Multiscale Very High Resolution Topographic Models in Alpine Ecology: Pros and Cons of Airborne LiDAR and Drone-Based Stereo-Photogrammetry Technologies

Guillaume, Annie S.; Leempoel, Kevin; Rochat, Estelle; Rogivue, Aude; Kasser, Michel; Gugerli, Félix; Parisod, Christian; Joost, Stéphane

doi:10.3390/rs13081588

Cited by 13 publications

(22 citation statements)

References 54 publications

(89 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We intentionally developed our models with fine‐scale environmental data that are increasingly adopted for SDMs (e.g. de Vries et al, 2021; Guillaume et al, 2021; Mitchell et al, 2017). Although so far, such data are typically used in models developed to assess species–environment relationships at a landscape scale, it has been highlighted that they can be crucial for understanding species distributions at global scales (Lembrechts, Lenoir, et al, 2019; Lembrechts, Nijs, & Lenoir, 2019; Stark & Fridley, 2022; Zellweger et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Positional errors in species distribution modelling are not overcome by the coarser grains of analysis

Gábor

Jetz

et al. 2022

Methods Ecol Evol

View full text Add to dashboard Cite

1. The performance of species distribution models (SDMs) is known to be affected by analysis grain and positional error of species occurrences. Coarsening of the analysis grain has been suggested to compensate for positional errors.Nevertheless, this way of dealing with positional errors has never been thoroughly tested. With increasing use of fine-scale environmental data in SDMs, it is important to test this assumption. Models using fine-scale environmental data are more likely to be negatively affected by positional error as the inaccurate occurrences might easier end up in unsuitable environment. This can result in inappropriate conservation actions.2. Here, we examined the trade-offs between positional error and analysis grain and provide recommendations for best practice. We generated narrow niche virtual species using environmental variables derived from LiDAR point clouds at 5 × 5 m fine-scale. We simulated the positional error in the range of 5 m to 99 m and evaluated the effects of several spatial grains in the range of 5 m to 500 m. In total, we assessed 49 combinations of positional accuracy and analysis grain. We used three modelling techniques (MaxEnt, BRT and GLM) and evaluated their discrimination ability, niche overlap with virtual species and change in realized niche.3. We found that model performance decreased with increasing positional error in species occurrences and coarsening of the analysis grain. Most importantly, we showed that coarsening the analysis grain to compensate for positional error did not improve model performance. Our results reject coarsening of the analysis grain as a solution to address the negative effects of positional error on model performance.

show abstract

Section: Discussionmentioning

confidence: 99%

Positional errors in species distribution modelling are not overcome by the coarser grains of analysis

Gábor

Jetz

et al. 2022

Methods Ecol Evol

View full text Add to dashboard Cite

show abstract

“…Using SAGA GIS (Conrad et al, 2015), we computed 13 topography-derived environmental factors (Table S2) from a precise and high-resolution DEM at 0.5 m pixel resolution, based on light detection and ranging (LiDAR) data obtained from the regional authorities (Canton Vaud, Switzerland). These topographic factors are known proxies for ecologically relevant descriptors, including elevation (and therefore temperature), climate, hydrology, soil conditions, light availability and exposure (Guillaume et al, 2021;Lecours et al, 2017;Leempoel et al, 2015;Wilson & Gallant, 2000).…”

Section: Environmental Factorsmentioning

confidence: 99%

Scale matters: genome-wide signatures of local adaptation to high-resolution environmental variation in an alpine plant

Rogivue,

Leempoel,

Guillaume

et al. 2023

Preprint

View full text Add to dashboard Cite

Microevolutionary processes shape adaptive responses to heterogeneous environments, where these effects vary both among and within species. However, the degree to which signatures of adaptation to environmental drivers can be detected based on spatial scale and genomic marker remains largely unknown. We studied signatures of local adaptation across different spatial extents, investigating complementary types of genomic variants–single nucleotide polymorphisms (SNPs) and polymorphic transposable elements (TEs)–in populations of the alpine model plant species Arabis alpina. We coupled high-resolution (0.5m) environmental factors, derived from remote sensing digital elevation models, with whole-genome sequenced data of 304 individuals across four populations. We demonstrate that responses of A. alpina to similar amounts of abiotic variation are largely governed by local evolutionary processes and find minimally overlapping signatures of local adaptation between SNPs and polymorphic TEs. Notably, functional annotations of high-impact genomic variants revealed several defence-related genes associated with the abiotic factors studied, which could indicate indirect selective pressure of biotic agents. Our results highlight the importance of considering different spatial extents and types of genomic polymorphisms when searching for signatures of adaptation to environmental variation. Such insights provide key information on microevolutionary processes and could guide management decisions to mitigate negative impacts of climate change on alpine plant populations.

