Tools & Packages

Open-source software for geospatial ecology: iLAND Workbench (QGIS plugin for forest landscape modeling) and forestPulse (R package for forest change analysis).

Open-source software is central to reproducible ecological research. Here are the tools I develop and maintain.

iLAND Workbench

🌲 iLAND Workbench — A QGIS Plugin for Forest Landscape Modeling

A dockable QGIS plugin that brings the iLAND individual-based forest landscape and disturbance model into a GIS workflow. It provides runtime management, module exploration, data-preparation pipelines for climate, soil, and stand-grid inputs, and reproducible analysis workflows — all from within QGIS.

DOI

GitHub Repository QGIS Plugin Repository

iLAND Workbench bridges the gap between the iLAND simulation model and the QGIS ecosystem, making forest landscape modeling accessible without leaving your GIS environment. Key capabilities include:

Runtime management with automatic download and verification of iLANDc across Windows, macOS, and Linux
Module and submodule exploration directly from the iLAND source tree
Complete climate workflows: future climate download, historical data, and conversion/validation pipelines
Soil data acquisition from SSURGO and SoilGrids
Stand-grid source workflows via LANDFIRE EVT and ESA WorldCover
Landscape pre-flight validation before model creation
Interactive SQLite plot browser for inspecting exported iLand output databases
Full QGIS Processing provider integration

# Requires QGIS Desktop 3.28 or newer
# In QGIS: Plugins → Manage and Install Plugins → Install from ZIP
# Download iland_qgis_plugin.zip from the GitHub Releases page

The plugin ships as a small, runtime-free package. The native iLAND console runtime (iLANDc) needed to run simulations is acquired automatically on first use.

@software{paudel2026iland,
  author    = {Paudel, Sushil},
  title     = {{iLAND Workbench: QGIS-based iLAND Workbench for
                hassle-free installation and reproducible analysis
                workflows}},
  year      = {2026},
  version   = {4.1.0},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.19300115},
  url       = {https://doi.org/10.5281/zenodo.19300115}
}

forestPulse

📦 forestPulse — An R Package for Forest Structure, Disturbance & iLand Inputs

forestPulse turns forest field plots and drone or satellite imagery into analysis-ready summaries, and assembles the spatial and tabular inputs needed to drive the iLand forest landscape model. Current release v0.3.0, MIT licensed.

DOI

GitHub Repository Releases Report an Issue

Together with iLAND Workbench, forestPulse completes the pipeline: it prepares the landscape, climate, and tree-initialisation inputs, and the plugin runs the simulation inside QGIS.

The package covers two connected areas, aimed at researchers, ecologists, and forest managers moving from field plots and imagery through to process-based modeling.

Field and remote-sensing analysis

Index geotagged raster imagery into a spatial catalogue and match it to field plots
Compute canopy height models (CHM), classify canopy gaps, and derive per-plot gap geometry
Calculate 37 RGB-derived vegetation indices from drone orthomosaics
Locate individual trees from compass bearings and distance measurements
Summarise plot-level basal area, crown density, and species composition
Standardise messy species names and audit what changed

iLand model inputs

Impute field plots to pixels with modified random-forest imputation to build wall-to-wall tree lists, flagging poorly matched pixels
Derive daily climate variables — vapour pressure deficit, radiation — from gridMET following the FAO-56 approach, and write a per-cluster SQLite climate database in iLand’s exact format
Build the stand grid, resource-unit environment grid and file, and the tree-initialisation file, then cross-validate them — step by step or through a single build_iland_landscape() call

# install.packages("remotes")
remotes::install_github("paudelsushil/forestPulse")

Requires R 4.1.0 or newer. Some capabilities use optional packages declared in Suggests: install RSQLite for climate databases, yaImpute for plot-to-pixel imputation, and future/future.apply for parallel spatial sampling.

Field and remote-sensing analysis:

library(forestPulse)

# 1. Build a spatial catalogue of geotagged rasters and match it to field plots
catalog      <- build_catalog(input = "path/to/imagery", output = "catalog.geojson")
plots        <- sf::read_sf("plots.shp")
plot_imagery <- match_imagery(catalog, plots)

# 2. Canopy height model, then per-plot canopy gap metrics
chm     <- compute_chm("dsm.tif", "dtm.tif", output = "chm.tif")
metrics <- extract_plot_metrics(chm = chm, plots = plots,
                                radius = 15.24, gap_threshold = 3)

# 3. An RGB vegetation index and a plot-level basal-area summary
vegetation_index("ExG", image_path = "orthomosaic.tif")
calculate_basal_area(tree_table, summary = "plot")

Preparing inputs for the iLand model:

# Daily climate from a gridMET extraction -> per-cluster SQLite climate database
clim <- gridmet_preprocessing(
  data        = gridmet_daily,   # one row per cluster-day
  cluster_col = "cluster",
  date_col    = "date",
  temp_unit   = "K",
  vpd_source  = "compute"        # derive VPD from temperature + humidity
)
write_iland_climate(clim, "database/climate.sqlite", split_col = "cluster")

# Stand grid + wall-to-wall tree initialisation via plot-to-pixel imputation
stand_grid <- create_stand_grid(stands, id_field = "stand_id",
                                filename = "gis/stand_grid.asc")
target     <- make_target_from_rasters(predictor_stack, stand_grid)
pixel_plot <- impute_plots_to_pixels(reference, target, predictors, responses)
build_iland_init(pixel_plot, tree_lists, out_path = "init/init_trees.txt")

Topic	Functions
Raster catalogue	`build_catalog()`, `read_catalog()`, `query_catalog()`, `match_imagery()`, `footprint()`
Canopy height & gaps	`compute_chm()`, `classify_gaps()`, `gap_metrics()`, `extract_plot_metrics()`
Vegetation indices	`vegetation_index()` — 37 RGB indices
Tree location	`locate_tree()`
Basal area & summaries	`calculate_basal_area()`, `calculate_species_basal_area()`, `calculate_plot_species_basal_area()`, `calculate_plot_summary()`
Crown density	`calculate_crownDensity()`, `show_calculation_details()`, `plot_summary()`
Species standardisation	`clean_species_names()`, `get_species_mapping()`, `check_unmapped_species()`, `compare_species_changes()`
Climate (iLand)	`gridmet_preprocessing()`, `calc_vpd()`, `calc_saturation_vapor_pressure()`, `convert_radiation()`, `write_iland_climate()`
Landscape (iLand)	`create_stand_grid()`, `create_environment_grid()`, `create_environment_file()`, `build_iland_landscape()`, `validate_landscape()`
Imputation & init	`impute_plots_to_pixels()`, `make_target_from_rasters()`, `flag_far_imputations()`, `build_iland_init()`, `create_init_file()`

Full documentation is available in the package help pages (?forestPulse) and the repository README.

@software{paudel2025forestpulse,
  author    = {Paudel, Sushil},
  title     = {{forestPulse: Analyse Forest Structure and Disturbance
                from Field and Remote Sensing Data}},
  year      = {2025},
  version   = {0.3.0},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.17887640},
  url       = {https://doi.org/10.5281/zenodo.17887640}
}

Other Resources

🎥

GeoAI for Ecology (YouTube)

Video tutorials on remote sensing, GIS, R programming, and geospatial AI methods for ecological research.

Visit Channel

Tutorials R GIS

💻

Code & Data

Source code for both packages, plus reproducible code and datasets accompanying publications and tutorials.

Browse Repositories

Open Source Reproducible