Assessment of Burned Forest Area Severity and Postfire Regrowth in Chapada Diamantina National Park (Bahia, Brazil) Using dNBR and RdNBR Spectral Indices

Santos, Sarah Moura Batista dos; Bento‐Gonçalves, António; Franca-Rocha, Washington; Baptista, Gustavo Macêdo de Mello

doi:10.3390/geosciences10030106

Cited by 30 publications

(33 citation statements)

References 22 publications

(40 reference statements)

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“…Using NBR as defined in Equation ( 1), we calculated the dNBR as illustrated in Equation (2). The dNBR has become a standard fire severity measurement [60] as it is a criterion to understand burned severity levels [66]. The dNBR ranges from -2 to +2 with high positive values representing severely burned areas.…”

Section: Burned Severity and Quantification Of Burn Area For Each Land Use/cover Classmentioning

confidence: 99%

“…Sentinel-2 has become an attractive source for burned area monitoring because of its superior spectral properties, including the red-edge bands suited for chlorophyll content characterization [58], which provides the means to create novel indices for burn severity mapping [59]. Several previous studies have evaluated forest fires using the normalized burn ratio (NBR) with a high degree of success [47,60]. The NBR exploits the near-infrared (NIR) and short-wave infrared (SWIR) regions of the electromagnetic spectrum [61].…”

Section: Introductionmentioning

confidence: 99%

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Burned Area Mapping over the Southern Cape Forestry Region, South Africa Using Sentinel Data within GEE Cloud Platform

Xulu

Mbatha

Peerbhay

2021

IJGI

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Planted forests in South Africa have been affected by an increasing number of economically damaging fires over the past four decades. They constitute a major threat to the forestry industry and account for over 80% of the country’s commercial timber losses. Forest fires are more frequent and severe during the drier drought conditions that are typical in South Africa. For proper forest management, accurate detection and mapping of burned areas are required, yet the exercise is difficult to perform in the field because of time and expense. Now that ready-to-use satellite data are freely accessible in the cloud-based Google Earth Engine (GEE), in this study, we exploit the Sentinel-2-derived differenced normalized burned ratio (dNBR) to characterize burn severity areas, and also track carbon monoxide (CO) plumes using Sentinel-5 following a wildfire that broke over the southeastern coast of the Western Cape province in late October 2018. The results showed that 37.4% of the area was severely burned, and much of it occurred in forested land in the studied area. This was followed by 24.7% of the area that was burned at a moderate-high level. About 15.9% had moderate-low burned severity, whereas 21.9% was slightly burned. Random forests classifier was adopted to separate burned class from unburned and achieved an overall accuracy of over 97%. The most important variables in the classification included texture, NBR, and the NIR bands. The CO signal sharply increased during fire outbreaks and marked the intensity of black carbon over the affected area. Our study contributes to the understanding of forest fire in the dynamics over the Southern Cape forestry landscape. Furthermore, it also demonstrates the usefulness of Sentinel-5 for monitoring CO. Taken together, the Sentinel satellites and GEE offer an effective tool for mapping fires, even in data-poor countries.

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Section: Burned Severity and Quantification Of Burn Area For Each Land Use/cover Classmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Burned Area Mapping over the Southern Cape Forestry Region, South Africa Using Sentinel Data within GEE Cloud Platform

Xulu

Mbatha

Peerbhay

2021

IJGI

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“…In this context, remote sensing techniques are a highly feasible and effective tool for describing patterns of the occurrence of fire in various ecosystems, as satellite imaging is an important means through which to delineate the perimeters of fire expansion and characterise the degree of intensity or severity reached by fires [12,13,[17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Research on Wildfires and Remote Sensing in the Last Three Decades: A Bibliometric Analysis

Santos

Bento‐Gonçalves

Vieira

2021

Forests

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Evaluating the impact of wildland fires on landscapes, a pursuit increasingly supported by remote sensing techniques, requires an understanding of wildfire dynamics. This research highlights the main insights from the literature related to “wildfires” and “remote sensing” published between 1991 and 2020. The Scopus database was used as a source of information regarding scientific production on these topics, after which bibliometric tools were employed as a means through which to reveal patterns in this network of journals, terms, countries, and authors. The results suggest that these subject areas have undergone significant developments in the last three decades, having been the focus of growing interest among the scientific community. The most relevant contributions to the literature available have been made by researchers working in the areas of earth and environmental sciences (54% of the publications), primarily in the United States, China, Spain, and Canada. Research trends in this field have undergone a significant evolution in recent decades, explained by the strong relationship between the technological evolution of detection methods and remote sensing data acquisition.

