Increased understanding of structural complexity in nature: relationship between shrub height and changes in spatial patterns

Volume 7, Issue 3, June 2023     |     PP. 78-95      |     PDF (4554 K)    |     Pub. Date: December 26, 2023
DOI: 10.54647/geosciences170298    39 Downloads     95899 Views  

Author(s)

Khodabakhsh Zabihi, Department of Ecosystem Science and Management, University of Wyoming, 1000 E University Ave, Laramie, WY 82071, USA; Faculty of Forestry and Wood Sciences, Czech University of Life Sciences (CULS), Kamýcká 129, 165 00 Praha 6-Suchdol, Czech Republic
Ginger B. Paige, Department of Ecosystem Science and Management, University of Wyoming, 1000 E University Ave, Laramie, WY 82071, USA
Amarina Wuenschel, U.S. Forest Service, Pacific Southwest Regional Ecology Program. Southern Sierra Province. North Fork, CA 93643, USA
Azadeh Abdollahnejad, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences (CULS), Kamýcká 129, 165 00 Praha 6-Suchdol, Czech Republic
Dimitrios Panagiotidis, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences (CULS), Kamýcká 129, 165 00 Praha 6-Suchdol, Czech Republic

Abstract
Characterizing and visualizing the vertical trends of three-dimensional (3D) structures help the science community and the public better conceptualize and perceive the structural complexity embedded in nature. We used terrestrial laser scanning (TLS) coupled with transect vegetation surveys to characterize vegetative structural complexity at vertical profiles in the shrubland ecosystems of western Wyoming, USA. We developed a homogeneity index for canopy cover spatial distributions using a reverse measure of lacunarity computed on 3D laser returns from the canopy covers. Height-dependent spatial homogeneity functions were defined by plotting the homogeneity index calculated at every 5 cm height of vegetation. We observed two distinct spatial homogeneity functions, indicating the structural diversity of shrubland vegetation. We also found a transitional zone of changes in the spatial patterns from being more homogenous to being more heterogeneous within a range between average shrub height (µ) and one standard deviation (σ) from the average [µ, (µ + σ)]. The revealed significant changes in the spatial patterns of vegetation structures in shrublands ([µ, (µ + σ)]) are likely to repeat within other structural features on earth if the heights of target structures follow normal distributions. The introduced approach to reveal significant changes in the lacunarity (or reversely homogeneity) measures along the height can be improved and programmed as a GIS spatial-analysis toolbox to compute the average height of target structures from the 3D lidar point clouds.

Keywords
Spatial patterns recognition; 3D structures; Vertical trends; Spatial homogeneity and heterogeneity; Terrestrial Laser Scanning (TLS); GIS

Cite this paper
Khodabakhsh Zabihi, Ginger B. Paige, Amarina Wuenschel, Azadeh Abdollahnejad, Dimitrios Panagiotidis, Increased understanding of structural complexity in nature: relationship between shrub height and changes in spatial patterns , SCIREA Journal of Geosciences. Volume 7, Issue 3, June 2023 | PP. 78-95. 10.54647/geosciences170298

