Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 329

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Forest biomass estimation from airborne LiDAR data using machine learning approaches

Gleason, Colin J., Im, Jungho·2012·Remote Sensing of Environment
DOI 10.1016/j.rse.2012.07.006
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.rse.2012.07.006

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 64

  1. Anderson, 2006, The use of waveform lidar to measure northern temperate mixed conifer and deciduous forest structure in New Hampshire, Remote Sensing of Environment, 105, 248, 10.1016/j.rse.2006.07.001 · DOI
  2. Ballabio, 2009, Spatial prediction of soil properties in temperate mountain regions using support vector regression, Geoderma, 151, 338, 10.1016/j.geoderma.2009.04.022 · DOI
  3. Blackard, 2008, Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information, Remote Sensing of Environment, 112, 1658, 10.1016/j.rse.2007.08.021 · DOI · Paper page
  4. 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. Brandtberg, 2007, Classifying individual tree species under leaf-off and leaf-on conditions using airborne lidar, ISPRS Journal of Photogrammetry and Remote Sensing, 61, 325, 10.1016/j.isprsjprs.2006.10.006 · DOI · Paper page
  6. Breidenbach, 2010, Prediction of species specific forest inventory attributes using a nonparametric semi-individual tree crown approach based on fused airborne laser scanning and multispectral data, Remote Sensing of Environment, 114, 911, 10.1016/j.rse.2009.12.004 · DOI
  7. Breiman, 2001, Random forests, Machine Learning, 45, 5, 10.1023/A:1010933404324 · DOI · Paper page
  8. Bunting, 2006, The delineation of tree crowns in Australian mixed species forests using hyperspectral Compact Airborne Spectrographic Imager (CASI) data, Remote Sensing of Environment, 101, 230, 10.1016/j.rse.2005.12.015 · DOI
  9. Chapelle, 2002, Choosing multiple parameters for support vector machines, Machine Learning, 46, 131, 10.1023/A:1012450327387 · DOI · Paper page
  10. Chen, 2007, Estimating basal area and stem volume for individual trees from lidar data, Photogrammetric Engineering and Remote Sensing, 73, 1355, 10.14358/PERS.73.12.1355 · DOI
  11. Chen, 2006, Isolating individual trees in a savanna woodland using small footprint lidar data, Photogrammetric Engineering and Remote Sensing, 72, 923, 10.14358/PERS.72.8.923 · DOI · Paper page
  12. Chen, 2011, Estimating aboveground forest biomass carbon and fire consumption in the U.S. Utah High Plateaus using data from the Forest Inventory and Analysis Program, Landsat, and LANDFIRE, Ecological Indicators, 11, 140, 10.1016/j.ecolind.2009.03.013 · DOI
  13. Donoghue, 2007, Remote sensing of species mixtures in conifer plantations using LiDAR height and intensity data, Remote Sensing of Environment, 110, 509, 10.1016/j.rse.2007.02.032 · DOI
  14. Durbha, 2007, Support vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer, Remote Sensing of Environment, 107, 348, 10.1016/j.rse.2006.09.031 · DOI · Paper page
  15. Falkowski, 2009, Characterizing forest succession with lidar data: An evaluation for the Inland Northwest, USA, Remote Sensing of Environment, 113, 946, 10.1016/j.rse.2009.01.003 · DOI · Paper page
  16. Fang, 2003, Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model, Remote Sensing of Environment, 85, 257, 10.1016/S0034-4257(03)00005-1 · DOI
  17. Foody, 2003, Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions, Remote Sensing of Environment, 85, 463, 10.1016/S0034-4257(03)00039-7 · DOI · Paper page
  18. Gleason, 2011, A review of remote sensing of forest biomass and biofuel: options for small scale forests, GIScience and Remote Sensing, 48, 141, 10.2747/1548-1603.48.2.141 · DOI
  19. Gleason, 2012, A fusion approach for tree crown delineation from LiDAR data, Photogrammetric Engineering and Remote Sensing, 78, 679, 10.14358/PERS.78.7.679 · DOI
  20. Gong, 2011, An artificial immune network approach to multi-sensor land use/land cover classification, Remote Sensing of Environment, 115, 600, 10.1016/j.rse.2010.10.005 · DOI
  21. Gonzalez, 2010, Forest carbon densities and uncertainties from Lidar, QuickBird, and field measurements in California, Remote Sensing of Environment, 114, 1561, 10.1016/j.rse.2010.02.011 · DOI
