English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We have examined the climate and forest fire data using Remote Sensing and GIS in the state of Himachal Pradesh and Uttarakhand states of India. The significant high forest fire events were observed in district of Pauri Garhwal (22.4%) followed by Naini Tal (16.4%), Tehri Garhwal (8.5%), Almora (7.7%), Chamoli (5.8%), Dehra Dun (4.6%), Uttarkashi (4.3%), Champawat (4.2%), Haridwar (3.6%), Una (3.4%), Bageshwar (3.1%), Udham Singh Nagar (2.9%), Sirmaur (2.7%), Solan (2.3%), Kangra (2.1%), Pithoragarh (1.7%) and Shimla (1.2%). The LULC forest category “Deciduous Broadleaf Forest” occupied 17.2% of total forest area and retain significantly high forest fire percent equivalent to 44.7% of total forest fire events. The study revealed that 79% of forest fire incidence was found in the month of April and May. The fire frequency was found highest in the month of April (among all months) whereas it was spread over the five grids (in the count) where the fire frequencies were greater than 100. The average monthly analysis (from January to June) for maximum temperature (°C), precipitation (mm), solar radiation (MJ/m^2), wind velocity (meter/sec.), wet-days frequency (number of days) and evapotranspiration (mm/day) were found to be in the range of (9.90 to 26.44), (26.06 to 134.71), (11738 to 24119), (1.397 to 2.237), (1.46 to 5.12) and (3.96 to 8.46) respectively. Rapid climate/weather severities which significantly enhance the forest fire events were observed in the month of April and May. The analysis of the Pearson Correlation Coefficient (PCC) values of climate parameters showed a significant correlation with forest fire events. The analysis of predicted (2050) climate anomalies data (RCP-6) for the month of April and annual precipitation manifest the significant rise in April temperature and reduction in annual precipitation observed over large part of high forest fire grids will certainly impact adversely to the future forest fire scenario.

Climate change and its impact on Forest Fire in the state of Himachal Pradesh and Uttarakhand states of India: Remote Sensing and GIS Analysis