Fine-scale Habitat Environmental Predictor Layers for rare Cacti and Herbaceous Plants in the Arid Southwest

This dataset contains raster layers of environmental characteristics used to predict habitat suitability for multiple species of perennial cactus and herbs in the arid southwest. These data were developed from Sentinel-2 imagery, a digital elevation model, and one obtained from the Google Earth Engine data catalog. These datasets can be used to model habitat suitability of species with distributions that are influenced by terrain and soil characteristics.

These layers are grouped into 2 categories:

sentinel.zip: this file includes each of the raster layers listed below, along with sentinel.xml
bgsi_10m.tif: Sentinel Bare Ground Index (bgsi): bare soil index to identify barren ground (Nguyen et al. 2021).
clay_10m.tif: Clay (clay): identify soils rich in Al-OH, such as those clay and sulphate minerals produced from hydrothermal fluids and associated with porphyry copper deposits and vegetation (Imbroane et al. 2007).
Fe2O3_10m.tif: Iron Oxide simple ratio (Fe203): Highlights surface soils that are rich in ferric iron oxide (limonite), for the hydrothermal alteration or the oxidation of Fe-Mg silicates (Imbroane et al., 2007).
Ferrous_10m.tif: Ferrous minerals simple ratio (ferrous): Highlights surface soils that are rich in ferrous iron (Imbroane et al., 2007).
gyp_10m.tif: Gypsum simple ratio (gypsum): Highlights surface soils that are rich in gypsum (Radwin & Bowen 2021).
ri_10m.tif: Rock index (ri): Highlights surface soils that are more likely to contain minerals (Imbroane et al. 2007).

terrain.zip: this file includes each of the raster layers listed below, along with terrain.xml
mtpi_10m.tif: Multi-Scale Topographic Position Index (mtpi): mTPI distinguishes ridge from valley forms. It is calculated using elevation data for each location subtracted by the mean elevation within a neighborhood. (Theobald et al. 2015)
twi_10m.tif: Terrain wetness index (twi): Describes how terrain controls hydrology and soil moisture and identifies potential zones of water accumulation, runoff generation, and soil saturation based entirely on a digital elevation model (Moore et al. 1991)
chili_10m.tif: Continuous Heat-Insolation Load Index (chili): surrogate for effects of insolation and topographic shading on evapotranspiration represented by calculating insolation at early afternoon, sun altitude equivalent to equinox (Theobald et al. 2015).

All layers were produced as geotiff raster files.

References:
Imbroane, M. A., Melenti, C. and Gorgan, D. "Mineral Explorations by Landsat Image Ratios," Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2007), Timisoara, Romania, 2007, pp. 335-340, doi: 10.1109/SYNASC.2007.52
Moore, I.D., R.B. Grayson, and A.R. Ladson, 1991. Digital Terrain Modeling: A Review of Hydrological, Geomorphological, and Biological Applications. Hydrological Processes 5:3-30. https://doi.org/10.1002/hyp.3360050103
Nguyen, C.T., Chidthaisong A., Diem, P.K., Huo, L. 2021. A Modified Bare Soil Index to Identify Bare Land Features during Agricultural Fallow-Period in Southeast Asia Using Landsat 8. 10, 231. Page 3. URL: https://www.mdpi.com/2073-445X/10/3/231/pdf, https://doi.org/10.3390/land10030231
Radwin, M. and Bowen, B. B. 2021. "Mapping mineralogy in evaporite basins through time using multispectral Landsat Data: Examples from the Bonneville Basin, Utah, USA." Earth Surface Processes and Landforms, 46(6), 1111-1126. https://doi.org/10.1002/esp.5089.
Theobald, D. M., Harrison-Atlas, D., Monahan, W. B., and Albano, C. M. 2015. Ecologically-relevant maps of landforms and physiographic diversity for climate adaptation planning. PloS one, 10(12), e0143619. https://doi.org/10.1371/journal.pone.0143619