  22. Hawbaker, 2010, Light detection and ranging-based measures of mixed hardwood forest structure, Forest Science, 56, 313, 10.1093/forestscience/56.3.313 · DOI
  23. Huang, 2006, A GA-based feature selection and parameters optimization for support vector machines, Expert Systems with Applications, 31, 231, 10.1016/j.eswa.2005.09.024 · DOI · Paper page
  24. Huang, 2008, Use of a dark object concept and support vector machines to automate forest cover change analysis, Remote Sensing of Environment, 112, 970, 10.1016/j.rse.2007.07.023 · DOI · Paper page
  25. Hudak, 2008, Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data, Remote Sensing of Environment, 112, 2232, 10.1016/j.rse.2007.10.009 · DOI · Paper page
  26. Hyde, 2006, Mapping forest structure for wildlife habitat analysis using multi-sensor (LiDAR, SAR/InSAR, ETM+, Quickbird) synergy, Remote Sensing of Environment, 102, 63, 10.1016/j.rse.2006.01.021 · DOI · Paper page
  27. Im, 2009, Hyperspectral remote sensing analysis of short rotation woody crops grown with controlled nutrient and irrigation treatment, Geocarto International, 24, 293, 10.1080/10106040802556207 · DOI
  28. Im, 2011, A genetic algorithm approach to moving threshold optimization for binary change detection, Photogrammetric Engineering and Remote Sensing, 77, 167, 10.14358/PERS.77.2.167 · DOI
  29. Im, 2012, Fusion of feature selection and optimized immune networks for hyperspectral image classification of urban landscapes, Geocarto International, 27, 373, 10.1080/10106049.2011.642898 · DOI
  30. Im, 2012, Impervious surface quantification using a synthesis of artificial immune networks and decision/regression trees from multi-sensor data, Remote Sensing of Environment, 117, 102, 10.1016/j.rse.2011.06.024 · DOI
  31. Ioki, 2010, Estimating stand volume in broad-leaved forest using discrete-return LiDAR: Plot-based approach, Landscape and Ecological Engineering, 6, 29, 10.1007/s11355-009-0077-4 · DOI
  32. Jang, 2008, The use of airborne lidar for orchard tree inventory, International Journal of Remote Sensing, 29, 1767, 10.1080/01431160600928591 · DOI
  33. Jenkins, 2003, National-scale biomass estimators for United States tree species, Forest Science, 49, 12, 10.1093/forestscience/49.1.12 · DOI · Paper page
  34. Kato, 2009, Capturing tree crown formation through implicit surface reconstruction using airborne lidar data, Remote Sensing of Environment, 113, 1148, 10.1016/j.rse.2009.02.010 · DOI
  35. Kavzoglu, 2009, A kernel functions analysis for support vector machines for land cover classification, International Journal of Applied Earth Observation and Geoinformation, 11, 352, 10.1016/j.jag.2009.06.002 · DOI
  36. Ke, 2010, Active contour and hill climbing for tree crown detection and delineation, Photogrammetric Engineering and Remote Sensing, 76, 1169, 10.14358/PERS.76.10.1169 · DOI
  37. Keerthi, 2003, Asymptotic behaviors of support vector machines with Gaussian kernel, Neural Computation, 15, 1667, 10.1162/089976603321891855 · DOI · Paper page
  38. Korpela, 2009, Airborne small-footprint discrete-return LiDAR data in the assessment of boreal mire surface patterns, vegetation, and habitats, Forest Ecology and Management, 258, 1549, 10.1016/j.foreco.2009.07.007 · DOI
  39. Kuemmerle, 2009, Forest cover change and illegal logging in the Ukrainian Carpathians in the transition period from 1988 to 2007, Remote Sensing of Environment, 113, 1194, 10.1016/j.rse.2009.02.006 · DOI
  40. Kwak, 2010, Estimating stem volume and biomass of Pinus koraiensis using LiDAR data, Journal of Plant Research, 123, 421, 10.1007/s10265-010-0310-0 · DOI
  41. Liaw, 2002, Classification and regression by randomForest, 2(3), 18
  42. Lin, 2011, A multi-level morphological active contour algorithm for delineating tree crowns in mountainous forest, Photogrammetric Engineering and Remote Sensing, 77, 241, 10.14358/PERS.77.3.241 · DOI
  43. Lu, Z., Im, J., & Hodgson, M. (in review). Building type classification using spatial attributes derived from LiDAR remote sensing data, ISPRS Journal of Photogrammetry and Remote Sensing, in review.
  44. Muinonen, 2001, Forest stand characteristics estimation using a most similar neighbor approach and image spatial structure information, Remote Sensing of Environment, 78, 223, 10.1016/S0034-4257(01)00220-6 · DOI
  45. Næsset, 2004, Practical large-scale forest stand inventory using a small-footprint airborne scanning laser, Scandinavian Journal of Forest Research, 19, 164, 10.1080/02827580410019544 · DOI
  46. Næsset, 2005, Assessing sensor effects and effects of leaf-off and leaf-on canopy conditions on biophysical stand properties derived from small-footprint airborne laser data, Remote Sensing of Environment, 98, 356, 10.1016/j.rse.2005.07.012 · DOI
  47. Næsset, 2002, Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve, Remote Sensing of Environment, 79, 105, 10.1016/S0034-4257(01)00243-7 · DOI
  48. Persson, 2002, Detecting and measuring individual trees using an airborne laser scanner, Photogrammetric Engineering and Remote Sensing, 68, 925
  49. Popescu, 2007, Estimating biomass of individual pine trees using airborne lidar, Biomass and Bioenergy, 31, 646, 10.1016/j.biombioe.2007.06.022 · DOI · Paper page
  50. 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 and Remote Sensing, 70, 589, 10.14358/PERS.70.5.589 · DOI · Paper page
  51. Popescu, 2004, Fusion of small-footprint lidar and multispectral data to estimate plot-level volume and biomass in deciduous and pine forests in Virginia, USA, Forest Science, 50, 551, 10.1093/forestscience/50.4.551 · DOI
  52. Popescu, 2002, Estimating plot-level tree heights with lidar: Local filtering with a canopy-height based variable window size, Computers and Electronics in Agriculture, 37, 71, 10.1016/S0168-1699(02)00121-7 · DOI · Paper page
  53. Powell, 2010, Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches, Remote Sensing of Environment, 114, 1053, 10.1016/j.rse.2009.12.018 · DOI · Paper page
  54. Salas, 2010, Modelling tree diameter from airborne laser scanning derived variables: A comparison of spatial statistical models, Remote Sensing of Environment, 114, 1277, 10.1016/j.rse.2010.01.020 · DOI
  55. Shevade, 2000, Improvements to the SMO algorithm for SVM regression, IEEE Transactions on Neural Networks, 11, 1188, 10.1109/72.870050 · DOI · Paper page
  56. Solberg, 2010, Estimating spruce and pine biomass with interferometric X-band SAR, Remote Sensing of Environment, 114, 2353, 10.1016/j.rse.2010.05.011 · DOI
  57. Tseng, 2008, A genetic algorithm rule-based approach for land-cover classification, ISPRS Journal of Photogrammetry and Remote Sensing, 63, 202, 10.1016/j.isprsjprs.2007.09.001 · DOI
  58. Vauhkonen, 2010, Imputation of single-tree attributes using airborne laser scanning-based height, intensity, and alpha shape metrics, Remote Sensing of Environment, 114, 1263, 10.1016/j.rse.2010.01.016 · DOI
  59. Walker, 2007, An empirical InSAR-optical fusion approach to mapping vegetation canopy height, Remote Sensing of Environment, 109, 482, 10.1016/j.rse.2007.02.001 · DOI
  60. Walton, 2008, Subpixel urban land cover estimation: Comparing cubist, random forests, and support vector regression, Photogrammetric Engineering and Remote Sensing, 74, 1213, 10.14358/PERS.74.10.1213 · DOI
  61. Wang, 2004, Individual tree-crown delineation and treetop detection in high-spatial-resolution aerial imagery, Photogrammetric Engineering and Remote Sensing, 70, 351, 10.14358/PERS.70.3.351 · DOI · Paper page
  62. Yu, 2006, Incorporating remote sensing information in modeling house values: A regression tree approach, Photogrammetric Engineering and Remote Sensing, 72, 129, 10.14358/PERS.74.10.1213 · DOI
  63. 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, 10.14358/PERS.70.3.351 · DOI · Paper page
  64. Zhao, 2009, Lidar remote sensing of forest biomass: A scale-invariant estimation approach using airborne lasers, Remote Sensing of Environment, 113, 182, 10.14358/PERS.72.2.129 · DOI

Cited by

See all 329

Metadata

Authors
Gleason, C. J., & Im, J.
Venue
Remote Sensing of Environment
Publisher
Elsevier BV
Published
2012-10
Volume
125
Pages
80-91
ISSN
0034-4257 (ISSN-L)
DOI
10.1016/j.rse.2012.07.006
OpenAlex
W1978642993
Cited by
329
References
1
FWCI
11.80
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)

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

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.