Abstract

A recent trend is to estimate landscape metrics using sample data and cost-efficiency is one important reason for this development. In this study, line intersect sampling (LIS) was used as an alternative to wall-to-wall mapping for estimating Shannon's diversity index and edge length and density. Monte Carlo simulation was applied to study the statistical performance of the estimators. All combinations of two sampling designs (random and systematic distribution of transects), four sample sizes, five transect configurations (straight line, L, Y, triangle, and quadrat), two transect orientations (fixed and random), and three configuration lengths were tested, each with a large number of simulations. Reference was 50 photos of size 1 km(2), already manually delineated in vector format by photo interpreters using GIS environment. The performance was compared by root mean square error (RMSE) and bias. The best combination for all three metrics was found to be the systematic design and as response design the straight line configuration with random orientation of transects, with little difference between the fixed and random orientation of transects. The rate of decrease of RMSE for increasing sample size and line length was studied with a mixed linear model. It was found that the RMSE decreased to a larger degree with the systematic design than the random one, especially with increasing sample size. Due to the nonlinearity in the definition of Shannon diversity estimator its estimator has a small and negative bias, decreasing with sample size and line length. Finally, a time study was conducted, measuring the time for registration of line intersections and their lengths on non-delineated aerial photos. The time study showed that long sampling lines were more cost-efficient than short ones for photo-interpretation.

Keywords

Landscape pattern analysis; Metric; Monte Carlo simulation; Bias; Root mean square error; Wall-to-wall mapping; Cost-efficiency

Published in

Environmental and Ecological Statistics
2011, Volume: 18, number: 1, pages: 109-130
Publisher: SPRINGER

SLU Authors

• Ramezani, Habib

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

• Holm, Sören

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Probability Theory and Statistics
Forest Science
Other Earth and Related Environmental Sciences


Publication identifier

DOI: https://doi.org/10.1007/s10651-009-0123-2

Permanent link to this page (URI)

https://res.slu.se/id/publ/47181