Estimating landscape pattern metrics from a sample of land cover

Abstract

Although landscape pattern metrics can be computed directly from wall-to-wall land-cover maps, statistical sampling offers a practical alternative when complete coverage land-cover information is unavailable. Partitioning a region into spatial units and then selecting a subset (sample) of these units introduces artificial patch edge and patch truncation effects that may lead to biased sample-based estimators of landscape pattern metrics. The bias and variance of sample-based estimators of status and change in landscape pattern metrics were evaluated for four 120-km × 120-km test regions with land cover provided by the 1992 and 2001 National Land-Cover Data of the United States. Bias was generally small for both the estimators of status and estimators of change in landscape pattern, but exceptions to this favorable result exist and it is advisable to assess bias for the specific metrics and region of interest in any given application. A 10-km × 10-km sample block generally yielded larger biases but smaller variances for the estimators relative to a 20-km × 20-km sample block. Stratified random sampling improved precision of the estimators relative to simple random sampling. The methodology developed to determine properties of sample-based estimators can be readily extended to evaluate other landscape pattern metrics, regions, and sample block sizes.