Dario Papale Contributi: Vern Vanderbilt, TA- Quinn Hart, M. Meroni, CCRS

1. Remote sensing and modeling in forestry Lecture 9 Vegetation indexes Dario Papale Contributi: Vern Vanderbilt, TA- Quinn Hart, M. Meroni, CCRS

2. What is affecting canopy reflectance 1. Absorption and reflection of the canopy elements at different wavelengths (leaves, branches, trunks, flowers, fruits, understory, soil etc.) 2. Canopy architecture (overstory biomass, LAI, leaves distribution, degree of coverage, etc.) 3. Remote sensing system (geometry Sun-target-sensor, atmosphere interaction etc.)

3. Our aim is to use remote sensing data to assess parameters such LAI or Biomass Remember that spectral reflectance varies with leaf presence and consequently with LAI

4. The reflectance in red and NIR are correlated with LAI in canopies Red, dense canopy NIR, dense canopy Red, sparse canopy NIR, sparse canopy

5. FAPAR Another important variable in remote sensing is the FAPAR – Fraction Absorbed of the PAR (photosynthetically active radiation, between 400 and 700 nm). It is clearly function of the canopy characteristics but also environmental conditions and it is linked with the photosynthetic capacity of the plants

6. Also the chlorophyll concentration has an effect on the reflectance of blue and red but the available radiation is almost entirely absorbed and changes of the chlorophyll content don’t have effects on the reflectance after some level of concentration Reflectance in red and blue Chlorophyll concentration LAI, FAPAR, chlorophyll content can be then estimated starting from spectral reflectance measurements.

7. Vegetation indexes VIs are algebraic combination of spectral reflectance data with the aim to move from a multi-dimensional dataset to a single value

8. Analyzing the correlation between canopy reflectance at different wavelengths and LAI, it is negative in the visible and positive in the NIR 0.5 1.0 very high leaf area 0.4 Positive corr. very low leaf area 0.3 sunlit soil Correlation coefficient Reflectance(%) 0.0 0.2 Negative corr. Correlation= 0.0 at about l = 0.71mm 0.1 0.0 -1.0 400 600 800 1000 1200 400 600 800 1000 1200 Wavelength Wavelength

9. Δ A β Vegetation indexes For this reason the vegetation indexes are based in particular on reflectance in red and NIR

10. Vegetation indexes In general, VIs can be classified in three categories: Intrinsic indexes, based only of reflectance Indexes linked to the soil line which try to reduce the effect of bare soil Indexes correcterd for the atmospheric effect

11. Indici intrinseci SR (Simple Ratio) It is the easiest VI. It can have values between 0 and infinite, in particular between 0 and 1 for soils and between 6 and 10 for green vegetation. NDVI (Normalized Difference Vegetation Index) The most used and common. Values between -1 and 1, in particular less than 0 for water, just above 0 for bare soils and between 0.4 and 0.7 for vegetation. Only very dense vegetations reach NDVI = 0.8.

12. NDVI image example Roccarespampani coppice forest – July 1999

13. NDVI meaning

14. Indexes based on the Red edge region Different approaches exist, based on ratios, normalized ratios, slopes, flex point position, integrals etc. Clearly to use these indexes measuremetns precise and with very narrow bands are needed. Based on measurements around the flex point of the vegetation reflectance curve between red and NIR

15. Indexes linked to the soil line If the vegetation is not dense enough to cover 100% of the terrain, the spectral reflectance is influenced by the soil characteristics (color, water content etc.) Soils in general have higher reflectance in the NIR respect to the red and it has been observed a linear relation of the reflectance in these two bands when different soils are considered (with similar general characteristics). This regression line is known ad “Soil line”

16. Indexes linked to the soil line PVI (Perpendicular Vegetation Index) Evaluate how much the object is different from a soil. PVI is zero on the Soil line (LAI = 0) and its value increase linearly moving far from the Soil line.

17. Indexes linked to the soil line SAVI (Soil Adjusted Vegetation Index) It is a normalized index that has a factor that takes into consideration also the soil type with a correction factor. L is measured experimentally and in general it is between 0.25 (dense veg) and 0.75 (sparse v.)

18. GEMI (Global Environment Monitoring Index ) Indexes that (try to) correct the atmospheric effect ARVI (Atmospherically Resistant Vegetation Index) The term rb is a combination of reflectance in blue and red, the  is function of the presence and type of aerosol. Generally  = 1 .

19. Photochemical Reflectance Index - PRI It is an index based on the Xanthophylls cycle When a leaf receive too much energy from the Sun respect to the amount that it is able to use, this energy must be dissipated to avoid damages. The xanthophylls are carotenoids. Violaxanthin, in case of energy excess, is de-epoxidized to Antherexanthine and then in Zeaxanthine, dissipating the energy not used as heat. epoxide Gamon et al. 1990, 1992

20. Photochemical Reflectance Index - PRI Zeaxanthine formation leads to changes in the absorbance at 531 nm, reducing the reflectance and this process is fast and reversible. Example: leaves in the dark and then exposed to direct light Gamon et al. 1990, 1992

21. Photochemical Reflectance Index - PRI Grace et al. 2007 Garbulsky et al. 2008 PRI calculated using Modis bands: 11 (526-536 nm), 12 (546-556 nm) PRI=(PRI+1)/2 to have positive values

22. FLUORESCENCE Energy dissipation through chlorophyll fluorescence (re-emission of energy absorbed at higher wavelengths) As for the dissipation seen before it is in competition with the photosynthesis The fluorescence picks are at about 685 and 730 nm. Meroni et al. 2009

23. FLUORESCENCE We can measure fluorescence with passive methods only in narrow spectral bands there solar irradiance in strongly reduced (Fraunhofer lines). In the NIR there are two of these bands at 687 and 760.4 nm due to oxygen absorption. Meroni et al. 2010

24. Use of vegetation indexes for spatialization Satellite images Variable of interest measured with geographic position (GPS) Georeferencing VIs calculation at the ground Measurement points Regression VIs/variable Best VI selection VI calculated on the full image Regression application Map ofthe variable

25. Spectral indexes characteristics • Simple and based only on measurements • Generally based on few spectral bands • Generally not based on radiance and for this reason less dependent on corrections

26. Spectral indexes disadvantages • Empirical relations not based on the knowledge of the reflectance physic • Use only few bands (good or bad??) Advantages and disadvantages are similar… • It is difficult to find simple relations that explain the variables of interest

27. Vegetation Index LAI, biomass, … Saturation One of the most important limits of the vegetation indexes is that above a certain limit they saturate. For this reason they are more indicated for sparse vegetation The alternative to the VIs are the models, in particular the models based on the physics of the reflection processes of leaves and canopy