vostokR: Solar Potential Calculation for LiDAR Point Clouds using VOSTOK

vostokR provides an R interface to the VOSTOK (Voxel Octree Solar Toolkit) algorithm for calculating solar potential on LiDAR point clouds. This package uses the original C++ implementation by Bechtold and Höfle (2020) to perform efficient ray casting and solar position calculations, computing solar irradiance for each point while accounting for shadowing effects from surrounding points.

The package integrates seamlessly with the lidR ecosystem for LiDAR data processing and uses the SOLPOS library from the U.S. Department of Energy National Renewable Energy Laboratory for accurate solar position calculations.

Authors and Attribution

Primary Author: Andrew Sánchez Meador (Northern Arizona University)\ Original VOSTOK Implementation: Sebastian Bechtold and Bernhard Höfle (2020)

This R package is based on the original VOSTOK toolkit developed by Bechtold and Höfle. The core C++ solar potential calculation algorithms remain unchanged from the original implementation.

Citation

When using this package, please cite both the R package and the original VOSTOK toolkit:

``` bibtex @manual{vostokR, title = {vostokR: Solar Potential Calculation for Point Clouds using VOSTOK}, author = {Andrew J. {Sánchez Meador}}, year = {2025}, note = {R package version 0.1.1}, url = {https://github.com/bi0m3trics/vostokR} }

@data{bechtold2020vostok, author = {Sebastian Bechtold and Bernhard Höfle}, publisher = {heiDATA}, title = {VOSTOK - The Voxel Octree Solar Toolkit}, year = {2020}, version = {V1}, doi = {10.11588/data/QNA02B}, url = {https://doi.org/10.11588/data/QNA02B} } ```

Important Note

The current version assumes clear sky conditions in the calculation of solar irradiance. Cloud coverage is not considered. To account for overcast conditions, you'll need to apply a correction to the modelled values using measurements from meteorological stations to derive the ratio between overcast and clear sky values for your specific location.

The Linke atmospheric turbidity coefficient, which models the atmospheric absorption and scattering of solar radiation under clear sky, is currently set to a fixed value of 3. This value is near the annual average for rural-city areas in Europe (i.e., mild climate in the Northern hemisphere). The factor should be adjusted for other study areas, see reference literature.

Installation

``` r

Install dependencies first

install.packages(c("Rcpp", "lidR", "data.table", "terra", "sf"))

Install vostokR from GitHub

devtools::install_github("bi0m3trics/vostokR") ```

Usage

Here's a simple introductory example:

``` r library(lidR) library(vostokR) library(terra)

Load test data included with the package

LASfile <- system.file("extdata", "test.laz", package="vostokR") las <- readLAS(LASfile)

Add normal vectors (required for solar calculations)

las <- add_normals(las, k = 10)

Calculate solar potential for summer period

lassolar <- calculatesolarpotential(las, year = 2025, startdate = "2025-06-01", enddate = "2025-08-31", daystep = 30, minute_step = 60)

View results

plot(lassolar, color = "solarpotential")

Create ground raster

groundraster <- solargroundraster(lassolar, res = 1.0) plot(ground_raster) ```

Key Features

• Automatic CRS detection: Extracts latitude, longitude, and timezone from LAS coordinate system
• Flexible date input: Use either date ranges ("2025-06-01" to "2025-08-31") or day numbers (1-365)
• lidR integration: Seamless integration with lidR point cloud processing workflows\
• Modern spatial support: Uses terra package for raster operations instead of deprecated raster
• Multiple visualization options: Point cloud plots and ground raster maps
• Efficient C++ core: Uses original VOSTOK C++ implementation for fast shadow calculations
• OpenMP parallelization: Multi-threaded processing for better performance

Performance Features

vostokR v0.1.1 includes comprehensive performance optimizations delivering 2-3x faster processing:

• OpenMP parallelization: Multi-threaded solar calculations with thread control functions
• Spatial optimization: Morton code Z-order sorting for improved cache performance
• Intelligent caching: SOLPOS result caching and thread-safe shadow caching
• Batch processing: Optimized memory access patterns with spatial batching
• Thread control: set_vostokr_threads(), get_vostokr_threads(), and performance monitoring

Parameters

Core Parameters

• year: Year for calculation (default: 2025)
• start_date/end_date: Date range as strings (e.g., "2025-06-01", "2025-08-31")
• day_start/day_end: Alternative day-of-year numbers (1-365)
• day_step: Interval between days (default: 30)
• minute_step: Time interval in minutes within each day (default: 30)
• min_sun_angle: Minimum sun elevation angle in degrees (default: 5)
• voxel_size: Voxel size for octree shadow calculation (default: 1)

Location Parameters (auto-detected if not provided)

• lat: Latitude of study area (auto-detected from CRS)
• lon: Longitude of study area (auto-detected from CRS)
• timezone: Time zone offset from UTC (auto-detected from CRS)

Functions

• calculate_solar_potential(): Main solar potential calculation with S3 methods for LAS and LAScatalog
• add_normals(): Calculate surface normal vectors using k-nearest neighbors
• solar_ground_raster(): Extract ground points and convert to terra SpatRaster\
• plot_solar_potential(): Visualize solar potential directly on point cloud
• set_vostokr_threads(): Configure OpenMP thread count for optimal performance
• get_vostokr_threads(): Get current thread configuration
• get_vostokr_performance_info(): Check OpenMP capabilities and status
• clear_vostokr_caches(): Clear performance caches

Example Output

The output point cloud contains the original coordinates, normal vectors (nx, ny, nz), and a new solar_potential column with values in kWh/m²/year. Ground rasters show spatial patterns of solar potential across the landscape.

Implementation Details

The solar potential calculation uses the original VOSTOK algorithm:

1. Computing sun positions throughout the day using the SOLPOS algorithm
2. Ray casting through an octree structure to determine shadowing
3. Calculating direct and diffuse radiation components
4. Accounting for surface orientation using normal vectors

The minimum sun angle parameter can be useful for forest plots or other cases where the shadow point cloud extent is limited. It prevents unrealistic illumination from very low sun angles that would normally be blocked by surrounding terrain not included in the point cloud.

Acknowledgments

This package is based on VOSTOK (Voxel Octree Solar Toolkit) developed by:

3DGeo Research Group\ Institute of Geography\ Heidelberg University\ http://www.uni-heidelberg.de/3dgeo

When using this package, please cite the original VOSTOK work:

Sánchez Meador, A.J. (2025): vostokR – Solar Potential Calculation for Point Clouds in R using VOSTOK. R package version 0.1.1. URL: https://github.com/bi0m3trics/vostokR

Bechtold, S. & Höfle, B. (2020): VOSTOK - The Voxel Octree Solar Toolkit. heiDATA, V1. DOI: 10.11588/data/QNA02B.

License

This package is licensed under the GPL (>= 2) license.