he zip file contains a R raster file (spat_vars_Soysambu.grd, spat_vars_Soysambu.gri).This is a number of rasters ("brick"), with the variables:"DEM","SAVI","bushes","bush_edges","dist_communities","dist_roads","dist_boundaries","dist_infrastructure".DEM=Digital Elevation Model from ASTERSAVI=Soil Adjusted Vegetation Index, via Sentinel imagebushes=kNN clustered SAVI file, simplified for bush/open areabush_edges=bushes file after focal (moving window) operation, aimed at bringing out bush edges.dist_communities=distance to communities surrounding the conservancy;dist_roads=distance to roads dissecting the conservancy;dist_boundaries=distance to the conservancy's boundaries;dist_infrastructure=distance to park infrastructure (gates,settlements,offices).Read the file, after starting R, by:library(raster)raster::brick("/path/to/file/spat_vars_Soysambu.grd")

In this course, you will learn to work within the free and open-source R environment with a specific focus on working with and analyzing geospatial data. We will cover a wide variety of data and spatial data analytics topics, and you will learn how to code in R along the way. The Introduction module provides more background info about the course and course set up. This course is designed for someone with some prior GIS knowledge. For example, you should know the basics of working with maps, map projections, and vector and raster data. You should be able to perform common spatial analysis tasks and make map layouts. If you do not have a GIS background, we would recommend checking out the West Virginia View GIScience class. We do not assume that you have any prior experience with R or with coding. So, don't worry if you haven't developed these skill sets yet. That is a major goal in this course. Background material will be provided using code examples, videos, and presentations. We have provided assignments to offer hands-on learning opportunities. Data links for the lecture modules are provided within each module while data for the assignments are linked to the assignment buttons below. Please see the sequencing document for our suggested order in which to work through the material. After completing this course you will be able to: prepare, manipulate, query, and generally work with data in R. perform data summarization, comparisons, and statistical tests. create quality graphs, map layouts, and interactive web maps to visualize data and findings. present your research, methods, results, and code as web pages to foster reproducible research. work with spatial data in R. analyze vector and raster geospatial data to answer a question with a spatial component. make spatial models and predictions using regression and machine learning. code in the R language at an intermediate level.

ESRI GRID raster datasets were created to display and quantify oil shale resources for seventeen zones in the Piceance Basin, Colorado as part of a 2009 National Oil Shale Assessment. The oil shale zones in descending order are: Bed 44, A Groove, Mahogany Zone, B Groove, R-6, L-5, R-5, L-4, R-4, L-3, R-3, L-2, R-2, L-1, R-1, L-0, and R-0. Each raster cell represents a one-acre square of the land surface and contains values for either oil yield in barrels per acre, gallons per ton, or isopach thickness, in feet, as defined by the grid name: _b (barrels per acre), _g (gallons per ton), and _i (isopach thickness) where "" can be replaced by the name of the oil shale zone.

ESRI GRID raster datasets were created to display and quantify nahcolite resources for eight oil shale zones in the Piceance Basin, Colorado as part of a 2009 National Oil Shale and Nahcolite Assessment. The zones in descending order are: L-5, R-5, L-4, R-4, L-3, R-3, L-2, and R-2. Each raster cell represents a one-acre square of the land surface and contains values for nahcolite tonnage. The gridnames follow the naming convention _n, where "" can be replaced by the name of the oil shale zone.

This data is necessary to run the i-SoMPE WP1 R Project available on GitLab (https://gitlab.com/heoi/i-sompe-wp1)

The raster_runif data source covers the Flanders and Brussels region and has a resolution of 32 meters. The raster cells with non-missing values match the value-cells of the GRTSmaster_habitats data source with a small buffer added. Every raster cell has a random value between 0 and 1 according to the uniform distribution.

An example usage of this raster is its combination with the GRTSmaster_habitats and habitatmap_stdized data sources in order to draw an equal probability sample of habitat types in Flanders. Habitatmap_stdized contains polygons that are partially or fully covered by habitat types. The proportion of a certain type within a polygon is provided by the phab value. We can draw an equal probability sample size n for a certain habitat type as follows:

select all raster cells of GRTSmaster_habitats that overlap with the sampling frame of the target habitat type

keep the raster cells for which the raster_runif value is lower than the phab value of the habitat type within the polygon

finally select the n raster cells with the lowest GRTS ranking number.

The R-code for creating the raster_runif data source can be found in the GitHub repository 'n2khab-preprocessing' at commit ede43a4.

A reading function to return the data source in a standardized way into the R environment is provided by the R-package n2khab.

Aligning rasters such that their bounding extent and cell sizes match precisely is a tedious, time consuming, and challenging task. East-to-use tools have been lacking up until now. Many modeling approaches require rasters to be perfectly aligned. For example, a common workflow using R would be to stack rasters and then do subsequent predictive modeling using the stacked rasters as covariates. The Align Rasters Toolbox allows users to quickly and easily align rasters. It has options for working with rasters of differing cell sizes and extents. The Align Rasters without Expansion tool is suitable for situations in which the template raster is smaller than all inputs.

The zip file contains a R raster file (spat_vars_Soysambu.grd, spat_vars_Soysambu.gri). This is a number of rasters ("brick"), with the variables: "DEM","SAVI","bushes","bush_edges","dist_communities","dist_roads","dist_boundaries","dist_infrastructure".DEM=Digital Elevation Model from ASTERSAVI=Soil Adjusted Vegetation Index, via Sentinel imagebushes=kNN clustered SAVI file, simplified for bush/open areabush_edges=bushes file after focal (moving window) operation, aimed at bringing out bush edges.dist_communities=distance to communities surrounding the conservancy;dist_roads=distance to roads dissecting the conservancy;dist_boundaries=distance to the conservancy's boundaries;dist_infrastructure=distance to park infrastructure (gates,settlements,offices).Read the file, after starting R, by:library(raster)raster::brick("/path/to/file/spat_vars_Soysambu.grd")

ESRI GRID raster datasets were created to display and quantify oil shale resources for eighteen zones in the Uinta Basin, Utah and Colorado as part of a 2010 National Oil Shale Assessment. The oil shale zones in descending order are: Bed 76, Bed 44, A Groove, Mahogany Zone, B Groove, R-6, L-5, R-5, L-4, R-4, L-3, R-3, L-2, R-2, L-1, R-1, L-0, and R-0. Each raster cell represents a one-acre square of the land surface and contains values for either oil yield in barrels per acre, gallons per ton, or isopach thickness, in feet, as defined by the grid name: _b (barrels per acre), _g (gallons per ton), and _i (isopach thickness) where "" can be replaced by the name of the oil shale zone.

This collection of files are part of a larger dataset uploaded in support of Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB, DOE Project DE-EE0006726). Phase 1 of the GPFA-AB project identified potential Geothermal Play Fairways within the Appalachian basin of Pennsylvania, West Virginia and New York. This was accomplished through analysis of 4 key criteria: thermal quality, natural reservoir productivity, risk of seismicity, and heat utilization. Each of these analyses represent a distinct project task, with the fifth task encompassing combination of the 4 risks factors. Supporting data for all five tasks has been uploaded into the Geothermal Data Repository node of the National Geothermal Data System (NGDS).

This submission comprises the data for Thermal Quality Analysis (project task 1) and includes all of the necessary shapefiles, rasters, datasets, code, and references to code repositories that were used to create the thermal resource and risk factor maps as part of the GPFA-AB project. The identified Geothermal Play Fairways are also provided with the larger dataset. Figures (.png) are provided as examples of the shapefiles and rasters. The regional standardized 1 square km grid used in the project is also provided as points (cell centers), polygons, and as a raster. Two ArcGIS toolboxes are available: 1) RegionalGridModels.tbx for creating resource and risk factor maps on the standardized grid, and 2) ThermalRiskFactorModels.tbx for use in making the thermal resource maps and cross sections. These toolboxes contain item description documentation for each model within the toolbox, and for the toolbox itself. This submission also contains three R scripts: 1) AddNewSeisFields.R to add seismic risk data to attribute tables of seismic risk, 2) StratifiedKrigingInterpolation.R for the interpolations used in the thermal resource analysis, and 3) LeaveOneOutCrossValidation.R for the cross validations used in the thermal interpolations.

Some file descriptions make reference to various 'memos'. These are contained within the final report submitted October 16, 2015.

Each zipped file in the submission contains an 'about' document describing the full Thermal Quality Analysis content available, along with key sources, authors, citation, use guidelines, and assumptions, with the specific file(s) contained within the .zip file highlighted.

UPDATE: Newer version of the Thermal Quality Analysis has been added here: https://gdr.openei.org/submissions/879 (Also linked below) Newer version of the Combined Risk Factor Analysis has been added here: https://gdr.openei.org/submissions/880 (Also linked below) This is one of sixteen associated .zip files relating to thermal resource interpolation results within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains the binary grid (raster) for the predicted depth to 100 degrees C.

The sixteen files contain the results of the thermal resource interpolation as binary grid (raster) files, images (.png) of the rasters, and toolbox of ArcGIS Models used. Note that raster files ending in “pred” are the predicted mean for that resource, and files ending in “err” are the standard error of the predicted mean for that resource. Leave one out cross validation results are provided for each thermal resource.

Several models were built in order to process the well database with outliers removed. ArcGIS toolbox ThermalRiskFactorModels contains the ArcGIS processing tools used. First, the WellClipsToWormSections model was used to clip the wells to the worm sections (interpolation regions). Then, the 1 square km gridded regions (see series of 14 Worm Based Interpolation Boundaries .zip files) along with the wells in those regions were loaded into R using the rgdal package. Then, a stratified kriging algorithm implemented in the R gstat package was used to create rasters of the predicted mean and the standard error of the predicted mean. The code used to make these rasters is called StratifiedKrigingInterpolation.R Details about the interpolation, and exploratory data analysis on the well data is provided in 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (Smith, 2015), contained within the final report.

