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Related Experiment Video

Updated: Jul 9, 2026

Wind Tunnel Experiments to Study Chaparral Crown Fires

Wind Tunnel Experiments to Study Chaparral Crown Fires

Published on: November 14, 2017

Spatial patterns in vegetation fires in the Indian region.

Krishna Prasad Vadrevu¹, K V S Badarinath, Eaturu Anuradha

¹Agroecosystem Management Program, The Ohio State University, Wooster, USA. krisvkp@yahoo.com

Environmental Monitoring and Assessment

|December 18, 2007

Summary

Satellite data reveals that fire occurrences in India exhibit a clustered spatial pattern, concentrated in agricultural areas and low-elevation regions. Madhya Pradesh and Gujarat show the highest fire counts, with significant hotspots identified in central and northeastern India. This research aids in environmental monitoring and fire management strategies.

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Published on: October 11, 2016

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Last Updated: Jul 9, 2026

Wind Tunnel Experiments to Study Chaparral Crown Fires

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Published on: November 14, 2017

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

Published on: October 11, 2016

Area of Science:

Environmental Science
Remote Sensing
Spatial Statistics

Background:

Understanding spatial patterns of fire occurrences is crucial for effective environmental management and mitigation strategies in diverse geographical regions.
The Indian region presents complex geographical, vegetation, and topographic gradients, necessitating detailed analysis of fire distribution.

Purpose of the Study:

To characterize the spatial patterns of fire occurrences across India using satellite-derived fire count data.
To identify 'hotspots' of fire incidents and analyze their distribution concerning various environmental factors.
To evaluate the applicability of spatial point pattern statistics for environmental monitoring of fire events.

Main Methods:

Utilized fire count datasets from the Along Track Scanning Radiometer (ATSR) satellite.
Applied spatial point pattern analysis techniques: quadrat analysis, nearest neighbor analysis, and Ripley's K function to test for complete spatial randomness (CSR).
Employed hierarchical nearest neighbor clustering to identify fire 'hotspots'.

Main Results:

Fire events in India exhibit a clustered spatial pattern, deviating significantly from a complete spatial random distribution.
Ripley's K statistic indicated high clustering of fire events at a lag-distance of 125 miles.
Highest fire counts were observed in Madhya Pradesh (14.77%) and Gujarat (10.86%), predominantly in agricultural regions (40.26%) and at low elevations with gentle slopes.

Conclusions:

Spatial point pattern statistics are valuable tools for environmental monitoring, particularly for analyzing fire events in the Indian region.
Identified significant fire 'hotspots' in central and northeastern India, providing critical data for targeted fire management and mitigation efforts.
The findings underscore the importance of considering vegetation type, elevation, and slope in understanding and managing fire occurrences.