This product contains plot location data for LCMAP Hawaii Reference Data in a .shp format as well as annual land cover, land use, and change process variables for each reference data plot in a separate .csv table. The same information available in the.csv file is also provided in a .xlsx format. The LCMAP Hawaii Reference Data Product was utilized for evaluation and validation of the Land Change Monitoring, Assessment, and Projection (LCMAP) land cover and land cover change products. The LCMAP Hawaii Reference Data Product includes the collection of an independent dataset of 600 30-meter by 30-meter plots across the island chain of Hawaii. The LCMAP Hawaii Reference Data Products collected variables related to primary and secondary land use, primary and secondary land cover(s), change processes, and other ancillary variables annually across Hawaii from 2000-2019. The sites in this dataset were collected via manual image interpretation. These samples were selected using a stratified random sampling process via stratification using a hybrid NLCD 2001 Hawaii and NOAA C-Cap 2011 land cover map.

Overview

This dataset is the repository for the following paper submitted to Data in Brief:

Kempf, M. A dataset to model Levantine landcover and land-use change connected to climate change, the Arab Spring and COVID-19. Data in Brief (submitted: December 2023).

The Data in Brief article contains the supplement information and is the related data paper to:

Kempf, M. Climate change, the Arab Spring, and COVID-19 - Impacts on landcover transformations in the Levant. Journal of Arid Environments (revision submitted: December 2023).

Description/abstract

The Levant region is highly vulnerable to climate change, experiencing prolonged heat waves that have led to societal crises and population displacement. Since 2010, the area has been marked by socio-political turmoil, including the Syrian civil war and currently the escalation of the so-called Israeli-Palestinian Conflict, which strained neighbouring countries like Jordan due to the influx of Syrian refugees and increases population vulnerability to governmental decision-making. Jordan, in particular, has seen rapid population growth and significant changes in land-use and infrastructure, leading to over-exploitation of the landscape through irrigation and construction. This dataset uses climate data, satellite imagery, and land cover information to illustrate the substantial increase in construction activity and highlights the intricate relationship between climate change predictions and current socio-political developments in the Levant.

Folder structure

The main folder after download contains all data, in which the following subfolders are stored are stored as zipped files:

“code” stores the above described 9 code chunks to read, extract, process, analyse, and visualize the data.

“MODIS_merged” contains the 16-days, 250 m resolution NDVI imagery merged from three tiles (h20v05, h21v05, h21v06) and cropped to the study area, n=510, covering January 2001 to December 2022 and including January and February 2023.

“mask” contains a single shapefile, which is the merged product of administrative boundaries, including Jordan, Lebanon, Israel, Syria, and Palestine (“MERGED_LEVANT.shp”).

“yield_productivity” contains .csv files of yield information for all countries listed above.

“population” contains two files with the same name but different format. The .csv file is for processing and plotting in R. The .ods file is for enhanced visualization of population dynamics in the Levant (Socio_cultural_political_development_database_FAO2023.ods).

“GLDAS” stores the raw data of the NASA Global Land Data Assimilation System datasets that can be read, extracted (variable name), and processed using code “8_GLDAS_read_extract_trend” from the respective folder. One folder contains data from 1975-2022 and a second the additional January and February 2023 data.

“built_up” contains the landcover and built-up change data from 1975 to 2022. This folder is subdivided into two subfolder which contain the raw data and the already processed data. “raw_data” contains the unprocessed datasets and “derived_data” stores the cropped built_up datasets at 5 year intervals, e.g., “Levant_built_up_1975.tif”.

Code structure

1_MODIS_NDVI_hdf_file_extraction.R

This is the first code chunk that refers to the extraction of MODIS data from .hdf file format. The following packages must be installed and the raw data must be downloaded using a simple mass downloader, e.g., from google chrome. Packages: terra. Download MODIS data from after registration from: https://lpdaac.usgs.gov/products/mod13q1v061/ or https://search.earthdata.nasa.gov/search (MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061, last accessed, 09th of October 2023). The code reads a list of files, extracts the NDVI, and saves each file to a single .tif-file with the indication “NDVI”. Because the study area is quite large, we have to load three different (spatially) time series and merge them later. Note that the time series are temporally consistent.

