Postfire Vegetation Recovery And Spectral Separability Over Amazonian Savanna Ecosystems Using Remote Sensing Time Series And Fuel Loads Measurements
DOI:
https://doi.org/10.18172/cig.6889Keywords:
Burned areas, Landsat, Sentinel 2, NDVI, tropical savannasAbstract
Monitoring and understanding vegetation responses to fire in Amazonian savanna ecosystems remains a very important scientific challenge to improve the landscape management practices of these areas. In this sense, the present study analyzes the dynamics of spectral separability as well as the postfire vegetation recovery process related to fire experiments carried out in open savanna ecosystems of the Campos Amazônicos National Park (Brazil). For this purpose, a harmonized Landsat and Sentinel-2 dataset was processed and analyzed. The time series of the Normalized Difference Vegetation Index (NDVI) and the Normalized Burned Ratio 2 (NBR2) spectral indices were also generated from this same dataset for the period from 2019 to 2023 and evaluated in combination with fine fuel load in-situ measurements. M-Statistics and mean absolute difference were calculated comparing data from burned and unburned plots, considering different treatments of fire seasonality (Early-Dry Season – EDS; Middle-Dry Season – MDS fires) and time since last fire (2-year-old fuel age; 3-year-old fuel age; and 10-year-old or older fuel age fires). The combined use of Sentinel-2 and Landsat resulted in an availability of cloud-free or partially cloud-free images ≈0.6 times greater than that obtained when using Landsat images exclusively. The potential of the NBR2 stood out, generating statistically significant mean absolute difference values when comparing EDS and MDS fires, and also when comparing 2-year-old fuel age areas with 3-year-old or 10-year-old or older fuel age areas. Satellite and field information converged in the detection of a rapid response of vegetation to fire in these ecosystems, demonstrating that conditions similar to those observed before the fire were reached after three rainy seasons. The results reinforce the potential of Landsat and Sentinel-2 harmonized remote sensing datasets to assess and monitor fire-affected areas over Amazonian savanna ecosystems, providing ecological meaning and establishing connections between remote sensing and field datasets.
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Copyright (c) 2025 Daniel Borini Alves, Antonio Laffayete Pires da Silveira, Bruno Contursi Cambraia, José Falcão Sobrinho, Thiago Sanna Freire Silva, Fernando Pérez-Cabello
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Funding data
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Fundação Cearense de Apoio ao Desenvolvimento Científico e Tecnológico
Grant numbers BP6-00241-00158.01.00/25 -
Fundação de Amparo à Pesquisa do Estado de São Paulo
Grant numbers 2019/07357-8 -
Agencia Estatal de Investigación
Grant numbers PID2020-118886RB-I00
