Monitoring grassland dryness and fire potential in australia with NOAA/AVHRR data

Paltridge, G. W.; Barber, James A.

doi:10.1016/0034-4257(88)90110-1

Cited by 148 publications

(96 citation statements)

References 10 publications

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“…The indices selected to estimate the biophysical variables included bands in the water absorption SWIR region (Faurtyot and Baret, 1997) and bands sensitive to vegetation greenness and structure in the NIR region (Paltridge and Barber, 1988;Yebra et al, 2008b).…”

Section: Vegetation Indicesmentioning

confidence: 99%

Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site

et al. 2015

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Abstract. This study evaluates three different metrics of water content of an herbaceous cover in a Mediterranean wooded grassland (dehesa) ecosystem. Fuel moisture content (FMC), equivalent water thickness (EWT) and canopy water content (CWC) were estimated from proximal sensing and MODIS satellite imagery. Dry matter (Dm) and leaf area index (LAI) connect the three metrics and were also analyzed. Metrics were derived from field sampling of grass cover within a 500 m MODIS pixel. Hand-held hyperspectral measurements and MODIS images were simultaneously acquired and predictive empirical models were parametrized. Two methods of estimating FMC and CWC using different field protocols were tested in order to evaluate the consistency of the metrics and the relationships with the predictive empirical models. In addition, radiative transfer models (RTM) were used to produce estimates of CWC and FMC, which were compared with the empirical ones.Results revealed that, for all metrics spatial variability was significantly lower than temporal. Thus we concluded that experimental design should prioritize sampling frequency rather than sample size. Dm variability was high which demonstrates that a constant annual Dm value should not be used to predict EWT from FMC as other previous studies did. Relative root mean square error (RRMSE) evaluated the performance of nine spectral indices to compute each variable. Visible Atmospherically Resistant Index (VARI) provided the lowest explicative power in all cases. For proximal sensing, Global Environment Monitoring Index (GEMI) showed higher statistical relationships both for FMC (RRMSE = 34.5 %) and EWT (RRMSE = 27.43 %) while Normalized Difference Infrared Index (NDII) and Global Vegetation Monitoring Index (GVMI) for CWC (RRMSE = 30.27 % and 31.58 % respectively). When MODIS data were used, results showed an increase in R 2 and Enhanced Vegetation Index (EVI) as the best predictor for FMC (RRMSE = 33.81 %) and CWC (RRMSE = 27.56 %) and GEMI for EWT (RRMSE = 24.6 %). Differences in the viewing geometry of the platforms can explain these differences as the portion of vegetation observed by MODIS is larger than when using proximal sensing including the spectral response from scattered trees and its shadows. CWC was better predicted than the other two water content metrics, probably because CWC depends on LAI, that shows a notable seasonal variation in this ecosystem. Strong statistical relationship was found between empirical models using indices sensible to chlorophyll activity (NDVI or EVI which are not directly related to water content) due to the close relationship between LAI, water content and chlorophyll activity in grassland cover, which is not true for other types of vegetation such as forest or shrubs. The empirical methods tested outperformed FMC and CWC products based on radiative transfer model inversion.

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Section: Vegetation Indicesmentioning

confidence: 99%

Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site

et al. 2015

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“…For our case, the increases of reflectance in blue and red regions were more likely due to the reduced chlorophyll content of dead materials. The decrease in reflectance of near-infrared and increase in mid-infrared were potentially attributed to the larger intercellular space and less water content of dead materials when compared with green vegetation [35]. The spectral signature of the study area illustrated that dead materials were one of the major surface components controlling the spectral behavior of vegetation canopies.…”

Section: Discussionmentioning

confidence: 93%

Quantifying Responses of Spectral Vegetation Indices to Dead Materials in Mixed Grasslands

Yang

Guo

2014

Remote Sensing

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Spectral vegetation indices have been the primary resources for characterizing grassland vegetation based on remotely sensed data. However, the use of spectral indices for vegetation characterization in grasslands has been challenged by the confounding effects from external factors, such as soil properties, dead materials, and shadowing of vegetation canopies. Dead materials refer to the dead component of vegetation, including fallen litter and standing dead grasses accumulated from previous years. The abundant dead materials have been presenting challenges to accurately estimate green vegetation using spectral vegetation indices (VIs) derived from remote sensing data in mixed grasslands. Therefore, a close investigation of the relationship between VIs and dead materials is needed. The identified relationships could provide better insight into not only using remote sensing data for quantitative estimation of dead materials, but also the improvement of green vegetation estimation in the mixed grassland that has a high proportion of dead materials. In this article, the spectral reflectance of dead materials and green vegetation mixtures and dead material cover were measured in mixed grasslands located in Grassland National Park (GNP), Saskatchewan, Canada. Nine VIs were derived from the measured spectral reflectance. The relationship between dead material cover and VIs was quantified using the regression model and sensitivity analysis. Results indicated that the relationship between dead material cover and VIs is a function of the amount of dead material cover. Weak positive relationship was found between VIs and dead materials where the cover was less than 50%, and a significant high negative relationship was OPEN ACCESSRemote Sens. 2014, 6 4290 evident when cover was greater than 50%. When the combined exponential and linear model was applied to fit the negative relationships, more than 90% variation in dead material cover could be explained by VIs. Sensitivity analysis was further applied to the developed models, indicating that sensitivities of all VIs were significant over the entire range of dead material cover except for the triangular vegetation index (TVI), which has insignificant sensitivity when dead material cover was greater than 94%. Among all VIs, the weighted difference vegetation index (WDVI) had the highest sensitivity to changes in dead material cover higher than 50%. The results from this study indicated that vegetation indices based on combination of reflectance in red and NIR bands can be used to estimate dead material cover that is greater than 50%.

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“…The first remote sensing studies on fuel moisture conditions monitoring used optical data, mainly NOAA-AVHRR NDVI images (e.g., Paltridge and Barber, 1988;Burgan et al, 1998;Hardy and Burgan, 1999). This supposes that timing and extent of drought can be assessed from vegetation greenness, as retrieved from satellite data.…”

Section: Optical Remote Sensingmentioning

confidence: 99%

“…NDVI-based operational systems have been proposed to assess fire potentials ( Figure 1) (Burgan et al, 1998) and crop droughts or fire dangers (Kogan, 2001 Figure 1. An operational system to compute fire potential maps from NOAA-AVHRR NDVI images (adapted from Burgan et al, 1998) These studies listed several problems related to the use of NDVI images in fuel moisture mapping, namely the saturation of relationships (Paltridge and Barber, 1988), the influence of site wetness on relationships (Deblonde and Cihlar, 1993) and the difficulty of using NDVI over forests, due to the spectral mixture of the overstory with the understory, both being different in nature and in moisture content (e.g., Hardy and Burgan, 1999;. In fact, NDVI and associated vegetation indices are only indirectly related to fuel moisture conditions, because it rather measures the greenness and the chlorophyllous activity of the vegetation (Ceccato et al, 2001;Leblon, 2005).…”

Section: Optical Remote Sensingmentioning

confidence: 99%

Use of Remote Sensing in Wildfire Management

Leblon¹,

Bourgeau‐Chavez²,

San-Miguel-Ayanz³

2012

Sustainable Development - Authoritative and Leading Edge Content for Environmental Management

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Monitoring grassland dryness and fire potential in australia with NOAA/AVHRR data

Cited by 148 publications

References 10 publications

Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site

Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site

Quantifying Responses of Spectral Vegetation Indices to Dead Materials in Mixed Grasslands

Use of Remote Sensing in Wildfire Management

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