Package {rLakeHabitat}

Type:	Package
Title:	Interpolate Bathymetry and Quantify Physical Aquatic Habitat
Version:	1.0.3
Date:	2026-07-29
URL:	https://gitlab.com/tristanblechinger/rlakehabitat
BugReports:	https://gitlab.com/tristanblechinger/rlakehabitat/-/issues
Depends:	R (≥ 4.3.0)
Imports:	dplyr, terra, gstat, sf, ggplot2, gganimate, tidyterra, rLakeAnalyzer, isoband
Maintainer:	Tristan Blechinger <tblechin@uwyo.edu>
Description:	Offers bathymetric interpolation using Inverse Distance Weighted and Ordinary Kriging via the 'gstat' and 'terra' packages. Other functions focus on quantifying physical aquatic habitats (e.g., littoral, epliminion, metalimnion, hypolimnion) from interpolated digital elevation models (DEMs). Functions were designed to calculate these metrics across water levels for use in reservoirs but can be applied to any DEM and will provide values for fixed conditions. Parameters like Secchi disk depth or estimated photic zone, thermocline depth, and water level fluctuation depth are included in most functions.
License:	GPL (≥ 3)
VignetteBuilder:	knitr
Encoding:	UTF-8
RoxygenNote:	7.3.3
Suggests:	testthat (≥ 3.0.0), knitr, rmarkdown, spbal, ggspatial, httr, maptiles, mapview, tidyr
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2026-07-29 23:56:00 UTC; tblechin
Author:	Tristan Blechinger [aut, cre], Sean Bertalot [aut]
Repository:	CRAN
Date/Publication:	2026-07-30 11:10:08 UTC

Generate Animated Plot

Description

Generate an animated plot of littoral area at different water level increments from a raster digital elevation model (DEM).

Usage

animBathy(
  DEM,
  units = "m",
  littoral = TRUE,
  secchi = NULL,
  photic = NULL,
  stop = NULL,
  by = 1
)

Arguments

Details

We recommend using the calcPhotic function to estimate photic depth instead of using a Secchi disk measurement

Value

an animated ggplot object

Author(s)

Tristan Blechinger, Department of Zoology & Physiology, University of Wyoming

Examples

#load raster
DEM <- terra::rast(system.file("extdata", "example_raster.tif", package = 'rLakeHabitat'))

#run function
animBathy(DEM, units = 'm', littoral = TRUE, secchi = 1, by = 5)

Plot Bathymetry Map

Description

Generate a bathymetry map from a provided DEM raster with optional contours and depth labels.

Usage

bathyMap(
  DEM,
  contours = TRUE,
  start = NULL,
  end = NULL,
  by = 5,
  breaks = NULL,
  units = "m",
  labels = TRUE,
  textSize = 1.5,
  plotTitle = NULL
)

Arguments

Value

ggplot object

Author(s)

Tristan Blechinger, Department of Zoology & Physiology, University of Wyoming

Examples

#load raster
DEM <- terra::rast(system.file("extdata", "example_raster.tif", package = 'rLakeHabitat'))
#run function
bathyMap(DEM, contours = TRUE, units = 'm', labels = TRUE)

Calculate Hypsography

Description

Calculates area at each depth for a given waterbody.

Usage

calcHyps(DEM, DEMunits = "m", depthUnits = "m", by = 1, output = "values")

Arguments

Value

data frame of areas at each depth unit ("values") or a hypsography plot ("plot")

Author(s)

Tristan Blechinger, Department of Zoology & Physiology, University of Wyoming

Examples

#load raster
DEM <- terra::rast(system.file("extdata", "example_raster.tif", package = 'rLakeHabitat'))
#run function
calcHyps(DEM, DEMunits = 'm', depthUnits = 'm', by = 1, output = 'values')

Calculate Littoral Area

Description

Calculates littoral surface area (2D) of a given waterbody across water levels based on an average photic depth value.

