[Pheno-climatic profiles of vegetation based on multitemporal analysis of satellite data].
Satellite Remote Sensing offers numerous advantages: study of large areas in a short time, study of areas with not easy accessibility, synoptic observation of territory, multitemporal observations of the same area, monitoring land modifications and change detection studies. The effectiveness of using satellite images for studying and mapping vegetation and land use has been stressed since the early 1980s. The photosynthetically active vegetation presents a very characteristic spectral response. In fact, leaves absorb red radiation (RED) in order to do photosynthetic process and reflect almost completely near infrared (NIR) wavelengths. The most diffused index for quantifying photosynthetically active biomass is the NDVI (Normalized Difference Vegetation Index): NDVI = (NIR-RED)/(NIR+RED). The NDVI is calculated, for each pixel of the images analysed, through an appropriate software. Low values of NDVI correspond to scarcely vegetated areas, while high values indicate densely vegetated ones. In order to distinguish among vegetation typologies we need some images of the same territory, well distributed during the year, showing seasonal variations of vegetation photosynthetic activity. Then it will be e.g. very easy distinguish between evergreen species (with NDVI almost steady during the year) and deciduous ones. Several types of sensors aboard some satellites allow different investigations to be done. AVHRR sensor on NOAA and TM sensor on Landsat are among the best known sensors available. They have different characteristics as for spectral resolution (number of spectral bands), spatial resolution (size of each elementary cell) and temporal resolution (the period of the satellite passes on the same territory). Vegetation phenology (including biomass and photosynthetic activity) heavily depends on climatic factors. The most important are: solar radiance, with an annual cycle and maximum at summer solstice; air temperature, (depending on solar radiance) with an annual cycle and maximum more than one month later; water availability, which is strongly dependent on rainfalls; in the Mediterranean area they can have an annual cycle (maximum during winter) or a six-monthly one (maxima near the equinoxes). Having a set of multitemporal satellite data (e.g. 12 monthly NOAA-AVHRR images) we can use a mathematical model able to discriminate annual and six-monthly cycles. Through Fourier analysis, the mathematical model calculate, for each pixel of the image, the parameters of the annual NDVI profile and create a synthetic image (pheno-climatic map), in which the values of the three RGB components (Red, Green, Blue ) are proportional to the integral of the NDVI profile for the following three periods: B=Nov-Feb G=Mar-Jun R=Jul-Oct. A similarly analysis is possible with Landsat satellite data, which have a higher spatial resolution, given that some shrewdness are taken. In fact, it is necessary to select satellite images according to the presence of cloud cover, which is--over the Italian peninsula--quite common during the March-April and October-November intervals. The purpose of carrying out pheno-climatic maps can be accomplished using 6 Landsat-TM images well-distributed during a year, every two months, even if the images have been taken during different years.
Taddei
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Centro di Competenza INNOVA e Dipartimento di Biologia Vegetale dell'Università di Napoli Federico II.
D018533:Biomass; D002980:Climate; D004463:Ecology; D005583:Fourier Analysis; D040362:Geographic Information Systems / Q000295:instrumentation; D007558:Italy; D008954:Models, Biological; D010788:Photosynthesis; D010944:Plants; D011891:Rain; D017215:Satellite Communications; D012621:Seasons; D013472:Sunlight; D013696:Temperature; D019371:Topography, Medical / Q000295:instrumentation / Q000379:methods