Integrated fire severity–land cover mapping using very-high-spatial-resolution aerial imagery and point clouds
Jeremy Arkin
A E , Nicholas C. Coops A , Txomin Hermosilla B , Lori D. Daniels C and Andrew Plowright D
+ Author Affiliations
- Author Affiliations
A Integrated Remote Sensing Studio, Department of Forest Resources Management, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
B Canadian Forest Service (Pacific Forestry Centre), Natural Resources Canada, 506 West Burnside Road, Victoria, BC V8Z 1M5, Canada.
C Tree Ring Lab, Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
D FYBR Solutions Inc., 138 E 7th Avenue, Suite 100, Vancouver, BC V5T 1M6, Canada.
E Corresponding author. Email: jeremy.arkin@alumni.ubc.ca
International Journal of Wildland Fire 28(11) 840-860 https://doi.org/10.1071/WF19008
Submitted: 19 January 2019 Accepted: 27 June 2019 Published: 13 August 2019
Abstract
Fire severity mapping is conventionally accomplished through the interpretation of aerial photography or the analysis of moderate- to coarse-spatial-resolution pre- and post-fire satellite imagery. Although these methods are well established, there is a demand from both forest managers and fire scientists for higher-spatial-resolution fire severity maps. This study examines the utility of high-spatial-resolution post-fire imagery and digital aerial photogrammetric point clouds acquired from an unmanned aerial vehicle (UAV) to produce integrated fire severity–land cover maps. To accomplish this, a suite of spectral, structural and textural variables was extracted from the UAV-acquired data. Correlation-based feature selection was used to select subsets of variables to be included in random forest classifiers. These classifiers were then used to produce disturbance-based land cover maps at 5- and 1-m spatial resolutions. By analysing maps produced using different variables, the highest-performing spectral, structural and textural variables were identified. The maps were produced with high overall accuracies (5 m, 89.5 ± 1.4%; 1 m, 85.4 ± 1.5%), with the 1-m classification produced at slightly lower accuracies. This reduction was attributed to the inclusion of four additional classes, which increased the thematic detail enough to outweigh the differences in accuracy.
Additional keywords: digital aerial photogrammetry, random forest, supervised classification.
References
Abbott G, Chapman M (2018) Addressing the new normal: 21st century disaster management in British Columbia. An independent report prepared for Government and British Columbians. Available at https://www2.gov.bc.ca/assets/gov/public-safetyand-emergency-services/emergency-preparedness-response-recovery/embc/bc-flood-andwildfire-review-addressing-the-new-normal-21st-century-disaster-management-in-bcweb.pdf [Verified 19 July 2019].Arnett JTTR, Coops NC, Daniels LD, Falls RW (2015) Detecting forest damage after a low-severity fire using remote sensing at multiple scales. International Journal of Applied Earth Observation and Geoinformation 35, 239–246.
| Detecting forest damage after a low-severity fire using remote sensing at multiple scales.Crossref | GoogleScholarGoogle Scholar |
Balaguer A, Ruiz LA, Hermosilla T, Recio JA (2010) Definition of a comprehensive set of texture semivariogram features and their evaluation for object-oriented image classification. Computers & Geosciences 36, 231–240.
| Definition of a comprehensive set of texture semivariogram features and their evaluation for object-oriented image classification.Crossref | GoogleScholarGoogle Scholar |
Balaguer-Beser A, Ruiz LA, Hermosilla T, Recio JA (2013) Using semivariogram indices to analyse heterogeneity in spatial patterns in remotely sensed images. Computers & Geosciences 50, 115–127.
| Using semivariogram indices to analyse heterogeneity in spatial patterns in remotely sensed images.Crossref | GoogleScholarGoogle Scholar |
Baraldi A, Parmiggiani F (1995) An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters. IEEE Transactions on Geoscience and Remote Sensing 33, 293–304.
| An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters.Crossref | GoogleScholarGoogle Scholar |
BC Wildfire Service (2019) Wildfire statistics. Available at http://www2.gov.bc.ca/gov/content/safety/wildfirestatus/wildfire-statistics/wildfire-averages [Verified 23 March 2019]
Belgiu M, Drăgu L (2016) Random forest in remote sensing: a review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing 114, 24–31.
| Random forest in remote sensing: a review of applications and future directions.Crossref | GoogleScholarGoogle Scholar |
Bouvier M, Durrieu S, Fournier RA, Renaud JP (2015) Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data. Remote Sensing of Environment 156, 322–334.
| Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data.Crossref | GoogleScholarGoogle Scholar |
Breiman L (2001) Random forests. Machine Learning 45, 5–32.
| Random forests.Crossref | GoogleScholarGoogle Scholar |
Burton PJ, Parisien MA, Hicke JA, Hall RJ, Freeburn JT (2008) Large fires as agents of ecological diversity in the North American boreal forest. International Journal of Wildland Fire 17, 754–767.
| Large fires as agents of ecological diversity in the North American boreal forest.Crossref | GoogleScholarGoogle Scholar |
Carrivick JL, Smith MW, Quincey DJ (2016) ‘New analytical methods in Earth and environmental science: structure from motion in the geosciences’, 1st edn. (Wiley-Blackwell: ChichesterUK)
Cochran WG (1977) ‘Sampling techniques’, 3rd edn. (John Wiley & Sons: New York, NY, USA)
Coops NC, Varhola A, Bater CW, Teti P, Boon S, Goodwin N, Weiler M (2009) Assessing differences in tree and stand structure following beetle infestation using lidar data. Canadian Journal of Remote Sensing 35, 497–508.
| Assessing differences in tree and stand structure following beetle infestation using lidar data.Crossref | GoogleScholarGoogle Scholar |
Coops NC, Duffe J, Koot C (2010) Assessing the utility of lidar remote sensing technology to identify mule deer winter habitat. Canadian Journal of Remote Sensing 36, 81–88.
| Assessing the utility of lidar remote sensing technology to identify mule deer winter habitat.Crossref | GoogleScholarGoogle Scholar |
Coops NC, Hermosilla T, Wulder MA, White JC, Bolton DK (2018) A thirty-year, fine-scale characterization of area burned in Canadian forests shows evidence of regionally increasing trends in the last decade. PLoS One 13, e0197218
| A thirty-year, fine-scale characterization of area burned in Canadian forests shows evidence of regionally increasing trends in the last decade.Crossref | GoogleScholarGoogle Scholar | 29995923PubMed |
Eidenshink JC, Schwind B, Brewer K, Zhu Z-L, Quayle B, Howard SM (2007) A project for monitoring trends in burn severity. Fire Ecology 3, 3–21.
| A project for monitoring trends in burn severity.Crossref | GoogleScholarGoogle Scholar |
Feng Q, Liu J, Gong J (2015) UAV remote sensing for urban vegetation mapping using random forest and texture analysis. Remote Sensing 7, 1074–1094.
| UAV remote sensing for urban vegetation mapping using random forest and texture analysis.Crossref | GoogleScholarGoogle Scholar |
Fernández-Guisuraga JM, Sanz-Ablanedo E, Suárez-Seoane S, Calvo L (2018) Using unmanned aerial vehicles in post-fire vegetation survey campaigns through large and heterogeneous areas: opportunities and challenges. Sensors 18, 586
| Using unmanned aerial vehicles in post-fire vegetation survey campaigns through large and heterogeneous areas: opportunities and challenges.Crossref | GoogleScholarGoogle Scholar |
Finley CD, Glenn NF (2010) Fire and vegetation type effects on soil hydrophobicity and infiltration in the sagebrush-steppe: I. Field analysis. Journal of Arid Environments 74, 660–666.
| Fire and vegetation type effects on soil hydrophobicity and infiltration in the sagebrush-steppe: I. Field analysis.Crossref | GoogleScholarGoogle Scholar |
Forkel M, Wutzler T (2015) greenbrown – land surface phenology and trend analysis. R package version 2.2. Available at http://greenbrown.r-forge.r-project.org/ [Verified 19 July 2019]
Frank E, Hall MA, Witten IH (2016) The WEKA workbench. Online Appendix for ‘Data mining: practical machine learning tools and techniques’, 4th edn. (Morgan Kaufmann) Available at https://www.cs.waikato.ac.nz/ml/weka/ [Verified 19 July 2019]
Fritz A, Kattenborn T, Koch B (2013) UAV-based photogrammetric point clouds – tree stem mapping in open stands in comparison to terrestrial laser scanner point clouds. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1, 141–146.
| UAV-based photogrammetric point clouds – tree stem mapping in open stands in comparison to terrestrial laser scanner point clouds.Crossref | GoogleScholarGoogle Scholar |
Gobakken T, Bollandsås OM, Næsset E (2015) Comparing biophysical forest characteristics estimated from photogrammetric matching of aerial images and airborne laser scanning data. Scandinavian Journal of Forest Research 30, 73–86.
