Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1

Ceccato, Pietro; Gobron, Nadine; Flasse, S.; Pinty, B.; Tarantola, Stefano

doi:10.1016/s0034-4257(02)00037-8

Cited by 551 publications

(294 citation statements)

References 30 publications

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“…The forest cover type, the presence of multi-layered or young stands and the fuel load have a substantial impact on the probability of wildfire occurrence (Castro et al, 2003;Ceccato et al, 2002;Cumming, 2001;Reed, 1994;Velez, 1990). Modification of any of these fuel strata by silvicultural operations will thus have implications on wildfire occurrence (Jactel et al, 2009;Peterson et al, 2005).…”

Section: Methodsmentioning

confidence: 99%

Assessing wildfire occurrence probability in Pinus pinaster Ait. stands in Portugal

Marques¹,

García-Gonzalo²,

Botequim³

et al. 2012

For. syst.

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Maritime pine (Pinus pinaster Ait.) is an important conifer from the western Mediterranean Basin extending over 22% of the forest area in Portugal. In the last three decades nearly 4% of Maritime pine area has been burned by wildfires. Yet no wildfire occurrence probability models are available and forest and fire management planning activities are thus carried out mostly independently of each other. This paper presents research to address this gap. Specifically, it presents a model to assess wildfire occurrence probability in regular and pure Maritime pine stands in Portugal. Emphasis was in developing a model based on easily available inventory data so that it might be useful to forest managers. For that purpose, data from the last two Portuguese National Forest Inventories (NFI) and data from wildfire perimeters in the years from 1998 to 2004 and from 2006 to 2007 were used. A binary logistic regression model was build using biometric data from the NFI. Biometric data included indicators that might be changed by operations prescribed in forest planning. Results showed that the probability of wildfire occurrence in a stand increases in stand located at steeper slopes and with high shrubs load while it decreases with precipitation and with stand basal area. These results are instrumental for assessing the impact of forest management options on wildfire probability thus helping forest managers to reduce the risk of wildfires.Key words: forest management; risk; fire occurrence model; Pinus pinaster Ait. Resumen Evaluación de la probabilidad de ocurrencia de fuegos en rodales de Pinus pinaster en PortugalEl articulo presenta un modelo para evaluar la probabilidad de ocurrencia de incendios en masas regulares y puras de Pinus pinaster en Portugal. Se desarrolla un modelo basado en datos de inventario fácilmente disponibles de tal forma que pueda ser una herramienta útil para los gestores forestales. Los datos proceden de los dos Inventarios Nacionales de Portugal (NFI) y de los datos de los parámetros de incendios forestales durante los años 1998-2004 y de 2006 a 2007. Se ha utilizado un modelo de regresión logística binarias utilizando datos biométricos del NFI. Los datos biométricos incluyen indicadores que puedan ser cambios en las operaciones prescritas en los planes forestales. Los resultados muestran que la probabilidad de ocurrencia de incendios en un rodal aumenta en rodales localizados en grandes pendientes y con una carga alta de matorrales, mientras que decrece con la precipitación y con el área basimétrica. Estos resultados son instrumentos para evaluar el impacto de las opciones de gestión forestal en la probabilidad de incendios ayudando por tanto a los gestores a reducir el riesgo de incendio.Palabras clave: gestion forestal, riesgo, modelo de ocurrencia de incendios, Pinus pinaster Ait.

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Section: Methodsmentioning

confidence: 99%

Assessing wildfire occurrence probability in Pinus pinaster Ait. stands in Portugal

Marques¹,

García-Gonzalo²,

Botequim³

et al. 2012

For. syst.

View full text Add to dashboard Cite

show abstract

“…This estimate is made through an index called the Equivalent Water Thickness (EWT). We adopted the expression given by Ceccato (Ceccato, 2002) which allows to estimate the value of EWT as a function of GVMI (Global Vegetation Moisture Index), although this index has been defined for the sensor SPOT/VEGETATION. Therefore, it was necessary to compute new coefficients allowing to apply the Ceccato EWT relationship to MODIS images.…”

Section: Resultsmentioning

confidence: 99%

PREFER FP7 project for the management of the pre- and post-fire phases: presentation of the products

Laneve¹,

Bonis²,

Fusilli³

et al. 2014

Advances in Forest Fire Research

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A navegação consulta e descarregamento dos títulos inseridos nas Bibliotecas Digitais UC Digitalis, UC Pombalina e UC Impactum, pressupõem a aceitação plena e sem reservas dos Termos e Condições de Uso destas Bibliotecas Digitais, disponíveis em https://digitalis.uc.pt/pt-pt/termos.Conforme exposto nos referidos Termos e Condições de Uso, o descarregamento de títulos de acesso restrito requer uma licença válida de autorização devendo o utilizador aceder ao(s) documento(s) a partir de um endereço de IP da instituição detentora da supramencionada licença.Ao utilizador é apenas permitido o descarregamento para uso pessoal, pelo que o emprego do(s) título(s) descarregado(s) para outro fim, designadamente comercial, carece de autorização do respetivo autor ou editor da obra. Na medida em que todas as obras da UC Digitalis se encontram protegidas pelo Código do Direito de Autor e Direitos Conexos e demais legislação aplicável, toda a cópia, parcial ou total, deste documento, nos casos em que é legalmente admitida, deverá conter ou fazer-se acompanhar por este aviso.

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“…Drought classification based on SPI ranges [21]. GVMI: Global Moisture Vegetation Index [24]. These MVI were developed from the combination of the near infrared (NIR) (B2 of MOD09A1) and the SWIR (B5, B6 and B7 of MOD09A1); the latter are particularly sensitive to the water content of vegetation [25] as shown in Figure 3.…”

Section: Modis Time-series Datamentioning

confidence: 99%

Quality Assessment of MODIS Time Series Images and the Effect on Drought Monitoring

Chakroun¹

2017

OJAppS

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Drought Monitoring by remotely sensed moisture vegetation indexes is being an active research subject as the vegetation spectral responses are showed to be highly correlated to water content. The MODIS (MODerate resolution Imaging Spectro-radiometer) sensor of the Terra satellite provides MOD09A1 product of BRDF (Bidirectional Reflectance Distribution Function) used in computing moisture vegetation indexes (MVI). The exploration of an MVI time-series in the Kroumirie forest in Northern Tunisia showed important noise due to both clouds contamination and sensor defaults that had to be removed. Amongst methods for removing these imperfections, TIMESAT tool was designed for correcting time-series of satellite data and also to retrieve seasonal parameters from smoothed vegetation indexes. The methodology of smoothing functions to fit the time series data is based on two stages. First, a least square fit to the upper envelope of the vegetation indices series is applied. The second stage is achieved by local and adaptive fitting functions. The corrections have been made by spikes removal due to abrupt change of MVI variations and by fitting the MVI time-series to the upper envelop to correct the negative biases of remote sensing vegetation indexes. The adaptive SavitskyGolay function filter compared to local filtering process produces variations that conserve local variations for all the tested MVI. Seasonal vegetation parameters were extracted for each year of the time-series analysis and compared to the Standardized Precipitation Index (SPI) calculated at meteorological station level and for different time scales. Positive relations were found between SPI and the seasonal parameters expressed by the length and the amplitude of the season, indicating MODIS derived MVI sensitivity to water deficit or surplus conditions. The 6-month SPI showed the best performance when related to water sensitive indexes suggesting that MODIS derived indexes are more correlated to the precipitation variations over seasons.

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Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1

Cited by 551 publications

References 30 publications

Assessing wildfire occurrence probability in Pinus pinaster Ait. stands in Portugal

Assessing wildfire occurrence probability in Pinus pinaster Ait. stands in Portugal

PREFER FP7 project for the management of the pre- and post-fire phases: presentation of the products

Quality Assessment of MODIS Time Series Images and the Effect on Drought Monitoring

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