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“…A criação de políticas de prevenção e monitoramento de incêndios tem se tornado possível através da aplicação de técnicas de Sensoriamento Remoto (SR), pois permitem extrair informações de forma rápida e eficiente, para serem utilizadas na identificação, controle e fiscalização de queimadas (CHUVIECO, 1996;DOS SANTOS et al, 2018;FREITAS et al, 2005;PEREIRA, 2009;SANTOS et al, 2020). A análise bitemporal de imagens de satélite tem sido uma técnica amplamente utilizada na detecção e monitoramento de mudanças ambientais, principalmente no que diz respeito à estimação de áreas queimadas e à detecção da severidade do fogo (CARDOZO et al, 2011;CARVAJAL-RAMIREZ et al, 2019;ESCUÍN;NAVARRO;FERNÁNDEZ, 2008;SANTOS et al 2020). O método de classificação de imagens orientada a objetos tem se destacado por considerar não apenas a informação espectral, mas também relações espaciais como forma e contexto, resultando em classificações mais sofisticadas e próximas da interpretação humana, sem o efeito "salt and pepper" comum no método pixel a pixel (McDERMID, et al, 2003;WONG et al, 2003).…”

Section: Introductionunclassified

“…Entretanto, a detecção de áreas queimadas por SR tem apresentado limitações, devido à dificuldade de discriminação do fogo de possíveis falhas de imageamento, que podem muitas vezes limitar a detecção (COCHRANE, 2003). Além disso, a tendência de confusão entre superfícies queimadas e outros usos e coberturas do solo, tem levado à necessidade de uma melhor caracterização de sua assinatura espectral, o que tem sido possível por meio dos índices espectrais, que auxiliam na discriminação de alvos da superfície terrestre com base em seu comportamento espectral, por meio de cálculos aritméticos entre bandas (CARVAJAL-RAMIREZ et al, 2019;DOS SANTOS et al, 2018;LEITE et al, 2017;SANTOS et al, 2020). Dessa forma, os principais objetivos desta pesquisa foram aplicar o método de classificação orientada a objeto e avaliar a melhor performance do uso conjunto de índices espectrais para o mapeamento de áreas queimadas na Mata Atlântica do Sul da Bahia.…”

Section: Introductionunclassified

Análise bitemporal de áreas queimadas na Mata Atlântica

Sacramento

Michel

Siqueira

2020

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O estudo de áreas queimadas serve como subsídio para os planos de controle e monitoramento do fogo nas unidades de conservação. Diante dos desafios de caracterização do comportamento espectral de áreas queimadas, este estudo objetivou aplicar o método de classificação orientada a objeto e avaliar a melhor performance do uso conjunto de índices espectrais para o mapeamento de área queimadas na Mata Atlântica do Sul da Bahia. Para tanto, foi realizada análise bitemporal entre 2014 e 2016, considerando a diferença de cada índice espectral entre duas imagens LANDSAT 8: pré e pós-fogo. A classificação orientada a objeto foi executada de maneira supervisionada e automatizada por meio de algoritmos de segmentação, classificação e otimização em ambiente SIG. A classe de intensidade de queimada fraca foi a mais expressiva, com 13,65% da área mapeada, enquanto a classe severa ocupou apenas 0,3%. As áreas queimadas apresentaram um aumento da reflectância na faixa do vermelho e do infravermelho médio e uma diminuição na faixa do infravermelho próximo. O ΔNBR foi o melhor discriminador de áreas queimadas e o conjunto ΔNBR, ΔNBR2, ΔNDMI, ΔSAVI, ΔNDVI, ΔGEMI e ΔMSAVI, apresentou o maior limiar de separação. A validação da classificação feita pelo coeficiente de concordância Kappa obteve um bom resultado (0,72). A seleção de variáveis mostrou-se eficiente na determinação do conjunto de índices espectrais de melhor performance para detecção das classes de queimadas, melhorando a acurácia da classificação e a confiabilidade dos resultados. A etapa de segmentação também se mostrou importante para a eficácia da classificação orientada a objeto, sendo influenciada diretamente pela resolução espacial da imagem.

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Assessment of Burned Forest Area Severity and Postfire Regrowth in Chapada Diamantina National Park (Bahia, Brazil) Using dNBR and RdNBR Spectral Indices

Cited by 30 publications

References 22 publications

Burned Area Mapping over the Southern Cape Forestry Region, South Africa Using Sentinel Data within GEE Cloud Platform

Burned Area Mapping over the Southern Cape Forestry Region, South Africa Using Sentinel Data within GEE Cloud Platform

Research on Wildfires and Remote Sensing in the Last Three Decades: A Bibliometric Analysis

Análise bitemporal de áreas queimadas na Mata Atlântica

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