Modelling vegetation understory cover using LiDAR metrics


Autoři: Lisa A. Venier aff001;  Tom Swystun aff001;  Marc J. Mazerolle aff002;  David P. Kreutzweiser aff001;  Kerrie L. Wainio-Keizer aff001;  Ken A. McIlwrick aff001;  Murray E. Woods aff003;  Xianli Wang aff001
Působiště autorů: Canadian Forest Service, Great Lakes Forestry Centre, Natural Resources Canada, Saul Ste Marie, ON, Canada aff001;  Department of Wood and Forest Sciences, Center for forest research, Université Laval, Quebec, QC, Canada aff002;  Ontario Ministry of Natural Resources and Forestry, North Bay, ON, Canada aff003
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0220096

Souhrn

Forest understory vegetation is an important characteristic of the forest. Predicting and mapping understory is a critical need for forest management and conservation planning, but it has proved difficult with available methods to date. LiDAR has the potential to generate remotely sensed forest understory structure data, but this potential has yet to be fully validated. Our objective was to examine the capacity of LiDAR point cloud data to predict forest understory cover. We modeled ground-based observations of understory structure in three vertical strata (0.5 m to < 1.5 m, 1.5 m to < 2.5 m, 2.5 m to < 3.5 m) as a function of a variety of LiDAR metrics using both mixed-effects and Random Forest models. We compared four understory LiDAR metrics designed to control for the spatial heterogeneity of sampling density. The four metrics were highly correlated and they all produced high values of variance explained in mixed-effects models. The top-ranked model used a voxel-based understory metric along with vertical stratum (Akaike weight = 1, explained variance = 87%, cross-validation error = 15.6%). We found evidence of occlusion of LiDAR pulses in the lowest stratum but no evidence that the occlusion influenced the predictability of understory structure. The Random Forest model results were consistent with those of the mixed-effects models, in that all four understory LiDAR metrics were identified as important, along with vertical stratum. The Random Forest model explained 74.4% of the variance, but had a lower cross-validation error of 12.9%. We conclude that the best approach to predict understory structure is using the mixed-effects model with the voxel-based understory LiDAR metric along with vertical stratum, because it yielded the highest explained variance with the fewest number of variables. However, results show that other understory LiDAR metrics (fractional cover, normalized cover and leaf area density) would still be effective in mixed-effects and Random Forest modelling approaches.

Klíčová slova:

Ecosystems – Forest ecology – Forests – Machine learning – Pines – Shrubs – Wildlife – Lidar


Zdroje

1. Yarie J. The role of understory vegetation in the nutrient cycle of forested ecosystems in Mountain Hemlock Biogeoclimatic Zone. Ecology. 1980;61:1498–514.

2. Nilsson M-C, D.A. W. Understory vegetation as a forest ecosystem driver: evidence from the Northern Swedish boreal forest. Front Ecol Environ. 2005;3:421–8.

3. MacArthur RH, MacArthur JW. On bird species diversity. Ecology. 1961;42:594–8.

4. Venier LA, Pearce JL. Boreal forest landbirds in relation to forest composition, structure, and landscape: Implications for forest management. Can J For Res. 2007;37(7):1214–26.

5. Lesak AA, Radeloff VC, Hawbaker TJ, Pidgeon AM, Gobakken T, Contrucci K. Modeling forest songbird species richness using LiDAR-derived measures of forest structure. Remote Sens Environ. 2011;115(11):2823–35.

6. Bessie WC, Johnson EA. The relative importance of fuels and weather on fire behavior in subalpine forests. Ecology. 1995;76:747–62.

7. Call PT, Albini FA. Aerial and surface fuel consumption in crown fires. Int J Wildland Fire. 1997;7:259–64.

8. Hély C, Bergeron Y, Flannigan MD. Effects of stand composition on fire hazard in mixed-wood Canadian boreal forest. J Veg Sci. 2000;11:813–24.

9. Roxburgh SH, Karunaratne SB, Paul KI, Lucas RM, Armston D, Sun J. A revised above-ground maximum biomass layer for the Australian continent. For Ecol Manage. 2019;432:264–75.

10. Kerns BK, Ohmann JL. Evaluation and predicition of shrub cover in coastal Oregon forests (USA). Ecol Indic. 2004;4:83–98.

11. Suchar VA, Crookston NL. Understory cover and biomass indices predictions for forest ecosystems of Northwestern United States. Ecol Indic. 2010;10:602–9.

