Potential for the wider application of national forest inventories to estimate the contagion metric for landscapes.

National forest inventories (NFIs) have traditionally been designed to assess the production value of forests as well as forest biodiversity. However, in this study, the aim is to show a new application of NFIs, namely the estimation of the landscape metric contagion. This metric is commonly calculated on raster-based land cover/use maps. In this study, a sample-based dataset from the Swedish NFI was used. The estimated contagion metric is based on a distance-dependent function so that the value of the metric is small for longer distances, whereas the corresponding estimated variance is large for longer distances. With this procedure, comparisons can be made for different landscapes at a given time and or to compare any given landscape over time. The main advantages are that the approach can be applied where raster-based land cover/use maps of the landscape are not available and that the data obtained from NFIs (e.g., land cover type) typically are of high quality in comparison with remotely sensed data due to being based on direct observation in the field survey. The procedure applied here accommodates both the patch-mosaic and the gradient-based model approach to landscape structure.

A comparison of two procedures to estimate three basic monitoring landscape metrics for monitoring.

An interesting alternative to wall-to-wall mapping approaches for the estimation of landscape metrics is to use sampling. Sample-based approaches are cost-efficient, and measurement errors can be reduced considerably. The previous efforts of sample-based estimation of landscape metrics have mainly been focused on data collection methods, but in this study, we consider two estimation procedures. First, landscape metrics of interest are calculated separately for each sampled image and then the image values are averaged to obtain an estimate of the entire landscape (separated procedure, SP). Second, metric components are calculated in all sampled images and then the aggregated values are inserted into the landscape metric formulas (aggregated procedure, AP). The national land cover map (NLCM) of Sweden, reflecting the status of land cover in the year 2000, was used to provide population information to investigate the statistical performance of the estimation procedures. For this purpose, sampling simulation with a large number of replications was used. For all three landscape metrics, the second procedure (AP) produced a lower relative RMSE and bias than the first one (SP). A smaller sample unit size (50 ha) produced larger bias than a larger one (100 ha), whereas a smaller sample unit size produced a lower variance than a larger sample unit. The efficiency of a metric estimator is highly related to the degree of landscape fragmentation and the selected procedure. Incorporating information from all of the sampled images into a single one (aggregated procedure, AP) is one way to improve the statistical performance of estimators.

Monitoring landscape metrics by point sampling: accuracy in estimating Shannon's diversity and edge density.

Environmental monitoring of landscapes is of increasing interest. To quantify landscape 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 samplings 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 increasing sample size. The estimators of both metrics were slightly biased, but the bias of Shannon's diversity estimator was shown to decrease when 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 estimators that eliminate the bias of the edge length estimator. The rates of decrease of RMSE with increasing sample size and buffer width were quantified by a regression model. Finally, indicative cost-accuracy relationships were derived showing that point sampling could be a competitive alternative to complete wall-to-wall mapping.