The output rasters from R are brought into ArcGIS for further spatial processing. First, the BufferedRasterToClippedRaster tool is used to clip the interpolations back to the Worm Sections. Then, the Mosaic tool in ArcGIS is used to merge all predicted mean rasters into a single raster, and all error rasters into a single raster for each thermal resource.

A leave one out cross validation was performed on each of the thermal resources. The code used to implement the cross validation is provided in the R script LeaveOneOutCrossValidation.R. The results of the cross validation are given for each thermal resource.

Other tools provided in this toolbox are useful for creating cross sections of the thermal resource. ExtractThermalPropertiesToCrossSection model extracts the predicted mean and the standard error of predicted mean to the attribute table of a line of cross section. The AddExtraInfoToCrossSection model is then used to add any other desired information, such as state and county boundaries, to the cross section attribute table. These two functions can be combined as a single function, as provided by the CrossSectionExtraction model.

This raster represents a continuous surface of sage-grouse habitat suitability index (HSI, created using ArcGIS 10.2.2) values for Nevada during spring, which is a surrogate for habitat conditions during the sage-grouse breeding and nesting period. Summary of steps to create Habitat Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial distribution) that contrasted data from multiple environmental factors at used sites (telemetry locations) and available sites (random locations). Predictor variables for the model represented vegetation communities at multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness, and slope. Vegetation data was derived from various mapping products, which included NV SynthMap (Petersen 2008, SageStitch (Comer et al. 2002, LANDFIRE (Landfire 2010), and the CA Fire and Resource Assessment Program (CFRAP 2006). The analysis was updated to include high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation, and bare ground (C. Homer, unpublished; based on the methods of Homer et al. 2014, Xian et al. 2015 ) and conifer (primarily pinyon-juniper, P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014); additional telemetry location data from field sites in 2014 were added to the dataset. The dataset was then split according calendar date into three seasons (spring, summer, winter). Summer included telemetry locations (n = 14,058) from mid-March to June. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum of 100 locations from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven subregions in winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed of HSI values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females (i.e., nesting and with broods). Insufficient data were available to allow for estimation of this habitat type for all seasons throughout the study area extent. A Resource Selection Function (RSF) was calculated using R Software (v 3.13) for each subregion and using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models. Subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall statewide HSI whereby a relative probability of occurrence was calculated for each raster cell during the spring. In order to account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north and south extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic and xeric regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale between zero and one, then mosaicked once more into a state-wide extent. REFERENCES: California Forest and Resource Assessment Program (CFRAP). 2006. Statewide Land Use / Land Cover Mosaic. [Geospatial data.] California Department of Forestry and Fire Protection, http://frap.cdf.ca.gov/data/frapgisdata-sw-rangeland-assessment_data.php Census 2010. TIGER/Line Shapefiles. Urban Areas [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2014. TIGER/Line Shapefiles. Roads [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2015. TIGER/Line Shapefiles. Blocks [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online) Comer, P., Kagen, J., Heiner, M., and Tobalske, C. 2002. Current distribution of sagebrush and associated vegetation in the western United States (excluding NM). [Geospatial data.] Interagency Sagebrush Working Group, http://sagemap.wr.usgs.gov Homer, C.G., Aldridge, C.L., Meyer, D.K., and Schell, S.J. 2014. Multi-Scale Remote Sensing Sagebrush Characterization with Regression Trees over Wyoming, USA; Laying a Foundation for Monitoring. International Journal of Applied Earth Observation and Geoinformation 14, Elsevier, US. LANDFIRE. 2010. 1.2.0 Existing Vegetation Type Layer. [Geospatial data.] U.S. Department of the Interior, Geological Survey, http://landfire.cr.usgs.gov/viewer/ Mason, R.R. 1999. The National Flood-Frequency Program—Methods For Estimating Flood Magnitude And Frequency In Rural Areas In Nevada U.S. Geological Survey Fact Sheet 123-98 September, 1999, Prepared by Robert R. Mason, Jr. and Kernell G. Ries III, of the U.S. Geological Survey; and Jeffrey N. King and Wilbert O. Thomas, Jr., of Michael Baker, Jr., Inc. http://pubs.usgs.gov/fs/fs-123-98/ Peterson, E. B. 2008. A Synthesis of Vegetation Maps for Nevada (Initiating a 'Living' Vegetation Map). Documentation and geospatial data, Nevada Natural Heritage Program, Carson City, Nevada, http://www.heritage.nv.gov/gis Xian, G., Homer, C., Rigge, M., Shi, H., and Meyer, D. 2015. Characterization of shrubland ecosystem components as continuous fields in the northwest United States. Remote Sensing of Environment 168:286-300. NOTE: This file does not include habitat areas for the Bi-State management area and the spatial extent is modified in comparison to Coates et al. 2014

