Evaluating the best spectral indices for burned areas in the tropical Pantanos de Centla Biosphere Reserve, Southeastern Mexico

Pérez, Casandra C.; Olthoff, Adriana E.; Hernández-Trejo, Humberto; Rullán-Silva, Cristóbal Daniel

doi:10.1016/j.rsase.2021.100664

Cited by 7 publications

(3 citation statements)

References 48 publications

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“…The BAI used to determine the effect of fires on vegetation shows suitability for detecting post-fire burned areas, taking advantage of the visible (B3) and NIR spectral characteristics and making clear the observation of the spatial pattern of fire behavior, as well as its severity level, mainly associated with grassland cover. This spectral index and the NDVI in tropical wetlands allow an adequate discrimination of fire-damaged surfaces [100]. The use of a multispectral sensor makes it easier to record high-precision, rapid, and indirect measurements based on the vegetation spectral response [101].…”

Section: Discussionmentioning

confidence: 99%

Use of Remotely Piloted Aircraft System Multispectral Data to Evaluate the Effects of Prescribed Burnings on Three Macrohabitats of Pantanal, Brazil

Pineda Valles,

Nunes,

Berlinck

et al. 2023

Remote Sensing

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The controlled use of fires to reduce combustible materials in prescribed burning helps to prevent the occurrence of forest fires. In recent decades, these fires have mainly been caused by anthropogenic activities. The study area is located in the Pantanal biome. In 2020, the greatest drought in 60 years happened in the Pantanal. The fire affected almost one third of the biome. The objective of this study is to evaluate the effect of prescribed burnings carried out in 2021 on three macrohabitats (M1: natural grassland flooded with a proliferation of Combretum spp., M2: natural grassland of seasonal swamps, and M3: natural grassland flooded with a proliferation of Vochysia divergens) inside the SESC Pantanal Private Natural Heritage Reserve. Multispectral and thermal data analyses were conducted with remotely piloted aircraft systems in 1 ha plots in three periods of the dry season with early, mid, and late burning. The land use and land cover classification indicate that the predominant vegetation type in these areas is seasonally flooded grassland, with percentages above 73%, except in zone three, which has a more diverse composition and structure, with the presence of arboreal specimens of V. divergem Pohl. The pattern of the thermal range showed differentiation pre- and post-burning. The burned area index indicated that fire was more efficient in the first two macrohabitats because they are natural grasslands, reducing the grass species in the burnings. Early and mid prescribed burnings are a good option to reduce the continuous accumulation of dry forest biomass fuel material and help to promote landscape heterogeneity. The use of multispectral sensor data with high spatial/spectral resolution can show the effects of fires, using highly detailed scales for technical decision making.

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Section: Discussionmentioning

confidence: 99%

Use of Remotely Piloted Aircraft System Multispectral Data to Evaluate the Effects of Prescribed Burnings on Three Macrohabitats of Pantanal, Brazil

Pineda Valles,

Nunes,

Berlinck

et al. 2023

Remote Sensing

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“…Given the constant forest fires in Mexico, in [16] proposed to analyze the impact on the health of these fires through the quantification of particulate matter (PM) 2.5 particles and carbon dioxide (CO2) emissions. As a methodology, satellite images from Google Earth and Vsmoke were used with an algorithm written in Fortran 77.…”

Section: Literature Reviewmentioning

confidence: 99%

MAPATON 2023: implementing a tool for the analysis of forest fire zones in Arequipa

Vargas-Cuentas,

Paico-Campos,

Cahuana

et al. 2024

IJEECS

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The "Mapaton 2023," a collaborative initiative by the Peruvian Space Agency, Paraguayan Space Agency, and AMERIGEOS, introduces an innovative approach to analyze fire-prone areas in Arequipa, a pivotal endeavor for disaster prevention. Focused on the provinces of Arequipa and Caylloma, recent studies identified over 1,500 affected hectares by forest fires, emphasizing the urgency of employing satellite images and geospatial analysis techniques. Leveraging Landsat 8 satellite imagery, the research calculated indices, including the normalized difference vegetation index (NDVI) for vegetation analysis and the differenced normalized burn ratio (dNBR) for fire severity assessment. Results revealed varying impacts, with some areas exhibiting increased vegetation and others displaying significant damage. The use of ArcGIS online facilitated the presentation of geospatial data, emphasizing the utility of remote sensing in comprehending and addressing forest fires. Drawing insights from analogous studies in Mexico and the Amazon, this research underscores the importance of remote sensing and geospatial analysis in informing preventive measures against wildfires. The findings, crucial for environmental management, are recommended for sharing with relevant authorities, and the continued use of diverse satellite imagery sources is encouraged to enhance accuracy in monitoring and mitigating forest fires.

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“…Secondly, our dataset covers the whole region of Greece, thus land cover types, biomes and vegetation structures can vary greatly among the events. Several studies have shown that spectral indices respond differently to different land cover types and thresholds are usually not transferable from one to another [59], [85], [27], [86]. As shown in Fig.…”

Section: A Examination Of Spectral Indicesmentioning

confidence: 99%

Deep Learning for Downscaling Remote Sensing Images: Fusion and super-resolution

Sdraka

Papoutsis

Psomas

et al. 2022

IEEE Geosci. Remote Sens. Mag.

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Over the last decade there has been an increasing frequency and intensity of wildfires across the globe, posing significant threats to human and animal lives, ecosystems, and socio-economic stability. Therefore urgent action is required to mitigate their devastating impact and safeguard Earth's natural resources. Robust Machine Learning methods combined with the abundance of high-resolution satellite imagery can provide accurate and timely mappings of the affected area in order to assess the scale of the event, identify the impacted assets and prioritize and allocate resources effectively for the proper restoration of the damaged region. In this work, we create and introduce a machine-learning ready dataset we name FLOGA (Forest wiLdfire Observations for the Greek Area). This dataset is unique as it comprises of satellite imagery acquired before and after a wildfire event, it contains information from Sentinel-2 and MODIS modalities with variable spatial and spectral resolution, and contains a large number of events where the corresponding burnt area ground truth has been annotated by domain experts. FLOGA covers the wider region of Greece, which is characterized by a Mediterranean landscape and climatic conditions. We use FLOGA to provide a thorough comparison of multiple Machine Learning and Deep Learning algorithms for the automatic extraction of burnt areas, approached as a change detection task. We also compare the results to those obtained using standard specialized spectral indices for burnt area mapping. Finally, we propose a novel Deep Learning model, namely BAM-CD. Our benchmark results demonstrate the efficacy of the proposed technique in the automatic extraction of burnt areas, outperforming all other methods in terms of accuracy and robustness. Our dataset and code are publicly available at: https://github.com/Orion-AI-Lab/FLOGA.

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Evaluating the best spectral indices for burned areas in the tropical Pantanos de Centla Biosphere Reserve, Southeastern Mexico

Cited by 7 publications

References 48 publications

Use of Remotely Piloted Aircraft System Multispectral Data to Evaluate the Effects of Prescribed Burnings on Three Macrohabitats of Pantanal, Brazil

Use of Remotely Piloted Aircraft System Multispectral Data to Evaluate the Effects of Prescribed Burnings on Three Macrohabitats of Pantanal, Brazil

MAPATON 2023: implementing a tool for the analysis of forest fire zones in Arequipa

Deep Learning for Downscaling Remote Sensing Images: Fusion and super-resolution

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