A 2026 study by Arade, Rama used Random Forest classification on high-resolution aerial imagery to map residential lawns in the City of North Vancouver, British Columbia. The analysis found that lawns covered approximately 21% of the municipality's total land cover. This dataset provides a binary classification of lawn and non-lawn surfaces to support spatial distribution analysis.
Use Cases
- Quantifying urban green infrastructure based on lawn area percentage mentioned in the description
- Analyzing habitat fragmentation and connectivity based on the spatial distribution of lawns
- Supporting urban planning strategies for biodiversity and ecosystem services based on the study's framework
- Validating remote sensing classification techniques like Random Forest and NDGRI for land cover mapping
Strengths
- Provides a specific quantitative finding: lawns cover approximately 21% of the City of North Vancouver's land cover.
- Uses a defined methodological framework involving Random Forest classification and the NDGRI vegetation index on high-resolution imagery.
- Focuses on a specific, often-overlooked component of urban ecology at a municipal scale.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count and file formats are unknown, which may limit suitability assessment.
- The temporal coverage of the source imagery is not specified.
Provenance
- Source
- Borealis Harvested Dataverse, author Arade, Rama.
- Collection Method
- Random Forest supervised classification applied to multispectral aerial imagery, with pixel-based analysis.
- Freshness
- Last updated 2026-05-02 04:10:42; freshness should be verified.
- Geography
- City of North Vancouver, British Columbia, Canada.