This data package includes spatial environmental and social layers for Shivamogga District, Karnataka, India that were considered as potential predictors of patterns in human cases of Kyasanur Forest Disease (KFD). KFD is a fatal tick-borne viral haemorrhagic disease of humans, that is spreading across degraded forest ecosystems in India. The layers encompass a range of fifteen metrics of topography, land use and land use change, livestock and human population density and public health resources for Shivamogga District across 1km and 2km study grids. These spatial proxies for risk factors for KFD that had been jointly identified between cross-sectoral stakeholders and researchers through a co-production approach. Shivamogga District is the District longest affected by KFD in south India. The layers are distributed as 1km and 2km GeoTiffs in Albers equal area conic projection. For KFD, spatial models incorporating these layers identified characteristics of forest-plantation landscapes at higher risk for human KFD. These layers will be useful for modelling spatial patterns in other environmentally sensitive infectious diseases and biodiversity within the district.

Methods Processing of environmental predictors of Kyasanur Forest Disease distribution

This file details the sources and processing of environmental predictors offered to the statistical analysis in the paper. All processing was performed in the raster package [1] of the R program [2] unless otherwise specified, with function names as specified below.

Topography predictors

Elevation data was extracted in tiles from Shuttle Radar Topography Mission data version 4 [3] an original resolution of 0.000833 degrees Latitude and Longitude resolution (approximately 90m by 90m grid cells). Tiles were mosaicked across the study region using the merge function. A slope value for each pixel was calculated (in degrees) using the terrain function of the raster package, and a focal window of 3 by 3 cells. Both the resulting elevation and slope rasters were cropped to the administrative boundaries of the Shivamogga District (raster package: crop function) and re-projected to an equal area projection (Albers equal area conic projection) using the projectRaster function (method=”bilinear”). Mean elevation and slope values were then calculated across the study 1km and 2km grid cells, using the aggregate function to average values across the appropriate number of ~90m grid cells and then the resample function to align the resulting grid to the study grids.

Landscape predictors

Metrics of the current availability (and fragmentation) of forest, agricultural and built-up land use types as well as that of water-bodies were extracted from the MonkeyFeverRisk Land Use Land Cover map of Shimoga. The latter was produced from classification of earth observation data from 2016 to 2018 using the methods described in the Supplementary information S3 file of the paper linked to this dataset. The LULC map had an original grid square resolution of 0.000269 degrees Latitude and Longitude resolution (or 30m x 28m grid cells) and nine different LULC classes. It was cropped to the administrative boundaries of the Shimoga District (raster package: crop function) and re-projected to the equal area projection (Albers equal area conic projection) using the projectRaster function (method=”ngb” for categorical data). The agriculture and fallow land classes were combined before landscape analysis (due to the difficulty of separating them accurately in the classification process).

An algorithm was developed in R to identify which of the pixels in the LULC map coincided with each 1km and 2km grid cell of the study area. The ClassStat function of the SDM Tools package [4] was used to calculate the proportional area of each 1km or 2km grid cell landscape that was made up of a particular land class, as well patch density and edge density metrics for the forest classes as indicators of fragmentation and forest-agriculture interface habitat respectively (Fig. S2B). The proportional area values (pi) of the n different forest classes (wet evergreen forest, moist deciduous forest, dry deciduous forest and plantation) were used to calculate an index of forest type diversity per grid cell as follows, after Shannon & Weaver (1949) [5]:

H'= -1npi(lognpi)

Metrics of longer term forest changes in Shimoga since 2000 were derived from a global product by Hansen et al. (2013) [6] available at a spatial resolution of 1 arc-second per pixel, (~ 30 meters per pixel at equator). Forest loss during the period 2000–2014, is defined as a stand-replacement disturbance, or a change from a forest to non-forest state, encoded as either 1 (loss) or 0 (no loss). Forest gain during the period 2000–2012, is defined as a non-forest to forest change entirely within the study period, encoded as either 1 (gain) or 0 (no gain).These layers were again cropped to the administrative boundaries of the Shimoga District (raster package: crop function) and re-projected to an equal area projection (Albers equal area conic projection) using the projectRaster function (method=”ngb”) in R. An algorithm was developed in R to identify which of the pixels in the loss and gain rasters coincided with each 1km and 2km grid cell of the study area. The ClassStat function of the SDM Tools package [4] was used to calculate the proportional area of each 1km or 2km grid cell that was made up of loss pixels or gain pixels. Forest gain and loss are very highly correlated (r=0.986) and occur in similar places in the landscape (Fig. S2C). Forest loss was a much more common transition than a forest gain affecting 1.2% of land pixels rather than 0.16% of land pixels for forest gain.

To assess how forest loss or gain from a global product like Hansen et al. (2013) should be interpreted locally in south India, we analysed how the loss and gain pixels from Hansen et al. 2013 coincided with classes in the MonkeyFeverRisk LULC map (by extracting the value of the LULC map for the centroids of loss or gain pixels).