12. Eskelson BNI, Madsen L, Hagar JC, Temesgen H. Estimating riparian understory vegetation cover with beta regression and copula models. Forest Sci. 2011;57:212–21.

13. Lim K, Treitz P, Baldwin K, Morrison I, Green J. LiDAR remote sensing of biophysical properties of tolerant northern hardwood forests. Can J Remote Sens. 2003;29:658–78.

14. Naesset E. Practical large-scale forest stand inventory using a small-footprint airborne scanning laser. Scand J Forest Res. 2004;19:164–79.

15. Thomas V, Treitz P, McCaughey JH, Morrison I. Mapping stand-level forest biophysical variables for a mixedwood boreal forest using lidar: an examination of scanning density. Can J For Res. 2006;36:34–47.

16. Woods M, Lim K, Treitz P. Predicting forest stand variables from LiDAR data in the Great Lakes-St. Lawrence Forest of Ontario. Forest Chron. 2008;84:827–39.

17. Bergen KM, Goetz SJ, Dubayah RO, Henebry GM, Hunsaker CT, Imhoff ML, et al. Remote sensing of vegetation 3-D structure for biodiversity and habitat: Review and implications for lidar and radar spaceborne missions. Journal of Geophysical Research: Biogeosciences. 2009;114(4).

18. Goodwin NR, Coops NC, Culvenor DS. Assessment of forest structure with airborne LiDAR and the effects of platform altitude. Remote Sens Environ. 2006;103:140–52.

19. Ruiz LA, Hermosilla T, Mauro F, Godino M. Analysis of the influence of plot size and LiDAR density on forest structure attribute estimates. Forests 2014;5:936–51.

20. Jakubowski MK, Guo Q, Kelly M. Tradeoffs between lidar pulse density and forest measurement accuracy. Remote Sens Environ. 2013;130:245–53.

21. Campbell MJ, Dennison PEH, A., Parham LM, Butler BW. Quantifying understory vegetation density using small-footprint airborne lidar. Remote Sens Environ. 2018;215:330–42.

22. Cutler DR, Edwards TC, Beard KH, Cutler A, Hess KT, Gibson J, et al. Random Forests for classification in ecology. Ecology. 2007;88(11):2783–92. doi: 10.1890/07-0539.1 18051647

23. Latifi H, Hill S, Schumann B, Heurich M, Dech S. Multi-model estimation of understory shrub, herb and moss cover in temperate forest stands by laser scanner data. Forestry. 2017;90:496–514.

24. Penner M, Pitt DG, Woods ME. Parametric vs. nonparametric LiDAR models for operational forest inventory in boreal Ontario. Can J Remote Sens. 2013;39(5):426–43.

25. De'ath G, Fabricius KE. Classification and Regression Trees: A Powerful Yet Simple Technique for Ecological Data Analysis. Ecology. 2000;81(11):3178–92.

26. Hill RA, Broughton RK. Mapping the understory of deciduous woodland from leaf-on and leaf-off airborne LiDAR data: a case study in lowland Britain. ISPRS J Photogramm. 2009;64:223–33.

27. Morsdorf F, Marell A, Koetz B, Cassagne N, Pimont F, Rigolot E, et al. Discrimination of vegetation strata in a multi-layered Mediterranean forest ecosystem using height and intensity information derived from airborne laser scanning. Remote Sens Environ. 2010;114:1404–15.

28. Wing BM, Ritchie MW, Boston K, Cohen WB, Gitelman A, Olsen MJ. Prediction of understory vegetation cover with airborne LiDAR in an interior ponderosa pine forest. Remote Sens Environ. 2012;124:730–41.

29. Latifi H, Heurich M, Hartig F, Müller J, Krzystek P, Jehl H, et al. Estimating over- and understorey canopy density of temperate mixed stands by airborne LiDAR data. Forestry. 2016;89:69–81.

30. Bouvier M, Durrieu S, Rounier RA. Generalizing predictive models of forest inventory attributes usin an area-based approach with airborne LiDAR data. Remote Sens Environ. 2015;156:322–34.

31. Kim E, Woo-Kyun L, Yoon M, Lee J-Y, Son Y, Salim KA. Estimation of voxel-based above-ground biomass using airborne LiDAR Data in an intact tropical rain forest, Brunei. Forests. 2016;7:259.

