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November 30, 2025Remote Sensing3 citationsOpen Access

Assessing Wildfire Impacts from the Perspectives of Social and Ecological Remote Sensing

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XWXiaolin WangSLShao-yang Liu

Key Points

  • Severe compound disturbances were identified in the Eaton and Palisades fire clusters, impacting regional resilience.
  • Geographically Weighted Regression analysis showed an Adj. R2 of 0.354, indicating significant spatial heterogeneity.
  • The Composite Disturbance Index quantifies ecological and socioeconomic disruption post-wildfires.
  • High-density clusters faced a notable 'Structural Burden,' emphasizing targeted recovery needs in the WUI.

Abstract

Wildfires in the Wildland–Urban Interface (WUI) pose escalating threats to socio-ecological systems, challenging regional resilience and sustainable recovery. Understanding the compound impacts of such fires requires an integrated, data-driven assessment of both ecological disturbance and social response. This study develops a multi-dimensional framework combining multisource remote sensing data (Landsat/Sentinel-2 NDVI and VIIRS nighttime light) with socio-structural indicators. A Composite Disturbance Index (ImpactIndex) was constructed to quantify ecological, population, and socioeconomic disruption across six fire clusters in the January 2025 Southern California wildfires. Mechanism analysis was conducted using Fixed-Effects OLS (M2) and Geographically Weighted Regression (GWR, M3) models. The ImpactIndex revealed that Eaton and Palisades experienced the most severe compound disturbances, while Border 2 showed purely ecological impacts. During-disaster CNLI signals were statistically decoupled from ecological disturbance (ΔNDVI) and dominated by site-specific effects (p < 0.001). GWR results (Adj. R2 = 0.354) confirmed asymmetric spatial heterogeneity: high-density clusters (Palisades, Kenneth) exhibited a significant “Structural Burden” effect, whereas low-density areas showed weak, nonsignificant recovery trends. This “Index-to-Mechanism” framework redefines the interpretation of nighttime light in disaster contexts and provides a robust, spatially explicit tool for targeted WUI resilience planning and post-fire recovery management.

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Cite This Study

Wang et al. (2025) studied this question.

synapsesocial.com/papers/692b9da91d383f2b2a37a6b2https://doi.org/10.3390/rs17233851
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