The distribution of loss and gain pixels across forest classes from the MonkeyFeverRisk LULC map is shown in Table 1. Locations categorised as a loss by Hansen et al. were most commonly classified currently as plantation, followed by moist evergreen forest, followed by

moist or dry deciduous forest by the MonkeyFeverRisk LULC map. The pattern was similar for the gain pixels. Since not all forest loss pixels were non-forest in the current day and not all forest gain pixels were forest in the current day, the precise meaning of the Hansen et al. (2013) forest loss layer was unclear for south India, though we expect that it is at least indicative of areas where the forest has undergone a larger degree of change since 2000.

Table 1: Percentage of loss (n= 108398) and gain (n= 14646) land pixels from the global Hansen et al. (2013) product that fall into different forest classes according to the MonkeyFeverRisk LULC map

    Land use class


    Gain


    Loss




    moist evergreen


    30.4


    26.1




    moist deciduous


    6.5


    16.2




    dry deciduous


    3.0


    9.7




    plantation


    46.2


    37.2




    Non-forest classes


    14.0


    10.9

Host and public health predictors

Livestock host density data, namely buffalo and indigenous cattle densities in units of total head per village were obtained from Department of Animal Husbandry, Dairying and Fisheries, Government of India Census from 2011 at village level. These were linked to village boundaries from the Survey of India using the village census codes in R. The village areas were calculated from the spatial polygons dataframe of villages using the rgeos package in R, so that the total head per village metrics could be convert into an areal density of buffalo and indigenous cattle per km and then rasterized at 1km and 2km using the rasterize function of the raster package.

The human population size and public health metrics were obtained from the Government of India Population Census 2011. The human population size (census field TOT_P) was again linked to the spatial polygon village boundaries using the census village code (census field VCT_2011) and converted to an areal metric of population density per km and rasterized at 1km and 2km as above. The number of medics per head of population was derived by summing all doctors and para-medicals “in position” across all types of health centres, clinics and dispensaries per village and dividing by the total population of the village (TOT_P) and then linked to village boundaries and rasterized as above. The proximity to health centres was a categorical variable derived from the “Primary.Health.Centre..Numbers” field, where 1 = Primary Health Centre (PHC) within village boundary, 2 = PHC within 5km of village, 3=PHC within 5-10km of village, 4= PHC further than 10km from village. It was linked to village boundaries and rasterized as above.

The resulting raster layers for all predictors were saved in GeoTiff format.

References

Robert J. Hijmans (2017). raster: Geographic Data Analysis and Modeling. R package version 2.6-7. https://CRAN.R-project.org/package=raster
R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.URL https://www.R-project.org/ 
Jarvis, A., Reuter, I., Nelson, A., Guevara, E. Hole-filled SRTM for the globe Version 4. 2008.
VanDerWal, J., Falconi, L., Januchowski, S., Shoo, L., & Storlie, C. (2014). SDMTools: Species Distribution Modelling Tools: Tools for processing data associated with species distribution modelling exercises. R

This is the updated tephra fall distribution map dataset for assessing tephra fall hazards (thickness in mm) in Japan. This database was created using the database of Suto et al. (2007) (https://www.jstage.jst.go.jp/article/bullgsj/58/9-10/58_261/_article/-char/ja/) with ArcGIS.

This version is a revised version of 1.3. An error in thickness data was found in Unzen_1991_6_8 in ver 1.2 and 1.3. Please use this version instead of older versions. We have confirmed that this error does not affect the discussion in Uesawa et al. (2022).

The exact method is available in Uesawa et al. (2022) appliedvolc.biomedcentral.com/articles/10.1186/s13617-022-00126-x. Please find the scripts to create the hazard curve at https://github.com/s-uesawa/Prototype-TephraDB-Japan.

The coordinate ranges handled in this database are as follows:
125E/150E/25N/47N (JGD2000)

Acknowledgments: Comments from Dr. K. Komura@CRIEPI were helpful.

The data source file is a monolayered GeoTIFF in the FLT8S datatype. In GRTSmh_base4frac, the decimal (i.e. base 10) integer values from the raster data source GRTSmaster_habitats (link) have been converted into base 4 fractions, using a precision of 13 digits behind the decimal mark (as needed to cope with the range of values). For example, the integer 16 (= 4^2) has been converted into 0.0000000000100 and 4^12 has been converted into 0.1000000000000.

Long base 4 fractions seem to be handled and stored easier than long (base 4) integers. This approach follows the one of Stevens & Olsen (2004) to represent the reverse hierarchical order in a GRTS sample as base-4-fraction addresses.

See R-code in the GitHub repository 'n2khab-preprocessing' at commit ecadaf5 for the creation from the GRTSmaster_habitats data source.

A reading function to return the data source in a standardized way into the R environment is provided by the R-package n2khab.