2_MERGE_MODIS_tiles.R

In this code, we load and merge the three different stacks to produce large and consistent time series of NDVI imagery across the study area. We further use the package gtools to load the files in (1, 2, 3, 4, 5, 6, etc.). Here, we have three stacks from which we merge the first two (stack 1, stack 2) and store them. We then merge this stack with stack 3. We produce single files named NDVI_final_*consecutivenumber*.tif. Before saving the final output of single merged files, create a folder called “merged” and set the working directory to this folder, e.g., setwd("your directory_MODIS/merged").

3_CROP_MODIS_merged_tiles.R

Now we want to crop the derived MODIS tiles to our study area. We are using a mask, which is provided as .shp file in the repository, named "MERGED_LEVANT.shp". We load the merged .tif files and crop the stack with the vector. Saving to individual files, we name them “NDVI_merged_clip_*consecutivenumber*.tif. We now produced single cropped NDVI time series data from MODIS.
The repository provides the already clipped and merged NDVI datasets.

4_TREND_analysis_NDVI.R

Now, we want to perform trend analysis from the derived data. The data we load is tricky as it contains 16-days return period across a year for the period of 22 years. Growing season sums contain MAM (March-May), JJA (June-August), and SON (September-November). December is represented as a single file, which means that the period DJF (December-February) is represented by 5 images instead of 6. For the last DJF period (December 2022), the data from January and February 2023 can be added. The code selects the respective images from the stack, depending on which period is under consideration. From these stacks, individual annually resolved growing season sums are generated and the slope is calculated. We can then extract the p-values of the trend and characterize all values with high confidence level (0.05). Using the ggplot2 package and the melt function from reshape2 package, we can create a plot of the reclassified NDVI trends together with a local smoother (LOESS) of value 0.3.
To increase comparability and understand the amplitude of the trends, z-scores were calculated and plotted, which show the deviation of the values from the mean. This has been done for the NDVI values as well as the GLDAS climate variables as a normalization technique.

5_BUILT_UP_change_raster.R

Let us look at the landcover changes now. We are working with the terra package and get raster data from here: https://ghsl.jrc.ec.europa.eu/download.php?ds=bu (last accessed 03. March 2023, 100 m resolution, global coverage). Here, one can download the temporal coverage that is aimed for and reclassify it using the code after cropping to the individual study area. Here, I summed up different raster to characterize the built-up change in continuous values between 1975 and 2022.

6_POPULATION_numbers_plot.R

For this plot, one needs to load the .csv-file “Socio_cultural_political_development_database_FAO2023.csv” from the repository. The ggplot script provided produces the desired plot with all countries under consideration.

7_YIELD_plot.R

In this section, we are using the country productivity from the supplement in the repository “yield_productivity” (e.g., "Jordan_yield.csv". Each of the single country yield datasets is plotted in a ggplot and combined using the patchwork package in R.

8_GLDAS_read_extract_trend

The last code provides the basis for the trend analysis of the climate variables used in the paper. The raw data can be accessed https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20Noah%20Land%20Surface%20Model%20L4%20monthly&page=1 (last accessed 9th of October 2023). The raw data comes in .nc file format and various variables can be extracted using the [“^a variable name”] command from the spatraster collection. Each time you run the code, this variable name must be adjusted to meet the requirements for the variables (see this link for abbreviations: https://disc.gsfc.nasa.gov/datasets/GLDAS_CLSM025_D_2.0/summary, last accessed 09th of October 2023; or the respective code chunk when reading a .nc file with the ncdf4 package in R) or run print(nc) from the code or use names(the spatraster collection).
Choosing one variable, the code uses the MERGED_LEVANT.shp mask from the repository to crop and mask the data to the outline of the study area.
From the processed data, trend analysis are conducted and z-scores were calculated following the code described above. However, annual trends require the frequency of the time series analysis to be set to value = 12. Regarding, e.g., rainfall, which is measured as annual sums and not means, the chunk r.sum=r.sum/12 has to be removed or set to r.sum=r.sum/1 to avoid calculating annual mean values (see other variables). Seasonal subset can be calculated as described in the code. Here, 3-month subsets were chosen for growing seasons, e.g. March-May (MAM), June-July (JJA), September-November (SON), and DJF (December-February, including Jan/Feb of the consecutive year).
From the data, mean values of 48 consecutive years are calculated and trend analysis are performed as describe above. In the same way, p-values are extracted and 95 % confidence level values are marked with dots on the raster plot. This analysis can be performed with a much longer time series, other variables, ad different spatial extent across the globe due to the availability of the GLDAS variables.

