An improved spatio-temporal clustering method for extracting fire footprints based on MCD64A1 in the Daxing’anling Area of north-eastern China

Abstract

Background. Understanding the spatio-temporal dynamics associated with a wildfire event is essential for projecting a clear profile of its potential ecological influences. Aims. To develop a reliable framework to extract fire footprints from MODIS-based burn products to facilitate the understanding of fire event evolution. Methods. This study integrated the Jenks natural breaks classification method and the density-based spatial clustering of applications with noise (DBSCAN) algorithm to extract the fire footprints in Daxing'anling region of China between 2001 and 2006 from MCD64A1 burned area data. Key results. The results showed that the fire footprints extracted by the model gained an overall accuracy of 80% in spatial and temporal domains after an intensive validation by using the historical fire records provided by the local agency. The agreement of burned area between the extracted fire patches and the historical fire records for those matched fire points was characterised by an overall determination coefficient R 2 at 0.91. Conclusions. The proposed framework serves as an efficient and convenient wildfire management tool for areas requiring large-scale and long-term wildfire monitoring. Implications. The current framework can be used to create a reliable large-scale fire event database by providing an important alternative for the improvement of field investigation.

Biomass Burning in Northeast China over Two Decades: Temporal Trends and Geographic Patterns

“…This algorithm is an extension of the well-known DBSCAN algorithm [50]. DBSCAN has been widely used to identify dense fire clusters [51][52][53][54][55]. Unlike DBSCAN, which relies on a single global density parameter ε (epsilon), HDBSCAN operates by performing clustering over varying ε values and integrating the results to identify the clustering that demonstrates the best stability across these values [56].…”

Erosion Gully Networks Extraction Based on InSAR Refined Digital Elevation Model and Relative Elevation Algorithm—A Case Study in Huangfuchuan Basin, Northern Loess Plateau, China

“…It is reported that data can be properly classified using this method, with minimal information loss [64]. The method and its variants have been successfully applied in the field of remote sensing and GIS in classification and object extraction tasks based on quantitative data [65][66][67]. After REA processing, DEM data in gully areas exhibited aggregation patterns in the gully floor and inter-gully regions.…”