Usage

calcLittoral(
  DEM,
  photic = NULL,
  secchi = NULL,
  DEMunits = "m",
  depthUnits = "m",
  by = 1,
  stop = NULL
)

Arguments

Details

We recommend using the calcPhotic function to estimate photic depth instead of using a Secchi disk measurement

Value

data frame of areas in specified units for each depth, as well as the littoral percentage of total surface area

Author(s)

Tristan Blechinger, Department of Zoology & Physiology, University of Wyoming

Examples

#load raster
DEM <- terra::rast(system.file("extdata", "example_raster.tif", package = 'rLakeHabitat'))
#run function
calcLittoral(DEM, secchi = 1, depthUnits = "m", DEMunits = "m")

Calculate Euphotic Depth

Description

Calculates euphotic depth from a Secchi disk measuremnt (meters) based on relationships between light attenuation (Kd), optical properties of water (F), and a measured Secchi depth (Zsd). Based on equations specified by Kirk (1994) and Koenings and Edmundson (1991).

Usage

calcPhotic(Z, F = 1.99)

Arguments

Details

Kirk, J. T. O. (1994). Light and Photosynthesis in Aquatic Ecosystems. Cambridge University Press. Koenings, J. P., & Edmundson, J. A. (1991). Secchi disk and photometer estimates of light regimes in Alaskan lakes: Effects of yellow color and turbidity. Limnology and Oceanography, 36(1), 91–105. https://doi.org/10.4319/lo.1991.36.1.0091

We recommend finding a value from the literature that most accurately reflects the water quality of the study system, but values for generalized conditions are included from Koenings and Edmundson (1991). F = 2.76 for stained lakes F = 1.99 for clear lakes F = 1.05 for turbid lakes

Author(s)

Tristan Blechinger, Department of Zoology & Physiology, University of Wyoming

Examples

calcPhotic(Z = 4, F = 1.99)

Calculate Shoreline Development Index

Description

Calculates Shoreline Development Index value across water levels for a given waterbody.

Usage

calcSDI(DEM, units = "m", by = 1, stop = NULL)

Arguments

Value

data frame of perimeter lengths and SDI values for given depths

Author(s)

Tristan Blechinger, Department of Zoology & Physiology, University of Wyoming

Examples

#load raster
DEM <- terra::rast(system.file("extdata", "example_raster.tif", package = 'rLakeHabitat'))
#run function
calcSDI(DEM, units = 'm')

Calculate Pelagic Habitat Volumes

Description

Calculates epilimnion, metalimnion, and hypolimnion volumes based on defined thermocline depths across water levels.

Usage

calcVolume(
  DEM,
  thermo_depth = NULL,
  thermo_high,
  thermo_low,
  DEMunits = "m",
  depthUnits = "m",
  by = 1,
  stop = NULL
)

Arguments

Value

a data frame of volumes in cubic meters calculated for each habitat (epilimnion, metalimnion, hypolimnion)

Author(s)

Tristan Blechinger, Department of Zoology & Physiology, University of Wyoming

Examples

#load raster
DEM <- terra::rast(system.file("extdata", "example_raster.tif", package = 'rLakeHabitat'))
#run function
calcVolume(DEM, thermo_depth = 3, DEMunits = 'm', depthUnits = 'm')

Contour Lines to Points

Description

Get point coordinates and depth values along predetermined contours at a specified density.

Usage

contourPoints(object, depths = NULL, geometry = "geometry", density = 10)

Arguments

Value

dataframe of coordinates and associated depths

Author(s)

Tristan Blechinger, Department of Zoology & Physiology, University of Wyoming

Examples

# load test data
data <- sf::read_sf(system.file("extdata", "example_contour.shp", package = 'rLakeHabitat'))
#run function
contourPoints(data, depths = "Z", geometry = "geometry", density = 50)

Cross Validate Interpolated Bathymetry

Description

Obtain residual mean square error (RMSE) from K-fold cross validation of bathymetry interpolation.

