Mapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data

Abstract

Background The age of forest stands is critical information for forest management and conservation, for example for growth modelling, timing of management activities and harvesting, or decisions about protection areas. However, area-wide information about forest stand age often does not exist. In this study, we developed regression models for large-scale area-wide prediction of age in Norwegian forests. For model development we used more than 4800 plots of the Norwegian National Forest Inventory (NFI) distributed over Norway between latitudes 58° and 65° N in an 18.2 Mha study area. Predictor variables were based on airborne laser scanning (ALS), Sentinel-2, and existing public map data. We performed model validation on an independent data set consisting of 63 spruce stands with known age. Results The best modelling strategy was to fit independent linear regression models to each observed site index (SI) level and using a SI prediction map in the application of the models. The most important predictor variable was an upper percentile of the ALS heights, and root mean squared errors (RMSEs) ranged between 3 and 31 years (6% to 26%) for SI-specific models, and 21 years (25%) on average. Mean deviance (MD) ranged between − 1 and 3 years. The models improved with increasing SI and the RMSEs were largest for low SI stands older than 100 years. Using a mapped SI, which is required for practical applications, RMSE and MD on plot level ranged from 19 to 56 years (29% to 53%), and 5 to 37 years (5% to 31%), respectively. For the validation stands, the RMSE and MD were 12 (22%) and 2 years (3%), respectively. Conclusions Tree height estimated from airborne laser scanning and predicted site index were the most important variables in the models describing age. Overall, we obtained good results, especially for stands with high SI. The models could be considered for practical applications, although we see considerable potential for improvements if better SI maps were available.

The Norwegian Forest Resource Map SR16: Mapping Forest Attributes Using Heterogeneous Sets of Airborne Laser Scanning and National Forest Inventory Data

“…The combination of NFI and remotely sensed data is useful for mapping forest attributes and the improvement of estimates on various scales. It is currently analysed whether further variables, for example those related to stand age (Schumacher et al, 2020) and the protective functions of forests, can be included in the forest resource map SR16. Because of their fine spatial resolution and relatively high accuracy, forest resource maps offer new insights and decision support for example with respect to harvest monitoring (Breidenbach et al, 2021) and road network planning.…”

A century of national forest inventories – informing past, present and future decisions

“…However, this information is usually not available at fine resolution for large geographic scales. Two studies in this Collection describe the development of regression models for largearea mapping of forest age using a combination of NFI, ALS, and other data (Maltamo et al 2020;Schumacher et al 2020). Using Norwegian NFI data, Schumacher et al (2020) model stand age by exploiting tree height predicted from ALS, a site index prediction map, and Sentinel-2 data as predictor variables.…”

“…They highlight that the utility of age predictions varies according to applications. In contract to Schumacher et al (2020), Maltamo et al (2020) focus on managed forests younger than 100 years of age.…”

Southwestern ponderosa pine forest patterns following wildland fires managed for resource benefit differ from reference landscapes

“…While the Sentinel-2 imagery can fail to detect single trees and small tree groups due to the size of object of interest relative to the 10-m resolution of the imagery, the resolution is su cient to detect mature individual trees or tree groups of ponderosa pine due to their average size (e.g. we expect a mature ponderosa to have a radius in the range of 6 meters) and thus appropriate for examining forest structure over large landscapes (Wasserman et al 2019;Grabska et al 2020;Schumacher et al 2020). See Wasserman and others (2019) for a comparison of landscape metrics resulting from a gradient of data resolution.…”