Remote sensing and forest inventories in Nordic countries – roadmap for the future

Abstract

The Nordic countries have long traditions in forest inventory and remote sensing (RS). In sample-based national forest inventories (NFIs), utilization of aerial photographs started during the 1960s, satellite images during the 1980s, laser scanning during the 2000s, and photogrammetric point clouds during the 2010s. In forest management inventories (FMI), utilization of aerial photos started during the 1940s and laser scanning during the 2000s. However, so far, RS has mostly been used for map production and research rather than for estimation of regional parameters or inference on their accuracy. In recent years, the RS technology has been developing very fast. At the same time, the needs for information are constantly increasing. New technologies have created possibilities for cost-efficient production of accurate, large area forest data sets, which also will change the way forest inventories are done in the future. In this study, we analyse the state-of-the-art both in the NFIs and FMIs in the Nordic countries. We identify the benefits and drawbacks of different RS materials and data acquisition approaches with different user perspectives. Based on the analysis, we identify the needs for further development and emerging research questions. We also discuss alternatives for ownership of the data and cost-sharing between different actors in the field. ARTICLE HISTORY

Predicting Forest Inventory Attributes Using Airborne Laser Scanning, Aerial Imagery, and Harvester Data

“…The amount and quality of the field plots is essential for ABA [10][11][12][13]. In Finland, the typical inventory area varies from 100,000 to 200,000 ha, and approximately 700 field sample plots are measured from the inventory area for enabling the development of the prediction models for forest inventory attributes (e.g., References [13][14][15]). In addition to the field-measured sample plots, required modeling data could be supplemented with data measured by sensors already integrated into the harvesters and recorded by the harvester's information system [16][17][18].…”

Gaussian Process Regression for Forest Attribute Estimation From Airborne Laser Scanning Data

“…Typically, sample mean and sample variance are estimated to the area of interest (e.g. 1 1000000 ha) using a certain number (e.g. 500) of sample plots and auxiliary variables covering all population elements.…”

Improving capacity for large-area monitoring of forest disturbance and recovery

“…Finland is also a signatory to many of the same international treaties and conventions as Canada, and similar to Canada, Finland generates a national annual summary of forest statistics (Natural Resources Institute Finland 2017). The Finnish NFI was established in the 1920s (Tomppo et al 2010;Kangas et al 2018) and is based on the statistical sampling of field measurements of standard forest inventory attributes, as well as indicators of forest health and biodiversity. National and regional-level strategic forest-planning scenarios for timber procurement, harvests, and silvicultural treatments utilize information from the NFI (e.g.…”