Data · dataset · 2026
FineKarstAGB: A Fine-Grained, High-Resolution Dataset of Aboveground Biomass in Southwest China
Listed in ScienceDB
FineKarstAGB is the first fine-grained, high-resolution aboveground biomass (AGB) dataset specifically developed for evaluating the effectiveness of forest management in the karst regions of Southwest China.
Description
The dataset provides a 30-meter resolution AGB map for Southwest China in 2024, generated using a deep learning algorithm constructed from GaoFen sub-meter remote sensing imagery and National Forest Inventory (NFI) plot data.
It has been validated against independent test plots and airborne LiDAR Canopy Height Models (CHM). FineKarstAGB offers critical fine-grained data support for regional carbon cycle modeling, carbon accounting, and the assessment of ecological restoration projects.Usage: The unit of the dataset is megagrams per hectare (Mg/ha), representing the AGB density within each 30 m × 30 m grid cell. The file is named FineKarstAGB.tif with a WGS84 coordinate system.
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Note
FineKarstAGB aim to comprehensively quantify the AGB contribution of trees in Southwest China, including Trees Outside Forests (TOF). users are advised to apply appropriate masks based on their research objects (e.g., Forest) when using the data.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.33452 ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Computer Science & AI · Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Forestry sciences 71% · Satellite remote sensing 65%
Provenance · 1 source records, 13 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.33452 | 11 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].anzsrc:group:3007 | enrichment · scidb cn | taxonomy-embedding@1.0.0 | title+keywords+description (71%) |
| concepts[field].local:field:computer-science-ai | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:social-science | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[modality].local:modality:remote-sensing | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (65%) |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/rights |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |