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

Abstract

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 5 × 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 3 m 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.

Remote Sensing and GIS for Habitat Quality Monitoring: New Approaches and Future Research

“…In our Special Issue, Lindgren et al [20] provide an operational example of data integration for systematic landscape inventorying on a national scale.…”

The Role of Improved Ground Positioning and Forest Structural Complexity When Performing Forest Inventory Using Airborne Laser Scanning

“…In the context of large-scale NFI inventories, the usability of the data can be boosted not only by reducing the co-registration error during ground operations but also by meeting the schedule of nationwide surveys using ALS [30]. A disadvantage of NFI sampling is that the periodicity of the data collection can range from a few years to more than a decade, considering the growth conditions and operational delay in the last ground campaign.…”

Estimating vertical canopy cover using dense image-based point cloud data in four vegetation types in southern Sweden

“…Accurate estimates of VCC would be useful both for separation of open area from forest, i.e. at 10% VCC (FAO 2010;Lindgren et al 2015), but also for use in studying habitat and biodiversity (Bergen et al 2009), which is linked to canopy cover (Müller and Vierling 2014). The raster-based method used by Waser et al (2015) in the production of a forest map resulted in overall accuracy of 97%, with lower accuracies along forest borders and at altitudes above 1400 m above sea level.…”

“…Such data would for instance be beneficial for the National Inventory of Landscapes in Sweden (NILS) programme (Ståhl et al 2011), which aims to monitor the condition and changes in the Swedish landscape. NILS would benefit from affordable metrics for estimation of VCC to separate open areas from forested areas, based on a threshold of 10% VCC and vegetation height ≥3 m (Lindgren et al 2015). Currently, such measurements are done manually by aerial photo interpretation (Allard et al 2003).…”