Large-Scale Mapping of Tree Species and Dead Trees in Šumava National Park and Bavarian Forest National Park Using Lidar and Multispectral Imagery

Abstract

Knowledge of forest structures—and of dead wood in particular—is fundamental to understanding, managing, and preserving the biodiversity of our forests. Lidar is a valuable technology for the area-wide mapping of trees in 3D because of its capability to penetrate vegetation. In essence, this technique enables the detection of single trees and their properties in all forest layers. This paper highlights a successful mapping of tree species—subdivided into conifers and broadleaf trees—and standing dead wood in a large forest 924 km2 in size. As a novelty, we calibrate the critical stopping criterion of the tree segmentation based on a normalized cut with regard to coniferous and broadleaf trees. The experiments were conducted in Šumava National Park and Bavarian Forest National Park. For both parks, lidar data were acquired at a point density of 55 points/m2. Aerial multispectral imagery was captured for Šumava National Park at a ground sample distance (GSD) of 17 cm and for Bavarian Forest National Park at 9.5 cm GSD. Classification of the two tree groups and standing dead wood—located in areas of pest infestation—is based on a diverse set of features (geometric, intensity-based, 3D shape contexts, multispectral-based) and well-known classifiers (Random forest and logistic regression). We show that the effect of under- and oversegmentation can be reduced by the modified normalized cut segmentation, thereby improving the precision by 13%. Conifers, broadleaf trees, and standing dead trees are classified with overall accuracies better than 90%. All in all, this experiment demonstrates the feasibility of large-scale and high-accuracy mapping of single conifers, broadleaf trees, and standing dead trees using lidar and aerial imagery.

Detection of Standing Deadwood from Aerial Imagery Products: Two Methods for Addressing the Bare Ground Misclassification Issue

“…Not-detected polygons with a median area of 1-3 pixels in all classification scenarios indicated the biggest problems in the detection of snags and small dead crowns. This is in line with Wing et al [85], who showed detection reliability rising with the tree's diameter at breast height (DBH) and Krzystek et al [40] who found lower accuracies for snags (UA = 0.56, PA = 0.66) than for dead trees (UA and PA > 0.90). Although our method was pixel-based, the deadwood-detection accuracy corresponded with the accuracy of 0.71-0.77 (by 1 to 1 pixel correspondence) achieved by Polewski et al [86] using object detection methods based on ALS and CIR aerial imagery.…”

“…To enhance deadwood detection structural information on canopy heights is helpful. For automated analyses, ALS data in combination with CIR aerial imagery showed the best results to date with OA of about 0.90 [38][39][40]. Detection of standing deadwood from ALS data was also tested and it delivered heterogeneous results, depending on forest type and detection method [41,42], with OAs from 0.65-0.73 [43][44][45] to 0.86-0.92 [35,46].…”

Tourism management in national parks: Šumava and Bayerischer Wald (Bavarian Forest) in the Czech-German borderland

“…Nevertheless, the process of tourism development was not stopped and the German side of the mountain range was still quite popular (Mayer et al 2010). A recent ecological phenomenon that influenced the landscape of the region and its tourist attractiveness was the large-scale impoverishment of coniferous forests as a result of air pollution and the weakening of trees by insect infestation (Krzystek et al 2020;Šamonil and Vrška 2007;Vacek and Podrázský 2003;Zýval et al 2016).…”

A Review of Tree Species Classification Based on Airborne LiDAR Data and Applied Classifiers

“…The importance of tree species maps has been highlighted in publications, as standalone products for forest management and planning [3,4], as input for species-specific growth and yields model, or for species-specific allometric models [5]. During the last four decades, remote sensing (RS) techniques have developed into a standard in large-scale forest inventory [1,[6][7][8][9][10][11][12][13][14]. The acquisition of high-resolution data using RS techniques with a relatively small set of field sample plots allows for the efficient and automated forest inventory of large areas.…”