Improving forest aboveground carbon storage estimation by fusing spaceborne LiDAR features and three-dimensional enhanced spectral indices.

Yi Long, Hua Sun, Binbin Wang, Fugen Jiang

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In one paragraph

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

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

4 authors.

Yi LongHunan Provincial Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security, Central South University of Forestry and Technology, Changsha 410004, China.

Hua SunHunan Provincial Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security, Central South University of Forestry and Technology, Changsha 410004, China.

Binbin WangHunan Provincial Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security, Central South University of Forestry and Technology, Changsha 410004, China.

Fugen JiangHunan Provincial Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security, Central South University of Forestry and Technology, Changsha 410004, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate forest aboveground carbon (AGC) storage estimation is critical for the global carbon cycle and ecosystem sequestration. Optical data suffer from spectral saturation, and spaceborne LiDAR from spatial discontinuity, both hindering seamless high-precision mapping. This study integrated TECIS LiDAR data with Sentinel-2 imagery to construct three-dimensional enhanced spectral indices (TDESIs) by pixel-wise fusing forest canopy height with optical spectral, textural, and fractional vegetation cover features for machine-learning-based AGC estimation. TDESIs outperformed optical-only models, increasing R

Indexed as

Artificial intelligenceEnvironmental managementEnvironmental science

Identifiers

PMID42375533

DOI10.1016/j.isci.2026.116451

PMCPMC13311212

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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.