Usage

crossValidate(
  outline,
  df,
  x,
  y,
  z,
  zeros = FALSE,
  separation = NULL,
  k = 5,
  res = 5,
  seed = NULL,
  method = "IDW",
  nmax = 20,
  idp = 2,
  model = "Sph",
  psill = NULL,
  range = NULL,
  nugget = 0,
  kappa = NULL,
  trend_order = 1,
  zero_threshold = 0.05
)

Arguments

Details

Folds are assigned in two stages: points are first split into 5 depth strata (quintiles of observed depth), then within each stratum, points are spatially clustered into 'k' groups (via k-means on their coordinates) and each spatial cluster becomes one fold. This keeps depth ranges reasonably balanced across folds while avoiding the optimistic bias that comes from randomly scattering spatially autocorrelated points across folds. 'res' is required and is always in meters, regardless of the CRS 'outline' was originally supplied in.

For the model argument there are four different methods included here that are supported by gstat::vgm ("Sph", "Exp", "Gau", "Mat"). "Sph" = The default gstat::vgm method. Spherical model characterized by a curve that rises steeply to defined range then flattens, indicates no spatial correlation between points beyond that range. "Exp" = Exponential model characterized by spatial correlation decaying rapidly with distance, results in a rougher surface. "Gau" = Gaussian model similar to spatial model but with slower decay over distance, results in a smoother surface. "Mat" = Matern model that uses kappa to define the variogram relationship. High kappa values approach a Guassian model (smooth surface), and low kappa values approach the Exponential model (kappa = 0.5 is equivalent to Exponential). Three parameters (psill, range, kappa) are incorporated from a fitted variogram (default = NULL). If specified in function input, chosen values will overwrite variogram values.

Value

a named numeric value giving the mean RMSE across k folds

Author(s)

Tristan Blechinger, Department of Zoology & Physiology, University of Wyoming

Examples

#load example outline
outline <- terra::vect(system.file("extdata", "example_outline.shp", package = 'rLakeHabitat'))
#load example xyz data
data <- read.csv(system.file("extdata", "example_depths.csv", package = 'rLakeHabitat'))
#run function
crossValidate(outline, data, "x", "y", "z", zeros = FALSE, separation = 10, k = 5,
res = 50, method = "IDW", nmax = 4, idp = 1.5, seed = 123)

Estimate Average Thermocline Depth

Description

Estimate average thermocline depth across multiple sites and dates.

Usage

estThermo(data, site, date, depth, temp, combine = "all", type = "midpoint")

Arguments

Value

a data frame. When type = "midpoint": thermocline midpoint depths, standard deviations, and n (combine = "sites", "dates", or "all"), or one row per site/date combination giving that profile's midpoint depth (combine = "none"). When type = "top_bottom": upper and lower metalimnion boundary depths, standard deviations, and n (combine = "sites", "dates", or "all"), or one row per site/date combination giving that profile's upper and lower boundary depths (combine = "none").

Author(s)

Tristan Blechinger, Department of Zoology & Physiology, University of Wyoming

Examples

# load test profile data
data <-  read.csv(system.file("extdata", "example_profile_data.csv", package = 'rLakeHabitat'))
data$date <- base::as.Date(data$date)
#run function
estThermo(data = data, site = "site", date = "date",
depth = "depth", temp = "temp", combine = "all")
#or, to get each site/date profile's thermocline depth individually:
estThermo(data = data, site = "site", date = "date",
depth = "depth", temp = "temp", combine = "none")
#or, to estimate upper and lower metalimnion boundaries instead of a single midpoint:
estThermo(data = data, site = "site", date = "date",
depth = "depth", temp = "temp", combine = "none", type = "top_bottom")

Create Raster Stack

Description

Create a raster stack from a single raster, option to save as file.

