ForestTools <img src="man/figures/logo.png" align="right" width ="200"/>

The ForestTools R package offers functions to analyze remote sensing forest data. Please consult the NEWS.md file for updates.

To get started, consult the canopy analysis tutorial. For a quick guide on generating spatial statistics from ForestTools outputs, consult the spatial statistics tutorial

To cite the package use citation("ForestTools") from within R.

Plowright A. (2023). ForestTools: Tools for Analyzing Remote Sensing Forest Data. R package version 1.0.2,
https://github.com/andrew-plowright/ForestTools.

Features

Detect and segment trees

Individual trees can be detected and delineated using a combination of the variable window filter (vwf) and marker-controlled watershed segmentation (mcws) algorithms, both of which are applied to a rasterized canopy height model (CHM). CHMs are typically derived from aerial LiDAR or photogrammetric point clouds.

Compute textural metrics

Grey-level co-occurrence matrices (GLCMs) and their associated statistics can be computed for individual trees using a single-band image and a segment raster (which can be produced using mcws). These metrics can be used as predictors for tree classification.

References

This library implements techniques developed in the following studies:

• Variable window filter: Seeing the trees in the forest by Popescu, S. C., & Wynne, R. H. (2004)
• Marker-controlled watershed segmentation: Morphological segmentation by Meyer, F., & Beucher, S. (1990)
• Grey-level co-occurrence matrices: Robust radiomics feature quantification using semiautomatic volumetric segmentation by Parmar, C., Velazquez, E.R., Leijenaar, R., Jermoumi, M., Carvalho, S., Mak, R.H., Mitra, S., Shankar, B.U., Kikinis, R., Haibe-Kains, B. and Lambin, P. (2014)

Research

The following is a non-exhaustive list of studies that use the ForestTools library. Several of these papers discuss topics such as algorithm parameterization, and may be informative for users of this library.

📈 LiDAR Applications in Forest Inventories
Check out this ArcGIS StoryMap showcasing a forest inventory analysis in Kisatchie National Forest (Louisiana, USA) using the tree detection and segmentation algorithms implemented in ForestTools.

2025

• Photogrammetry to Assess the Recovery of a Forest: Case Study of Guadalupe Island by Vera-Ortega, L. A., Hinojosa, A., & Luna, L. (2025)

• Responses of spectral indices to heat and drought differ by tree size in Douglas-fir by Waite, O.J., Coops, N.C., Grubinger, S., Isaac-Renton, M., Degner, J., King, J. and Liu, A. (2025)

2024

• Monitoring climate change impacts on vegetation canopies in Central Europe with passive remote sensing techniques: new insights and perspectives by Kloos, S. (2024)

• A comparison and development of methods for estimating shrub volume using drone-imagery-derived point clouds by Harrison, G.R., Shrestha, A., Strand, E.K. and Karl, J.W. (2024)

• From LiDAR data to vegetation biophysical variables by Ventura Rodríguez, P. (2024)

• A Viewscape-based Approach for Assessing Perceived Walkability in Cities by Yang, X., Lindquist, M., Van Berkel, D. and Grace, D. (2024)

• Use of a Consumer-Grade UAV Laser Scanner to Identify Trees and Estimate Key Tree Attributes across a Point Density Range by Watt, M.S., Jayathunga, S., Hartley, R.J., Pearse, G.D., Massam, P.D., Cajes, D., Steer, B.S. and Estarija, H.J.C. (2024)

• Mapping tree canopy thermal refugia for birds using biophysical models and LiDAR by Strydom, L.H., Conradie, S.R., Smit, I.P., Greve, M., Boucher, P.B., Davies, A.B. and McKechnie, A.E. (2024)

• Early Detection of Southern Pine Beetle Attack by UAV-Collected Multispectral Imagery by Kanaskie, C.R., Routhier, M.R., Fraser, B.T., Congalton, R.G., Ayres, M.P. and Garnas, J.R. (2024)

