Description

This dataset includes the inputs and outputs generated in the spatial modeling of CES using social media data for eight mountain parks in Spain and Portugal (Aigüestortes, Sierra de Guadarrama, Ordesa, Peneda-Gerês, Picos de Europa, Sierra de las Nieves, Sierra Nevada and Teide). This spatial modeling is addressed in the article in preparation entitled: "What drives cultural ecosystem services in mountain protected areas? An AI-assisted answer using social media."

The variables used as inputs to generate the models come from different sources:

-CES presence points come from social media photos (Flickr and Twitter) labeled using AI models and validated by experts. The models used for automatic labeling were Dino v2 and OPENAI's GPT 4.1 model. Consensus was sought on the labels from these two label sources, which showed F1 values above 0.75, and these labels were used as presence data.

The environmental variables used are mainly derived from:

• OpenStreetMap (OSM) https://www.openstreetmap.org/

• Variables derived from remote sensing

• Topographic variables

• Current and future climate variables derived from CHELSA (https://chelsa-climate.org/)

• Landscape metrics (calculated with Fragstats software https://www.fragstats.org/)

• Viewshed

• Land use and land cover maps (https://land.copernicus.eu/en/products/corine-land-cover)

The models were generated with the maximum entropy (MaxEnt) algorithm using the biomod2 R package, leveraging its suitability for presence-only data, low sample sizes, and mixed predictor types. To address sampling bias, we generated 10 pseudo-absence replicates based on the “target-group background” method. Models were evaluated using AUC-ROC and True Skill Statistic (TSS), with performance validation via 10-fold cross-validation, resulting in 100 runs per model. Ensemble models were created from runs with AUC-ROC > 0.6, using the median for spatial projections of CES and the coefficient of variation to estimate uncertainty. We implemented two modelling approaches: one assuming consistent CES preferences across parks, and another assuming park-specific preferences shaped by local environmental contexts.

Table 1. Categories used in social media photo tagging: Stoten, based on the scientific framework proposed by Moreno-Llorca et al. (2020) (https://doi.org/10.1016/j.scitotenv.2020.140067).

Stoten

Cultural

Fauna/Flora

Gastronomy

Nature & Landscape

Not relevant

Recreational

Religious

Rural tourism

Sports

Sun and beach

Urban

Table 2. Table of contents of the dataset

Folder

format

Description

Inputs

Base layers

by National Park

100-meter grid

grid_wgs84_atrib

.shp

100 x 100 meter grid for each of the studied national parks that cover the study area

Biosphere Reserve

MAB_wgs84

.shp

Biosphere reserve layers present in each of the national parks studied

Municipality

Municipality

.shp

Layers of municipalities that overlap with the park area, biosphere reserve, Natura 2000 and the socioeconomic influence area with a 100-meter buffer

National park limit

National_park_limit

.shp

Boundaries of each of the national parks studied

Natura 2000

RN2000

.shp

Layers of the Natura 2000 for each of the national parks studied

Socioeconomic influence area

AIS

.shp

Area of socioeconomic influence of each of the parks studied

Readme

.txt

File containing layer metadata, including download locations and descriptions of shape attributes.

by National Park

Accessibility

.tif

Accessibility variables that include routes, streets, parking, and train tracks

Climate

.tif

Chelsea-derived climate variable layers and solar radiation layers

Ecosystem functioning

.tif

Layers derived from remote sensing that are related with the functional attributes of ecosystems

Ecosystem structure

.tif

Landscape and spectral diversity metrics

Geodiversity

.tif

Topographic and derived variables

Land use Land cover

.tif

Layers related to land use and cover

Tourism and Culture

.tif

Layers related to infrastructure associated with tourism such as bars, restaurants, lodgings and places of cultural interest such as monuments

Scripts

Modeling to get output data

Biomod_modelling_by_park

.R

Script used for modeling CES using data from social media by fitting one ENM for each park and CES.

Biomod_modelling_all_parks

.R

Script used for modeling CES using data from social media by fitting one ENM for each CES.

Modeling to get output data

Downloading and processing variables

EFAS

EFAs code

.js

GEE scripts used to download the Ecosystem Functional Attributes (EFAs) (Paruelo et al.2001; Alcaraz-Segura et al. 2006) derived from Sentinel 2 dataset for each of the national parks studied

OSM

1) Download layers

.py

Python scripts used to download the OpenStreetMap layers of interest for each of the national parks studied.

2) Join layers

.py

Scripts used to merge OSM layers belonging to the same category. e.g., primary, secondary, and tertiary highways.

3) Count point

.py

Scripts used to count the number of points in each of the 100 grid cells for each park, used in case of point type data

4) Presence and absence

.py

Scripts used to assess presence in each of the cells of the 100-square grid for each park, used in the case of data types such as points, lines, and polygons.