Beware that not all GRTS ranking numbers are present in the data source, as the original GRTS raster has been clipped with the Flemish outer borders (i.e., not excluding the Brussels Capital Region).

Overview:
The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.

The Copernicus DEM for Europe at 3 arcsec (0:00:03 = 0.00083333333 ~ 90 meter) in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/).

Processing steps:
The original Copernicus GLO-30 DEM contains a relevant percentage of tiles with non-square pixels. We created a mosaic map in VRT format and defined within the VRT file the rule to apply cubic resampling while reading the data, i.e. importing them into GRASS GIS for further processing. We chose cubic instead of bilinear resampling since the height-width ratio of non-square pixels is up to 1:5. Hence, artefacts between adjacent tiles in rugged terrain could be minimized:

gdalbuildvrt -input_file_list list_geotiffs_MOOD.csv -r cubic -tr 0.000277777777777778 0.000277777777777778 Copernicus_DSM_30m_MOOD.vrt

In order to reduce the spatial resolution to 3 arc seconds, weighted resampling was performed in GRASS GIS (using r.resamp.stats -w and the pixel values were scaled with 1000 (storing the pixels as integer values) for data volume reduction. In addition, a hillshade raster map was derived from the resampled elevation map (using r.relief, GRASS GIS). Eventually, we exported the elevation and hillshade raster maps in Cloud Optimized GeoTIFF (COG) format, along with SLD and QML style files.

Projection + EPSG code:
Latitude-Longitude/WGS84 (EPSG: 4326)

Spatial extent:
north: 82:00:30N
south: 18N
west: 32:00:30W
east: 70E

Spatial resolution:
3 arc seconds (approx. 90 m)

Pixel values:
meters * 1000 (scaled to Integer; example: value 23220 = 23.220 m a.s.l.)

Software used:
GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief)

Original dataset license:
https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf

Processed by:
mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/)

A spatial tiling index designed for storage of file-based image and other raster (i.e., LiDAR elevation, landcover) data sets. An irregular grid of overlapping polygons, each enclosing its respective Public Land Survey System (PLSS) township in an orthogonal polygon minimally encompassing all portions of that township, i.e., minimum bounding rectangle. The amount of overlap between adjacent tiles varies depending on the geometry of the underlying township. Currently extended to include all townships within or partially within King County as well as those townships in the southwestern portion of Snohomish County included within King County's ESA/SAO project area. The name of the spatial index is derived from the acronym (I)n(D)e(X) (P)olygons for (T)ownship-(R)ange, (M)inimum (B)ounding (R)ectangle, or idxptrmbr. Tile label is the t(township number)r(range number)as in t24r02. The meridian zone identifiers, N for townships and E for range is inferred as this index is intended as a local index for ease of use by the majority of users of GIS data. Lowercase identifiers are used for consistency between Unix and Windows OS storage. This index or tile level is the primary user-access level for most LiDAR elevation, orthoimagery and high-resolution raster landcover data. However, not all image and raster data is stored at the tiling level if a given data's resolution does not justify storing the data as multiple tiles.

