CoChat
Sign Up
Cited by 347

The DOI matches its Crossref record.

Generating Pit-free Canopy Height Models from Airborne Lidar

Khosravipour, Anahita, Skidmore, Andrew K., Isenburg, Martin, Wang, Tiejun, Hussin, Yousif A.·2014·Photogrammetric Engineering & Remote Sensing
DOI 10.14358/pers.80.9.863
Open this paper in CoChat — free account

Abstract

Canopy height models (CHMs) derived from lidar data have been applied to extract forest inventory parameters. However, variations in modeled height cause data pits, which form a challenging problem as they disrupt CHM smoothness, negatively affecting tree detection and subsequent biophysical measurements. These pits appear where laser beams penetrate deeply into a tree crown, hitting a lower branch or the ground before producing the first return. In this study, we develop a new algorithm that generates a pit-free CHM raster, by using subsets of the lidar points to close pits. The algorithm operates robustly on high-density lidar data as well as on a thinned lidar dataset. The evaluation involves detecting individual trees using the pit-free CHM and comparing the findings to those achieved by using a Gaussian smoothed CHM. The results show that our pit-free CHMs derived from high-and low-density lidar data significantly improve the accuracy of tree detection.

Overview

Integrity signals

Registry

Crossref · 10.14358/pers.80.9.863

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 50

  1. 2008, LAS file format specifications
  2. P. Axelsson, 1999, Processing of laser scanner data - Algorithms and applications, ISPRS Journal of Photogrammetry and Remote Sensing, 54, 138, 10.1016/S0924-2716(99)00008-8 · DOI
  3. J.R. Ben-Arie, 2009, Development of a pit filling algorithm for lidar canopy height models, Computers & Geosciences, 35, 1940, 10.1016/j.cageo.2009.02.003 · DOI
  4. Z.J. Bortolot, 2005, Estimating forest biomass using small footprint LiDAR data: An individual tree-based approach that incorporates training data, ISPRS Journal of Photogrammetry and Remote Sensing, 59, 342, 10.1016/j.isprsjprs.2005.07.001 · DOI
  5. T. Brandtberg, 2003, Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density lidar data from the eastern deciduous forest in North America, Remote Sensing of Environment, 85, 290, 10.1016/S0034-4257(03)00008-7 · DOI
  6. R.H. Chan, 2005, Salt-and-pepper noise removal by median-type noise detectors and detail-pre-serving regularization, IEEE Transactions on Image Processing, 14, 1479, 10.1109/TIP.2005.852196 · DOI
  7. Q. Chen, 2006, Isolating individual trees in a savanna woodland using small footprint lidar data, Photogrammetric Engineering & Remote Sensing, 72, 923, 10.14358/PERS.72.8.923 · DOI
  8. K. Dralle, 1996, Stem number estimation by kernel smoothing of aerial photos, Canadian Journal of Forest Research, 26, 1228, 10.1139/x26-137 · DOI
  9. J.-C. Flageollet, 1999, Land-slides and climatic conditions in the Barcelonnette and Vars basins (Southern French Alps, France), Geomorphology, 30, 65, 10.1016/S0169-555X(99)00045-8 · DOI
  10. G. Forzieri, 2009, Multiple attribute decision making for individual tree detection using high-resolution laser scanning, Forest Ecology and Management, 258, 2501, 10.1016/j.foreco.2009.09.006 · DOI
  11. D.L. Gaveau, 2003, Quantifying canopy height under-estimation by laser pulse penetration in small-footprint airborne laser scanning data, Canadian Journal of Remote Sensing, 29, 650, 10.5589/m03-023 · DOI
  12. R. Hall, 2009, Quantifying structural physical habitat attributes using LIDAR and hyperspectral imagery, Environmental Monitoring and Assessment, 159, 63, 10.1007/s10661-008-0613-y · DOI