show abstract

“…Resolution and coverage of elevation data have increased dramatically over the last few decades. Current global DEMs such as the MERIT DEM [1] and the NASADEM [2] provide global or semi-global coverage of the land with a resolution of 30-90 m. Less extensive airborne LIDAR or optically derived DEMs may have resolutions from the first meters up to the first centimeters [3]. Since finer resolution is often considered to be an indicator of higher DEM quality, it is not uncommon to see small-scale maps produced from such detailed sources without generalization.…”

Section: Introductionmentioning

confidence: 99%

Granularity of Digital Elevation Model and Optimal Level of Detail in Small-Scale Cartographic Relief Presentation

Samsonov

2022

Remote Sensing

View full text Add to dashboard Cite

One of the key applications of digital elevation models (DEMs) is cartographic relief presentation. DEMs are widely used in mapping, most commonly in the form of contours, hypsometric tints, and hill shading. Recent advancements in the coverage, quality, and resolution of global DEMs facilitate the overall improvement of the detail and reliability of terrain-related research. At the same time, geographic problem solving is conducted in a wide variety of scales, and the data used for mapping should have the corresponding level of detail. Specifically, at small scales, intensive generalization is needed, which is also true for elevation data. With the widespread accessibility of detailed DEMs, this principle is often violated, and the data are used for mapping at scales far smaller than what is appropriate. Small-scale relief shading obtained from fine-resolution DEMs is excessively detailed and brings an unclear representation of the Earth’s surface instead of emphasizing what is important at the scale of visualization. Existing coarse-resolution global DEMs do not resolve the issue, since they accumulate the maximum possible information in every pixel, and therefore also require reduction in detail to obtain a high-quality cartographic image. It is clear that guidelines and effective principles for DEM generalization at small scales are needed. Numerous algorithms have been developed for the generalization of elevation data represented either in gridded, contoured, or pointwise form. However, the answer to the most important question—When should we stop surface simplification?—remains unclear. Primitive error-based measures such as vertical distance are not effective for cartography, since they do not account for the landform structure of the surface perceived by the map reader. The current paper approached the problem by elaborating the granularity—a newly developed property of DEMs, which characterizes the typical size of a landform represented on the DEM surface. A methodology of estimating the granularity through a landform width measure was conceptualized and implemented as software. Using the developed program tools, the optimal granularity was statistically learned from DEMs reconstructed for multiple fragments of manually drawn 1:200,000, 1:500,000, and 1:1,000,000 topographic maps covering different relief types. It was shown that the relative granularity should be 5–6 mm at the mapping scale to achieve the clearness of relief presentation typical for manually drawn maps. We then demonstrate how the granularity measure can be used effectively as a constraint during DEM generalization. Experimental results on a combination of contours, hypsometric tints, and hill shading indicated clearly that the optimal level of detail in small-scale cartographic relief presentation can be achieved by DEM generalization constrained by granularity in combination with fine DEM resolution, which facilitates high-quality rendering.

show abstract

Multiscale Very High Resolution Topographic Models in Alpine Ecology: Pros and Cons of Airborne LiDAR and Drone-Based Stereo-Photogrammetry Technologies

Cited by 13 publications

References 54 publications

Positional errors in species distribution modelling are not overcome by the coarser grains of analysis

Positional errors in species distribution modelling are not overcome by the coarser grains of analysis

Scale matters: genome-wide signatures of local adaptation to high-resolution environmental variation in an alpine plant

Granularity of Digital Elevation Model and Optimal Level of Detail in Small-Scale Cartographic Relief Presentation

Contact Info

Product

Resources

About