PÁRAMO SOC MODELING: REPRODUCIBLE WORKFLOW ========================================== Overview -------- This repository contains two R scripts to (1) fit and validate a spatially-aware Random Forest model for soil organic carbon (SOC) in Colombian paramos, and (2) generate national wall-to-wall SOC predictions and sector-level summaries. Scripts ------- 1) soilCmodel.R - Builds land-cover labels (Disturbed, Forest, Paramo). For modeling, the former "Nosoil" class is collapsed into Disturbed. - Extracts rasters to points and clusters points on a 100 m grid to avoid leakage across train/test folds. - Runs grouped v-fold spatial cross-validation, tunes RF by inner OOB RMSE, computes diagnostics (OOB, random 5-fold, spatial CV) in SOC space using Duan smearing for unbiased back-transform. - Saves the finalized model and artifacts for prediction and reporting. 2) soilCprediction.R - Loads the finalized model and the Duan smearing factor. - Assembles the predictor stack, predicts log-SOC, applies smearing, and outputs SOC density in Mg C ha^-1. Pixels flagged as Nosoil are set to 0. - Converts density to Mg per cell using true cell area in hectares. - Aggregates totals and statistics by paramo sector and land-cover class. - Produces figures and CSVs for the paper. Directory layout (edit paths in scripts if different) ----------------------------------------------------- geo_dir = .../Paramo carbon map/GEographic stats_dir = .../Paramo carbon map/stats2 Required inputs --------------- Points (CSV): - carbon_site.csv with columns: Longitude, Latitude, CarbonMgHa Predictor rasters (aligned to land-cover grid, ~100 m): - dem3_100.tif, TPI100.tif, slope100.tif - temp2.tiff (mean T), tempmax2.tiff, precip2.tiff, soilmoist2.tiff - Cobertura100.tif (grid target) Vectors: - corine_paramo2.* (CORINE polygons; fields include corinetext, Clasificac) - paramos.* (paramo sectors; field NOMBRE_COM) - paramos_names.csv (two columns: NOMBRE_COM, Sector) for short plot labels CRS expectations: - Input points in EPSG:4326 - Clustering for spatial CV uses EPSG:3116 (MAGNA-SIRGAS / Bogota) - Rasters are internally aligned to the Cobertura100.tif grid Software requirements --------------------- Tested with R >= 4.3 and packages: terra, sf, dplyr, tidyr, ranger, rsample, yardstick, vip, ggplot2, purrr, forcats, scales, stringr, bestNormalize (optional) Install once in R: install.packages(c( "terra","sf","dplyr","tidyr","ranger","rsample","yardstick","vip", "ggplot2","purrr","forcats","scales","stringr","bestNormalize" )) Each script starts with: suppressPackageStartupMessages({ library(terra); library(sf); library(dplyr); library(tidyr) library(ranger); library(rsample); library(yardstick); library(vip) library(ggplot2); library(purrr); library(forcats); library(scales); library(stringr) }) How to run ---------- 1) Fit + validate the model Rscript soilCmodel.R Outputs (in stats_dir): - rf_full.rds (finalized ranger model) - smear_full.txt (Duan smearing factor) - variable_importance.csv (permutation importance, mean and sd) - diagnostics.txt (OOB, random 5-fold, spatial CV metrics) - OVP_spatialCV.png (observed vs predicted, pooled folds) - imp_bar_RF.png (RF importance with error bars) 2) Predict wall-to-wall + summarize Rscript soilCprediction.R Outputs (in stats_dir): - SOC_pred_final_RF_GAM.tif (SOC density, Mg C ha^-1) - SOC_totals_by_sector.csv (Tg C by sector x land-cover) - SOC_by_sector_LC_Tg_mean_sd.csv (Tg C plus area-weighted mean/sd in Mg C ha^-1) - SOC_national_mean_sd_by_LC.csv (national area-weighted mean/sd in Mg C ha^-1) - sector_bars_TgC.png (stacked bars by sector using short labels) Units ----- - SOC density outputs are in Mg C ha^-1. - Totals are in Mg and reported as Tg (Mg / 1e6). - Cell areas are computed with terra::cellSize(..., unit="m")/10000 to ensure hectares. Modeling notes -------------- - Learner: ranger Random Forest, permutation importance, respect.unordered.factors="partition". - Response transform: log or Yeo-Johnson (when enabled), with Duan smearing to remove retransformation bias when returning to SOC space. - Spatial CV: grouped v-fold using 100 m clusters to prevent leakage. - Land cover: modeling uses three classes (Disturbed includes former Nosoil). In mapping, Nosoil pixels are forced to 0 SOC. Troubleshooting --------------- - If a write fails with "source and target filename cannot be the same", write to a new filename. - If sector labels appear misaligned in plots, normalize strings and join short names via paramos_names.csv. - If national means look ~100x too small, ensure means are area-weighted over valid pixels only (LC present AND SOC not NA), and that areas are in hectares. - If any join fails, confirm the sector name field (NOMBRE_COM) exists in paramos.shp and in paramos_names.csv. Reproducibility --------------- - set.seed(120) is used throughout. - All area computations are in hectares. - Scripts are deterministic given the same inputs and package versions.

Abstract \r

\r This dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement.\r \r The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.\r \r \r \r The Groundwater (GW) quantiles are extracted from the Groundwater modelling outputs. Dataset prepared for import into the Impact and Risk Analysis Database.\r \r

Dataset History \r

\r Drawdown percentile and exceedance probability values was extracted from groundwater model outputs. This was performed using a GIS routine to extract groundwater model raster values using the assessment units (as points) attributed with the regional water table aquifer layer and assigning the model value from the corresponding layer to each assessment unit.\r \r

Dataset Citation \r

\r XXXX XXX (2017) GAL GW Quantile Interpolation 20161013. Bioregional Assessment Derived Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/49f20390-3340-4b08-b1dc-370fb919d34c.\r \r

Dataset Ancestors \r

\r * Derived From Surface Geology of Australia, 1:2 500 000 scale, 2012 edition\r \r * Derived From Galilee Drawdown Rasters\r \r * Derived From Galilee model HRV receptors gdb\r \r * Derived From Queensland petroleum exploration data - QPED\r \r * Derived From Galilee groundwater numerical modelling AEM models\r \r * Derived From Galilee drawdown grids\r \r * Derived From Three-dimensional visualisation of the Great Artesian Basin - GABWRA\r \r * Derived From Geoscience Australia GEODATA TOPO series - 1:1 Million to 1:10 Million scale\r \r * Derived From Phanerozoic OZ SEEBASE v2 GIS\r \r * Derived From Galilee Hydrological Response Variable (HRV) model\r \r * Derived From QLD Department of Natural Resources and Mines Groundwater Database Extract 20142808\r \r * Derived From GAL Assessment Units 1000m 20160522 v01\r \r * Derived From Galilee Groundwater Model, Hydrogeological Formation Extents v01\r \r * Derived From BA ALL Assessment Units 1000m Reference 20160516_v01\r \r * Derived From GAL Aquifer Formation Extents v01\r \r * Derived From Queensland Geological Digital Data - Detailed state extent, regional. November 2012\r \r * Derived From BA ALL Assessment Units 1000m 'super set' 20160516_v01\r \r * Derived From GAL Aquifer Formation Extents v02\r \r

