ArticleNature communications2024
Characterizing the structural complexity of the Earth's forests with spaceborne lidar.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Declining grassland canopy height in China under asymmetric biomass allocation.Nature communications · 2026Article
- Weakly supervised forest canopy extraction and multi-dimensional joint canopy entropy for quantifying canopy structural complexity using large-scale forest UAV LiDAR data.Plant phenomics (Washington, D.C.) · 2026Article
- Comprehensive uncrewed aerial system data for Amazon rainforest at Tiputini Biodiversity Station, Ecuador.Scientific data · 2026Article
- A 10 m High-precision Canopy Height Product for Nanping City, Fujian Province, China.Scientific data · 2026Article
- Expanding forest research with terrestrial LiDAR technology.Nature communications · 2025Review
- Canopy Structure Exhibits Linear and Nonlinear Links to Biome-Level Maximum Light Use Efficiency.Ecology letters · 2025Article
- Application of UAV remote sensing for vegetation identification: a review and meta-analysis.Frontiers in plant science · 2025Review
- Predicting individual tree diameter at breast height for genetically diverseFrontiers in plant science · 2025Article
- Ladder fuels rather than canopy volumes consistently predict wildfire severity even in extreme topographic-weather conditions.Communications earth & environment · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Forest structural complexity is a key element of ecosystem functioning, impacting light environments, nutrient cycling, biodiversity, and habitat quality. Addressing the need for a comprehensive global assessment of actual forest structural complexity, we derive a near-global map of 3D canopy complexity using data from the GEDI spaceborne lidar mission. These data show that tropical forests harbor most of the high complexity observations, while less than 20% of temperate forests reached median levels of tropical complexity. Structural complexity in tropical forests is more strongly related to canopy attributes from lower and middle waveform layers, whereas in temperate forests upper and middle layers are more influential. Globally, forests exhibit robust scaling relationships between complexity and canopy height, but these vary geographically and by biome. Our results offer insights into the spatial distribution of forest structural complexity and emphasize the importance of considering biome-specific and fine-scale variations for ecological research and management applications. The GEDI Waveform Structural Complexity Index data product, derived from our analyses, provides researchers and conservationists with a single, easily interpretable metric by combining various aspects of canopy structure.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.