Evidence map›Paper›PMID 41748648›Full record

ArticleScientific data2026

Comprehensive uncrewed aerial system data for Amazon rainforest at Tiputini Biodiversity Station, Ecuador.

Minyoung Jung, Anjin Chang, Charles H Cannon, Gonzalo Rivas-Torres, Jinha Jung

Abstract readDataset
In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Minyoung JungLyles School of Civil and Construction Engineering, Purdue University, West Lafayette, IN, 47907, USA.ORCID http://orcid.org/0000-0002-6419-5236
Anjin ChangDepartment of Biosystems & Agricultural Engineering, College of Agriculture & Natural Resources, Michigan State University, East Lansing, MI, 48824, USA. changanj@msu.edu.
Charles H CannonForest and Tree Research, Singapore Botanic Gardens, National Parks Board, Singapore.
Gonzalo Rivas-TorresUniversidad San Francisco de Quito, Estación de Biodiversidad Tiputini, Av. Diego de Robles e Interoceánica, Quito, Ecuador.ORCID http://orcid.org/0000-0002-2704-8288
Jinha JungLyles School of Civil and Construction Engineering, Purdue University, West Lafayette, IN, 47907, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Amazon rainforest, the most biodiverse biome on Earth, represents a critical natural laboratory for studying species interactions, ecosystem functioning, and vulnerability to climate change. High-quality datasets are crucial for advancing a detailed understanding of this complex biome; however, open-access uncrewed aerial system (UAS) datasets for this region remain notably scarce. Here, we present UAS-derived high-resolution products that seamlessly cover over 700 hectares of the upper and hyperdiverse Amazon. To the best of our knowledge, this is the first large-scale, comprehensive, open, and seamless UAS dataset of the Amazon, integrating multispectral imagery and lidar (light detection and ranging) data collected during a single mission, ensuring temporal consistency. The dataset provides complementary spectral and structural information from the Tiputini Biodiversity Station, one of the regions with the highest tree species richness per unit area on Earth. This resource supports diverse applications such as biodiversity and forest-structure analyses and establishes a methodological reference for future large-scale UAS research in the Amazon.

Indexed as

BiodiversityRainforestEcuadorTrees

Identifiers

PMID41748648
PMCPMC13056996

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

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.