Ravi Thapaliya

Mr. Ravi Thapaliya

EEGS, University of North Carolina at Charlotte, Charlotte, Michigan, USA
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Research Focus & Metrics

Academic Career Length

1Year (2026-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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9 October 2026 in Geohazards
Verified
Journal Article
Open access
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Ravi ThapaliyaRavi Thapaliya
Ravi Thapaliya
KEKibri Hutchison Everett
Kibri Hutchison Everett
Gang ChenGang Chen
Gang Chen
This study examines historical wildfire frequency and the burned area in the Palisades region from 1928 to 2021 and the spatial distribution of burn severity within the 2025 Palisades Fire perimeter in Los Angeles County, California. Sentinel-2 imagery was used to calculate the Normalized Difference Vegetation Index (NDVI), differenced NDVI (dNDVI), and differenced Normalized Burn Ratio (dNBR). Burn-severity clustering was evaluated with Global and Local Moran’s I and Getis-Ord Gi*, and prefire land cover was summarized from Dynamic World V1. The historical record showed an overall increase in annual fire frequency, while annual burned area remained highly variable. Within the 2025 fire perimeter, the highest dNBR severity class represented 61.92% of the classified area, and Global Moran’s I indicated strong positive spatial autocorrelation (I = 0.874, z = 2656.89, p < 0.001). NDVI and dNDVI showed widespread reductions in vegetation greenness between the prefire and early postfire observation periods. An external field-informed cross-check against the final USGS Burned Area Emergency Response Soil Burn Severity product produced a moderately strong positive rank correlation (Spearman’s ρ = 0.70), although study-specific field validation was unavailable. Area-weighted American Community Survey data provide a descriptive demographic profile of intersecting census tracts and are not interpreted as a population-weighted social vulnerability assessment. The results characterize spatial fire effects and demographic context while avoiding causal attribution to environmental or social drivers that were not directly tested.