• Characterizing heterogeneous forest structure in ponderosa pine forests via UAS-derived structure from motion by Hanna, L., Tinkham, W.T., Battaglia, M.A., Vogeler, J.C., Ritter, S.M. and Hoffman, C.M. (2024)

• Ground-based calibration for remote sensing of biomass in the tallest forests by Sillett, S.C., Graham, M.E., Montague, J.P., Antoine, M.E. and Koch, G.W. (2024)

• Integrating Drone-Based LiDAR and Multispectral Data for Tree Monitoring by Savinelli, B., Tagliabue, G., Vignali, L., Garzonio, R., Gentili, R., Panigada, C. and Rossini, M. (2024)

• Accounting for the impact of tree size and soil spatial variability on leaching from orchards by Turkeltaub, T., Peltin, B., Dagan, A., Paz-Kagan, T., Rave, E. and Baram, S. (2024)

• Canopy Structural Changes in Black Pine Trees Affected by Pine Processionary Moth Using Drone-Derived Data by Domingo, D., Gómez, C., Mauro, F., Houdas, H., Sangüesa-Barreda, G. and Rodríguez-Puerta, F. (2024)

• Active Remote Sensing Assessment of Biomass Productivity and Canopy Structure of Short-Rotation Coppice American Sycamore (Platanus occidentalis L.) by Ukachukwu, O.J., Smart, L., Jeziorska, J., Mitasova, H. and King, J.S. (2024)

• Automated detection of an insect-induced keystone vegetation phenotype using airborne LiDAR by Wang, Z., Huben, R., Boucher, P.B., Van Amburg, C., Zeng, J., Chung, N., Wang, J., King, J., Knecht, R.J., Ng'iru, I. and Baraza, A. (2024)

• Individual urban trees detection based on point clouds derived from UAV-RGB imagery and local maxima algorithm, a case study of Fateh Garden, Iran by Azizi, Z., & Miraki, M. (2024)

• Cutting the Greenness Index into 12 Monthly Slices: How Intra-Annual NDVI Dynamics Help Decipher Drought Responses in Mixed Forest Tree Species by Acosta-Hernández, A. C., Pompa-García, M., Martínez-Rivas, J. A., & Vivar-Vivar, E. D. (2024)

• Coupling UAV and satellite data for tree species identification to map the distribution of Caspian poplar by Miraki, M., Sohrabi, H., Fatehi, P., & Kneubuehler, M. (2024)

• Diameter estimation based on LiDAR point clouds at stand level of loblolly pine plantations by Talmage, C., Weng, Y., Zhang, Y., & Grogan, J. (2024)

• A Lidar-based Method for 3D Urban Forest Evaluation and Microclimate Assessment, a Case Study in Portland, Oregon, USA by Yao, X., and Minho, K. (2024)

• Remote Estimation of Above Ground Forest Biomass Using LiDAR and Drone Imagery by Parlato, C., Loftus, N., McGrath, S., Narman, H.S. and Gage, R. (2024)

• UAV-based LiDAR and Multispectral images for forest trait retrieval by Vignali, L., Panigada, C., Tagliabue, G., Savinelli, B., Garzonio, R., Colombo, & Rossini, M. (2024)

• Use of unmanned aerial vehicles for estimating carbon storage in subtropical shrubland aboveground biomass by Vega-Puga, M. G., Romo-León, J. R., Castellanos, A. E., Castillo-Gámez, R. A., & Garatuza-Payán, J. (2024)

2023

• A novel post-fire method to estimate individual tree crown scorch height and volume using simple RPAS-derived data by Arkin, J., Coops, N. C., Daniels, L. D., & Plowright, A. (2023)

• Prediction of Open Woodland Transpiration Incorporating Sun-Induced Chlorophyll Fluorescence and Vegetation Structure by Gao, S., Woodgate, W., Ma, X., & Doody, T. M. (2023)

• From Local to Micro: Exploratory Data Analysis on Urban Forests and Microclimates in Portland, Oregon, USA by Yao, X., & Kim, M.