Remote sensing

Canopy

.js

GEE scripts used to download the canopy (https://gee-community-catalog.org/projects/canopy/) downloaded and cropped for each of the national parks studied

ESPI

.js

GEE scripts used to download the ESPI index (Ecosystem Service Provision Index) downloaded and cropped for each of the national parks studied

European disturbance map

.js

GEE scripts used to download European disturbance maps (//https://www.eea.europa.eu/data-and-maps/figures/biogeographical-regions-in-europe-2)

downloaded and cropped for each of the national parks studied

LST

.js

GEE scripts used to download LST maps (from Landsat Collection)

downloaded and cropped for each of the national parks studied

Night lights

.js

GEE scripts used to download nighttime light maps (https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_ANNUAL_V22)

downloaded and cropped for each of the national parks studied

Population density

.js

GEE scripts used to download population density maps (https://developers.google.com/earth-engine/datasets/catalog/CIESIN_GPWv411_GPW_Population_Density)

downloaded and cropped for each of the national parks studied

Soil groups

.js

GEE scripts used to download Hydrologic Soil Group maps (https://gee-community-catalog.org/projects/hihydro_soil/)

downloaded and cropped for each of the national parks studied

Solar radiation

.js

GEE scripts used to download solar radiation maps (https://globalsolaratlas.info/support/faq)

downloaded and cropped for each of the national parks studied

RGB diversity

Seasonal KMeans clustering

.js

GEE scripts were used to calculate seasonal clusters using Sentinel 2 RGB bands with GEE's .wekaKMeans algorithm. These layers were downloaded and cropped for each of the national parks studied.

Colour diversity analysis

.R

R script used to calculate spectral diversity (Shannon, Simpson and inverse Simpson) using the cluster layers and RGB bands derived from Sentinel 2.

Post processing

Align_and_Clip_rasters

.py

Python scripts used to align and clip the downloaded layers to a 100-meter grid reference layer for each of the national parks studied.

Outputs

CES projections

proj_Aiguestortes_Sports_ensemble

.tif

Spatial projections for the best models obtained for each CES and park

References:

Alcaraz-Segura, D., Paruelo, J., and Cabello, J. 2006: Identification of current ecosystem functional types in the Iberian Peninsula, Global Ecol. Biogeogr., 15, 200–212, https://doi.org/10.1111/j.1466-822X.2006.00215.x

Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M., 2017. Climatologies at high resolution for the earth’s land surface areas. Sci Data 4, 170122. https://doi.org/10.1038/sdata.2017.122

Lobo, J.M., Jiménez-Valverde, A., Hortal, J., 2010. The uncertain nature of absences and their importance in species distribution modelling. Ecography 33, 103–114. https://doi.org/10.1111/j.1600-0587.2009.06039.x

Paruelo, J. M., Jobbágy, E. G., and Sala, O. E. 2001: Current Distribution of Ecosystem Functional Types in Temperate South America, Ecosystems, 4, 683–698, https://doi.org/10.1007/s10021-001-0037-9

Phillips, S.J., Anderson, R.P., Schapire, R.E., 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling 190, 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026

Phillips, S.J., Dudík, M., Elith, J., Graham, C.H., Lehmann, A., Leathwick, J., Ferrier, S., 2009. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological Applications 19, 181–197. https://doi.org/10.1890/07-2153.1

Thuiller, W., Georges, D., Gueguen, M., Engler, R., Breiner, F., Lafourcade, B., Patin, R., 2023. biomod2: Ensemble Platform for Species Distribution Modeling.

Sillero, N., Arenas-Castro, S., Enriquez‐Urzelai, U., Vale, C.G., Sousa-Guedes, D., Martínez-Freiría, F., Real,

Global Urban Heat Island Intensity DatasetA novel dynamic equal-area (DEA) method was proposed for UHII estimations. This method can minimize the influence of various confounding factors through a dynamic cyclic process and finally obtain the background reference area (BRA) with its size equal to the central urban area. Utilizing the DEA method and leveraging eight different temperature data, we developed a global-scale (>10,000 cities), long-term (over 20 years by month), and multi-faceted (clear-sky surface, all-sky surface, and canopy) UHII dataset. Please refer to "Readme" for details.We also produced gridded UHII data with a spatial resolution of 1km. The data can be downloaded from Baidu Cloud Drive. Link: https://pan.baidu.com/s/1XcYsRKFuVINCrvCG3nj3Ng Code: 1234This gridded UHII data is now on the Earth Engine data catalog: https://gee-community-catalog.org/projects/uhii/In addition, all datasets have been uploaded to the Google Drive and can be downloaded from https://drive.google.com/drive/folders/1dKwW2ceg457iM2UHLJWmMcNhkx5OfBY9?usp=drive_link. More information about downloading and using the datasets can be found at https://github.com/samapriya/awesome-gee-community-datasets/issues/276.Citation: Qiquan Yang.Global Urban Heat Island Intensity Dataset. Figshare. https://doi.org/10.6084/m9.figshare.24821538, 2024.Reference: Qiquan Yang, Yi Xu, TC Chakraborty, et al. A global urban Heat Island intensity dataset: Generation, comparison, and analysis. Remote Sensing of Environment, 2024, 312. https://doi.org/10.1016/j.rse.2024.114343.