Using lidar to assess the development of structural diversity in forests undergoing passive rewilding in temperate Northern Europe

Abstract

Forested areas are increasing across Europe, driven by both reforestation programs and farmland abandonment. While tree planting remains the standard reforestation strategy, there is increased interest in spontaneous regeneration as a cost-effective method with equal or potentially greater benefits. Furthermore, expanding areas of already established forests are left for passive rewilding to promote biodiversity conservation. Effective and objective methods are needed for monitoring and analyzing the development of forest structure under these management scenarios, with airborne laser scanning (lidar: light detection and ranging) being a promising methodology. Here, we assess the structural characteristics and development of unmanaged forests and 28- to 78-year old spontaneously regenerated forests on former agricultural land, relative to managed forests of similar age in Denmark, using 25 lidar-derived metrics in 10- and 30-m grid cells. We analyzed the lidar-derived cell values in a principal component analysis (PCA) and interpreted the axes ecologically, in conjunction with pairwise tests of median and variance of PCA-values for each forest. Spontaneously regenerated forest in general had increased structural heterogeneity compared to planted and managed forests. Furthermore, structural heterogeneity kept increasing in spontaneously regenerated forest across the maximal 78-year timespan investigated. Natural disturbances showed strong impacts on vegetation structure, leading to both structural homogeneity and heterogeneity. The results illustrate the utility of passive rewilding for generating structurally heterogeneous forested nature areas, and the utility of lidar surveys for monitoring and interpreting structural development of such forests.

Deriving Stand Structural Complexity from Airborne Laser Scanning Data—What Does It Tell Us about a Forest?

“…Past research showed that a large number of forest metrics can be derived from ALS point clouds, including species identities [56,57], a plethora of structural measures [9,58], and even forest type classifications [10,59,60]. As a measure of structural complexity, D b was related to all measures tested in our study except basal area.…”

“…Great advances have been made in addressing the three-dimensional (3D) character of forest structure by approaching it with airborne remote sensing as well as ground-based (close-range) remote sensing techniques. Among these techniques, radio detection and ranging (RaDAR; e.g., [6][7][8]), airborne light detection and ranging (LiDAR; e.g., [9,10]), spaceborne LiDAR [11], terrestrial laser scanning (TLS; e.g., [12][13][14]), aerial and satellite imagery (photogrammetry; e.g., [15,16]), or structure-from-motion (SfM, e.g., [17]) can be named as prominent examples. All of them aim at capturing the real forest and representing it based on 3D data in a virtual model space, probably most commonly in the form of 3D point clouds or digital elevation models.…”

Scrub encroachment promotes biodiversity in wetland restoration under eutrophic conditions

“…Field observations had led us to hypothesize that thickets and woodlands on wet, nutrient poor soils grow more heterogeneous in structure, leaving many canopy openings, compared to thickets and woodlands on more nutrient rich and/or less wet soils. This complexity of vegetation structure can be measured using light detection and ranging (lidar), which is a cost-effective way of gaining fine-resolution data on vegetation structure as compared to field measurements (Lefsky et al 2002) and which has been shown to capture aspects of vegetation structure that are important and otherwise overlooked for biodiversity (Moeslund et al 2019b, Thers et al 2019). A range of variables representing vegetation structure can be derived from lidar, although the translation to and correlation with well-understood properties is not always straightforward.…”

Landslide age, elevation and residual vegetation determine tropical montane forest canopy recovery and biomass accumulation after landslide disturbances in the Peruvian Andes

“…And, (c) How does this large, high‐resolution dataset help us refine estimated biomass recovery rates for landslides in TMF? We focus both on mean top of canopy height (TCH) and its variability (SD TCH ), two important indicators of forest recovery after disturbance (Letcher & Chazdon, 2009; Thers et al., 2019; Weishampel et al., 2007). Whereas TCH characterizes the general state of forest structure and recovery, SD TCH shows the within‐landslide successional process as patches of vegetation coalesce and canopies close and undergo thinning.…”

“…Applying LiDAR across hundreds of landslides in a highly variable landscape may help constrain estimates of AGB accumulation rates and recovery times and/or highlight additional data needed for this endeavour. Dislich and Huth (2012) (Letcher & Chazdon, 2009;Thers et al, 2019;Weishampel et al, 2007). Whereas TCH characterizes the general state of forest structure and recovery, SD TCH shows the within-landslide successional process as patches of vegetation coalesce and canopies close and undergo thinning.…”