| Comparing biophysical forest characteristics estimated from photogrammetric matching of aerial images and airborne laser scanning data.Crossref | GoogleScholarGoogle Scholar |
Goodbody TRH, Coops NC, Marshall PL, Tompalski P, Crawford P (2017) Unmanned aerial systems for precision forest inventory purposes: a review and case study. Forestry Chronicle 93, 71–81.
| Unmanned aerial systems for precision forest inventory purposes: a review and case study.Crossref | GoogleScholarGoogle Scholar |
Goodbody TRH, Coops NC, Hermosilla T, Tompalski P, McCartney G, MacLean DA (2018) Digital aerial photogrammetry for assessing cumulative spruce budworm defoliation and enhancing forest inventories at a landscape level. ISPRS Journal of Photogrammetry and Remote Sensing 142, 1–11.
| Digital aerial photogrammetry for assessing cumulative spruce budworm defoliation and enhancing forest inventories at a landscape level.Crossref | GoogleScholarGoogle Scholar |
Graham A, Coops NC, Wilcox M, Plowright A (2019) Evaluation of ground surface models derived from unmanned aerial systems with digital aerial photogrammetry in a disturbed conifer forest. Remote Sensing 11, 84
| Evaluation of ground surface models derived from unmanned aerial systems with digital aerial photogrammetry in a disturbed conifer forest.Crossref | GoogleScholarGoogle Scholar |
Guerra-Hernández J, Cosenza DN, Rodriguez LCE, Silva M, Tomé M, Díaz-Varela RA, González-Ferreiro E (2018) Comparison of ALS- and UAV (SfM)-derived high-density point clouds for individual tree detection in eucalyptus plantations. International Journal of Remote Sensing 39, 5211–5235.
| Comparison of ALS- and UAV (SfM)-derived high-density point clouds for individual tree detection in eucalyptus plantations.Crossref | GoogleScholarGoogle Scholar |
Hall M, Smith LA (1999) Feature selection for machine learning: comparing a correlation-based filter approach to the wrapper. In In Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference, 235, 239.
Hall MA, Holmes G (2003) Benchmarking attribute selection techniques for data mining. IEEE Transactions on Knowledge and Data Engineering 15, 1437–1447.
| Benchmarking attribute selection techniques for data mining.Crossref | GoogleScholarGoogle Scholar |
Hall RJ, Freeburn JT, De Groot WJ, Pritchard JM, Lynham TJ, Landry R (2008) Remote sensing of burn severity: experience from western Canada boreal fires. International Journal of Wildland Fire 17, 476–489.
| Remote sensing of burn severity: experience from western Canada boreal fires.Crossref | GoogleScholarGoogle Scholar |
Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics SMC-3, 610–621.
| Textural features for image classification.Crossref | GoogleScholarGoogle Scholar |
Hermosilla T, Wulder MA, White JC, Coops NC, Hobart GW, Campbell LB (2016) Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. International Journal of Digital Earth 9, 1035–1054.
| Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring.Crossref | GoogleScholarGoogle Scholar |
Hijmans RJ (2018) Raster: geographic data analysis and modeling. R package version 2.8–4. Available at https://cran.r-project.org/web/packages/raster/raster.pdf [Verified 19 July 2019]
Hope ES, McKenney DW, Pedlar JH, Stocks BJ, Gauthier S (2016) Wildfire suppression costs for Canada under a changing climate. PLoS One 11, e0157425
| Wildfire suppression costs for Canada under a changing climate.Crossref | GoogleScholarGoogle Scholar | 27513660PubMed |
Hunt ER, Doraiswamy PC, McMurtrey JE, Daughtry CST, Perry EM, Akhmedov B (2013) A visible band index for remote sensing leaf chlorophyll content at the canopy scale. International Journal of Applied Earth Observation and Geoinformation 21, 103–112.
| A visible band index for remote sensing leaf chlorophyll content at the canopy scale.Crossref | GoogleScholarGoogle Scholar |
Hyndman RJ, Fan Y (1996) Sample quantiles in statistical packages The American Statistician 50, 361–365.
| Sample quantiles in statistical packagesCrossref | GoogleScholarGoogle Scholar |
Jensen JR (2006) ‘Remote sensing of the environment: an earth resource perspective’, 2nd edn. (Prentice Hall: Upper Saddle River, NJ, USA)
Keeley JE (2009) Fire intensity, fire severity and burn severity: a brief review and suggested usage. International Journal of Wildland Fire 18, 116–126.
