Abstract
Traditionally fuel maps are built in terms of ‘fuel types’, thus considering the structural characteristics of vegetation only. The aim of this work is to derive a phenological fuel map based on the functional attributes of coarse-scale vegetation phenology, such as seasonality and productivity. MODIS NDVI 250m images of Sardinia (Italy), a large Mediterranean island with high frequency of fire incidence, were acquired for the period 2000–2012 to construct a mean annual NDVI profile of the vegetation at the pixel-level. Next, the following procedure was used to develop the phenological fuel map: (i) image segmentation on the Fourier components of the NDVI profiles to identify phenologically homogeneous landscape units, (ii) cluster analysis of the phenological units and post-hoc analysis of the fire-proneness of the phenological fuel classes (PFCs) obtained, (iii) environmental characterization (in terms of land cover and climate) of the PFCs. Our results showed the ability of coarse-resolution satellite time-series to characterize the fire-proneness of Sardinia with an adequate level of accuracy. The remotely sensed phenological framework presented may represent a suitable basis for the development of fire distribution prediction models, coarse-scale fuel maps and for various biogeographic studies.
Highlights
Phenological studies of vegetation can be carried out at the species level using in-situ field techniques [1]
The aim of this work is to derive a phenological fuel map based on the functional attributes of coarse-scale vegetation phenology, such as seasonality and productivity
Our results showed the ability of coarseresolution satellite time-series to characterize the fire-proneness of Sardinia with an adequate level of accuracy
Summary
Phenological studies of vegetation can be carried out at the species level (bud break, flowering, leaf flush, etc.) using in-situ field techniques [1]. Only remote sensing can offer information on ecosystem phenology and productivity over several temporal scales and continuous spatial coverage. Space-borne optical sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS) provide daily measurements of a variety of biophysical parameters of the land surface [2]. MODIS is a key instrument aboard the Terra and Aqua NASA satellites viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands Mapping Forest Fuels through Vegetation Phenology design, data collection and analysis, decision to publish, or preparation of the manuscript
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