Abstract
The vulnerability of alpine environments to climate change presses an urgent need to accurately model and understand these ecosystems. Popularity in the use of digital elevation models (DEMs) to derive proxy environmental variables has increased over the past decade, particularly as DEMs are relatively cheaply acquired at very high resolutions (VHR; <1 m spatial resolution). Here, we implement a multiscale framework and compare DEM-derived variables produced by Light Detection and Ranging (LiDAR) and stereo-photogrammetry (PHOTO) methods, with the aim of assessing their relevance and utility in species distribution modelling (SDM). Using a case study on the arctic-alpine plant, Arabis alpina, in two valleys in the western Swiss Alps, we show that both LiDAR and PHOTO technologies can be relevant for producing DEM-derived variables for use in SDMs. We demonstrate that PHOTO DEMs, up to a spatial resolution of at least 1 m, rivalled the accuracy of LiDAR DEMs, largely owing to the customizability of PHOTO DEMs to the study sites compared to commercially available LiDAR DEMs. We obtained DEMs at spatial resolutions of 6.25 cm–8 m for PHOTO and 50 cm–32 m for LiDAR, where we determined that the optimal spatial resolutions of DEM-derived variables in SDM were between 1 and 32 m, depending on the variable and site characteristics. We found that the reduced extent of PHOTO DEMs altered the calculations of all derived variables, which had particular consequences on their relevance at the site with heterogenous terrain. However, for the homogenous site, SDMs based on PHOTO-derived variables generally had higher predictive powers than those derived from LiDAR at matching resolutions. From our results, we recommend carefully considering the required DEM extent to produce relevant derived variables. We also advocate implementing a multiscale framework to appropriately assess the ecological relevance of derived variables, where we caution against the use of VHR-DEMs finer than 50 cm in such studies.
Highlights
Alpine environments are among the most sensitive ecosystems to climate change and associated extreme weather fluctuations [1]
Method using a drone compared to state-obtained Light Detection and Ranging (LiDAR) using airplanes, with the motivation for cost-effective repeatability of the project in other areas. This presented a good opportunity to compare digital elevation models (DEMs) produced by LiDAR and PHOTO technologies, as well as their associated derived variables, as a lack of vegetation and man-made structures at the study sites meant that the digital surface model (DSM) obtained by PHOTO
We discuss the influence of spatial scale and DEM-acquisition technologies on the relevance of derived variables in species distribution models (SDM) at the two study sites, after which we provide an overview of the technologies to assist in selecting the most appropriate method for producing data to use in alpine ecology studies
Summary
Alpine environments are among the most sensitive ecosystems to climate change and associated extreme weather fluctuations [1]. Adopted in the fields of geology and hydrology to describe terrain steepness and orientation, the primary terrain attributes of slope, aspect, and curvature derived from DEMs can be used to calculate more complex secondary terrain attributes These secondary derived variables have been developed to accurately model ecologically-relevant environmental factors, such as soil depth, nutrient status, solar radiation, terrain ruggedness, humidity, and soil wetness [7,10,12,14,15,16], for example, which have been successfully used to model species distributions [7,17,18], for studying responses to environmental change [8,9,11], and for evaluating capacities for local adaptation [19]
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