| Fire intensity, fire severity and burn severity: a brief review and suggested usage.Crossref | GoogleScholarGoogle Scholar |
Kramer HA, Collins BM, Kelly M, Stephens SL (2014) Quantifying ladder fuels: a new approach using LiDAR. Forests 5, 1432–1453.
| Quantifying ladder fuels: a new approach using LiDAR.Crossref | GoogleScholarGoogle Scholar |
Leduc A, Bernier PY, Mansuy N, Raulier F, Gauthier S, Bergeron Y (2015) Using salvage logging and tolerance to risk to reduce the impact of forest fires on timber supply calculations. Canadian Journal of Forest Research 45, 480–486.
| Using salvage logging and tolerance to risk to reduce the impact of forest fires on timber supply calculations.Crossref | GoogleScholarGoogle Scholar |
Lentile LB, Holden ZA, Smith AMS, Falkowski MJ, Hudak AT, Morgan P, Benson NC (2006) Remote sensing techniques to assess active fire characteristics and post-fire effects. International Journal of Wildland Fire 15, 319–345.
| Remote sensing techniques to assess active fire characteristics and post-fire effects.Crossref | GoogleScholarGoogle Scholar |
Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2, 18–22.
Louhaichi M, Borman MM, Johnson DE (2001) Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto International 16, 65–70.
| Spatially located platform and aerial photography for documentation of grazing impacts on wheat.Crossref | GoogleScholarGoogle Scholar |
Lucieer A, Malenovský Z, Veness T, Wallace L (2014) HyperUAS – imaging spectroscopy from a multirotor unmanned aircraft system. Journal of Field Robotics 31, 571–590.
| HyperUAS – imaging spectroscopy from a multirotor unmanned aircraft system.Crossref | GoogleScholarGoogle Scholar |
Ma L, Cheng L, Li M, Liu Y, Ma X (2015) Training set size, scale, and features in geographic object-based image analysis of very-high-resolution unmanned aerial vehicle imagery. ISPRS Journal of Photogrammetry and Remote Sensing 102, 14–27.
| Training set size, scale, and features in geographic object-based image analysis of very-high-resolution unmanned aerial vehicle imagery.Crossref | GoogleScholarGoogle Scholar |
Matese A, Toscano P, Di Gennaro S, Genesio L, Vaccari F, Primicerio J, Belli C, Zaldei A, Bianconi R, Gioli B (2015) Intercomparison of UAV, aircraft and satellite remote sensing platforms for precision viticulture. Remote Sensing 7, 2971–2990.
| Intercomparison of UAV, aircraft and satellite remote sensing platforms for precision viticulture.Crossref | GoogleScholarGoogle Scholar |
McKenna P, Erskine PD, Lechner AM, Phinn S (2017) Measuring fire severity using UAV imagery in semi-arid central Queensland, Australia. International Journal of Remote Sensing 38, 4244–4264.
| Measuring fire severity using UAV imagery in semi-arid central Queensland, Australia.Crossref | GoogleScholarGoogle Scholar |
Meidinger DV, Pojar J (1991) ‘Ecosystems of British Columbia.’ (Research Branch, Ministry of Forests: Victoria, BC, Canada) 6.
Michez A, Piégay H, Lisein J, Claessens H, Lejeune P (2016) Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system. Environmental Monitoring and Assessment 188, 146
| Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system.Crossref | GoogleScholarGoogle Scholar | 26850712PubMed |
Mitri GH, Gitas IZ (2006) Fire type mapping using object-based classification of Ikonos imagery. International Journal of Wildland Fire 15, 457–462.
| Fire type mapping using object-based classification of Ikonos imagery.Crossref | GoogleScholarGoogle Scholar |
Montealegre AL, Lamelas MT, Tanase MA, De la Riva J (2014) Forest fire severity assessment using ALS data in a Mediterranean environment. Remote Sensing 6, 4240–4265.
| Forest fire severity assessment using ALS data in a Mediterranean environment.Crossref | GoogleScholarGoogle Scholar |
Motohka T, Nasahara KN, Oguma H, Tsuchida S (2010) Applicability of green–red vegetation index for remote sensing of vegetation phenology. Remote Sensing 2, 2369–2387.
| Applicability of green–red vegetation index for remote sensing of vegetation phenology.Crossref | GoogleScholarGoogle Scholar |
Näsi R, Honkavaara E, Lyytikäinen-Saarenmaa P, Blomqvist M, Litkey P, Hakala T, Holopainen M (2015) Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree level. Remote Sensing 7, 15467–15493.
| Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree level.Crossref | GoogleScholarGoogle Scholar |
Natural Resources Canada (2016) The state of Canada’s forests annual report 2017, Canadian Forest Service, Headquarters, Ottawa pp. 1–76 Available at https://cfs.nrcan.gc.ca/publications?id=38871 [Verified 19 July 2019].