  13. J. Holmgren, 2008, Species identification of individual trees by combining high resolution LIDAR data with multi-spectral images, International Journal of Remote Sensing, 29, 1537, 10.1080/01431160701736471 · DOI
  14. F. Hosoi, 2012, Accurate detection of tree apexes in coniferous canopies from airborne scanning light detection and ranging images based on crown-extraction filtering, Journal of Applied Remote Sensing, 6
  15. J. Hyyppä, 2008, Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests, International Journal of Remote Sensing, 29, 1339, 10.1080/01431160701736489 · DOI
  16. J. Hyyppä, 2001, A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners, IEEE Transactions on Geoscience and Remote Sensing, 39, 969, 10.1109/36.921414 · DOI
  17. J. Hyyppä, 2000, Elevation accuracy of laser scanning-derived digital terrain and target models in forest environment
  18. J. Hyyppä, 2012, Advances in forest inventory using airborne laser scanning, Remote Sensing, 4, 1190, 10.3390/rs4051190 · DOI
  19. M. Isenburg, 2006, Streaming computation of Delaunay triangulations, 1049
  20. H. Kaartinen, 2012, An international comparison of individual tree detection and extraction using airborne laser scanning, Remote Sensing, 4, 950, 10.3390/rs4040950 · DOI
  21. Y. Ke, 2010, Synergistic use of Quick-Bird multispectral imagery and LIDAR data for object-based forest species classification, Remote Sensing of Environment, 114, 1141, 10.1016/j.rse.2010.01.002 · DOI
  22. K. Kraus, 1998, Determination of terrain models in wooded areas with airborne laser scanner data, ISPRS Journal of Photogrammetry and Remote Sensing, 53, 193, 10.1016/S0924-2716(98)00009-4 · DOI
  23. D. Leckie, 2003, Combined high-density lidar and multispectral imagery for individual tree crown analysis, Canadian Journal of Remote Sensing, 29, 633, 10.5589/m03-024 · DOI
  24. J. Li, 2013, Classification of tree species based on structural features derived from high density LiDAR data, Agricultural and Forest Meteorology, 171-172, 104, 10.1016/j.agrformet.2012.11.012 · DOI
  25. W.K. Li, 2012, A new method for segmenting individual trees from the lidar point cloud, Photogrammetric Engineering & Remote Sensing, 78, 75, 10.14358/PERS.78.1.75 · DOI
  26. K. Lim, 2003, LiDAR remote sensing of biophysical properties of tolerant northern hardwood forests, Canadian Journal of Remote Sensing, 29, 658, 10.5589/m03-025 · DOI
  27. K. Lim, 2003, LiDAR remote sensing of forest structure, Progress in Physical Geography, 27, 88, 10.1191/0309133303pp360ra · DOI
  28. S. Magnussen, 2010, Reliability of LiDAR derived predictors of forest inventory attributes: A case study with Norway spruce, Remote Sensing of Environment, 114, 700, 10.1016/j.rse.2009.11.007 · DOI
  29. R. Nelson, 1988, Estimating forest biomass and volume using airborne laser data, Remote Sensing of Environment, 24, 247, 10.1016/0034-4257(88)90028-4 · DOI
  30. 2000, The National Forests Office (France)
  31. A. Persson, 2002, Detecting and measuring individual trees using an airborne laser scanner, Photogrammetric Engineering & Remote Sensing, 68, 925
  32. J. Pitkänen, 2004, Adaptive methods for individual tree detection on airborne laser based canopy height model, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36, 187
  33. S.C. Popescu, 2007, Estimating biomass of individual pine trees using airborne lidar, Biomass and Bioenergy, 31, 646, 10.1016/j.biombioe.2007.06.022 · DOI
  34. S.C. Popescu, 2004, Seeing the trees in the forest: Using lidar and multispectral data fusion with local filtering and variable window size for estimating tree height, Photogrammetric Engineering & Remote Sensing, 70, 589, 10.14358/PERS.70.5.589 · DOI
  35. D. Pouliot, 2005, Development and evaluation of an automated tree detection delineation algorithm for monitoring regenerating coniferous forests, Canadian Journal of Forest Research, 35, 2332, 10.1139/x05-145 · DOI
