CoChat
Sign Up
Cited by 150

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

Prediction of understory vegetation cover with airborne lidar in an interior ponderosa pine forest

Wing, Brian M., Ritchie, Martin W., Boston, Kevin, Cohen, Warren B., Gitelman, Alix, Olsen, Michael J.·2012·Remote Sensing of Environment
DOI 10.1016/j.rse.2012.06.024
Open this paper in CoChat — free account

Abstract

Forest understory communities are important components in forest ecosystems providing wildlife habitat and influencing nutrient cycling, fuel loadings, fire behavior and tree species composition over time. One of the most widely utilized understory component metrics is understory vegetation cover, often used as a measure of vegetation abundance. To date, understory vegetation cover estimation and prediction has proven to be inherently difficult using traditional explanatory variables such as: leaf area index, basal area, slope, and aspect. We introduce airborne lidar-derived metrics into the modeling framework for understory vegetation cover. A new airborne lidar metric, understory lidar cover density, created by filtering understory lidar points using intensity values increased traditional explanatory power from non-lidar understory vegetation cover estimation models (non-lidar R²-values: 0.2–0.45 vs. lidar R²-values: 0.7–0.8). Beta regression, a relatively new modeling technique for this type of data, was compared with a traditional weighted linear regression model using a leave-one-out cross‐validation procedure. Both models provided similar understory vegetation cover accuracies (±22%) and biases (~0%) using 40.5m² circular plots (n=154). The method presented in this paper provides the ability to accurately obtain census understory vegetation cover information at fine spatial resolutions over a broad range of stand conditions for the interior ponderosa pine forest type. A…

