Björn Nilsson

National Inventory of Landscapes in Sweden (NILS) - scope, design, and experiences from establishing a multiscale biodiversity monitoring system

The landscape-level and multiscale biodiversity monitoring program National Inventory of Landscapes in Sweden (NILS) was launched in 2003. NILS is conducted as a sample-based stratified inventory that acquires data across several spatial scales, which is accomplished by combining aerial photo interpretation with field inventory. A total of 631 sample units are distributed across the land base of Sweden, of which 20% are surveyed each year. By 2007 NILS completed the first 5-year inventory phase. As the reinventory in the second 5-year phase (2008–2012) proceeds, experiences and insights accumulate and reflections are made on the setup and accomplishment of the monitoring scheme. In this article, the emphasis is placed on background, scope, objectives, design, and experiences of the NILS program. The main objective to collect data for and perform analyses of natural landscape changes, degree of anthropogenic impact, prerequisites for natural biological diversity and ecological processes at landscape scale. Different environmental conditions that can have direct or indirect effects on biological diversity are monitored. The program provides data for national and international policy and offers an infrastructure for other monitoring program and research projects. NILS has attracted significant national and international interest during its relatively short time of existence; the number of stakeholders and cooperation partners steadily increases. This is constructive and strengthens the incentive for the multiscale monitoring approach.

Using Optical Satellite Data and Airborne Lidar Data for a Nationwide Sampling Survey

A workflow for combining airborne lidar, optical satellite data and National Forest Inventory (NFI) plots for cost efficient operational mapping of a nationwide sample of 5x 5 km squares in the National Inventory of Landscapes in Sweden (NILS) landscape inventory in Sweden is presented. Since the areas where both satellite data and lidar data have a common data quality are limited, and impose a constraint on the number of available NFI plots, it is not feasible to perform classifications in a single step. Instead a stratified approach where canopy cover and canopy height are first predicted from lidar data trained with NFI plots is proposed. From the lidar predictions a forest stratum is defined as grid cells with more than 3m mean tree height and more than 10% vertical canopy cover, the remaining grid cells are defined as open land. Both forest and open land are then classified into broad vegetation classes using optical satellite data. The classification of open land is trained with aerial photo interpretation and the classification of the forest stratum is trained with a new set of NFI plots. The result is a rational procedure for nationwide sample based vegetation characterization.