ReviewNature communications2025
Expanding forest research with terrestrial LiDAR technology.
Review in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Hybrid Deep Learning-Geostatistical Mapping of Forest Aboveground Biomass in Lishui, China.Plants (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
The three-dimensional arrangement of plant components, both within and among individual trees, is fundamental for characterizing forest ecosystems. This structure not only influences but also responds to environmental changes, playing a key role in regulating light regimes, forest productivity, as well as physiological and biophysical processes. Over the past few decades, terrestrial laser scanning (TLS, or terrestrial LiDAR) has provided a unique perspective of this 3D structure, offering new insights into ecological processes and forest disturbances, as well as enhancing structural assessments in forest and carbon inventories. Here, we examine recent advancements in TLS and its applications in forest science. We also explore how increasing computational power, alongside the rise of artificial intelligence, is empowering researchers to tackle more complex questions, paving the way for breakthroughs in understanding forest ecosystem dynamics in a changing world.
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.