Abstract

This dataset was derived by the Bioregional Assessment Programme. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

This dataset converts the original Digital Atlas of Australian Soils (GUID: 9e7d2f5b-ff51-4f0f-898a-a55be8837828) shapefile into the Australian soil classification, as per data from Conversion of the Atlas of Australian Soils to the Australian Soil Classification (GUID: 295707d5-2774-4ca5-a539-6c0426bbd662). A Layer file is also supplied using the RGB colour reference table, also found in the Conversion of the Atlas of Australian Soils to the Australian Soil Classification dataset.

Purpose

Provides a spatial and cartographic representation of the Digital Atlas of Australian Soils shapefile into the new Australian soil classification.

Dataset History

From the Conversion of the Atlas of Australian Soils to the Australian Soil Classification dataset (GUID: 295707d5-2774-4ca5-a539-6c0426bbd662) the file asclut.txt was converted to .csv format and field headings added (MAP_UNIT, SOIL_CODE, SOIL_SYMBOL, SOIL).

This csv file (asclut.csv) was joined to the Digital Atlas of Australian Soils (GUID: 9e7d2f5b-ff51-4f0f-898a-a55be8837828), soilAtlas2M shapefile on the common 'MAP_UNIT' field. The resulting join was saved as 'soilAtlas2M_ASC_Conversion.shp'

The symbology of this shapefile was updated by matching the RGB values provided in the 'asc_colours.xls' spreadsheet from the Conversion of the Atlas of Australian Soils to the Australian Soil Classification dataset (GUID: 295707d5-2774-4ca5-a539-6c0426bbd662) to the 'SOIL' field. A Layer File was created 'soilAtlas2M_ASC_Conversion.lyr'

Dataset Citation

Bioregional Assessment Programme (2015) Spatial Data Conversion of the Atlas of Australian Soils to the Australian Soil Classification v01. Bioregional Assessment Derived Dataset. Viewed 29 September 2017, http://data.bioregionalassessments.gov.au/dataset/6f804e8b-2de9-4c88-adfa-918ec327c32f.

Dataset Ancestors

• Derived From Digital Atlas of Australian Soils

• Derived From Conversion of the Atlas of Australian Soils to the Australian Soil Classification

This repository hosts data and code presented in the article "A climate envelope model for the projection of carbon storage at global scale". The repository contains a compressed file containing all the code needed to reproduce the methodology that we developed and to analyse its results. We did not upload all the temporary and intermediate data files that are created during the execution of the method. We rather uploaded "milestone" data, i.e. final results or important intermediate ones. This includes the final training dataset (CSV and ESRI Shapefile), model calibration data (CSV), the final trained model (Python pickle file), the global data for prediction (CSV), the final global map of potential aboveground biomass density (AGBD) at present times (raster files at 1km2 and 10km2 resolution), maps depicting regions where climatic conditions are outside of the training range of positive AGBD instances and maps depicting world regions without trees.

Files:

code.zip : Compressed directory with all the code needed to reproduce the methodology presented in the manuscript. Contains a README file.

agbd_climate_training_dataset_spatialkfolds_1000km.zip : the training dataset in Shapefile format.

agbd_climate_training_dataset_spatialkfolds_1000km.csv : the training dataset in CSV format.

potential_AGBD_Mgha_1km2_contemporary_climate.tif : the predicted global potential AGBD under contemporary climate conditions and at a resolution of 1 squared kilometer.

potential_AGBD_Mgha_10km2_contemporary_climate.tif : the predicted global potential AGBD under contemporary climate conditions downsampled at a resolution of 10 squared kilometers.

model_calibration_error_estimation.zip : data on the calibration experiments.

number_predictors_out_of_range.zip : tiled maps representing the number of climatic predictors outside of the training range before including 0 AGBD instances in the training dataset.

tree_absence_map.zip : tiled maps representing world regions without trees. Based on Crowther et al. (2015) (https://elischolar.library.yale.edu/yale_fes_data/1/).

potential_agbd_Mgha_climate_envelope.pkl : Calibrated model for the prediction of potential AGBD given bioclimatic conditions.

predictors_data_global.zip : Global predictors data to apply the model on.