Overview

Integrity signals

Registry

Crossref · 10.1016/j.rse.2012.06.024

Cites

1 of 1 references verified0 citation errors · 0 not checkable

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

Flagged first, then by citations

Research works printed but not linked: 57

  1. Andersen, 2005, Estimating forest canopy fuel parameters using lidar data, Remote Sensing of Environment, 94, 441, 10.1016/j.rse.2004.10.013 · DOI · Paper page
  2. Chen, 2006, Distribution model and spatial variation of cover in grassland vegetation, Grassland Science, 52, 167, 10.1111/j.1744-697X.2006.00065.x · DOI
  3. Chen, 2008, Frequency distribution models for spatial patterns of vegetation abundance, Ecological Modelling, 211, 403, 10.1016/j.ecolmodel.2007.09.017 · DOI
  4. Chen, 2008, Plant cover estimation based on the beta distribution in grassland vegetation, Ecological Research, 23, 813, 10.1007/s11284-007-0443-3 · DOI
  5. Clawges, 2008, The use of airborne lidar to assess avian species diversity, density, and occurrence in a pine/aspen forest, Remote Sensing of Environment, 112, 2064, 10.1016/j.rse.2007.08.023 · DOI
  6. Cohen, 2004, Landsat's role in ecological applications of remote sensing, Bioscience, 54, 535, 10.1641/0006-3568(2004)054[0535:LRIEAO]2.0.CO;2 · DOI
  7. Coops, 2007, Estimating canopy structure of Douglas-fir forest stands from discrete-return lidar, Trees-Structure and Function, 21, 295, 10.1007/s00468-006-0119-6 · DOI
  8. Cribari-Neto, 2010, Beta regression in R, Journal of Statistical Software, 34, 1, 10.18637/jss.v034.i02 · DOI · Paper page
  9. Damgaard, 2009, On the distribution of plant abundance data, Ecological Informatics, 4, 76, 10.1016/j.ecoinf.2009.02.002 · DOI
  10. 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
  11. Dunning, 1928, A tree classification for the selection forests of the Sierra Nevada, Journal of Agricultural Research, 36, 755
  12. Eskelson, 2011, Estimating riparian understory vegetation cover with beta regression and copula models, Forest Science, 57, 212, 10.1093/forestscience/57.3.212 · DOI
  13. Espinheira, 2008, On beta regression residuals, Journal of Applied Statistics, 35, 407, 10.1080/02664760701834931 · DOI
  14. 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
  15. Ferrari, 2004, Beta regression modeling rates and proportions, Journal of Applied Statistics, 31, 799, 10.1080/0266476042000214501 · DOI · Paper page
  16. Goetz, 2007, Laser remote sensing of canopy habitat heterogeneity as a predictor of bird species richness in an eastern temperate forest, USA, Remote Sensing of Environment, 108, 254, 10.1016/j.rse.2006.11.016 · DOI
  17. Heurich, 2004, Detecting and measuring individual trees with laser scanning in mixed mountain forest of central Europe using an algorithm developed for Swedish boreal forest conditions, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 36, 307
  18. Hill, 2009, Mapping the understorey of deciduous woodland from leaf-on and leaf-off airborne Lidar data: A case study in lowland Britain, ISPRS Journal of Photogrammetry and Remote Sensing, 64, 223, 10.1016/j.isprsjprs.2008.12.004 · DOI
  19. Holmgren, 2004, Indentifying species of individual trees using airborne laser scanning, Remote Sensing of Environment, 90, 415, 10.1016/S0034-4257(03)00140-8 · DOI
  20. Hopkinson, 2006, Towards a universal lidar canopy height indicator, Canadian Journal of Remote Sensing, 32, 139, 10.5589/m06-006 · DOI
  21. Hudak, 2006, Regression modeling and mapping of coniferous forest basal area and tree density from discrete-return lidar and multispectral satellite data, Canadian Journal of Remote Sensing, 32, 126, 10.5589/m06-007 · DOI
  22. Hudak, 2008, Nearest neighbor imputation modeling of species-level, plot-scale structural attributes from lidar data, Remote Sensing of Environment, 112, 2232, 10.1016/j.rse.2007.10.009 · DOI
  23. Hyyppä, 2001, A segmentation based method to retrieve stem volume estimates from 3-dimensional tree height models produced by laser scanner, IEEE Transactions on Geoscience and Remote Sensing, 39, 969, 10.1109/36.921414 · DOI
  24. Kaasalainen, 2009, Radiometric calibration of LiDAR intensity with commercially available reference targets, IEEE Transactions on Geoscience and Remote Sensing, 47, 588, 10.1109/TGRS.2008.2003351 · DOI
  25. Kass, 1995, Bayes factors, Journal of the American Statistical Association, 90, 773, 10.1080/01621459.1995.10476572 · DOI · Paper page
  26. Kerns, 2004, Evaluation and prediction of shrub cover in coastal Oregon forests (USA), Ecological Indicators, 4, 83, 10.1016/j.ecolind.2003.12.002 · DOI
  27. Kerr, 2003, From space to species: Ecological applications for remote sensing, Trends in Ecology and Evolution, 18, 299, 10.1016/S0169-5347(03)00071-5 · DOI
  28. Kieschnick, 2003, Regression analysis of variates observed on (0, 1): Percentages, proportions and fractions, Statistical Modelling, 3, 193, 10.1191/1471082X03st053oa · DOI
  29. Kim, 2009, Distinguishing between live and dead standing tree biomass on the North Rim of Grand Canyon National Park, USA using small-footprint lidar data, Remote Sensing of Environment, 113, 2499, 10.1016/j.rse.2009.07.010 · DOI
  30. Korhonen, 2007, Local models for forest canopy cover with beta regression, Silva Fennica, 41, 671, 10.14214/sf.275 · DOI
