Regional Equivalent Water Thickness Modeling from Remote Sensing across a Tree Cover/LAI Gradient in Mediterranean Forests of Northern Tunisia

Chakroun, Hédia; Mouillot, Florent; Hamdi, Abdelaziz

doi:10.3390/rs70201937

Cited by 16 publications

(20 citation statements)

References 68 publications

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“…NDVI is an indicator of the vegetation dryness [48] and could be used to assess dry woody vegetation matter, which is the main fuel source in Mediterranean forest wildfires. A decline in NDVIW would imply drying trends in woody vegetation and increasing dry matter for potential fires [47].…”

Section: Pre and Post Fire Assessment Using Ndviw And Ndvih In Mt Camentioning

confidence: 99%

A Phenology-Based Method for Monitoring Woody and Herbaceous Vegetation in Mediterranean Forests from NDVI Time Series

et al. 2015

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Abstract:We present an efficient method for monitoring woody (i.e., evergreen) and herbaceous (i.e., ephemeral) vegetation in Mediterranean forests at a sub pixel scale from Normalized Difference Vegetation Index (NDVI) time series derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). The method is based on the distinct development periods of those vegetation components. In the dry season, herbaceous vegetation is absent or completely dry in Mediterranean forests. Thus the mean NDVI in the dry season was attributed to the woody vegetation (NDVIW). A constant NDVI value was assumed for soil background during this period. In the wet season, changes in NDVI were attributed to the development of ephemeral herbaceous vegetation in the forest floor and its maximum value to the peak green cover (NDVIH). NDVIW and NDVIH agreed well with field estimates of leaf area index and fraction of vegetation cover in two differently structured Mediterranean forests. To further assess the method's assumptions, understory NDVI was retrieved form MODIS Bidirectional Reflectance Distribution Function (BRDF) data and compared with NDVIH. After calibration, leaf area index and woody and herbaceous vegetation covers were assessed for those forests. Applicability for pre-and post-fire OPEN ACCESSRemote Sens. 2015, 7 12315 monitoring is presented as a potential use of this method for forest management in Mediterranean-climate regions.

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Section: Pre and Post Fire Assessment Using Ndviw And Ndvih In Mt Camentioning

confidence: 99%

A Phenology-Based Method for Monitoring Woody and Herbaceous Vegetation in Mediterranean Forests from NDVI Time Series

et al. 2015

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“…In a previous research in the Kroumirie forest comparing field moisture data with MVI computed from MODIS [26], it has been shown that MVI are highly correlated to vegetation water content in this ecosystem. In other water limited ecosystems, a study led in Australia confirmed that MVI are sensitive to drought intensity and are highly correlated to SPI [27].…”

Section: Modis Time-series Datamentioning

confidence: 99%

“…Indeed in [26], it has been shown that in this peculiar ecosystem characterized by a gradient of vegetation density, the models linking water-sensitive indexes and the hydric status of the canopy are highly dependent of the biomass expressed by the LAI (Leaf Area Index). This could explain the variable performance of MVI from one site to another as they had different sensitivities to the biomass as reported in other works [27].…”

Section: Drought Analysis By Spi and Modis Time Seriesmentioning

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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“…Helman et al [56] and Chekroun et al [57] used a distinctly different approach to distinguish the contributions of herbaceous and woody vegetation to the VI time signals in their Mediterranean study areas. In contrast to conventional signal decomposition, their approaches were designed a priori to take strategic advantage of two intra-annual periods, the dry season and the wet season, when the seasonal phenology of herbaceous and woody vegetation diverge.…”

Section: Multispectral Image-based Approaches To Mapping Of Semiarid mentioning

confidence: 99%

“…These studies demonstrated that the signal component associated with seasonal herbaceous vegetation could be isolated from the woody signal by exploiting this seasonal divergence in the VI response. Chekroun et al [57] used this approach with several VI data types from the Moderate Resolution Imaging Spectroradiometer (MODIS) to achieve improved sensitivity to drought across landscapes of Northern Tunisia with large variations in the proportional surface cover of trees, shrubs and grass. Helman et al [56] showed this approach is effective for estimating and mapping spatial variation in the subpixel fractional surface cover of woody vegetation and herbaceous vegetation in their study area in Israel.…”

Section: Multispectral Image-based Approaches To Mapping Of Semiarid mentioning

confidence: 99%

Exploiting Differential Vegetation Phenology for Satellite-Based Mapping of Semiarid Grass Vegetation in the Southwestern United States and Northern Mexico

et al. 2016

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Abstract:We developed and evaluated a methodology for subpixel discrimination and large-area mapping of the perennial warm-season (C 4 ) grass component of vegetation cover in mixed-composition landscapes of the southwestern United States and northern Mexico. We describe the methodology within a general, conceptual framework that we identify as the differential vegetation phenology (DVP) paradigm. We introduce a DVP index, the Normalized Difference Phenometric Index (NDPI) that provides vegetation type-specific information at the subpixel scale by exploiting differential patterns of vegetation phenology detectable in time-series spectral vegetation index (VI) data from multispectral land imagers. We used modified soil-adjusted vegetation index (MSAVI 2 ) data from Landsat to develop the NDPI, and MSAVI 2 data from MODIS to compare its performance relative to one alternate DVP metric (difference of spring average MSAVI 2 and summer maximum MSAVI 2 ), and two simple, conventional VI metrics (summer average MSAVI 2 , summer maximum MSAVI 2 ). The NDPI in a scaled form (NDPI s ) performed best in predicting variation in perennial C 4 grass cover as estimated from landscape photographs at 92 sites (R 2 = 0.76, p < 0.001), indicating improvement over the alternate DVP metric (R 2 = 0.73, p < 0.001) and substantial improvement over the two conventional VI metrics (R 2 = 0.62 and 0.56, p < 0.001). The results suggest DVP-based methods, and the NDPI in particular, can be effective for subpixel discrimination and mapping of exposed perennial C 4 grass cover within mixed-composition landscapes of the Southwest, and potentially for monitoring of its response to drought, climate change, grazing and other factors, including land management. With appropriate adjustments, the method could potentially be used for subpixel discrimination and mapping of grass or other vegetation types in other regions where the vegetation components of the landscape exhibit contrasting seasonal patterns of phenology.

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Regional Equivalent Water Thickness Modeling from Remote Sensing across a Tree Cover/LAI Gradient in Mediterranean Forests of Northern Tunisia

Cited by 16 publications

References 68 publications

A Phenology-Based Method for Monitoring Woody and Herbaceous Vegetation in Mediterranean Forests from NDVI Time Series

A Phenology-Based Method for Monitoring Woody and Herbaceous Vegetation in Mediterranean Forests from NDVI Time Series

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

Exploiting Differential Vegetation Phenology for Satellite-Based Mapping of Semiarid Grass Vegetation in the Southwestern United States and Northern Mexico

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