Modeling and forecasting MODIS-based Fire Potential Index on a pixel basis using time series models

Huesca, Margarita; Litago, Javier; Miguel, S. Merino de; Cicuendez-López-Ocaña, Victor; Palacios-Orueta, Alicia

doi:10.1016/j.jag.2013.09.003

Cited by 29 publications

(33 citation statements)

References 36 publications

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“…The FDI index showed a reasonably good predictive capacity of observed fire occurrence, confirming previous findings relating the similar FPI index to fire occurrence in other ecosystems (e.g., [28][29][30][31][33][34][35][45][46][47][48]).…”

Section: Predicting Fire Hotspot Density (Fhd) By Vegetation Type Andsupporting

confidence: 84%

“…The FPI combines remotely sensed estimates of vegetation greenness-as measured by 10-day Normalized Difference Vegetation Index (NDVI) composites-with daily estimates of dead fuel moisture content [42,43] for mapping fuel dryness conditions and associated fire risk and danger. The FPI fuel dryness index has been operationally used for fire danger monitoring and occurrence risk prediction in the United States of America (USA) [28,[33][34][35], Indonesia [44], and on the European continent (e.g., [29][30][31]45]), including studies of regional application in northern Spain [46][47][48]. Several of these works have highlighted the need to understand how the same values of a fuel dryness index such as FPI result in different patterns of fire occurrence under different bioclimatic regions and vegetation types.…”

Section: Introductionmentioning

confidence: 99%

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Developing Models to Predict the Number of Fire Hotspots from an Accumulated Fuel Dryness Index by Vegetation Type and Region in Mexico

Vega-Nieva

Briseño-Reyes

Nava‐Miranda

et al. 2018

Forests

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Understanding the linkage between accumulated fuel dryness and temporal fire occurrence risk is key for improving decision-making in forest fire management, especially under growing conditions of vegetation stress associated with climate change. This study addresses the development of models to predict the number of 10-day observed Moderate-Resolution Imaging Spectroradiometer (MODIS) active fire hotspots-expressed as a Fire Hotspot Density index (FHD)-from an Accumulated Fuel Dryness Index (AcFDI), for 17 main vegetation types and regions in Mexico, for the period 2011-2015. The AcFDI was calculated by applying vegetation-specific thresholds for fire occurrence to a satellite-based fuel dryness index (FDI), which was developed after the structure of the Fire Potential Index (FPI). Linear and non-linear models were tested for the prediction of FHD from FDI and AcFDI. Non-linear quantile regression models gave the best results for predicting FHD using AcFDI, together with auto-regression from previously observed hotspot density values. The predictions of 10-day observed FHD values were reasonably good with R 2 values of 0.5 to 0.7 suggesting the potential to be used as an operational tool for predicting the expected number of fire hotspots by vegetation type and region in Mexico. The presented modeling strategy could be replicated for any fire danger index in any region, based on information from MODIS or other remote sensors.

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Section: Predicting Fire Hotspot Density (Fhd) By Vegetation Type Andsupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Developing Models to Predict the Number of Fire Hotspots from an Accumulated Fuel Dryness Index by Vegetation Type and Region in Mexico

Vega-Nieva

Briseño-Reyes

Nava‐Miranda

et al. 2018

Forests

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

“…The application of temporal series techniques to spatially continuous time series from the remote sensing data opens a new perspective in terms of environmental monitoring (Huesca et al 2014). Pixel-specific models are necessary in order to account for environmental heterogeneity (Huesca et al 2014). However, time-series analysis technique has been applied mainly to specific representative pixels or average series from selected group of pixels (Cunha and Richter 2014;Chang et al 2014;Krishnaswamy, John, and Joseph 2014;Song and Ma 2011).…”

Section: Introductionmentioning

confidence: 99%

A multi-scale spatiotemporal modeling approach to explore vegetation dynamics patterns under global climate change

Qiu

Wang

Tang

et al. 2016

GIScience & Remote Sensing

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Given the complexity of vegetation dynamic patterns under global climate change, multi-scale spatiotemporal explicit models are necessary in order to account for environmental heterogeneity. However, there is no efficient time-series tool to extract, reconstruct and analyze the multi-scale vegetation dynamic patterns under global climate change. To fill this gap, a Multi-Scale Spatio-Temporal Modeling (MSSTM) framework which can incorporate the pixel, scale, and time-specific heterogeneity was proposed. The MSSTM method was defined on proper time-series models for multitemporal components through wavelet transforms. The proposed MSSTM approach was applied to a subtropical mountainous and hilly agro-forestry ecosystem in southeast China using the moderate resolution imaging spectroradiometer enhanced vegetation index (EVI) time-series data sets from 2001 to 2011. The MSSTM approach was proved to be efficient in characterizing and forecasting the complex vegetation dynamic patterns. It provided good estimates of the peaks and valleys of the observed EVI and its average percentages of relative absolute errors of reconstruction was low (6.65). The complexity of the relationship between vegetation dynamics and meteorological parameters was also revealed through the MSSTM method: (1) at seasonal level, vegetation dynamic patterns are strongly associated with climatic variables, primarily the temperature and then precipitation, with correlations slight decreasing (EVI-temperature)/increasing (EVI-precipitation) with altitudinal gradients. (2) At inter-annual scale, obvious positive correlations were primarily observed between EVI and temperature. (3) Despite very low-correlation coefficients observed at intraseasonal scales, considerable proportions of EVI anomalies are associated with climatic variables, principally the precipitation and sunshine durations.

