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hydroweight: Inverse distance-weighted rasters and landscape attributes

Brian W Kielstra; Robert W Mackereth; Stephanie J Melles; Erik JS Emilson

Environmental scientists often want to understand how upland features like forest cover affect receiving waterbodies (e.g., water quality). Upland areas are characterized by deriving various landscape attributes (e.g., % forest cover in catchment). However, this approach often assumes that the influence of upland features on receiving waterbodies is independent of their proximity to the waterbodies. This may not adequately describe important spatial patterns within the upland area, for example, if there was higher forest cover near the waterbody and lower forest cover farther away. The R statistical software package hydroweight helps to account for these patterns.

hydroweight calculates landscape attributes based on distances to waterbodies — areas nearby have more influence than those farther away (i.e., inverse distance-weighting). We implement various scenarios described by Peterson et al. (2011) that include different types of straight-line and flow-path distances to waterbodies. We add to the literature of current implementations (IDW-Plus in ArcGIS software and rdwplus in R statistical software through GRASS GIS spatial software). However, hydroweight provides a set of simple and flexible functions to accommodate a wider set of scenarios and statistics (e.g., numerical and categorical raster and polygon inputs) in R using WhiteboxTools spatial software.

Please use hydroweight Github for most up-to-date version.

Citations

Lindsay, J.B. (2016). Whitebox GAT: A case study in geomorphometric analysis. Computers & Geosciences, 95: 75-84. https://doi.org/10.1016/j.cageo.2016.07.003

Peterson, E. E., Sheldon, F., Darnell, R., Bunn, S. E., & Harch, B. D. (2011). A comparison of spatially explicit landscape representation methods and their relationship to stream condition. Freshwater Biology, 56(3), 590–610. https://doi.org/10.1111/j.1365-2427.2010.02507.x

Peterson, E. E. & Pearse, A. R. (2017). IDW‐Plus: An ArcGIS Toolset for calculating spatially explicit watershed attributes for survey sites. Journal of the American Water Resources Association, 53(5): 1241–1249. https://doi.org/10.1111/1752-1688.12558

Pearse A., Heron G., & Peterson E. (2019). rdwplus: An Implementation of IDW-PLUS. R package version 0.1.0. https://CRAN.R-project.org/package=rdwplus

Wu, Q. (2020). whitebox: ‘WhiteboxTools’ R Frontend. R package version 1.4.0. https://github.com/giswqs/whiteboxR

We acknowledge the funding support of Natural Resources Canada, the Ontario Ministry of Natural Resources, and a Natural Sciences and Engineering Research Council of Canada Strategic Partnership Grant (STPGP 521405-2018).
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  • Lindsay, J.B. (2016). Whitebox GAT: A case study in geomorphometric analysis. Computers & Geosciences, 95: 75-84. https://doi.org/10.1016/j.cageo.2016.07.003

  • Peterson, E. E., Sheldon, F., Darnell, R., Bunn, S. E., & Harch, B. D. (2011). A comparison of spatially explicit landscape representation methods and their relationship to stream condition. Freshwater Biology, 56(3), 590–610. https://doi.org/10.1111/j.1365-2427.2010.02507.x

  • Peterson, E. E. & Pearse, A. R. (2017). IDW‐Plus: An ArcGIS Toolset for calculating spatially explicit watershed attributes for survey sites. Journal of the American Water Resources Association, 53(5): 1241–1249. https://doi.org/10.1111/1752-1688.12558

  • Pearse, A., Heron, G., & Peterson, E. (2019). rdwplus: An Implementation of IDW-PLUS. R package version 0.1.0. https://CRAN.R-project.org/package=rdwplus

  • R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

  • Wickham, H., Bryan, J. (2021). R Packages. 2nd edition. https://r-pkgs.org/.

  • Wu, Q. (2020). whitebox: 'WhiteboxTools' R Frontend. R package version 1.4.0. https://github.com/giswqs/whiteboxR

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