Sharma et al. (2025) Quantifying potential forestation-induced variability in land surface temperature across India: a percentile-based and class-specific assessment

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Environmental Research Climate
• Year: 2025
• Date: 2025-11-21
• Authors: Jyoti Sharma, Pankaj Kumar
• DOI: 10.1088/2752-5295/ae228d

Research Groups

Not explicitly stated in the provided abstract.

Short Summary

This study investigated forestation-induced changes in daytime land surface temperature (LST) across 14 major forest classes in India, revealing that the effects vary significantly by forest class and elevation, with low-elevation forests generally causing cooling and high-altitude forests tending towards warming.

Objective

• To investigate forestation-induced changes in daytime land surface temperature (LST) across 14 major forest classes in India.
• To propose and apply a percentile-based framework to quantify forestation-induced temperature changes, capturing seasonal and class-specific variability at fine spatial resolution.
• To identify the climatic drivers influencing forest greenness.

Study Configuration

• Spatial Scale: Across 14 major forest classes in India; fine spatial resolution; specific focus on the 12–25° N latitude band.
• Temporal Scale: Daytime; seasonal variability.

Methodology and Data

• Models used: Percentile-based framework (linking forest class fractions at 75th, 85th, and 95th thresholds with LST variations); Random forest regression.
• Data sources: Data analyzed included forest class fractions, daytime land surface temperature (LST), forest greenness (indicated by leaf area index), latent heat flux (LE), net photosynthesis, precipitation dynamics, drought conditions, and soil moisture availability. (Specific data acquisition methods like satellite, observation, or reanalysis are not detailed in the abstract.)

Main Results

• The effect of forestation on daytime LST varies considerably across forest classes and percentile thresholds, exhibiting both cooling and warming effects.
• Cooling effects dominate in nine of the 14 classes, ranging from substantial cooling (−0.081 °C) in littoral and swamp forests (mangroves) to notable warming (+0.095 °C) in montane dry temperate forests of the Himalaya.
• A clear spatial and ecological pattern emerged: low-elevation forest types generally exhibit cooling, while high-altitude forest types show a tendency toward warming.
• Spatially, forestation is generally associated with cooling between 12–25° N latitude, while regions outside this band tend to experience warming.
• Variability in forest greenness is primarily explained by latent heat flux (LE), accounting for over 70% of the variation in classes 4, 5, and 6, and by net photosynthesis, accounting for up to 69.42% in class 14.
• The strong association between LE and forest greenness reflects the underlying coupling between evapotranspiration and photosynthetic activity in actively transpiring canopies.
• LST responses to forestation depend strongly on forest type and elevation. Low-elevation tropical and subtropical forests in central India cool the surface via enhanced evapotranspiration, while high-altitude temperate forests show localized warming.

Contributions

• Proposes a novel percentile-based framework for quantifying forestation-induced temperature changes, effectively capturing seasonal and class-specific variability at fine spatial resolution.
• Provides a comprehensive assessment of forestation-induced daytime LST changes across 14 major forest classes in India, revealing distinct spatial and ecological patterns of cooling and warming.
• Identifies key climatic drivers (latent heat flux and net photosynthesis) influencing forest greenness, highlighting the coupling between evapotranspiration and photosynthetic activity in LST regulation.
• Underscores the critical role of forest functional diversity and elevation in regional climate regulation, demonstrating that LST responses are highly dependent on specific forest types and altitudes.

Funding

Not explicitly stated in the provided abstract.

Citation

@article{Sharma2025Quantifying,
  author = {Sharma, Jyoti and Kumar, Pankaj},
  title = {Quantifying potential forestation-induced variability in land surface temperature across India: a percentile-based and class-specific assessment},
  journal = {Environmental Research Climate},
  year = {2025},
  doi = {10.1088/2752-5295/ae228d},
  url = {https://doi.org/10.1088/2752-5295/ae228d}
}

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