A global-scale dataset of forest stand basal area estimates used for a machine-learning study. It integrates field inventory observations with environmental, climatic, topographic, and remote sensing covariates. The dataset was authored by ANKITA MITRA and last updated on 2026-06-03.
Use Cases
- Train predictive models for forest basal area based on integrated field and remote sensing data.
- Analyze relationships between forest structure and environmental covariates like climate and topography.
- Validate remote sensing-derived forest metrics against ground-based inventory observations.
Strengths
- Dataset size is 824.8 MB, indicating substantial data volume.
- Data integrates multiple covariate types including environmental, climatic, topographic, and remote sensing.
- It is licensed under CC-BY-4.0, allowing for open use and sharing.
Limitations
- Row count is unknown, which may limit suitability assessment.
- Column-level documentation is absent; field semantics must be inferred after download.
- Description metadata is limited; actual data quality requires manual inspection after download.
Provenance
- Source
- ANKITA MITRA via figshare.
- Collection Method
- Estimated using a machine-learning framework integrating field inventory observations with covariates.
- Freshness
- Last updated 2026-06-03 16:21:23; freshness should be verified.
- Geography
- Global scale.