  31. Lefsky, 2002, Lidar remote sensing for forest ecosystem studies, BioScience, 52, 19, 10.1641/0006-3568(2002)052[0019:LRSFES]2.0.CO;2 · DOI
  32. Legare, 2002, Influence of forest composition on understory cover in boreal mixed-wood forests of Western Quebec, Silva Fennica, 36, 353, 10.14214/sf.567 · DOI
  33. Lim, 2003, Lidar remote sensing of biophysical properties of tolerant northern hardwood forests, Canadian Journal of Remote Sensing, 29, 648, 10.5589/m03-025 · DOI
  34. Maltamo, 2005, Identifying and quantifying structural characteristics of heterogeneous boreal forests using laser scanner data, Forest Ecology and Management, 216, 41, 10.1016/j.foreco.2005.05.034 · DOI
  35. Maltamo, 2006, Nonparametric estimation of stem volume using airborne laser scanning, airborne photography, and stand-register data, Canadian Journal of Forest Research, 36, 426, 10.1139/x05-246 · DOI
  36. Martinuzzi, 2009, Mapping snags and understory shrubs for a lidar-based assessment of wildlife habitat suitability, Remote Sensing of Environment, 113, 2522, 10.1016/j.rse.2009.07.002 · DOI
  37. McDermid, 2005, Remote sensing for large-area habitat mapping, Progress in Physical Geography, 29, 449, 10.1191/0309133305pp455ra · DOI
  38. Morsdorf, 2010, Discrimination of vegetation strata in a multi-layered Mediterranean forest ecosystem using height and intensity information derived from airborne laser scanning, Remote Sensing of Environment, 114, 1404, 10.1016/j.rse.2010.01.023 · DOI
  39. Mutlu, 2008, Mapping surface fuel models using lidar and multispectral data fusion for fire behavior, Remote Sensing of Environment, 112, 274, 10.1016/j.rse.2007.05.005 · DOI
  40. Næsset, 2002, Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data, Remote Sensing of Environment, 80, 88, 10.1016/S0034-4257(01)00290-5 · DOI
  41. Ospina, 2012, A general class of zero-or-one inflated beta regression models, Computational Statistics and Data Analysis, 56, 1609, 10.1016/j.csda.2011.10.005 · DOI
  42. Packalén, 2006, Predicting the plot volume by tree species using airborne laser scanning and airborne photographs, Forest Science, 52, 611, 10.1093/forestscience/52.6.611 · DOI
  43. Pesonen, 2008, Airborne laser scanning based prediction of coarse woody debris volumes in a conservation area, Forest Ecology and Management, 255, 3288, 10.1016/j.foreco.2008.02.017 · DOI
  44. Raferty, 1995, Bayesian model selection in social research, Sociological Methodology, 25, 111, 10.2307/271063 · DOI · Paper page
  45. Riaňo, 2003, Modeling airborne laser scanning data for the spatial generation of critical forest parameters in fire behavior modeling, Remote Sensing of Environment, 86, 177, 10.1016/S0034-4257(03)00098-1 · DOI
  46. Ritchie, 2007, Probability of wildfire-induced tree mortality in an interior pine forest: effects of thinning and prescribed fire, Forest Ecology and Management, 247, 200, 10.1016/j.foreco.2007.04.044 · DOI
  47. Russell, 2007, Habitat suitability models for cavity-nesting birds in a postfire landscape, The Journal of Wildlife Management, 71, 2600, 10.2193/2007-034 · DOI
  48. Schroeder, 2007, Patterns of forest regrowth following clearcutting in Western Oregon as determined from a Landsat time-series, Forest Ecology and Management, 243, 259, 10.1016/j.foreco.2007.03.019 · DOI
  49. Seielstad, 2003, Using airborne laser altimetry to determine fuel models for estimating fire behavior, Journal of Forestry, 101, 10, 10.1093/jof/101.4.10 · DOI
  50. Stage, 2007, Interactions of elevation, aspect, and slope in models of forest species composition and productivity, Forest Science, 53, 486, 10.1093/forestscience/53.4.486 · DOI
  51. Suchar, 2010, Understory cover and biomass indices predictions for forest ecosystems of the Northwestern United States, Ecological Indicators, 10, 602, 10.1016/j.ecolind.2009.10.004 · DOI
  52. Tremblay, 2001, Seasonal dynamics of understory vegetation in four eastern Canadian forest types, International Journal of Plant Sciences, 162, 271, 10.1086/319582 · DOI
  53. Venier, 2007, Boreal forest land birds in relation to forest composition, structure, and landscape: Implications for forest management, Canadian Journal of Forest Research, 37, 1214, 10.1139/X07-025 · DOI
  54. Vierling, 2008, Lidar: Shedding new light on habitat modeling, Frontiers in Ecology and the Environment, 6, 90, 10.1890/070001 · DOI · Paper page
  55. Vosselman, 2004, Recognizing structure in laser scanning point clouds, The Information of the Science, 46, 1
  56. Wagner, 2008, 3D vegetation mapping using small-footprint full-waveform airborne laser scanners, International Journal of Remote Sensing, 29, 1433, 10.1080/01431160701736398 · DOI
  57. Zimble, 2003, Characterizing vertical forest structure using small-footprint airborne LIDAR, Remote Sensing of Environment, 87, 171, 10.1016/S0034-4257(03)00139-1 · DOI

Cited by

Sorted by citations

Metadata

Authors
Wing, B. M., Ritchie, M. W., Boston, K., Cohen, W. B., Gitelman, A., & Olsen, M. J.
Venue
Remote Sensing of Environment
Publisher
Elsevier BV
Published
2012-09
Volume
124
Pages
730-741
ISSN
0034-4257
DOI
10.1016/j.rse.2012.06.024
Cited by
150
References
1
Language
en
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