References

[ 1 ] Allain, C., Cloitre, M., 1991. Characterizing the lacunarity of random and deterministic fractal sets. Physics Review A 44, 3552-3558.
[ 2 ] Bestelmeyer, B. T., Brown, J. R., Havstad, K. M., Alexander, R., Chavez, G., Herrick, J. E., 2003. Development and use of state-and-transition models for rangelands. J. Range Manage. 56 (2), 114-126.
[ 3 ] Canty, A., Ripley, B., 2017. Package “boot”. Comprehensive R Archive Network. https://cran.r-project.org/web/packages/boot/boot.pdf (Accessed 12 January 2019).
[ 4 ] Dale, M. R. T., 2000. Lacunarity of spatial pattern: A comparison. Landsc. Ecol. 15, 467-478.
[ 5 ] Dong, P., 2009. Lacunarity analysis of raster datasets and 1D, 2D, and 3D point patterns. Comput. Geosci. 35, 2100-2110.
[ 6 ] Environmental Systems Resource Institute (ESRI). 2011. ArcGIS Desktop 10: Release 10. Redlands, California, USA.
[ 7 ] García-Feced, C., Tempel, D. J., Kelly, M., 2011. LiDAR as a Tool to Characterize Wildlife Habitat: California Spotted Owl Nesting Habitat as an Example. J. For. 436-443.
[ 8 ] Herrick, J. E., Van Zee, J. W., Havstad, K. M., Burkett, L. M., Whitford, W. G., 2009. Monitoring manual for grassland, shrubland and savannah ecosystems. U.S. Department of Agriculture, Agricultural Research Service, Jornada Experimental Range, Las Cruces, NM. USA.
[ 9 ] InnovMetric. Polyworks Software version 10. 2007. http://www.facility.unavco.org/project_support/polar/support/TLS/PolyWorksBeginnersGuide.pdf (Accessed 5 December 2011).
[ 10 ] James, F. C., 1971. Ordinations of habitat relationships among breeding birds. Wilson Bulletin 83, 215-236.
[ 11 ] Lennon, J. J., 2000. Red-shifts and red herrings in geographical ecology. Ecology 23, 101-113.
[ 12 ] Leu, M., Hanser, S. E., 2011. Influence of the human footprint 528 on sagebrush landscape patterns: implications for Sage-Grouse conservation. in: Knick, S. T., Connelly, J. W., (Eds.), Greater sage-grouse: ecology and conservation of a landscape species and its habitats. Studies in Avian Biology (vol. 38). University of California Press, Berkeley, CA, pp. 253-271.
[ 13 ] MacArthur, R. H., MacArthur, J. W., 1961. On bird species diversity. Ecology 42, 594-598.
[ 14 ] Mandelbrot, B. B., 1983. The Fractal Geometry of Nature. Freeman W. H, San Francisco, CA.
[ 15 ] Minitab 16 Statistical Software (2010). State College, PA: Minitab, Inc.
[ 16 ] Olsoy, P. J., Forbey, J. S., Rachlow, J. L., Nobler, J. D., Glenn, N. F., Shipley, L. A., 2015. Fearscapes: mapping functional properties of cover for prey with terrestrial lidar. BioScience 65, 74-80.
[ 17 ] Plotnick, R. E., Gardner, R. H., Hargrove, W. W., Pretegaard, K., Perlmutter, M., 1996. Lacunarity analysis: A general technique for the analysis of spatial patterns. Phys. Rev. E53, 5461-5468.
[ 18 ] Plotnick, R. E., Gardner, R. H., O’Neil, R. V., 1993. Lacunarity indices as measures of landscape patterns. Landsc. Ecol. 8, 201-211.
[ 19 ] R Core Team, 2018. R: A language and environment for statistical computing. Vienna, Austria. R Foundation for Statistical Computing.
[ 20 ] Society for Range Management, Task Group on Unity in Concepts and Terminology. 1995. New concepts for assessment of rangeland condition. J. Range Manage. 48, 271-282.
[ 21 ] Western Regional Climate Center. http://www.wrcc.dri.edu/cgi-bin/cliMAIN.pl?wypine (Accessed 10 March 2013).
[ 22 ] William, M. I., Paige, G. B., Throw, T. L., Hild, A. L., Gerow, K. G., 2011. Songbird Relationship to Shrub-Steppe Ecological Site Characteristics. Rangel. Ecol. Manage. 64 (2), 109-118.
[ 23 ] Wu, X. B., Sui, D. Z., 2001. An initial exploration of a lacunarity-based segregation measure. Environment and planning B: Planing and Design 28, 433-446.
[ 24 ] Zabihi, K., Driese, K. L., Paige, G. B., Hild, A. K., 2019. Application of Ground-Based Lidar and Gap Intercept Measurements to Quantify a Shrub Configuration Metric within Greater Sage-Grouse Nesting Habitat. West. N. Am. Nat. 79 (4), 500-514.