GAVIA_main_data_table.csv - This comma-separated text file contains the 27,723 alien bird records that form the core of the Global AVian Invasions Atlas (GAVIA) project. These records represent 971 species, introduced to 230 countries and administrative areas across all eight biogeographical realms, spanning the period 6000 BCE – AD 2014. The data comprises taxonomic (species-level), spatial (geographic location, realm, land type) and temporal (dates of introduction and spread) components, as well as details relating to the introduction event (how and why the species was introduced, whether or not it is established). Each line of data consists of an individual record concerning a specific alien bird species introduced to a specific location. The data derives from both published and unpublished sources, including atlases, country species lists, peer-reviewed articles, websites and via correspondence with in-country experts.GAVIA_abbreviations.csv - This comma-separated text file describes the abbreviations in the main text of the 'GAVIA_main_data_table.csv'.GAVIA_column_names.csv - This comma-separated text file describes the column heading used in the 'GAVIA_main_data_table.csv'.

GAVIA_references.csv - This comma-separated text file contains the full references referred to in the 'GAVIA_main_data_table.csv' column headed "Reference".GAVIA_rangemaps.zip - This compressed folder (.zip format) contains the species’ range maps stored as one ESRI shapefile per species (n = 362). Within these shapefiles are attribute tables which contain a unique species ID number and binomial which match up to the species ID number and binomial in the 'GAVIA main data table'.

This dataset contains data and code from the manuscript:Heintzman, L.J., McIntyre, N.E., Langendoen, E.J., & Read, Q.D. (2024). Cultivation and dynamic cropping processes impart land-cover heterogeneity within agroecosystems: a metrics-based case study in the Yazoo-Mississippi Delta (USA). Landscape Ecology 39, 29 (2024). https://doi.org/10.1007/s10980-024-01797-0There are 14 rasters of land use and land cover data for the study region, in .tif format with associated auxiliary files, two shape files with county boundaries and study area extent, a CSV file with summary information derived from the rasters, and a Jupyter notebook containing Python code.The rasters included here represent an intermediate data product. Original unprocessed rasters from NASS CropScape are not included here, nor is the code to process them.List of filesMS_Delta_maps.zipMSDeltaCounties_UTMZone15N.shp: Depiction of the 19 counties (labeled) that intersect the Mississippi Alluvial Plain in western Mississippi.MS_Delta_MAP_UTMZone15N.shp: Depiction of the study area extent.mf8h_20082021.zipmf8h_XXXX.tif: Yearly, reclassified and majority filtered LULC data used to build comboall1.csv - derived from USDA NASS CropScape. There are 14 .tif files total for years 2008-2021. Each .tif file includes auxiliary files with the same file name and the following extensions: .tfw, .tif.aux.xml, .tif.ovr., .tif.vat.cpg., .tif.vat.dbf.comboall1.csv: Combined dataset of LULC information for all 14 years in study period.analysis.ipynb_.txt: Jupyter Notebook used to analyze comboall1.csv. Convert to .ipynb format to open with Jupyter.This research was conducted under USDA Agricultural Research Service, National Program 211 (Water Availability and Watershed Management).

The permanent equipment base (EPB) is a statistical basis of INSEE. It lists a wide range of equipment and services, whether commercial or non-market, available to the public throughout France on 1 January of each year. Equipment is defined as a service rendered by an establishment. Thus, an institution can be counted several times in the base, if it provides multiple services. In 2020, it covers 188 different types of services and equipment, divided into seven main areas: services to individuals, shops, education, health and social services, transport and travel, sports-leisure-culture and tourism. The BPE is built from various administrative sources. It collects data on access points to services intended for the population, or equipment. It is distributed here in coordinates (x,y) on the territory of Occitania. For further information/data: * Presentation of source: https://www.insee.fr/fr/metadonnees/source/serie/s1161?debut=0 * The database: https://www.insee.fr/fr/statistiques/3568638?sommaire=3568656 OPenIG makes this database available in vector format (Shapefile) after processing: filter on the Occitanie region, delete equipment without XY coordinates, delete equipment located outside the region, attach labels and convert the.CSV tabular file into a SHAPEFILE vector file.

