Environ Monit Assess (2010) 164:403–421 DOI 10.1007/s10661-009-0902-0 Monitoring landscape metrics by point sampling: accuracy in estimating Shannon’s diversity and edge density Habib Ramezani · Sören Holm · Anna Allard · Göran Ståhl Received: 22 January 2009 / Accepted: 6 April 2009 / Published online: 5 May 2009 © Springer Science + Business Media B.V. 2009 Abstract Environmental monitoring of lands- capes is of increasing interest. To quantify land- scape patterns, a number of metrics are used, of which Shannon’s diversity, edge length, and density are studied here. As an alternative to complete mapping, point sampling was applied to estimate the metrics for already mapped landscapes selected from the National Inventory of Landscapes in Sweden (NILS). Monte-Carlo simulation was applied to study the performance of different designs. Random and systematic sam- plings were applied for four sample sizes and five buffer widths. The latter feature was relevant for edge length, since length was estimated through the number of points falling in buffer areas around edges. In addition, two landscape complexities were tested by applying two classification schemes with seven or 20 land cover classes to the NILS data. As expected, the root mean square error (RMSE) of the estimators decreased with increas- ing sample size. The estimators of both metrics were slightly biased, but the bias of Shannon’s diversity estimator was shown to decrease when H. Ramezani (B ) · S. Holm · A. Allard · G. Ståhl Department of Forest Resource Management, Swedish University of Agriculture Science, SLU, 901 83 Umeå, Sweden e-mail: Habib.Ramezani@srh.slu.se, Ramezani.habib@gmail.com sample size increased. In the edge length case, an increasing buffer width resulted in larger bias due to the increased impact of boundary conditions; this effect was shown to be independent of sample size. However, we also developed adjusted esti- mators that eliminate the bias of the edge length estimator. The rates of decrease of RMSE with in- creasing sample size and buffer width were quan- tified by a regression model. Finally, indicative cost–accuracy relationships were derived showing that point sampling could be a competitive alter- native to complete wall-to-wall mapping. Keywords Monitoring landscapes · Landscape pattern metrics · Root mean square error · Monte-Carlo simulation · Bias · Cost efficiency · Wall-to-wall · Buffer area Introduction Due to rapid and global changes in our environ- ment, there is a need for environmental monitor- ing and assessment at broad scales such as the landscape level (Turner et al. 2001; Ricotta et al. 2003). Large-scale monitoring programs have in- creasingly been established over the past decades; for instance, the Environmental Monitoring and Assessment Program of the US Environment Protection Agency (1994), the Norwegian 3Q