The Pyrogeography of the Gran Chaco’s Dry Forest: A Comparison of Clustering Algorithms and the Scale of Analysis

(1) Background: Changes in the spatial, temporal, and magnitude-related patterns of fires caused by humans are expected to exacerbate with climate change, significantly impacting ecosystems and societies worldwide. However, our understanding of fire regimes in many regions remains limited, largely due to the inherent complexity of fire as an ecological process. Pyrogeography, combined with unsupervised learning methods and the availability of long-term satellite data, offers a robust framework for approaching this problem. The purpose of the study is to identify the pyroregions of the Argentine Gran Chaco, the world’s largest continuous tropical dry forest region. (2) Methods: Using globally available fire occurrence datasets, we computed five fire metrics, related to the extent, frequency, intensity, size, and seasonality of fires at three spatial scales (5, 10, and 25 km). In addition, we tested two widely used cluster algorithms, the K-means algorithm and the Gaussian Mixture Model (GMM). (3) Results and Discussion: The identification of pyroregions was dependent on the clustering algorithm and scale of analysis. The GMM algorithm at a 25 km scale ultimately demonstrated more coherent ecological and spatial distributions. GMM identified six pyroregions, which were labeled based on three metrics in the following order: annual burned area (categorized in low, regular or high), interannual variability of fire (rare, occasional, frequent), and fire intensity (low, moderate, intense). The values were as follows: LRM (22% of study area), ROI (19%), ROM (14%), LOM (10%), ROL (9%), and HFL (4%). (4) Conclusions: Our study provides the most comprehensive delineation of the Argentine Gran Chaco’s Dry Forest pyroregions to date, and highlights both the importance of determining the optimal scale of analysis and the critical role of clustering algorithms in efforts to accurately characterize the diverse attributes of fire regimes. Furthermore, it emphasizes the importance of integrating fire ecology principles and fire management perspectives into pyrogeographic studies to ensure a more comprehensive and meaningful characterization of fire regimes.

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Publication Details

Journal
Forests
Published
2025-07-05
DOI
https://doi.org/10.3390/f16071114
Citations
2
Primary Topic
Landslides and related hazards
Type
article
Field-Weighted Citation Impact
4.84

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article

The Pyrogeography of the Gran Chaco’s Dry Forest: A Comparison of Clustering Algorithms and the Scale of Analysis

Vincenzo Giannico, Mario Elia, Juan Pablo Argañaraz, Laura M. Bellis et al.
2 citations
Forests
Landslides and related hazards
4.84
article

The Pyrogeography of the Gran Chaco’s Dry Forest: A Comparison of Clustering Algorithms and the Scale of Analysis

Vincenzo Giannico, Mario Elia, Juan Pablo Argañaraz, Laura M. Bellis, Sandra Bravo, María Cecilia Naval-Fernández
article en
2 citations

Abstract

(1) Background: Changes in the spatial, temporal, and magnitude-related patterns of fires caused by humans are expected to exacerbate with climate change, significantly impacting ecosystems and societies worldwide. However, our understanding of fire regimes in many regions remains limited, largely due to the inherent complexity of fire as an ecological process. Pyrogeography, combined with unsupervised learning methods and the availability of long-term satellite data, offers a robust framework for approaching this problem. The purpose of the study is to identify the pyroregions of the Argentine Gran Chaco, the world’s largest continuous tropical dry forest region. (2) Methods: Using globally available fire occurrence datasets, we computed five fire metrics, related to the extent, frequency, intensity, size, and seasonality of fires at three spatial scales (5, 10, and 25 km). In addition, we tested two widely used cluster algorithms, the K-means algorithm and the Gaussian Mixture Model (GMM). (3) Results and Discussion: The identification of pyroregions was dependent on the clustering algorithm and scale of analysis. The GMM algorithm at a 25 km scale ultimately demonstrated more coherent ecological and spatial distributions. GMM identified six pyroregions, which were labeled based on three metrics in the following order: annual burned area (categorized in low, regular or high), interannual variability of fire (rare, occasional, frequent), and fire intensity (low, moderate, intense). The values were as follows: LRM (22% of study area), ROI (19%), ROM (14%), LOM (10%), ROL (9%), and HFL (4%). (4) Conclusions: Our study provides the most comprehensive delineation of the Argentine Gran Chaco’s Dry Forest pyroregions to date, and highlights both the importance of determining the optimal scale of analysis and the critical role of clustering algorithms in efforts to accurately characterize the diverse attributes of fire regimes. Furthermore, it emphasizes the importance of integrating fire ecology principles and fire management perspectives into pyrogeographic studies to ensure a more comprehensive and meaningful characterization of fire regimes.

ForestsVol. 16(7)
Consejo Nacional de Investigaciones Científicas y Técnicas (AR), Universidad Nacional de Córdoba (AR), National University of Santiago del Estero (AR), Centro Científico Tecnológico - Córdoba (AR), National Space Activities Commission (AR), University of Bari Aldo Moro (IT)
Consejo Nacional de Investigaciones Científicas y Técnicas, Fondo para la Investigación Científica y Tecnológica
Openalex Percentile: Top 6%
Landslides and related hazards
4.84
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