Pre and Post fire vegetation behavioral trends from satellite MODIS/NDVI time series in semi-natural areas
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Pre and Post fire vegetation behavioral trends from satellite MODIS/NDVI time series in semi-natural areas Tiziana Montesano, Antonio Lanorte, Fortunato De Santis, Rosa Lasaponara - Institute of Methodologies for Environmental Analysis, National Research Council, Italy
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Pre and Post fire vegetation behavioral trends from satellite MODIS/NDVI time series in semi-natural areas
- 1. Pre and Post fire vegetation behavioral trends from satellite MODIS/NDVI time series in semi-natural areas Tiziana Montesano, Antonio Lanorte, Fortunato De Santis, Rosa Lasaponara Institute of Methodologies for Environmental Analysis, National Research Council, Tito, Italy
- 6. .4 .6 .8 1.0 1.2 Green and healthy Veg Dry and stressed Veg Red NIR Plant Reflectance Spectral bands NDVI = NIR - RED NIR + RED
- 8. July 3, 1998 Pre-fire NDVI map Post-fire NDVI map
- 9. Fractal Scaling Power-law statistics f=f(x) f(ax)=g(a)f(x) f(x)=bg(x) g(x)=x c FRACTAL : EXPECTED MEASURE OF THE SAMPLE PATH INCLUDED WITHIN SOME RADIUS SCALES WITH THE SIZE OF THE RADIUS
- 10. TEMPORAL FLUCTUATIONS CORRELATION STRUCTURES MEMORY PHENOMENA Purely random Antipersistent Persistent =0 >0 <0
- 11. Lasaponara et al., Chaos Solitons & Fractals, 24, 139-149, 2005 Telesca et al., Ecol. Model., 185, 531-544, 2005 Telesca and Lasaponara, Physica A, 359, 747-751, 2006 Telesca et al., Chaos Solitons & Fractals, 2006 Telesca et al., Comm. Nonlin. Sci. Numeric. Simul. , 2006 ARE FIRES A FRACTAL PROCESS? Allan Factor (Thurner et al., Fractals, 5, 1997) Data: Fire catalogues
- 12. Telesca and Lasaponara, Rem. Sens. Environ. , 101, 95-103, 2005 Telesca et al., Physica A, 361, 699-706, 2006 Lasaponara and Telesca, Int. J. Remot. Sens., 2006 IS VEGETATION A FRACTAL SYSTEM? F( ) <0.5 antipersistence =0.5 white noise > 0.5 persistence Detrended Fluctuation Analysis (Peng et al., CHAOS, 1995) Data: SPOT-VGT decadal NDVI time series
- 14. + burned: 23 sites + unburned: 10 sites Telesca et al., submitted, 2006 Data: SPOT-VGT decadal NDVI time series
- 15. Focus of the current areas Investigating the scaling properties of managed areas versus “natural”areas
- 17. Crotone site Land use from Corine
- 18. Andriace Land use from Corine
- 19. NDVI d = [NDVI - <NDVI>]/ In order to eliminate the phenological fluctuations, for each decadal composition of each pixel, we focused on the departure from the decadal mean <NDVI>. The decadal mean <NDVI> is calculated for each decade, e.g. 1st decade of January, by averaging over all years in the record. Investigations were conducte on the NDVI d departure series computed using formula Elimination of p henological fluctuations
- 20. Time variation of pixel_1 of the Crotone site of NDVI
- 21. Time variation of NDVI d for pixel_1 of the Crotone site
- 22. Time variation of NDVI for pixel_1 of the Andriace site
- 23. Time variation of NDVId for pixel_1 of the Andriace site
- 24. Scaling exponents relating to NDVI d for the Crotone (indicated as KR) and Andriace (indicated as AN) test sites, average and standard deviations for each set of scaling exponents.
- 25. CONCLUSIONS 1. Burned and unburned vegetation covers are characterized by fractal behaviour 2. The scaling exponents of both types (burned and unburned) suggest a persistent behaviour of the vegetation dynamics. 5. . Results from this paper clearly pointed out the diverse vegetation behavior observed for natural and managed areas before and after fire occurrence. 3. For managed areas (cultivated) Post-fire vegetation dynamics are less persistent than pre-fire vegetation dynamics 4. The difference between the two groups(burned/unburned or pre-fire/post-fire vegetation covers) is significant
- 26. THANKS!!
Editor's Notes
- In this work the analysis of the fractal properties a satellite vegetation index time series is proposed in order to discriminate between burned and unburned vegetation covers.