Olofsson P, Foody GM, Herold M, Stehman SV, Woodcock CE, Wulder MA (2014) Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment 148, 42–57.
| Good practices for estimating area and assessing accuracy of land change.Crossref | GoogleScholarGoogle Scholar |
Pal M, Foody GM (2010) Feature selection for classification of hyperspectral data by SVM. IEEE Transactions on Geoscience and Remote Sensing 48, 2297–2307.
| Feature selection for classification of hyperspectral data by SVM.Crossref | GoogleScholarGoogle Scholar |
Paneque-Gálvez J, McCall MK, Napoletano BM, Wich SA, Koh LP (2014) Small drones for community-based forest monitoring: an assessment of their feasibility and potential in tropical areas. Forests 5, 1481–1507.
| Small drones for community-based forest monitoring: an assessment of their feasibility and potential in tropical areas.Crossref | GoogleScholarGoogle Scholar |
Pretzsch H (2009) Description and analysis of stand structures. In ‘Forest dynamics, growth and yield: from measurement to model’, 1st edn. pp. 223–289. (Springer Publishing: Berlin, Germany)
Rishmawi KN, Gitas IZ (2001) Burned area mapping on the Mediterranean island of Thasos using low-, medium-high- and very-high-spatial-resolution satellite data. In ‘Remote Sensing and Photogrammetry Society (RSPS2001): conference proceedings’,12–14 September 2001, Westminster, London. pp. 26–32.
Robichaud PR, Ashmun LE (2013) Tools to aid post-wildfire assessment and erosion-mitigation treatment decisions. International Journal of Wildland Fire 22, 95–105.
| Tools to aid post-wildfire assessment and erosion-mitigation treatment decisions.Crossref | GoogleScholarGoogle Scholar |
Roussel J-R, Auty D (2018) lidR: airborne LiDAR data manipulation and visualization for forestry applications. R package version 1.4.1. Available at https://cran.r-project.org/web/packages/lidR/lidR.pdf [Verified 19 July 2019]
Ruiz LA, Recio JA, Fernandez-Sarria A, Hermosilla T (2011) A feature extraction software tool for agricultural object-based image analysis. Computers and Electronics in Agriculture 76, 284–296.
| A feature extraction software tool for agricultural object-based image analysis.Crossref | GoogleScholarGoogle Scholar |
San-Miguel I, Andison DW, Coops NC (2017) Characterizing historical fire patterns as a guide for harvesting planning using landscape metrics derived from long-term satellite imagery. Forest Ecology and Management 399, 155–165.
| Characterizing historical fire patterns as a guide for harvesting planning using landscape metrics derived from long-term satellite imagery.Crossref | GoogleScholarGoogle Scholar |
Schoennagel T, Balch JK, Brenkert-Smith H, Dennison PE, Harvey BJ, Krawchuk MA, Mietkiewicz N, Morgan P, Moritz MA, Rasker R, Turner MG, Whitlock C (2017) Adapt to more wildfire in western North American forests as climate changes. Proceedings of the National Academy of Sciences of the United States of America 114, 4582–4590.
| Adapt to more wildfire in western North American forests as climate changes.Crossref | GoogleScholarGoogle Scholar | 28416662PubMed |
Simpson JE, Wooster MJ, Smith TEL, Trivedi M, Vernimmen RRE, Dedi R, Shakti M, Dinata Y (2016) Tropical peatland burn depth and combustion heterogeneity assessed using UAV photogrammetry and airborne LiDAR. Remote Sensing 8, 1000
| Tropical peatland burn depth and combustion heterogeneity assessed using UAV photogrammetry and airborne LiDAR.Crossref | GoogleScholarGoogle Scholar |
Soverel NO, Perrakis DDB, Coops NC (2010) Estimating burn severity from Landsat dNBR and RdNBR indices across western Canada. Remote Sensing of Environment 114, 1896–1909.
