Data · dataset · 2025
A 10-m Vector Dataset of Mangrove Species Distribution in China
Listed in ScienceDB
This dataset provides a 10-m spatial-resolution vector map of mangrove species distribution along the Chinese coast, produced under an Environment-Enhanced Remote Sensing (EERS) framework.
Description
EERS performs feature-level fusion of single-date Sentinel-2 spectral and textural features with long-term climatological and oceanographic variables from WorldClim and Bio-ORACLE. Spectral separability among species was quantified across 36 monthly composites (2018–2020) using the Jeffries–Matusita distance, identifying June 2019 as the most discriminative window.
Lightweight, interpretable classifiers were evaluated with spatially blocked validation to map 26 mangrove species merged into 18 classes. Relative to a remote-sensing-only baseline, overall precision increased by 16.7% and AUC-ROC improved from 0.803 to 0.900. Mapping results indicate dominance by Avicennia marina (31.6%), Aegiceras corniculatum (17.0%), and Kandelia obovata (16.8%).
Read the rest (1 more)
Introduced species (Sonneratia apetala and Laguncularia racemosa) together account for ~14%, while threatened taxa such as Nypa fruticans cover <0.05%. Delivered as a vector dataset with an effective 10-m mapping resolution, it offers a current baseline for biodiversity assessment, invasion monitoring, and climate-informed coastal ecosystem management.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.31545 ↗
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
- Applied computing 70% · Satellite remote sensing 65%
Provenance · 1 source records, 13 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.31545 | 12 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].anzsrc:group:4601 | enrichment · scidb cn | taxonomy-embedding@1.0.0 | title+keywords+description (70%) |
| 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_text | source · scidb cn | connector:scidb_cn@1.0.0 | |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |