International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004 
NDVI - LAI 
R? 2 0,5845 
   
  
  
  
In spite of many troubles, satellite imagery can represent a new 
estimation biomass tool. In this way, we will be able to employ 
the natural resources in a sustainable way: so, we can to 
improve, for example, the forestry residuals management as 
energy renewable source (Favretto and Santoprete, 1994). 
  
  
  
  
  
  
  
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= | 7. REFERENCES “a, 
À | De 
s : . iis PDe 
| Balzarolo, M., Bocchi, S., Boschetti, M., Brivio, P.A., 2003. p 
| - Utilizzo di immagini Landsat ETM+ per lo studio della 
| * produttività del pascolo alpino. Caso di studio: la malga di KE 
at = ee = um po "A Trela, Parco Nazionale dello — Stelvio. ^ Bollettino 
NDVI dell'Associazione Italiana di Cartografia (AIC), 117-118-119, 
pp. 455-464. AB 
Fieure 9. Correlation between LAI and NDVI. Benedetti, R., Mambelli, G., Rossigni, P., Salsi, A., 1991. 
= L'indice di verde dei dati NOAA-AVHRR per il controllo Int 
= : : dell'annata agraria: la fenologia. i del conve : A 
5.4. Analysis of the correlation between NDVI values and eue ata Ag ar Me olog a. Atti del convegno for 
weishted biomass data Monitorare l'ambiente agrario e forestale, Porto Conte (SS), pp: crit 
E 161-176. feci 
As second step we compare NDVI values and weighted Forfecchia, F., De Cecco, L., Di Bari, C., lanetta, M., Martini, this 
: - . . > 3 adr i Schi e ^) 4 imi: 71 o o 1 
biomass data; also for this analysis we had a small number of S.. Pedrotti, F., Schino, G., 2001. Ottimizzazione della stima for 
samples. The R? correlation index reaches values around 0,55 della biomassa prativa nel parco nazionale dei monti sibillini 
when we compare the five values. This value increased till tramite dati satellitari e rilievi a terra, Atti V Conferenza 
tre - : : : avinnale IT; 7 279.284 
0,745 (fig.10) when we examined also theoretical values of Nazionale ASITA, Vol. I, pp. 279-284. 
. . ~ . . ~ . Never 2S / J se je 7s C i, me x 
wood biomass derived from the application of dendrometric Clevers, J.G. P.W., Van Leeuwen, H.J.C., 1996. Combined use 
table, on the second area test (parcel 36). of optical microwave remote sensing data for crop growth As 
Generally speaking, the relationship between wood biomass and monitoring. Remote Sensing of Environment, 56 (1), pp. 42-50. lanc 
s “js + ; : 7 p ys ete abc Q Ee ; : 
NDVI is sufficiently marked, but it would need a major number Cook, E.A., Iverson, L.R., Graham, L.R., 1989. Estimating ima 
of samples. forest productivity with Thematic Mapper and biogeographical mar 
data. Remote Sensing of Environment, 28, pp. 131-141. autc 
Del Favero, R. (a cura di), 1998. La vegetazione forestale e la has 
NDVI - Wood Biomass selvicoltura nella regione Friuli-Venezia Giulia, Vol. I, Regione fiel 
20 + Autonoma Friuli-Venezia Giulia, Direzione Regionale delle geo! 
Foreste e della Caccia - Servizio della Selvicoltura, Udine, pp. proc 
v 76-86. ima; 
a Favretto, A., Santoprete, G., 1994. Energia dai rifiuti e dalle 
© 4 — A ; am 1* "n + ^ 4. 
E biomasse. G. Giapichelli Editore, Torino, pp. 73-74. In t 
m Jodice, R., Nassimbeni, G., 1999. La disponibilità di biomasse Lan 
9 forestali utilizzabili per scopi energetici. Atti del convegno: geol 
= L'utilizzo della biomassa a scopi energetici: un incentivo per la the 
gestione dei nostri boschi, Comunità Montana della Carnia, DE! 
Arta Terme (Udine), pp. 52-56. abo 
0,835 0,84 0,845 0,85 0,855 0,86 0,865 0,87 0,875 0,88 i : : n ; : ; Y 7A and 
NDVI Liang, S., 2004. Quantitive Remote Sensing of Land Surfaces. de 
Wiley, New Jersey, pp. 200-201. i 
Rondeaux, G., Steven, M., Baret, F., 1995. Optimisation of F 
E: a ^ elati aa P : as SA + ; ; ^ Or 
Figure 10. Correlation between w ood biomass and NDVI. Soil-Adjusted Vegetation Indexes. Remote Sensing of 2 
is : - 0 
Environment, 55 (2), pp. 95-107. X 
^W aJ si x EN $ ; - 3 C 
6. CONCLUSIONS Tucker, C.J., Vanpraet, C.L., Sharman, M.J., Van Ittersum, G., em 
1985. Satellite remote sensing of total herbaceous biomass S 
In this work we got some important advancements about the 
different (forestry and satellite) methods of wood biomass 
estimation. 
production in Senegalese Sahel: 1980-1984. Remote Sensing of The 
Environment, 17, pp. 233-249. 
In fact, we could compare ground data (obtained from cutting : Con 
: e = Acknowledgements. We would like to thank the Forestall 
ac ; ahi t A Andar > unde 
Lives m two small test areas) with by standard method Direction of Friuli-Venezia Giulia Region and Experimental deor 
(dendrometric table) of biomass estimation, Then, we found out Agriculture Sector of Pordenone Province for full collaboration; ve 
the poor precision of traditional forestry methods. However, the Biology Department (Dr. Napolitano) of Trieste University is 
some instruments (Alpine dendrometric table to estimate wood : PH > : . ; ««tarical 
. Be > ot] - ^ - a storica 
biomass) and biophysical indexes (LAI to estimate canopy) for LA data: the GeoNetLab of Geographical and Histories type 
qud Dnysic: qi a Shimane canos Department of Trieste University for technological support. crite 
showed: high accuracy. In research developments, these tools e 
; : : co 
can be used in order to have ground data, without ecological fro | 
rom 
impact. 
Then, we could verify the high sensitivity of a vegetation index 
(NDVI) to wood biomass. This is very important when we use 
the satellite imagery to define forestall biomass, because it 
prove the remote sensing reliability. 
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