Using airborne lidar to characterize North European terrestrial high‐dark‐diversity habitats

Abstract

A key aspect of nature conservation is knowledge of which aspects of nature to conserve or restore to favor the characteristic diversity of plants in a given area. Here, we used a large plant dataset with >40 000 plots combined with airborne laser scanning (lidar) data to reveal the local characteristics of habitats having a high plant dark diversity—that is, absence of suitable species—at national extent (>43 000 km2). Such habitats have potential for reaching high realized diversity levels and hence are important in a conservation context. We calculated 10 different lidar based metrics (both terrain and vegetation structure) and combined these with seven different field‐based measures (soil chemistry and species indicators). We then used Integrated Nested Laplace Approximation for modelling plant dark diversity across 33 North European habitat types (open landscapes and forests) selected by the European communities to be important. In open habitat types high‐dark‐diversity habitats had relatively low pH, high nitrogen content, tall homogenous vegetation, and overall relatively homogenous terrains (high terrain openness) although with a rather high degree of local microtopographical variations. High‐dark‐diversity habitats in forests had relatively tall vegetation, few natural‐forest indicators, low potential solar radiation input and a low cover of small woody plants. Our results highlight important vegetation, terrain‐ and soil‐related factors that managers and policymakers should be aware of in conservation and restoration projects to ensure a natural plant diversity, for example low nutrient loads, natural microtopography and possibly also open forests with old‐growth elements such as dead wood and rot attacks.

Predicting Abiotic Soil Characteristics Using Sentinel-2 at Nature-Management-Relevant Spatial Scales and Extent

“…In recent years, massive vegetation plot databases have provided large amounts of data enabling studies of local plant diversity at national and continental extents [4][5][6]. Combining these with indicator values such as Ellenberg opens for novel insights into how local plant communities are distributed at broad spatial scales [7,8].…”

“…At the same time remote sensing techniques are slowly maturing and are approaching a stage where they can be used to predict local plant diversity over large areas [12,13]. A large number of studies have attempted to use remote sensing techniques to predict ecological factors like plant species richness, plant phenology, plant traits and habitat characteristics [4,12,14,15]. However, most of these studies have done this only at local scale or involving only one or a few species, traits or habitat types, preventing broad scale generalization.…”