area density with T-LiDAR: evaluating and calibrating the effects of vegetation heterogeneity and scanner properties. Remote Sensing 10, 1580.
| Crossref | Google Scholar |
Southwest Ecological Restoration Institutes (2025) Treatment and Wildfire Interagency Geodatabase v0.2.144 [Dataset]. Available at https://reshapewildfire.org/twig/ [verified 7 March 2025]
Szpakowski D, Jensen J (2019) A review of the applications of remote sensing in fire ecology. Remote Sensing 11, 2638.
| Crossref | Google Scholar |
Tango LL, Sankey TT, Leonard J, Sankey JB, Kasprak A (2024) Combining terrestrial lidar with single line transects to investigate geomorphic change: a case study on the Upper Verde River, Arizona. Geomorphology 457, 109231.
| Crossref | Google Scholar |
Vuolo F, Neugebauer N, Bolognesi S, Atzberger C, D’Urso G (2013) Estimation of leaf area index using DEIMOS-1 Data: application and transferability of a semi-empirical relationship between two agricultural areas. Remote Sensing 5, 1274-1291.
| Crossref | Google Scholar |
Wallace L, Hillman S, Hally B, Taneja R, White A, McGlade J (2022) Terrestrial laser scanning: an operational tool for fuel hazard mapping? Fire 5, 85.
| Crossref | Google Scholar |
Weiskittel AR, Temesgen H, Wilson DS, Maguire DA (2008) Sources of within- and between-stand variability in specific leaf area of three ecologically distinct conifer species. Annales Des Sciences Forestières 65, 103.
| Crossref | Google Scholar |
Whelan AW, Cannon JB, Bigelow SW, Rutledge BT, Sánchez Meador AJ (2023) Improving generalized models of forest structure in complex forest types using area- and voxel-based approaches from lidar. Remote Sensing of Environment 284, 113362.
| Crossref | Google Scholar |
Wildland Fire Leadership Council (2014) The National Strategy. Available at https://www.forestsandrangelands.gov/documents/strategy/strategy/CSPhaseIIINationalStrategyApr2014.pdf [verified 7 March 2025]
Wildland Fire Leadership Council (2023) National Cohesive Wildland Fire Management Strategy Addendum Update. Available at https://www.forestsandrangelands.gov/documents/strategy/natl-cohesive-wildland-fire-mgmt-strategy-addendum-update-2023.pdf [verified 7 March 2025]
Wilson N, Bradstock R, Bedward M (2021) Detecting the effects of logging and wildfire on forest fuel structure using terrestrial laser scanning (TLS). Forest Ecology and Management 488, 119037.
| Crossref | Google Scholar |
Xi Z, Chasmer L, Hopkinson C (2023) Delineating and reconstructing 3D Forest fuel components and volumes with terrestrial laser scanning. Remote Sensing 15, 4778.
| Crossref | Google Scholar |
Yin T, Cook BD, Morton DC (2022) Three-dimensional estimation of deciduous forest canopy structure and leaf area using multi-directional, leaf-on and leaf-off airborne lidar data. Agricultural and Forest Meteorology 314, 108781.
| Crossref | Google Scholar |