Usage

genStack(
  DEM,
  by = 1,
  stop = NULL,
  save = TRUE,
  file_name = NULL,
  file_type = "COG"
)

Arguments

Value

a raster stack of specified depth increments for a given waterbody. Raster stack is either stored as an object (save = FALSE) or written to a file in the directory (save = TRUE).

Author(s)

Tristan Blechinger, Department of Zoology & Physiology, University of Wyoming

Examples

#load raster
DEM <- terra::rast(system.file("extdata", "example_raster.tif", package = 'rLakeHabitat'))
#run function
genStack(DEM, by = 1, save = FALSE)

Interpolate bathymetry

Description

Generate a bathymetric digital elevation model (DEM) for a given waterbody using Inverse Distance Weighting (IDW), Ordinary Kriging (OK), or Universal Kriging (UK) interpolation. For high densities of point data, we recommend rarifying prior to interpolation to improve accuracy and reduce computation time (see rarify function).

Usage

interpBathy(
  outline,
  df,
  x,
  y,
  z,
  zeros = FALSE,
  separation = NULL,
  res = 10,
  method = "IDW",
  nmax = 20,
  idp = 2,
  model = "Sph",
  psill = NULL,
  range = NULL,
  nugget = NULL,
  kappa = NULL,
  trend_order = 1,
  zero_threshold = 0.05
)

Arguments

Details

The function automatically detects whether 'outline' (and therefore 'df', which is assumed to share its CRS) is in a geographic (decimal degree) or projected (meters) coordinate system. If geographic, the outline and point data are internally reprojected to their best-fit UTM zone so that all distance-based calculations (resolution, nmax neighbor selection, IDW power, kriging variogram parameters, and boundary point separation) operate on meters rather than degrees. The final DEM is reprojected back to the original CRS of 'outline' before being returned. The CRS used for interpolation, and progress through the major steps, are printed/reported as the function runs. 'res' is required and is always in meters, regardless of the CRS 'outline' was originally supplied in.

For the model argument there are four different methods included here that are supported by gstat::vgm ("Sph", "Exp", "Gau", "Mat"). "Sph" = The default gstat::vgm method. Spherical model characterized by a curve that rises steeply to defined range then flattens, indicates no spatial correlation between points beyond that range. "Exp" = Exponential model characterized by spatial correlation decaying rapidly with distance, results in a rougher surface. "Gau" = Gaussian model similar to spatial model but with slower decay over distance, results in a smoother surface. "Mat" = Matern model that uses kappa to define the variogram relationship. High kappa values approach a Guassian model (smooth surface), and low kappa values approach the Exponential model (kappa = 0.5 is equivalent to Exponential). Three parameters (psill, range, kappa) are incorporated from a fitted variogram (default = NULL). If specified in function input, chosen values will overwrite variogram values - and any parameter that is auto-fit is fit with knowledge of the others you did supply (including nugget), rather than fitting as if the rest were still at their gstat defaults. Universal Kriging ("UK") differs from Ordinary Kriging in that it fits a polynomial trend surface across x/y (see 'trend_order') and models spatial correlation in the residuals from that trend, rather than assuming a constant mean across the whole waterbody. This can help for reservoirs with a strong directional depth gradient (e.g. a river-fed arm sloping steadily toward a dam), where OK's constant-mean assumption doesn't hold well.

Value

the interpolated DEM. For "IDW", a single-layer SpatRaster. For "OK" and "UK", a two-layer SpatRaster: layer 'depth' (the interpolated values) and layer 'error' (the associated standard error of each estimate).

Author(s)

Tristan Blechinger & Sean Bertalot, Department of Zoology & Physiology, University of Wyoming

Examples

#load example outline
outline <- terra::vect(system.file("extdata", "example_outline.shp", package = 'rLakeHabitat'))
#load example xyz data
data <- read.csv(system.file("extdata", "example_depths.csv", package = 'rLakeHabitat'))
#run function
interpBathy(outline, data, "x", "y", "z", zeros = FALSE, separation = 10,
res = 5, method = "IDW", nmax = 4, idp = 2)

Calculate Littoral Volume

Description

Calculate littoral and pelagic volume across water levels from a DEM based on estimated photic depth.