- Among the environmental phenomena, vegatation can be considered as very complex.
- In particular, changes in vegetation cover play an active role in the surface energy and water balance, as well as in the carbon cycle. Then variation in composition and distribution of vegetation can arise in response to natural hazards (drougths, wind, floods, rain erosion) and anthropic stress (industry, land abandonment, fires)
- Traditionally, vegetation monitoring by remotely sensed data has been carried out by using vegetation indices, which are quantitative measures, based on vegetation spectral properties, mainly derived from reflectance data from the red (R) and near-infrared (NIR) bands.
- The NDVI is one of the widely used satellite-based indices for vegetation monitoring It is computed by means of the formula shown in the slide. For a given vegetation type, the NDVI is high for green and healthy plants; whereas it is lower for dry and/or stressed vegetation depending on the level of disease.
- Vegetation stress can be defined as any disturbance that adversely influences plants Among stresses, fires can be considered very important, because They significantly influence the dynamics of ecosystems and can lead to permanent changes in the composition of vegetation community Furthermore, fires can cause several effects as shown in the box
- This is an example of how the NVDI is able to detect the change in the vegetation vigour caused by fires. Figure a) hot spots from active fires as shown by AVHRR channel 3 image acquired on 3 July, 1998. Figure b) smoke plumes (A, B, C, and D) from active fires as shown by AVHRR channel 1 image acquired on 3 July, 1998. Figures c) and d) show NDVI maps acquired before c) and after fire occurrence d). Circles in Figure d) show the drastic variations in the NDVI values after fire occurrence.
- At the beginning I said that the discrimination between burned and unburned vegatation covers would be performed using scaling properties of the time series of a satellite index, which is the NDVI, as you’ve seen. So what is the concept of scaling. This concept is closely related to that of fractal. The concept of fractal, which is the basic concept of the methodologies that have been used to perform the analysis of these data. The concept of fractal is well-known. It describes a particular property of an object, called self-similarity, which means that if I take an object and I calculate some statistical property, this property is invariant if I change the scale, I mean if I take a smaller part of the object this part is characterized by the same statistical property. This feature is generally called scaling. Therefore, a fractal process is characterized by a scaling behaviour, and this leads naturally to a power-law statistics, used to descrive the process. As shown by this set of equations: suppose that we have a statistics f, which depends on the scale x, let us suppose that if we change the scale x by a factor g(a), then the only solution for this scaling equation is the power-law.
- The scaling properties of a geophysical process are generally investigated by means of the analysis of its temporal fluctuations, which inform about the presence of correlation structures and memory phenomena in the process itself. The standard method to analyze such kind of signals was the PSD, obtained by using the FT. The PSD informs about the power distribution of the signal at the different frequency bands. In particular if the PSD behaves as a power-law of the frequency, this is the fingerprint of the presence of scaling in the signal, and the scaling exponent alpha gives information about the type of temporal fluctuations which really underlie the geophysical process. I.E….
- So the problem is: Could the dynamics of vegetation covers be significantly influenced by fires? Is it possible to identify and quantify such effect? Several papers we published regarding this point.
- Both pixels show a scaling exponents larger than 0.5. In particular 1.14 for the fire-affected site and 0.87 for the fire unaffected site. This indicates that the temporal fluctuations of both time series are persistent. Persistence means that the investigated ecosystems are governed by positive feedback mechanisms, which tend to destabilize the system under external forces. We performed the same analysis on all the ten pixels for each sites, and this plot shows the results. The scaling exponents for Bolotana pixels range around the mean value of 1.11, while those for Salina pixels vary around the mean value of 0.78. The most striking feature is the clear difference in the scaling behavior of the two investigated sites. Bolotana, which is the fire-affected site, shows scaling exponents much larger than those shown by Salina, which is the fire-unaffected site. Therefore, the two sites are clearly discriminated from each other. This indicates that fires play an important role in the temporal evolution of the shrub-land, increasing the persistence of the vegetation dynamics. This seems to express the structural character of the fire-induced vegetation recovery processes, which suggest the existence of positive relation between the amounts of burned and regenerated biomass.
- In order to eliminate the phenological fluctuations, for each decadal composition, we focused on the departure NDVI d = NDVI - <NDVI> from the mean decadal <NDVI>. The mean decadal <NDVI> is calculated for each decade, e.g. 1st decade of January, by averaging over all years in the record. This is the annual phenological pattern. The Vegetal Phenology is the study of the annual cycles of plants and how they respond to seasonal changes in their environment.