DOI: 10.5281/zenodo.8163612
title: Mapping spatial microclimate patterns in mountain forests from LiDAR

description: Data respository for following paper: Vandewiele, Michiel; Lisa Geres; Annette Lotz; Lisa Mandl; Tobias Richter; Sebastian Seibold; Rupert Seidl and Cornelius Senf (in revision) Mapping spatial microclimate patterns in mountain forests from LiDAR. Agricultural and Forest Meteorology. Content of the repository: ## Data folder #### "disturbance_year_1986-2020_epsg25832_cropped.tif"<br> - map containing the year of the most severe forest disturbance from 1986 until 2020<br> - obtained from: Senf, Cornelius, and Rupert Seidl. 2021. 'Mapping the forest disturbance regimes of Europe', Nature Sustainability, 4: 63-70.<br> - cropped and reprojected to match the study area and raster files #### "Forest_Plot_Info" (folder)<br> - Coordinates and Elevation for forest plots, including shapefile of the locations #### "forest_types2020_epsg31464.tif" <br> - raster file containing the current distribution of forest types (from 2021) <br> - obtained from: Thom, Dominik, Werner Rammer, Patrick Laux, Gerhard Smiatek, Harald Kunstmann, Sebastian Seibold, and Rupert Seidl. 2022. 'Will forest dynamics continue to accelerate throughout the 21st century in the Northern Alps?', Global Change Biology, 28: 3260-74. #### "lidar_metrics_r12_6.csv"<br> - LiDAR based predictor variables representing forest structure<br> - used as input for the boosted regression trees<br> - standard metrics from the lidR package (stdmetrics)<br> - see also: Roussel, J.-R.; Auty, D.; Coops, N. C.; Tompalski, P.; Goodbody, T. R. H.; Meador, A. S.; Bourdon, J.-F.; de Boissieu, F.; Achim, A. lidR: An R package for analysis of Airborne Laser Scanning (ALS) data. Remote Sensing of Environment 2020, 251, 112061. DOI: https://doi.org/10.1016/j.rse.2020.112061. #### "temperature_data.csv"<br> - pre-processed microclimatic and macroclimatic temperatures for the study period<br> - used to calculate the temperature metrics #### "temperature_metrics.csv"<br> - 95th percentile and mean temperature offsets calculated for the study period and for each forest plot <br> - The metrics to be predicted by the models #### "topography_metrics_r50.csv"<br> - LiDAR based predictor variables (based on a LiDAR derived DEM) representing topography<br> - used as input for the boosted regression trees<br> - see also: Frey, S. J.; Hadley, A. S.; Johnson, S. L.; Schulze, M.; Jones, J. A.; Betts, M. G. Spatial models reveal the microclimatic buffering capacity of old-growth forests. Science advances 2016, 2 (4), e1501392.; Hofierka, J.; Suri, M. The solar radiation model for Open source GIS: implementation and applications. In Proceedings of the Open source GIS-GRASS users conference, 2002; Vol. 2002, pp 51-70. #### "predictions_rs.tif"<br> - ratser with predictor variables (same as in lidar_metrics_r12_6.csv and topography_metrics_r50.csv) calculated for a regular 20*20m grid<br> - the names of the predictors are stored in "names_rs_predictors.csv" ## Scripts folder #### Scripts in sequential ## Results folder #### accuracies_*.csv<br> - accuracies of the models for maximum (TO_max), mean (TO_mean) and minimum (TO_min) offsets #### effects_*.csv<br> - effect sizes from the models for maximum (TO_max), mean (TO_mean) and minimum (TO_min) offsets #### prediction_*.csv<br> - spatial predictions (mean) and uncertainty (standard error) of TO_max, TO_mean and TO_min #### stats_*:csv<br> - statistical overview of TO_max, TO_mean and TO_min derived from (i) the sample plots and (ii) the saptial predictions