Usage

littoralVol(
  DEM,
  photic,
  secchi = NULL,
  DEMunits = "m",
  depthUnits = "m",
  by = 1
)

Arguments

Details

We recommend using the calcPhotic function to estimate photic depth instead of using a Secchi disk measurement

Value

data frame of littoral and pelagic volume estimates

Author(s)

Tristan Blechinger, Department of Zoology & Physiology, University of Wyoming

Examples

#load raster
DEM <- terra::rast(system.file("extdata", "example_raster.tif", package = 'rLakeHabitat'))
#run function
littoralVol(DEM, photic = 2, DEMunits = "m", depthUnits = "m", by = 1)

Optimize Point Density and DEM Resolution From Existing Depth Data

Description

Given a dense depth dataset, determine what point spacing the data can be rarified to, and what DEM cell resolution to interpolate at, without meaningfully losing accuracy. Unlike samplingDensity(), this works directly with measuremed depth data: at every combination of candidate rarefaction spacing and candidate DEM resolution, the data are thinned with rarify() and scored with crossValidate(), building an RMSE surface across the full spacing x resolution grid.

Usage

optimizeParams(
  outline,
  df,
  x,
  y,
  z,
  spacings,
  res_values,
  k = 5,
  zeros = FALSE,
  separation = NULL,
  method = "IDW",
  nmax = 20,
  idp = 2,
  model = "Sph",
  psill = NULL,
  range = NULL,
  nugget = NULL,
  kappa = NULL,
  trend_order = 1,
  zero_threshold = 0.05,
  tolerance = 0.1,
  plot = TRUE,
  seed = NULL
)

Arguments

Details

Runtime scales with length(spacings) x length(res_values) x k, since each grid point runs a full crossValidate() call (which itself fits k separate DEMs). Start with a small grid (e.g. 3x3) to confirm the analysis runs end-to-end before scaling up to a finer search.

Value

a list with:

results

a data frame of spacing, res, mean RMSE, and the number of points remaining after rarefaction at each spacing

recommended_spacing

the recommended rarefaction spacing, in meters

recommended_res

the recommended DEM cell resolution, in meters

Author(s)

Tristan Blechinger, Department of Zoology & Physiology, University of Wyoming

Examples

outline <- terra::vect(system.file("extdata", "example_outline.shp", package = 'rLakeHabitat'))
data <- read.csv(system.file("extdata", "example_depths.csv", package = 'rLakeHabitat'))
optimizeParams(outline, data, "x", "y", "z", spacings = c(10, 25, 50),
res_values = c(5, 10, 20), seed = 123)

Random Stratified Site Selection

Description

Randomly assign locations across a waterbody using Halton Iterative Partitioning (HIP; Robertson et al. 2018, via the spbal package), stratified by depth. Depth bins are defined from an interpolated DEM (e.g. the output of interpBathy()), and a customizable number of spatially determined locations is drawn within each bin - so, for example, you can request more locations in a shallow littoral bin and fewer in a deep pelagic bin.

Usage

partitionSites(
  dem,
  depth_bins,
  n_per_bin,
  water_level_drop = 0,
  min_spacing = NULL,
  iterations = 7,
  plot = TRUE,
  seed = NULL
)

Arguments

Details

This function requires the 'spbal' and 'sf' packages to be installed (not hard dependencies of this package, since they are only needed for this function). The function automatically detects whether 'dem' is in a geographic (decimal degree) or projected (meters) coordinate system; if geographic, sampling and 'min_spacing' are carried out in the waterbody's best-fit UTM zone (determined from 'dem's own extent) so that distances are measured in meters, and returned locations are projected back to the original CRS of 'dem'. If a depth bin contains fewer candidate DEM cells than the number of locations requested for it, all available cells are used and a warning is issued. No outline shapefile is required - a boundary for the plot (if requested) is derived automatically from the non-NA footprint of 'dem' (or the water-level-adjusted DEM, if 'water_level_drop' > 0).