  36. D.A. Pouliot, 2002, Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration, Remote Sensing of Environment, 82, 322, 10.1016/S0034-4257(02)00050-0 · DOI
  37. 2013, LAStools Rapid LiDAR Processing
  38. K.A. Razak, 2011, Airborne laser scanning of forested landslides characterization: Terrain model quality and visualization, Geomorphology, 126, 186, 10.1016/j.geomorph.2010.11.003 · DOI
  39. R. Säynäjoki, 2008, Detection of aspens using high resolution aerial laser scanning data and digital aerial images, Sensors, 8, 5037, 10.3390/s8085037 · DOI
  40. A. Shamsoddini, 2013, Improving lidar-based forest structure mapping with crown-level pit removal, Journal of Spatial Science, 58, 29, 10.1080/14498596.2012.759092 · DOI
  41. S. Solberg, 2006, Single tree segmentation using airborne laser scanner data in a structurally heterogeneous spruce forest, Photogrammetric Engineering & Remote Sensing, 72, 1369, 10.14358/PERS.72.12.1369 · DOI
  42. C. Song, 2010, Estimating average tree crown size using spatial information from Ikonos and QuickBird images: Across-sensor and across-site comparisons, Remote Sensing of Environment, 114, 1099, 10.1016/j.rse.2009.12.022 · DOI
  43. J. Vallet, 2004, Development and experiences with a fully-digital handheld mapping system operated from a helicopter
  44. M. Van Leeuwen, 2010, Canopy surface reconstruction from a LiDAR point cloud using Hough transform, Remote Sensing Letters, 1, 125, 10.1080/01431161003649339 · DOI
  45. C. Véga, 2011, Multi-level filtering segmentation to measure individual tree parameters based on Lidar data: Application to a mountainous forest with heterogeneous stands, International Journal of Applied Earth Observation and Geoinformation, 13, 646, 10.1016/j.jag.2011.04.002 · DOI
  46. M. Wulder, 2000, Local maximum filtering for the extraction of tree locations and basal area from high spatial resolution imagery, Remote Sensing of Environment, 73, 103, 10.1016/S0034-4257(00)00101-2 · DOI
  47. W. Yao, 2012, Tree species classification and estimation of stem volume and DBH based on single tree extraction by exploiting airborne full-waveform LiDAR data, Remote Sensing of Environment, 123, 368, 10.1016/j.rse.2012.03.027 · DOI
  48. X. Yu, 2011, Predicting individual tree attributes from airborne laser point clouds based on the random forests technique, ISPRS Journal of Photogrammetry and Remote Sensing, 66, 28, 10.1016/j.isprsjprs.2010.08.003 · DOI
  49. D. Zhao, 2013, Filling invalid values in a lidar-derived canopy height model with morphological crown control, International Journal of Remote Sensing, 34, 4636, 10.1080/01431161.2013.779398 · DOI
  50. K. Zhao, 2009, Lidar remote sensing of forest biomass: A scale-invariant estimation approach using air-borne lasers, Remote Sensing of Environment, 113, 182, 10.1016/j.rse.2008.09.009 · DOI

Cited by

Sorted by citations

See all 347

Metadata

Authors
Khosravipour, A., Skidmore, A. K., Isenburg, M., Wang, T., & Hussin, Y. A.
Venue
Photogrammetric Engineering & Remote Sensing
Publisher
American Society for Photogrammetry and Remote Sensing
Published
2014-09-01
Volume
80
Issue
9
Pages
863-872
ISSN
0099-1112
DOI
10.14358/pers.80.9.863
Cited by
347
References
0
Language
en

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Generating Pit-free Canopy Height Models from Airborne Lidar. https://app.cochat.ai/papers/10.14358/pers.80.9.863
The paper
Khosravipour, A., Skidmore, A. K., Isenburg, M., Wang, T., & Hussin, Y. A. (2014). Generating Pit-free Canopy Height Models from Airborne Lidar. Photogrammetric Engineering & Remote Sensing. https://doi.org/10.14358/pers.80.9.863