Monitoring post-fire recovery of various vegetation biomes using multi-wavelength satellite remote sensing

Bousquet, Emma; Mialon, Arnaud; Rodríguez-Fernández, Nemesio; Mermoz, Stéphane; Kerr, Yann H.

doi:10.5194/bg-19-3317-2022

Cited by 17 publications

(5 citation statements)

References 91 publications

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“…NDVI proved more effective in tracking vegetation changes 31 . To analyse post-fire vegetation recovery, using multi-wavelength satellite data is an approach that utilizes various remotely sensed parameters, including visible–infrared vegetation indices 32 . The vegetation index credibility for effective vegetation monitoring can be further attributed to its use for drought monitoring 33 .…”

Section: Related Workmentioning

confidence: 99%

Vegetation change detection and recovery assessment based on post-fire satellite imagery using deep learning

Priya,

Vani

2024

Sci Rep

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Wildfires are uncontrolled fires fuelled by dry conditions, high winds, and flammable materials that profoundly impact vegetation, leading to significant consequences including noteworthy changes to ecosystems. In this study, we provide a novel methodology to understand and evaluate post-fire effects on vegetation. In regions affected by wildfires, earth-observation data from various satellite sources can be vital in monitoring vegetation and assessing its impact. These effects can be understood by detecting vegetation change over the years using a novel unsupervised method termed Deep Embedded Clustering (DEC), which enables us to classify regions based on whether there has been a change in vegetation after the fire. Our model achieves an impressive accuracy of 96.17%. Appropriate vegetation indices can be used to evaluate the evolution of vegetation patterns over the years; for this study, we utilized Enhanced Vegetation Index (EVI) based trend analysis showing the greening fraction, which ranges from 0.1 to 22.4 km2 while the browning fraction ranges from 0.1 to 18.1 km2 over the years. Vegetation recovery maps can be created to assess re-vegetation in regions affected by the fire, which is performed via a deep learning-based unsupervised method, Adaptive Generative Adversarial Neural Network Model (AdaptiGAN) on post-fire data collected from various regions affected by wildfire with a training error of 0.075 proving its capability. Based on the results obtained from the study, our approach tends to have notable merits when compared to pre-existing works.

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Section: Related Workmentioning

confidence: 99%

Vegetation change detection and recovery assessment based on post-fire satellite imagery using deep learning

Priya,

Vani

2024

Sci Rep

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“…NDVI proved more effective in tracking vegetation changes (Natalie et al, 2018). To analyze post-re vegetation recovery, usage of multi wavelength satellite data is an approach which utilized various remotely sensed parameters including visible-infrared vegetation indices (Bousquet et al, 2022). Vegetation indices credibility for effective vegetation monitoring can be further attributed to the fact that it is used for drought monitoring (Benedict et al, 2021).…”

Section: Data and Preprocessingmentioning

confidence: 99%

Vegetation Change Detection and Recovery Assessment on Post-fire Satellite Imagery using Deep Learning

Rajendran,

2024

Preprint

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Wildfire are uncontrolled fires fueled by dry conditions, high winds and flammable materials that tends to have a profound impact on vegetation due to the intense heat generated by it which can cause the destruction of trees, small plants and other vegetation leading to significant consequences including noteworthy changes to ecosystems. Due to the periodic wildfires, vegetation communities in forest systems have changed adaptively to deal with ecological rebuilding. In this study we provide a novel methodology, to understand and evaluate post-fire effects on vegetation. In regions which are affected by wildfire, earth-observation data provided by various satellite sources can be very vital in monitoring vegetation and assessing the effect a wildfire tends to have on it. These effects can be understood by detecting the change of vegetation over years using an unsupervised method termed Deep Embedded Clustering (DEC), which enables us to classify regions on whether there has been a change in vegetation after fire. Appropriate vegetation indices can be used to evaluate evolution of vegetation pattern over the years, for this study we utilized Enhanced Vegetation Index (EVI) based trend analysis. Vegetation recovery maps can be created to assess re-vegetation in regions affected by fire which is performed via a deep learning based unsupervised method, Adaptive Generative Adversarial Neural Network Model (AdaptiGAN) on postfire data collected from various regions affected by wildfire. Through the results obtained from the study we can arrive at a conclusion that our approach tends to have notable merits when compared to pre-existing works.

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“…While much attention has traditionally been given to fires in forest ecosystems and their consequences [French et al, 2008;Pacheco et al, 2021;Sulova, 2021], there are also various remote sensing methods employed to assess post-fire grass restoration. These methods evaluate their strengths, limitations, and discuss potential future directions in the field [Soulard, 2016;Bousquet et al, 2022].…”

Section: Introductionmentioning

confidence: 99%

Restoration of Western Siberia meadow vegetation after fires according to Sentinel-2 data

Karpachevskiy,

Tikhonov,

Koroleva

2023

E3S Web of Conf.

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The paper considers the dynamics of restoration of plant communities of meadows in the south of Western Siberia according to Sentinel data-2. Four subjects of the Russian Federation and nine specially protected natural territories within them were taken. Sentinel-2 data is characterized by a repetition of the survey once every five days and a spatial resolution of 10-20 m, which allows you to track the moment of the fire with high accuracy and record the state of vegetation before and after it. For quantitative evaluation, we used NDVI and NBR2 indices, which were compared for burnt and unburned areas before the fire, immediately after the fire, one year after the fire and two years after the fire. As a result of statistical analysis by the Mann-Whitney test, it was found out that both indices practically do not show the consequences of meadow fires in any way a year after the fire.

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Monitoring post-fire recovery of various vegetation biomes using multi-wavelength satellite remote sensing

Cited by 17 publications

References 91 publications

Vegetation change detection and recovery assessment based on post-fire satellite imagery using deep learning

Vegetation change detection and recovery assessment based on post-fire satellite imagery using deep learning

Vegetation Change Detection and Recovery Assessment on Post-fire Satellite Imagery using Deep Learning

Restoration of Western Siberia meadow vegetation after fires according to Sentinel-2 data

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