Value

a list with:

locations

a data frame of selected locations, with columns 'x', 'y' (in the original CRS of 'dem'), 'depth_bin' (the bin label), and 'bin_index' (the bin's position in 'depth_bins'). Depth bins/labels reflect the water-level-adjusted DEM if 'water_level_drop' > 0.

map

a recorded base R plot (see grDevices::recordPlot()) of the (adjusted) DEM and selected locations by depth bin, or NULL if plot = FALSE.

Author(s)

Tristan Blechinger, Department of Zoology & Physiology, University of Wyoming

Examples

outline <- terra::vect(system.file("extdata", "example_outline.shp", package = 'rLakeHabitat'))
data <- read.csv(system.file("extdata", "example_depths.csv", package = 'rLakeHabitat'))
dem <- interpBathy(outline, data, "x", "y", "z", zeros = FALSE,
separation = 10, res = 10, nmax = 8, method = "IDW")
partitionSites(dem, depth_bins = c(0, 5, 15, 30, Inf),
n_per_bin = c(8, 6, 4, 2), seed = 123)

# with a 2 m water level drop from full pool
partitionSites(dem, depth_bins = c(0, 5, 15, 30, Inf),
n_per_bin = c(8, 6, 4, 2), water_level_drop = 2, seed = 123)

Rarify Depth Data

Description

Reduce density of mapped depth data to improve accuracy and computation time.

Usage

rarify(outline, df, x, y, z, res = 10)

Arguments

Details

The function automatically detects whether 'outline' (and therefore 'df') is in a geographic (decimal degree) or projected (meters) coordinate system, the same way interpBathy() does. If geographic, points are rarified in the waterbody's best-fit UTM zone so that 'res' is honored as a true physical spacing in meters, then the rarified points are returned in the original CRS of 'outline'.

Value

dataframe of rarified xyz coordinates (columns named 'x', 'y', 'z' regardless of the input column names), in the same CRS as the original input

Author(s)

Sean Bertalot & Tristan Blechinger, Department of Zoology & Physiology, University of Wyoming

Examples

outline <- terra::vect(system.file("extdata", "example_outline.shp", package = 'rLakeHabitat'))
depths <- read.csv(system.file("extdata", "example_depths.csv", package = 'rLakeHabitat'))
rarify(outline = outline, df = depths, x = "x", y = "y", z = "z", res = 100)

Pre-Survey Power Analysis for Bathymetric Sampling Design

Description

Before any depth data are collected, estimate how far apart survey transects can be spaced and still produce an accurate bathymetric DEM. Because no real depth data exist yet, this works as a simulation study: a plausible "true" bathymetry is generated for the waterbody, a survey at each candidate transect spacing is simulated by sampling that truth, a DEM is reconstructed from those simulated samples (via interpBathy), and the reconstruction is compared back against the generated DEM. This is repeated many times per spacing to average out randomness, and across all candidate spacings, to build a curve of expected DEM accuracy vs. transect spacing - along with a recommended spacing and a map of what that survey design looks like on the actual waterbody outline.

Before any depth data are collected, estimate how far apart survey transects can be spaced and still produce an accurate bathymetric DEM. Because no real depth data exist yet, this works as a simulation study: a plausible "true" bathymetry is generated for the waterbody, a survey at each candidate transect spacing is simulated by sampling that truth, a DEM is reconstructed from those simulated samples (via interpBathy), and the reconstruction is compared back against the generated DEM. This is repeated many times per spacing to average out randomness, and across all candidate spacings, to build a curve of expected DEM accuracy vs. transect spacing - along with a recommended spacing and a map of what that survey design looks like on the actual waterbody outline.

