Ronie Silva Juvanhol, Helbecy Cristino Paraná de Sousa, José Wellington Batista Lopes
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引用次数: 0
Abstract
The objective of the study was to analyze the occurrence of forest fires in a conservation unit (CU) of the Brazilian savannah using remote sensing techniques and statistical methods developed for spatial punctual processes. To conduct the spatial analysis of fires, fire polygons mapped using Landsat 8 satellite images were used. The fires were considered into size classes to better illustrate the spatial patterns. The analysis of the spatial distribution of fires utilized Ripley's K-function, in addition to the Kcross function to verify spatial interaction. The results show that the year 2015 had the highest number of fires and burned area. Smaller fires represent a greater number of occurrences, located mostly on CU boundaries. The spatial distribution of forest fires is not random and can cluster on a scale of approximately 6 km. There is a strong spatial interaction between forest fires and traditional communities, particularly with fires smaller than 100 hectares. However, these communities are not responsible for large fires. These results contribute to better-targeted forest fire prevention and combat policies, serving as management tools for the protected area.
期刊介绍:
Stochastic Environmental Research and Risk Assessment (SERRA) will publish research papers, reviews and technical notes on stochastic and probabilistic approaches to environmental sciences and engineering, including interactions of earth and atmospheric environments with people and ecosystems. The basic idea is to bring together research papers on stochastic modelling in various fields of environmental sciences and to provide an interdisciplinary forum for the exchange of ideas, for communicating on issues that cut across disciplinary barriers, and for the dissemination of stochastic techniques used in different fields to the community of interested researchers. Original contributions will be considered dealing with modelling (theoretical and computational), measurements and instrumentation in one or more of the following topical areas:
- Spatiotemporal analysis and mapping of natural processes.
- Enviroinformatics.
- Environmental risk assessment, reliability analysis and decision making.
- Surface and subsurface hydrology and hydraulics.
- Multiphase porous media domains and contaminant transport modelling.
- Hazardous waste site characterization.
- Stochastic turbulence and random hydrodynamic fields.
- Chaotic and fractal systems.
- Random waves and seafloor morphology.
- Stochastic atmospheric and climate processes.
- Air pollution and quality assessment research.
- Modern geostatistics.
- Mechanisms of pollutant formation, emission, exposure and absorption.
- Physical, chemical and biological analysis of human exposure from single and multiple media and routes; control and protection.
- Bioinformatics.
- Probabilistic methods in ecology and population biology.
- Epidemiological investigations.
- Models using stochastic differential equations stochastic or partial differential equations.
- Hazardous waste site characterization.