Height-Diameter Data and R Code for Tropical Forest Biomass Models

Martin Sullivan from the University of Leeds provides input data and R code to fit and assess height-diameter models for tropical forests. The dataset supports a study on field methods for sampling tree height, using a pan-tropical dataset of 73 plots where at least 150 trees had ground-based height measurements. The research found that locally-derived allometries with just 20 measured trees could often predict height with lower error than regional models.

Use Cases

• Developing local height-diameter allometric equations based on a pan-tropical dataset of 73 plots.
• Assessing the performance of different tree height sampling strategies, such as measuring the ten largest diameter trees.
• Estimating stand-level biomass for tropical forests using refined height predictions.
• Cross-validating the predictive error of height-diameter models with varying sample sizes.

Strengths

• Dataset is based on a substantial pan-tropical collection of 73 inventory plots.
• Includes R code for model fitting and assessment, facilitating reproducibility.
• Study provides a concrete, data-backed sampling recommendation of 50 trees per location, including the ten largest.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and dataset size are unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

University of Leeds, associated with the paper 'Field methods for sampling tree height for tropical forest biomass estimation'.

Collection Method

Compiled from in-field ground-based height measurements across 73 tropical forest plots.

Geography

Pan-tropical