Before any depth data are collected, estimate how far apart survey transects can be spaced and still produce an accurate bathymetric DEM. Because no real depth data exist yet, this works as a simulation study: a plausible synthetic "true" bathymetry is generated for the waterbody (a bowl-shaped depth profile plus spatially-correlated random roughness), a survey at each candidate transect spacing is simulated by sampling that synthetic truth, a DEM is reconstructed from those simulated samples (via interpBathy), and the reconstruction is compared back against the known synthetic truth. This is repeated many times per spacing to average out randomness, and across all candidate spacings, to build a curve of expected DEM accuracy vs. transect spacing - along with a recommended spacing and a map of what that survey design looks like on the actual lake outline.

Usage

samplingDensity(
  outline,
  max_depth,
  spacings = NULL,
  n_sim = 10,
  shape = 1,
  truth_model = "Exp",
  truth_range = NULL,
  truth_sill = NULL,
  truth_nugget = 0,
  along_track_interval = NULL,
  orientation = NULL,
  res = NULL,
  method = "IDW",
  nmax = 20,
  idp = 2,
  model = "Sph",
  psill = NULL,
  range = NULL,
  nugget = 0,
  kappa = NULL,
  trend_order = 1,
  zero_threshold = 0.05,
  tolerance = 0.1,
  n_truth_examples = 3,
  plot = TRUE,
  seed = NULL
)

samplingDensity(
  outline,
  max_depth,
  spacings = NULL,
  n_sim = 10,
  shape = 1,
  truth_model = "Exp",
  truth_range = NULL,
  truth_sill = NULL,
  truth_nugget = 0,
  along_track_interval = NULL,
  orientation = NULL,
  res = NULL,
  method = "IDW",
  nmax = 20,
  idp = 2,
  model = "Sph",
  psill = NULL,
  range = NULL,
  nugget = 0,
  kappa = NULL,
  trend_order = 1,
  zero_threshold = 0.05,
  tolerance = 0.1,
  n_truth_examples = 3,
  plot = TRUE,
  seed = NULL
)

samplingDensity(
  outline,
  max_depth,
  spacings = NULL,
  n_sim = 10,
  shape = 1,
  truth_model = "Exp",
  truth_range = NULL,
  truth_sill = NULL,
  truth_nugget = 0,
  along_track_interval = NULL,
  orientation = NULL,
  res = NULL,
  method = "IDW",
  nmax = 20,
  idp = 2,
  model = "Sph",
  psill = NULL,
  range = NULL,
  nugget = 0,
  kappa = NULL,
  trend_order = 1,
  zero_threshold = 0.05,
  tolerance = 0.1,
  n_truth_examples = 3,
  plot = TRUE,
  seed = NULL
)

Arguments

Details

This is a power analysis under assumed, not observed, bathymetry - it can only be as realistic as the 'max_depth', 'shape', 'truth_range', and 'truth_sill' inputs. Once real depth data have been collected, use crossValidate(), interpBathy(), and optimizeParams() on the actual data to check whether observed accuracy matches what was predicted here, and adjust future survey effort accordingly. Runtime scales with n_sim x length(spacings) x (cost of one synthetic-surface simulation + one interpBathy call). For a first look, consider a smaller n_sim (e.g. 5) and a coarser 'res' before committing to a longer run.

This is a power analysis under assumed, not observed, bathymetry - it can only be as realistic as the 'max_depth', 'shape', 'truth_range', and 'truth_sill' inputs. Once real depth data have been collected, use crossValidate() and interpBathy() on the actual data to check whether observed accuracy matches what was predicted here, and adjust future survey effort accordingly.

