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Quantifying Post-Fire Changes in the Aboveground Biomass of an Amazonian Forest Based on Field and Remote Sensing Data

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Quantifying Post-Fire Changes in the Aboveground Biomass of an Amazonian Forest Based on Field and Remote Sensing Data. / Pontes-Lopes, Aline; Dalagnol, Ricardo; Dutra, Andeise Cerqueira et al.

In: Remote Sensing, Vol. 14, No. 7, e1545, 23.03.2022.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Pontes-Lopes, A, Dalagnol, R, Dutra, AC, de Jesus Silva, CV, de Alencastro Graça, PML & de Oliveira e Cruz de Aragão, LE 2022, 'Quantifying Post-Fire Changes in the Aboveground Biomass of an Amazonian Forest Based on Field and Remote Sensing Data', Remote Sensing, vol. 14, no. 7, e1545. https://doi.org/10.3390/rs14071545

APA

Pontes-Lopes, A., Dalagnol, R., Dutra, A. C., de Jesus Silva, C. V., de Alencastro Graça, P. M. L., & de Oliveira e Cruz de Aragão, L. E. (2022). Quantifying Post-Fire Changes in the Aboveground Biomass of an Amazonian Forest Based on Field and Remote Sensing Data. Remote Sensing, 14(7), [e1545]. https://doi.org/10.3390/rs14071545

Vancouver

Pontes-Lopes A, Dalagnol R, Dutra AC, de Jesus Silva CV, de Alencastro Graça PML, de Oliveira e Cruz de Aragão LE. Quantifying Post-Fire Changes in the Aboveground Biomass of an Amazonian Forest Based on Field and Remote Sensing Data. Remote Sensing. 2022 Mar 23;14(7):e1545. doi: 10.3390/rs14071545

Author

Pontes-Lopes, Aline ; Dalagnol, Ricardo ; Dutra, Andeise Cerqueira et al. / Quantifying Post-Fire Changes in the Aboveground Biomass of an Amazonian Forest Based on Field and Remote Sensing Data. In: Remote Sensing. 2022 ; Vol. 14, No. 7.

Bibtex

@article{a0b9e18b09454915ad5d00e7ef768e75,
title = "Quantifying Post-Fire Changes in the Aboveground Biomass of an Amazonian Forest Based on Field and Remote Sensing Data",
abstract = "Fire is a major forest degradation component in the Amazon forests. Therefore, it is important to improve our understanding of how the post-fire canopy structure changes cascade through the spectral signals registered by medium-resolution satellite sensors over time. We contrasted accumulated yearly temporal changes in forest aboveground biomass (AGB), measured in permanent plots, and in traditional spectral indices derived from Landsat-8 images. We tested if the spectral indices can improve Random Forest (RF) models of post-fire AGB losses based on pre-fire AGB, proxied by AGB data from immediately after a fire. The delta normalized burned ratio, non-photosynthetic vegetation, and green vegetation (ΔNBR, ΔNPV, and ΔGV, respectively), relative to pre-fire data, were good proxies of canopy damage through tree mortality, even though small and medium trees were the most affected tree size. Among all tested predictors, pre-fire AGB had the highest RF model importance to predicting AGB within one year after fire. However, spectral indices significantly improved AGB loss estimates by 24% and model accuracy by 16% within two years after a fire, with ΔGV as the most important predictor, followed by ΔNBR and ΔNPV. Up to two years after a fire, this study indicates the potential of structural and spectral-based spatial data for integrating complex post-fire ecological processes and improving carbon emission estimates by forest fires in the Amazon.",
keywords = "forest fire, degradation, biomass, change detection, Landsat-8, Google Earth Engine",
author = "Aline Pontes-Lopes and Ricardo Dalagnol and Dutra, {Andeise Cerqueira} and {de Jesus Silva}, {Camila Val{\'e}ria} and {de Alencastro Gra{\c c}a}, {Paulo Maur{\'i}cio Lima} and {de Oliveira e Cruz de Arag{\~a}o}, {Luiz Eduardo}",
year = "2022",
month = mar,
day = "23",
doi = "10.3390/rs14071545",
language = "English",
volume = "14",
journal = "Remote Sensing",
issn = "2072-4292",
publisher = "MDPI AG",
number = "7",

}

RIS

TY - JOUR

T1 - Quantifying Post-Fire Changes in the Aboveground Biomass of an Amazonian Forest Based on Field and Remote Sensing Data

AU - Pontes-Lopes, Aline

AU - Dalagnol, Ricardo

AU - Dutra, Andeise Cerqueira

AU - de Jesus Silva, Camila Valéria

AU - de Alencastro Graça, Paulo Maurício Lima

AU - de Oliveira e Cruz de Aragão, Luiz Eduardo

PY - 2022/3/23

Y1 - 2022/3/23

N2 - Fire is a major forest degradation component in the Amazon forests. Therefore, it is important to improve our understanding of how the post-fire canopy structure changes cascade through the spectral signals registered by medium-resolution satellite sensors over time. We contrasted accumulated yearly temporal changes in forest aboveground biomass (AGB), measured in permanent plots, and in traditional spectral indices derived from Landsat-8 images. We tested if the spectral indices can improve Random Forest (RF) models of post-fire AGB losses based on pre-fire AGB, proxied by AGB data from immediately after a fire. The delta normalized burned ratio, non-photosynthetic vegetation, and green vegetation (ΔNBR, ΔNPV, and ΔGV, respectively), relative to pre-fire data, were good proxies of canopy damage through tree mortality, even though small and medium trees were the most affected tree size. Among all tested predictors, pre-fire AGB had the highest RF model importance to predicting AGB within one year after fire. However, spectral indices significantly improved AGB loss estimates by 24% and model accuracy by 16% within two years after a fire, with ΔGV as the most important predictor, followed by ΔNBR and ΔNPV. Up to two years after a fire, this study indicates the potential of structural and spectral-based spatial data for integrating complex post-fire ecological processes and improving carbon emission estimates by forest fires in the Amazon.

AB - Fire is a major forest degradation component in the Amazon forests. Therefore, it is important to improve our understanding of how the post-fire canopy structure changes cascade through the spectral signals registered by medium-resolution satellite sensors over time. We contrasted accumulated yearly temporal changes in forest aboveground biomass (AGB), measured in permanent plots, and in traditional spectral indices derived from Landsat-8 images. We tested if the spectral indices can improve Random Forest (RF) models of post-fire AGB losses based on pre-fire AGB, proxied by AGB data from immediately after a fire. The delta normalized burned ratio, non-photosynthetic vegetation, and green vegetation (ΔNBR, ΔNPV, and ΔGV, respectively), relative to pre-fire data, were good proxies of canopy damage through tree mortality, even though small and medium trees were the most affected tree size. Among all tested predictors, pre-fire AGB had the highest RF model importance to predicting AGB within one year after fire. However, spectral indices significantly improved AGB loss estimates by 24% and model accuracy by 16% within two years after a fire, with ΔGV as the most important predictor, followed by ΔNBR and ΔNPV. Up to two years after a fire, this study indicates the potential of structural and spectral-based spatial data for integrating complex post-fire ecological processes and improving carbon emission estimates by forest fires in the Amazon.

KW - forest fire

KW - degradation

KW - biomass

KW - change detection

KW - Landsat-8

KW - Google Earth Engine

U2 - 10.3390/rs14071545

DO - 10.3390/rs14071545

M3 - Journal article

VL - 14

JO - Remote Sensing

JF - Remote Sensing

SN - 2072-4292

IS - 7

M1 - e1545

ER -