This dataset contains the results of Food Standards Agency inspections of food outlets in England and Wales. The FSA inspect and rate food outlets, scores of 0-5 are returned with 0/1 indicating urgent and immediate action is required and 5 being exemplary. To pass, an outlet must score 3 or above. Even 0 and 1 scores are allowed to remain open, only outlets that are deemed to pose an immediate risk to health anre closed down. 91% of outlets pass, but that leaves 9% that fail. The dataset contains over 300,000 records. Data source from the Guardian website () who in turn sourced it from the Food Standards Agency. It was downloaded from the Guardian as a CSV and converted to a shp file in QGIS. Data was cleaned and converted from Txt to float using MMQGIS. The original data contains 340,000 records but the shp file has 310,000. The lost records did not contain lat/lon. The most probable reason for this is that they were outside catering or mobile catering and had no fixed address for their kitchens. This data is free to use, but acknowledging The FSA and the Guardian would be courteous. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2013-08-09 and migrated to Edinburgh DataShare on 2017-02-22.

Data for Figure Atlas.2 from Atlas of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure Atlas.2 shows WGI reference regions used in the (a) AR5 and (b) AR6 reports.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citations: For the report component from which the figure originates: Gutiérrez, J.M., R.G. Jones, G.T. Narisma, L.M. Alves, M. Amjad, I.V. Gorodetskaya, M. Grose, N.A.B. Klutse, S. Krakovska, J. Li, D. Martínez-Castro, L.O. Mearns, S.H. Mernild, T. Ngo-Duc, B. van den Hurk, and J.-H. Yoon, 2021: Atlas. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1927–2058, doi:10.1017/9781009157896.021

Iturbide, M. et al., 2021: Repository supporting the implementation of FAIR principles in the IPCC-WG1 Interactive Atlas. Zenodo. Retrieved from: http://doi.org/10.5281/zenodo.5171760

Figure subpanels

The figure has two panels, with data provided for both panels in the master GitHub repository linked in the documentation.

Data provided in relation to figure

This dataset contains the corner coordinates defining each reference region for the second panel of the figure, which contain coordinate information at a 0.44º resolution. The repository directory 'reference-regions' contains data provided for the reference regions as polygons in different formats (CSV with coordinates, R data, shapefile and geojson) together with R and Python notebooks illustrating the use of these regions with worked examples.

Data for reference regions for AR5 can be found here: https://catalogue.ceda.ac.uk/uuid/a3b6d7f93e5c4ea986f3622eeee2b96f

CMIP5 is the fifth phase of the Coupled Model Intercomparison Project. CMIP6 is the sixth phase of the Coupled Model Intercomparison Project. CORDEX is The Coordinated Regional Downscaling Experiment from the WCRP. AR5 and AR6 refer to the 5th and 6th Annual Report of the IPCC. WGI stands for Working Group I

Notes on reproducing the figure from the provided data

Data and figures produced by the Jupyter Notebooks live inside the notebooks directory. The notebooks describe step by step the basic process followed to generate some key figures of the AR6 WGI Atlas and some products underpinning the Interactive Atlas, such as reference regions, global warming levels, aggregated datasets. They include comments and hints to extend the analysis, thus promoting reusability of the results. These notebooks are provided as guidance for practitioners, more user friendly than the code provided as scripts in the reproducibility folder.

Some of the notebooks require access to large data volumes out of this repository. To speed up the execution of the notebook, in addition to the full code to access the data, we provide a data loading shortcut, by storing intermediate results in the auxiliary-material folder in this repository. To test other parameter settings, the full data access instructions should be followed, which can take long waiting times.

Sources of additional information

The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the figure on the IPCC AR6 website - Link to the report component containing the figure (Atlas) - Link to the Supplementary Material for Atlas, which contains details on the input data used in Table Atlas.SM.15. - Link to the code for the figure, archived on Zenodo. - Link to the necessary notebooks for reproducing the figure from GitHub. - Link to IPCC AR5 reference regions dataset

Data used in manuscript results and figures. Zip file contains a series of files: - all.shp - shapefile containing spatial representation of analysis areas - summary.csv - aggregate summary data for flood hazard and population impacts - vulnerability_summary.csv - aggregate summary of vulnerability data - vulnerability_summary_urban.csv - aggregate summary of urban vulnerability data - vulnerability.csv - detailed vulnerability data - all_{flood type}_depth{return period}_{climate scenario}_{year}{uncertainty}_v2.csv, detailed flood hazard and exposure data for given flood types, return periods, climate scenario, years and uncertainty percentiles

This dataset provides a detailed delineation and classification of endorheic basins on the Qinghai-Tibet Plateau, which would be beneficial for ecological analysis. It includes tables (.xls, .xlsx, .csv, .txt) and shapefiles (.shp).