Applied Geography

The study area of 245 km2 was divided into 16,158 squares (SQ) in the spatial matrix with a grid cell of 125 x 125 m. Each of the SQ had the tree and shrub mean percentage coverage (MPC) assigned as a result of automatic analysis of the identification of tree and shrub coverage in 2015 year, and starting from 1966 year and ending in 2010 year as a result of manual image interpretation.

The data set contains tables and figures on landuse land cover pattern, distribution of soil properties collected from 251 grid cells with resolution of 1 km by 1 km, and as well as soil suitability analysis of winter crops (Sunflower, Chilli, potato and mustard) in the Sagar Island. Table 1 and figure 1 represent area and percent of total geographical area under different landuse land covers derived from satellite images. Tables 2 and 3 represent variation in soil properties and the estimated soil suitability index values for the winter crops in the Island. Figures 2, 3 and 4 represent percent area under different classes and variation of soil properties (pH, SOC, EC, particle size distribution, macro-nutrients NPK percent distribution) while figure 5 represents variation in soil suitability index across the Island. We also supplied row data on LULC classes (with pixel numbers), measured soil properties, classes of soil properties and in detail the methodology we adopted in estimation of soil quality indices for the four above mentioned different crops in excel sheets.

Monitoring data of visitors of the New Forest, UK. Each track has been collected with an eTrex or eTrex Venture GPS device. Explanation of the headers can be found in Table A.3. The dataset contains 1563 tracks of which 1553 could be used for further analyses in our research.

This repository contains the R scripts and data necessary to recreate the accompanying analysis.

A gridded representation of the global erodibility index described in the linked paper. This erodibility index is the first estimate of sediment production potential of different rock types at given environmental conditions aimed at regional to global scale application. The index is 1.0 for acid plutonic rocks, metamorphic rocks, and carbonate sedimentary rocks; medium erodibility: 1.1 for acid volcanic rocks; 1.2 for mixed sedimentary rocks; medium erodibility - 1.5 for basic plutonic rocks, siliciclastic rocks of all grain sizes; 1.4 for basic volcanic rocks; high erodibility – 3.2 for unconsolidated sediments. The gridding of 0.1 degrees to align with the gridding of the BQUART model.

Water use in the cities along China’s South-North Water Transfer Project during 2006-2014

This data has been used to estimate Shannon's entropy for each zone and ward of the study area with respect to time. Shannon's entropy has been calculated from the amount of impervious land-cover and their proportion in each zone or ward. The amount of impervious land-cover has been estimated from classified image.

Results from remote sensing analysis described in Heilmayr et al., 2020

The dataset contains three (1860s, 1970s, 2013) detailed Wildland-Urban Interface (WUI) maps of the Polish Carpathians, including information on building density. The maps, available in the form of 10m raster GeoTIFF files, are based on the WUI definition of US Federal Register (USDA and USDI, 2001) as operationalized by (Radeloff et al., 2005), which distinguishes two kinds of WUI: intermix, where housing intermingle with wildland vegetation, and interface, where settlement abuts the wildland areas. Either WUI type requires a housing density higher than 6.17 houses/km2 (1 house/40 acres in the US context). In intermix WUI, there has to be also > 50% wildland vegetation, while the interface WUI, has < 50% wildland vegetation but is within 2.4 km of a wildland vegetation patch larger > 5 km2. Given the ecological context of the Polish Carpathians, we defined wildland vegetation as forests, because forests are the climax vegetation type below the treeline. To assess settlements, we analysed all buildings locations (residential and non-residential), because all buildings reflect human activities. The building density and forest cover share were calculated by using a 500m circular moving window algorithm. Acknowledgements The study was supported by the National Science Centre, Poland, contract no. UMO-2019/35/D/HS4/00117 and by the NASA Land Use and Land Cover Change Program.

Les unités spatiales sont les îlots de recensement de Statistique Canada de 2006 pour l'île de Montréal