A Clustering Algorithm to Organize Satellite Hotspot Data for the Purpose of Tracking Bushfires Remotely

TL;DR

This paper introduces a novel spatiotemporal clustering algorithm implemented in R to organize satellite hotspot data, aiding in remote bushfire tracking and management.

Contribution

The paper presents an enhanced clustering algorithm that accounts for spatial and temporal movement, with adjustable parameters for diverse satellite data sources.

Findings

Effective clustering of bushfire hotspots in Victoria, Australia

Improved tracking of fire spread over time

Flexible parameter adjustment for different datasets

Abstract

This paper proposes a spatiotemporal clustering algorithm and its implementation in the R package spotoroo. This work is motivated by the catastrophic bushfires in Australia throughout the summer of 2019-2020 and made possible by the availability of satellite hotspot data. The algorithm is inspired by two existing spatiotemporal clustering algorithms but makes enhancements to cluster points spatially in conjunction with their movement across consecutive time periods. It also allows for the adjustment of key parameters, if required, for different locations and satellite data sources. Bushfire data from Victoria, Australia, is used to illustrate the algorithm and its use within the package.