Do Structural Traits Support Reliable Tree Species Classification in Savannas? Insights From Close‐Range Photogrammetry Data

ABSTRACT Understanding whether structural traits reliably reflect species identity is crucial for advancing automated tree classification, particularly in complex disturbance‐prone ecosystems like the West Sudanian savannas. In this study, we assessed the ecological consistency and taxonomic discriminability of 17 architectural traits, spanning crown, stem branch and whole‐tree components, derived from close‐range photogrammetry point clouds across five dominant savanna tree species. Using a trait‐by‐trait analysis grounded in the Trait Probability Density (TPD) framework, we quantified intraspecific versus interspecific variation through functional richness (FRic) and pairwise trait overlap. We computed a composite score combining these two indicators to rank traits according to their species‐discriminatory potential. ANOVA and the Student Newman Keuls post hoc test were performed on the composite score to identify groups of significantly distinct traits. Our findings showed that while structural traits exhibit considerable within‐species variability, a subset of traits, particularly crown evenness, stem branch angle and stem taper factor, demonstrated relatively high interspecific structuring and low trait overlap. Crown and stem branch traits emerged as the most reliable for distinguishing species. However, the top performing traits were distributed across all architectural domains, underscoring the value of a multi‐trait approach. We also observed species‐specific inconsistencies even among high‐ranking traits, emphasising the need to account for intraspecific plasticity in classification tasks. These results suggest that no single architectural trait offers universal taxonomic resolution, and call for further research into identifying context‐specific structural features, particularly those robust to environmental disturbance and ecotypic variation, for advancing automated tree species classification in savanna systems.

Institutions

Publication Details

Journal
African Journal of Ecology
Published
2025-10-01
DOI
https://doi.org/10.1111/aje.70120
Primary Topic
Irish and British Studies
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Do Structural Traits Support Reliable Tree Species Classification in Savannas? Insights From Close‐Range Photogrammetry Data

African Journal of Ecology
Irish and British Studies
article

Do Structural Traits Support Reliable Tree Species Classification in Savannas? Insights From Close‐Range Photogrammetry Data

article en

Abstract

ABSTRACT Understanding whether structural traits reliably reflect species identity is crucial for advancing automated tree classification, particularly in complex disturbance‐prone ecosystems like the West Sudanian savannas. In this study, we assessed the ecological consistency and taxonomic discriminability of 17 architectural traits, spanning crown, stem branch and whole‐tree components, derived from close‐range photogrammetry point clouds across five dominant savanna tree species. Using a trait‐by‐trait analysis grounded in the Trait Probability Density (TPD) framework, we quantified intraspecific versus interspecific variation through functional richness (FRic) and pairwise trait overlap. We computed a composite score combining these two indicators to rank traits according to their species‐discriminatory potential. ANOVA and the Student Newman Keuls post hoc test were performed on the composite score to identify groups of significantly distinct traits. Our findings showed that while structural traits exhibit considerable within‐species variability, a subset of traits, particularly crown evenness, stem branch angle and stem taper factor, demonstrated relatively high interspecific structuring and low trait overlap. Crown and stem branch traits emerged as the most reliable for distinguishing species. However, the top performing traits were distributed across all architectural domains, underscoring the value of a multi‐trait approach. We also observed species‐specific inconsistencies even among high‐ranking traits, emphasising the need to account for intraspecific plasticity in classification tasks. These results suggest that no single architectural trait offers universal taxonomic resolution, and call for further research into identifying context‐specific structural features, particularly those robust to environmental disturbance and ecotypic variation, for advancing automated tree species classification in savanna systems.

African Journal of EcologyVol. 63(7)
Université d'Abomey-Calavi (BJ)
Idea Wild
Reduced inequalities
Openalex Percentile: Top 35%
Irish and British Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Do Structural Traits Support Reliable Tree Species Classification in Savannas? Insights From Close‐Range Photogrammetry Data · African Journal of Ecology (2025) | TGRS Research Map | TGRS