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“…Na época de estiagem, onde há a possibilidade de aquisição de imagens com baixa cobertura de nuvens, a intensa atividade de queimadas e a consequente liberação de aerossóis dificultam a utilização dos métodos tradicionais de mapeamento baseados na faixa espectral do vermelho e infravermelho próximo (PEREIRA, 2003;LIBONATI et al, 2010). Desta forma, o emprego de sensores com alta resolução temporal, que possibilitam a aquisição de imagens com boa qualidade atmosférica, conciliados com os métodos estatísticos é essencial para o monitoramento de incêndios florestais (LENTILE et al, 2006;MOUILLOT et al, 2014;HUESCA et al, 2014).…”

Section: Introductionunclassified

Monitoramento de queimadas no sudoeste do Pará, a partir de séries temporais do sensor modis

Santana¹

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A Deus por todas as bênçãos concedidas.Aos meus pais Valdenise e José, pela educação, incentivos e por sempre me ensinar que a educação é a forma mais todas as conversas e discussões enriquecedoras! Ao Serviço Geológico dos Estados Unidos pela política de distribuição gratuita de suas imagens de Sensoriamento Remoto.A CAPES pelo apoio financeiro para a conclusão desta pesquisa. v RESUMOAs queimadas associadas à expansão da pecuária e agricultura têm se tornado um problema no bioma Amazônico, causando danos severos ao meio ambiente. Imagens de sensoriamento remoto têm sido amplamente utilizadas no monitoramento de queimadas na extensa Floresta Amazônica, porém há a necessidade de aprimoramentos metodológicos para uma detecção automatizada. Esta pesquisa tem como objetivo avaliar séries temporais MODIS para o mapeamento de áreas queimadas no município de Novo Progresso, Pará, e determinar suas ocorrência nos diferentes tipos de uso e cobertura da terra durante o período de 2000-2014.Na detecção de área queimada, os seguintes dados foram comparados: banda do infravermelho próximo e índices espectrais (NBR, NDVI e NBRT), considerando-se imagens diárias e produtos compostos de 8 dias. As séries temporais MODIS foram filtradas e normalizadas temporalmente para eliminar falsos eventos de queimadas. A determinação dos valores limites para a ocorrência de queimadas foi obtida a partir da comparação da série de imagens MODIS com classificações visuais de dados LANDSAT/TM e ETM+ usando o coeficiente Kappa. O melhor resultado alcançado considerou os seguintes fatores: banda de infravermelho próximo, imagens diárias e normalização pela média, obtendo o valor de coeficiente Kappa de 0,72 e Acurácia Geral de 99%. As áreas desmatadas são as responsáveis por mais de 70% dos eventos de incêndios. As propriedades privadas apresentaram maior porcentagem de área queimada, enquanto as Reservas Ambientais Particulares e Terras Indígenas apresentaram as menores taxas. O resultado do método proposto foi melhor do que o disponível pelo produto de áreas queimadas (MCD45), mas ainda apresenta interferências de cobertura de nuvens que devem ser melhoradas em trabalhos futuros. Palavras-chave:Áreas Queimadas, Séries Temporais, Sensoriamento Remoto, Floresta Amazônica. vi ABSTRACT Fires associated with the expansion of cattle ranching and agriculture has become a problem in the Amazon biome, causing severe environmental damages. Remote sensing images have been widely used in the fire monitoring on the extensive Amazon forest, but an accurate automated detection still need improvements. This research aims to evaluate MODIS time series spectral indices for mapping burned areas in the municipality of Novo Progresso, Para, and determine their occurrences in the different types of land use/land cover during the period 2000-2014. In burned area detection, the following data were compared: near-infrared band and spectral indices (NBR, NDVI and NBRT), considering daily images and 8-day composite products. MODIS time series were filtered and standardiz...

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Modeling and forecasting MODIS-based Fire Potential Index on a pixel basis using time series models

Cited by 29 publications

References 36 publications

Developing Models to Predict the Number of Fire Hotspots from an Accumulated Fuel Dryness Index by Vegetation Type and Region in Mexico

Developing Models to Predict the Number of Fire Hotspots from an Accumulated Fuel Dryness Index by Vegetation Type and Region in Mexico

A multi-scale spatiotemporal modeling approach to explore vegetation dynamics patterns under global climate change

Monitoramento de queimadas no sudoeste do Pará, a partir de séries temporais do sensor modis

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