Three dimensional arrangements of structural elements (Drăguţ et al., 2010; Walz et al., 2016) are crucial for understanding ecological processes and interactions in landscapes that have complex shapes (Scheuerell, 2004). Thus, indices quantifying the 3D configuration may be beneficial for landscape analysis, management and planning (Listopad et al., 2015).
Several approaches were already proposed for incorporating the third dimension into landscape analysis. First of all, the landscape metrics, suited to the 2D framework provided by the patch-corridor model, can be adapted to the three dimensional paradigm (Jenness, 2004). The vertical component calls for developing new measures, capturing features that are not visible when remaining in the planimetric context. Knowledge transfer from other research areas, such as surface metrology can be beneficial (Hoechstetter et al., 2008). Moreover, geomorphology provides a plethora of parameters (Mark, 1975) or elaborated concepts such as geometric signature (Pike, 1988). The lack of flatness is highlighted by measures such as curvatures (Evans, 1972), which are useful both for assessing terrain heterogeneity (Shary et al., 2002) or for detecting vegetation structures such as isolated trees (Stupariu, 2016). One could also explore whether techniques used in pattern recognition (Bribiesca, 2008) could be transferred for assessing characteristics of 3D landscapes.
There are some methodological issues that need to be taken into account when dealing with 3D metrics. The first one regards the representation of the terrain itself. There are two alternatives, each of them with advantages and inherent drawbacks. Thus, the regularly gridded elevation models are easy to handle and efficient algorithms can be easily developed (Pike et al., 2009). On the other hand, models based on triangular irregular networks can provide more realistic approximations of the true terrain (Stupariu et al., 2010) and create a framework that enables defining new metrics (Du Preez, 2015). Another issue of interest refers to the fact that terrain variability is actually a local property (Etzelmüller, 2000), while landscape indices need to be defined for larger units, such as patches. Various approaches are available and could be tested.
The aim of this flash presentation is to provide a schematic overview of approaches for computing 3D landscape metrics and to review some methodological challenges. Another goal is to present a software tool developed for assessing 3D measures of landscapes and to present some tests conducted for true terrain data.
Bribiesca, E. (2008). An easy measure of compactness for 2D and 3D shapes. Pattern Recognition, 41, 543-554.
Drăguţ, L., Walz, U., & Blaschke, T, (2010). The Third and Fourth Dimensions of Landscape: towards Conceptual Models of Topographically Complex Landscapes. Landscape Online, 22, 1-10.
Du Preez, C. (2015). A new arc-chord ratio (ACR) rugosity index for quantifying three-dimensional landscape structural complexity. Landscape Ecology, 30, 181-192.
Etzelmüller, B. (2000). On the Quantification of Surface Changes using Grid-based Digital Elevation Models (DEMs). Transactions in GIS, 4, 129-143.
Evans, I. (1972). General geomorphometry, derivatives of altitude, and descriptive statistics, in: Chorley, R. (Ed.), Spatial Analysis in Geomorphology. Methuen & Co., London, pp. 17-90.
Hoechstetter, S., Walz, U., Dang, L.H., & Thinh, N.X. (2008). Effects of topography and surface roughness in analyses of landscape structure – A proposal to modify the existing set of landscape metrics. Landscape Online, 3, 1-14.
Jenness, J. (2004). Calculating landscape surface area from digital elevation models. Wildlife Society Bulletin, 32, 829–839.
Listopad, C.M.C.S., Masters, R.E., Drake, J., Weishampel, J., & Branquinho, C. (2015). Structural diversity indices based on airborne LiDAR as ecological indicators for managing highly dynamic landscapes. Ecological Indicators, 57, 268-279.
Mark, D.M. (1975). Geomorphometric Parameters: A Review and Evaluation. Geografiska Annaler, 57A, 165-177.
Pike, R.J. (1988). The Geometric Signature: Quantifying Landslide-Terrain Types from Digital Elevation Models. Mathematical Geology, 20, 491-511.
Pike, R.J., Evans, I.S., & Hengl, T. (2009). Geomorphometry: A Brief Guide, in: Hengl, T. & Reuter, H. (Eds.), Geomorphometry. Concepts, software, applications, Elsevier, Amsterdam, pp. 3-30.
Scheuerell, M.D. (2004). Quantifying aggregation and association in three-dimensional landscapes. Ecology, 85, 2332-2340.
Shary, P., Sharaya, L., & Mitusov, A. (2002). Fundamental quantitative methods of land surface analysis. Geoderma, 107, 1-32.
Stupariu, M.S. (2016). An Application of Triangle Mesh Models in Detecting Patterns of Vegetation, in: Skala, V. (Ed.), Computer Science Research Notes 2603, Proceedings of WSCG 2016 - 24th Conference on Computer Graphics, Visualization and Computer Vision 2016, Poster Proceedings, pp. 87-90.
Stupariu, M.S., Pătru-Stupariu, I., & Cuculici, R. (2010). Geometric approaches in computing 3D landscape metrics. Landscape Online, 24, 1-12.
Walz, U., Hoechstetter, S., Drăguţ, L., & Blaschke, T. (2016). Integrating time and the third spatial dimension in landscape structure analysis, Landscape Research, 41, 279-293.
- Log in to post comments