This is a power analysis under assumed, not observed, bathymetry - it can only be as realistic as the 'max_depth', 'shape', 'truth_range', and 'truth_sill' inputs. Once real depth data have been collected, use crossValidate() and interpBathy() on the actual data to check whether observed accuracy matches what was predicted here, and adjust future survey effort accordingly. Runtime scales with n_sim x length(spacings) x (cost of one synthetic-surface simulation + one interpBathy call). For a first look, consider a smaller n_sim (e.g. 5) and a coarser 'res' before committing to a long run.

Value

a list with:

results

a data frame of spacing, mean RMSE, and SD of RMSE across simulations

recommended_spacing

the recommended transect spacing, in meters

transects

a SpatVector of the recommended transect lines, in the original CRS of 'outline'

total_transect_length

total length of the recommended transects, in meters

truth_examples

a multi-layer SpatRaster (one layer per saved example) of synthetic "true" bathymetries used during the simulation, in the original CRS of 'outline', for visually sanity-checking the assumed depth profile

a list with:

results

a data frame of spacing, mean RMSE, and SD of RMSE across simulations

recommended_spacing

the recommended transect spacing, in meters

transects

a SpatVector of the recommended transect lines, in the original CRS of 'outline'

total_transect_length

total length of the recommended transects, in meters

truth_examples

a multi-layer SpatRaster (one layer per saved example) of synthetic "true" bathymetries used during the simulation, in the original CRS of 'outline', for visually sanity-checking the assumed depth profile

a list with:

results

a data frame of spacing, mean RMSE, and SD of RMSE across simulations

recommended_spacing

the recommended transect spacing, in meters

transects

a SpatVector of the recommended transect lines, in the original CRS of 'outline'

total_transect_length

total length of the recommended transects, in meters

truth_examples

a multi-layer SpatRaster (one layer per saved example) of synthetic "true" bathymetries used during the simulation, in the original CRS of 'outline', for visually sanity-checking the assumed depth profile

Author(s)

Tristan Blechinger, Department of Zoology & Physiology, University of Wyoming

Examples

outline <- terra::vect(system.file("extdata", "example_outline.shp", package = 'rLakeHabitat'))
samplingDensity(outline, max_depth = 40, n_sim = 5, seed = 123)

outline <- terra::vect(system.file("extdata", "example_outline.shp", package = 'rLakeHabitat'))
samplingDensity(outline, max_depth = 40, n_sim = 5, seed = 123)

outline <- terra::vect(system.file("extdata", "example_outline.shp", package = 'rLakeHabitat'))
samplingDensity(outline, max_depth = 40, n_sim = 5, seed = 123)

Generate and Export Bathymetric Contours

Description

Generate depth contours from an interpolated DEM and save them to a file format usable outside R - a GPS unit or chartplotter (.gpx), Google Earth or similar (.kml), or standard GIS formats (.shp, .gpkg).

Usage

saveContours(
  dem,
  by = NULL,
  levels = NULL,
  file_type = "gpx",
  file_name = "contours"
)

Arguments

Details

GPX and KML both require geographic (WGS84 lon/lat) coordinates - if 'dem' is in a projected CRS, the contours are automatically reprojected to EPSG:4326 before being written (.shp/.gpkg outputs keep 'dem's original CRS).

Value

an sf object of the generated contour lines (with a 'depth' column), invisibly saved to 'file_name' as a side effect. Returned regardless of 'file_type' so you can plot or inspect it without re-reading the saved file.

Author(s)

Tristan Blechinger, Department of Zoology & Physiology, University of Wyoming

Examples

if (requireNamespace("sf", quietly = TRUE)) {
outline <- terra::vect(system.file("extdata", "example_outline.shp", package = 'rLakeHabitat'))
data <- read.csv(system.file("extdata", "example_depths.csv", package = 'rLakeHabitat'))
dem <- interpBathy(outline, data, "x", "y", "z", zeros = FALSE, separation = 10,
res = 10, method = "IDW", nmax = 4)
saveContours(dem, by = 2, file_type = "gpx", file_name = tempfile("contours"))
}