| Estimating burn severity from Landsat dNBR and RdNBR indices across western Canada.Crossref | GoogleScholarGoogle Scholar |
Sripada RP, Heiniger RW, White JG, Meijer AD (2006) Aerial color infrared photography for determining early in-season nitrogen requirements in corn. Agronomy Journal 98, 968–977.
| Aerial color infrared photography for determining early in-season nitrogen requirements in corn.Crossref | GoogleScholarGoogle Scholar |
Stephens SL, Agee JK, Fulé PZ, North MP, Romme WH, Swetnam TW, Turner MG (2013) Managing forests and fire in changing climates. Science 342, 41–42.
| Managing forests and fire in changing climates.Crossref | GoogleScholarGoogle Scholar | 24092714PubMed |
Stephens SL, Burrows N, Buyantuyev A, Gray RW, Keane RE, Kubian R, Liu S, Seijo F, Shu L, Tolhurst KG, van Wagtendonk JW (2014) Temperate and boreal forest mega-fires: characteristics and challenges. Frontiers in Ecology and the Environment 12, 115–122.
| Temperate and boreal forest mega-fires: characteristics and challenges.Crossref | GoogleScholarGoogle Scholar |
van Ewijk KY, Treitz PM, Scott NA (2011) Characterizing forest succession in central Ontario using Lidar-derived indices. Photogrammetric Engineering and Remote Sensing 77, 261–269.
| Characterizing forest succession in central Ontario using Lidar-derived indices.Crossref | GoogleScholarGoogle Scholar |
Vastaranta M, Wulder MA, White JC, Pekkarinen A, Tuominen S, Ginzler C, Kankare V, Holopainen M, Hyyppä J, Hyyppä H (2013) Airborne laser scanning and digital stereo imagery measures of forest structure: comparative results and implications to forest mapping and inventory update. Canadian Journal of Remote Sensing 39, 382–395.
| Airborne laser scanning and digital stereo imagery measures of forest structure: comparative results and implications to forest mapping and inventory update.Crossref | GoogleScholarGoogle Scholar |
Wallace L, Lucieer A, Watson C, Turner D (2012) Development of a UAV-LiDAR system with application to forest inventory. Remote Sensing 4, 1519–1543.
| Development of a UAV-LiDAR system with application to forest inventory.Crossref | GoogleScholarGoogle Scholar |
Wang C, Glenn NF (2009) Estimation of fire severity using pre- and post-fire LiDAR data in sagebrush steppe rangelands. International Journal of Wildland Fire 18, 848–856.
| Estimation of fire severity using pre- and post-fire LiDAR data in sagebrush steppe rangelands.Crossref | GoogleScholarGoogle Scholar |
Warner TA, Skowronski NS, Gallagher MR (2017) High-spatial-resolution burn severity mapping of the New Jersey Pine Barrens with WorldView-3 near-infrared and shortwave infrared imagery. International Journal of Remote Sensing 38, 598–616.
| High-spatial-resolution burn severity mapping of the New Jersey Pine Barrens with WorldView-3 near-infrared and shortwave infrared imagery.Crossref | GoogleScholarGoogle Scholar |
White JC, Wulder MA, Vastaranta M, Coops NC, Pitt D, Woods M (2013) The utility of image-based point clouds for forest inventory: a comparison with airborne laser scanning. Forests 4, 518–536.
| The utility of image-based point clouds for forest inventory: a comparison with airborne laser scanning.Crossref | GoogleScholarGoogle Scholar |
Wulder MA, White JC, Magnussen S, McDonald S (2007) Validation of a large area land cover product using purpose-acquired airborne video. Remote Sensing of Environment 106, 480–491.
| Validation of a large area land cover product using purpose-acquired airborne video.Crossref | GoogleScholarGoogle Scholar |
Wulder MA, White JC, Alvarez F, Han T, Rogan J, Hawkes B (2009) Characterizing boreal forest wildfire with multi-temporal Landsat and LIDAR data. Remote Sensing of Environment 113, 1540–1555.
| Characterizing boreal forest wildfire with multi-temporal Landsat and LIDAR data.Crossref | GoogleScholarGoogle Scholar |
Zhu Z, Gallant AL, Woodcock CE, Pengra B, Olofsson P, Loveland TR, Jin S, Dahal D, Yang L, Auch RF (2016) Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative. ISPRS Journal of Photogrammetry and Remote Sensing 122, 206–221.
| Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative.Crossref | GoogleScholarGoogle Scholar |
