Mapping temperate old-growth forests in Central Europe using ALS and Sentinel-2A multispectral data

Abstract

Old-growth forests are essential to preserve biodiversity and play an important role in sequestering carbon and mitigating climate change. However, their existence across Europe is vulnerable due to the scarcity of their distribution, logging, and environmental threats. Therefore, providing the current status of old-growth forests across Europe is essential to aiding informed conservation efforts and sustainable forest management. Remote sensing techniques have proven effective for mapping and monitoring forests over large areas. However, relying solely on remote sensing spectral or structural information cannot capture comprehensive horizontal and vertical structure complexity profiles associated with old-growth forest characteristics. To overcome this issue, we combined spectral information from Sentinel-2A multispectral imagery with 3D structural information from high-density point clouds of airborne laser scanning (ALS) imagery to map old-growth forests over an extended area. Four features from the ALS data and fifteen from Sentinel-2A comprising raw band (spectral reflectance), vegetation indices (VIs), and texture were selected to create three datasets used in the classification process using the random forest algorithm. The results demonstrated that combining ALS and Sentinel-2A features improved the classification performance and yielded the highest accuracy for old-growth class, with an F1-score of 92% and producer's and user's accuracies of 93% and 90%, respectively. The findings suggest that features from ALS and Sentinel-2A data sensitive to forest structure are essential for identifying old-growth forests. Integrating open-access satellite imageries, such as Sentinel-2A and ALS data, can benefit forest managers, stakeholders, and conservationists in monitoring old-growth forest preservation across a broader spatial extent.

Keywords: Airborne LiDAR; Data fusion; Forest structure; Multispectral; Stand age; Structural complexity.

Fig. 1

Map of Bavarian Forest National Park (BFNP) with the distribution of sample plots, which were randomly generated from the reference data of this study, i.e., BFNP stand age map. The random selection of plots was based on an old-growth stand age threshold of more than 150 years. Plots below 150 years were assigned as second-growth

Fig. 2

The frequency of the stand age of the old-growth and second-growth plots of this study

Fig. 3

Workflow process used for this study. The workflow includes preprocessing ALS and Sentinel-2A data, generating sample plots from reference datasets, classifying datasets, and evaluating accuracy assessment and important features

Fig. 4

A comparison of total old-growth forest area per dataset resulted from the random forest classification. The improvement is shown when ALS was added to the classification, as demonstrated by the total area close to the red line (total area of old-growth forest in reference data (3510 ha))

Fig. 5

The results of the old-growth forests classification maps of each dataset. The inset of each map shows the improvements of overestimated old-growth forest polygons within an example area. It can be seen that from the standalone Sentinel-2A to the combination of ALS + Sentinel-2A, the grainy polygons are decreased and become more compact and clustered within the reference old-growth area. The grainy polygon decrease was also followed by increased F1-score of the old-growth forest class accuracy in each dataset classification. The combination of ALS and Sentinel-2A obtained the highest F1-score

Fig. 6

The variable importance of all ALS and Sentinel-2A features generated from mean decrease accuracy (MDA) analysis. These features were used in the classification using an ALS + Sentinel-2A combination dataset