scripts.zip

arcgisTools.atbx: terrainDerivatives: make terrain derivatives from digital terrain model (Band 1 = TPI (50 m radius circle), Band 2 = square root of slope, Band 3 = TPI (annulus), Band 4 = hillshade, Band 5 = multidirectional hillshades, Band 6 = slopeshade). rasterizeFeatures: convert vector polygons to raster masks (1 = feature, 0 = background).

makeChips.R: R function to break terrain derivatives and chips into image chips of a defined size. makeTerrainDerivatives.R: R function to generated 6-band terrain derivatives from digital terrain data (same as ArcGIS Pro tool). merge_logs.R: R script to merge training logs into a single file. predictToExtents.ipynb: Python notebook to use trained model to predict to new data. trainExperiments.ipynb: Python notebook used to train semantic segmentation models using PyTorch and the Segmentation Models package. assessmentExperiments.ipynb: Python code to generate assessment metrics using PyTorch and the torchmetrics library. graphs_results.R: R code to make graphs with ggplot2 to summarize results. makeChipsList.R: R code to generate lists of chips in a directory. makeMasks.R: R function to make raster masks from vector data (same as rasterizeFeatures ArcGIS Pro tool).

terraceDL.zip

dems: LiDAR DTM data partitioned into training, testing, and validation datasets based on HUC8 watershed boundaries. Original DTM data were provided by the Iowa BMP mapping project: https://www.gis.iastate.edu/BMPs. extents: extents of the training, testing, and validation areas as defined by HUC 8 watershed boundaries. vectors: vector features representing agricultural terraces and partitioned into separate training, testing, and validation datasets. Original digitized features were provided by the Iowa BMP Mapping Project: https://www.gis.iastate.edu/BMPs.

The land use and cover category contains the "geostat25" and "wslhabmap" datasets.

The geostat25 dataset describes the land use and cover of Switzerland. After resampling the “Downscaled Land Use/Land Cover of Switzerland” source data (Giuliani et al., 2022) to the SWECO25 grid, we generated individual layers for the 65 land use and cover classes and the 3 time periods (1992-1997, 2004-2009, and 2013-2018) that were available. For each class and period, we provided the binary maps (0 or 1) and computed 13 focal statistics layers by applying a cell-level function calculating the average percentage cover value for a given class in a circular moving window of 13 radii ranging from 25m to 5km. This dataset includes a total of 2,730 layers. Final values were rounded and multiplied by 100.

The wslhabmap dataset (land use and cover category) describes the natural habitats of Switzerland. After rasterizing and resampling the “Habitat Map of Switzerland v1” source data (Price et al., 2021) to the SWECO25 grid, we generated individual layers for 41 categories (32 classes and 9 groups). The groups correspond to the first level of the TypoCH classification and the classes to the second level. For details on the TypoCH classification see Delarze, R., Gonseth, Y., Eggenberg, S., & Vust, M. (2015). Guide des milieux naturels de Suisse : Écologie, menaces, espèces caractéristiques. Rossolis. For each of the 41 categories, we provided the binary maps (0 or 1) and computed 13 focal statistics layers by applying a cell-level function calculating the average percentage cover value for a given category in a circular moving window of 13 radii ranging from 25m to 5km. This dataset includes a total of 574 layers. Final values were rounded and multiplied by 100.

The detailed list of layers available is provided in SWECO25_datalayers_details_lulc.csv and includes information on the category, dataset, variable name (long), variable name (short), period, sub-period, start year, end year, attribute, radii, unit, and path.

References:

G. Giuliani, D. Rodila, N. Külling, R. Maggini, A. Lehmann, Downscaling Switzerland Land Use/Land Cover Data Using Nearest Neighbors and an Expert System. Land 11, 615 (2022).

B. Price, Huber, N., Ginzler, C., Pazúr, R., Rüetschi, M., "The Habitat Map of Switzerland v1," (Birmensdorf, Switzerland, 2021)

Külling, N., Adde, A., Fopp, F., Schweiger, A. K., Broennimann, O., Rey, P.-L., Giuliani, G., Goicolea, T., Petitpierre, B., Zimmermann, N. E., Pellissier, L., Altermatt, F., Lehmann, A., & Guisan, A. (2024). SWECO25: A cross-thematic raster database for ecological research in Switzerland. Scientific Data, 11(1), Article 1. https://doi.org/10.1038/s41597-023-02899-1

V2: metadata update