32. Pinheiro J, Bates D, DebRoy S, Sarkar D, RCoreTeam. nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1–137 ed2018.

33. Burnham KP, Anderson DR. Model Selection and Multimodel Inference: a practical information-theoretic approach. 2 ed. New York: Springer-Verlag; 2002.

34. Nakagawa S, Schielzeth H. A general and simple method for obtaining R2 from generalized linear mixed effects models. Methods Ecol Evol. 2013;4:133–42.

35. Gneiting T. Making and evaluating point forecasts. Journal of American Statistical Association. 2011;106:746–62.

36. RCoreTeam. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria2018.

37. Liaw A, Wiener M. Classification and regression by randomForest. R News. 2002;2:18–22.

38. Gonzalez-Ferreiro E, Dieguez-Aranda, Miranda D. Estimation of stand variables in Pinus radiata D. Don plantations using different LiDAR pulse densities. Forestry. 2012;85:281–92.

39. Hyde P, Dubayah R, Walker W, Blair JB, Hofton M, Hunsaker C. Mapping forest structure for wildlife habitat analysis using multi-sensor (LiDAR, SAR/InSAR, ETM+, Quickbird) synergy. Remote Sens Environ. 2006;102:63–73.

40. Asner GP, Hughes RF, Vitousek PM, Knapp DE, Kennedy-Bowdoinm T, Boardmann J, et al. Invasive plants transform the three-dimensional structure of rain forests. PNAS. 2008;105:4519–23. doi: 10.1073/pnas.0710811105 18316720

41. Martinuzzi S, Vierling LA, Gould WA, Falkowski MJ, Evans JS, Hudak AT, et al. Prediction of understory vegetation cover with airborne LiDAR in an interior ponderosa pine forest. Remote Sens Environ. 2009;124:730–41.

42. Lefsky MA, Cohen WB, Parker GG, Harding DJ. LiDAR remote sensing for ecosystem studies. BioScience. 2002;52:19–30.

43. Bradbury RB, Hill RA, Mason DC, Hinsley SA, Wilson JD, Balzter H, et al. Modelling relationships between birds and vegetation structure using airborne LiDAR data: A review with case studies from agricultural and woodland environments. Ibis. 2005;147(3):443–52.

44. Vierling KT, Vierling LA, Gould WA, Martinuzzi S, Clawges RM. Lidar: Shedding new light on habitat characterization and modeling. Front Ecol Environ. 2008;6(2):90–8.

45. Davies AB, Asner GP. Advances in animal ecology from 3D-LiDAR ecosystem mapping. Trends in Ecology and Evolution 2014;29:681–91. doi: 10.1016/j.tree.2014.10.005 25457158

46. Rechsteiner C, Zellweger F, Gerber A, Breiner FT, Bollmann K. Remotely sensed forest habitat structures improve regional species conservation. Remote Sens Ecol Conserv. 2017;3:247–58.

47. Vogeler JC, Hudak AT, Vierling LA, Evans J, Green P, Vierling KT. Terrain and vegetation structural influences on local avian species richness in two mixed-conifer forests. Remote Sens Environ. 2014;147:13–22.

48. Coops NC, Tompaski P, Nijland W, Rickbeil GJM, Nielsen SE, Bater CW, et al. A forest structure habitat index based on airborne laser scanning. Ecol Indic. 2016;67:346–57.

49. Clawges R, Vierling K, Vierling L, Rowell E. The use of airborne lidar to assess avian species diversity, density, and occurrence in a pine/aspen forest. Remote Sens Environ. 2008;112(5):2064–73.

50. Müller J, Stadler J, Brandl R. Composition versus physiognomy of vegetation as predictors of bird assemblages: the role of LiDAR. Remote Sens Environ. 2010;114:490–5.

51. Vierling LA, Vierling KT, Adam P, Hudak AT. Using satellite and airborne LiDAR to model woodpecker habitat occupancy at the landscape scale. PloS ONE 2013;8:e80988. doi: 10.1371/journal.pone.0080988 24324655

52. Melin M, Mehtätalo L, Miettinen J, Tossavainen S, Packalen P. Forest structure as a determinant of grouse brood occurrence: an analysis linking LiDAR data with presence/absence field data. For Ecol Manage. 2016;380:202–11.


Článek vyšel v časopise

PLOS One


2019 Číslo 11