An R-based geospatial analysis workflow for mapping and monitoring ash dieback disease (Hymenoscyphus fraxineus) impacts on forest stands. This repository contains reproducible code for processing field survey data, creating stem distribution maps, and analyzing spatial patterns of disease progression in ash tree populations. The tool integrates forest inventory methods with spatial analysis techniques to support forest health monitoring and management decisions in response to this invasive fungal pathogen affecting European ash forests.

This comprehensive R-based Monte Carlo simulation framework quantifies uncertainty in REDD+ emission factors and activity data for ART-TREES Standard V2.0 compliance, featuring allometric modeling, biomass estimation uncertainty, cross-validation techniques, and automated uncertainty deduction calculations with 90% confidence intervals for jurisdictional and nested carbon verification projects.

We apply the “Height Variation Hypothesis” and associated methods to estimate tree height heterogeneity (MacArthur and MacArthu, 1961). Due to varied factors regarding yield class, soil moisture, browsing, species diversity, stocking density, line of sight and clinometer errors, tree height variability presents a key challenge to upholding accuracy targets in forest inventory operations. The following tool aims to assist inventory crews to identify areas of high tree height heterogeneity, where there may be need for an increase in number of sample plots or for a decrease in the size of sampling units. Using LiDAR derived tree metrics, we classify forest areas according simple standard deviation values of tree height to produce maps of Height Heterogeneity Areas, or HHA’s.