**7. References**


to the thermocline oscillations are also observed. The inter-annual SLA and CHL covary in areas dominated by mesoscale eddies, such as the Levantine basin and the southern part of the Western basin. Cyclonic eddies can enhance primary production by upwelling of nutrient rich water (McGillicuddy et al., 1998; Oschlies & Garçon, 1998). In the intra-annual variability, the coupling between SLA and CHL is exerted through a mechanical compression mechanism, which concentrates nutrients and phytoplankton cells into the surface layer. Nevertheless, the overall influence of the mesoscale eddies and the compression mechanism in the enhancement of phytoplankton in the Mediterranean Sea

The SVD analysis to link SLA and CHL is a quick, easily accessible and powerful method for assessing the ocean physical-biological coupling. Our results demonstrate its strength over the direct correlation. The correlation map indicates the spatial covariability of SLA and CHL but cannot provide any details about their temporal variability. SVD analysis extracts the dominant temporal and spatial components of covariability between SLA and CHL into a series of orthogonal functions or statistical modes, and their time evolution or expansion coefficients. In addition, the SVD modes can be related to different coupling mechanism. This methodology represents a simple alternative to more sophisticated coupled physicalbiological ocean models. There are also other conceptual methods that isolate coupled modes of variability between spatial time series, such as joint EOFs or canonical correlation analysis. According to Bretherton et al. (1992), the SVD analysis is simpler and superior than

This work was supported by EHRE (CTM2009-08270) project. A. Jordi's work was

Abbott, M.R. & Zion, P.M. (1987). Spatial and temporal variability of phytoplankton

Antoine, D.; Morel, A.; Gordon, H.R.; Banzon, V.F. & Evans, R.H. (2005). Bridging ocean

Antoine, D; Morel, A. & André, J. M. (1995). Algal pigment distribution and primary

Bailey, S. W. & Werdell, P. J. (2006). A multi-sensor approach for the on-orbit validation of ocean color satellite data products. *Remote Sensing of the Environment*, 102, 12–23. Barale, V.; Jaquet, J.M. & Ndiaye, M. (2008). Algal blooming patterns and anomalies in the

scanner observations*. Journal of Geophysical Research*, 100, 16193–16209. Arnone, R.A. & La Violette, P.E. (1986). Satellite definition of the bio-optical and thermal

pigment off northern California during coastal ocean dynamics experiment 1.

color observations of the 1980s and 2000s in search of long-term trends. *Journal of* 

production in the Eastern Mediterranean as derived from coastal zone color

variation of coastal eddies associated with the African Current. *Journal of* 

Mediterranean Sea as derived from the SeaWiFS data set (1998-2003). *Remote* 

supported by a Ramón y Cajal award from the Ministerio de Ciencia e Innovación.

*Geophysical Research*, 110, C06009, doi:10.1029/2004JC002620.

these other methods in most situations involving geophysical fields.

*Journal of Geophysical Research*, 92 (1), 745–1,755.

*Geophysical Research*, 91, 2351– 2364.

*Sensing of Environment*, 112 (8), 3300-3313.

deserves further study.

**6. Acknowledgment** 

**7. References** 


Using SVD Analysis of Combined Altimetry and Ocean Color

*Environment*, 111, 69–88.

*Sensing*, 20, 1681–1702.

359–13,379.

doi:10.1029/1999JC000117.

*Environment*, 107(4), 625 – 638.

10.1029/2001GL014063.

21009.

Satellite Data for Assessing Basin Scale Physical-Biological Coupling in the Mediterranean Sea 139

Morel, A. & Berthon, J.F. (1989). Surface pigments, algal biomass profiles, and potential

Morel, A.; Huot, Y.; Gentili, B.; Werdell, P.J.; Hooker, S.B. & Franz, B.A. (2007). Examining

Oschlies, A. & Garçon, V. (1998). Eddy-induced enhancement of primary production in a

Pascual, A.; Pujol, M.I.; Larnicol, G.; Le Traon, P.Y. & Rio, M.H. (2007). Mesoscale mapping

Platt, T. & Sathyendranath, S. (1988). Oceanic primary production: estimation by remote

Pujol, M.I. & Larnicol, G. (2005). Mediterranean Sea eddy kinetic energy variability from 11

Rast, M.; Bézy, J.L. & Bruzzi, S. (1999). The ESA Medium Resolution Imaging Spectrometer

Santoleri, R.; Banzon, V.; Marullo, S.; Napolitano, E.; D'Ortenzio, F. & Evans, R. (2003). Year-

Siegel, D.A.; Court, D.B.; Menzies, D.W.; Peterson, P.; Maritorena, S. & Nelson, N.B. (2008).

Stammer, D. (1997). Steric and wind-induced changes in TOPEX/POSEIDON large-scale sea

Taupier-Letage, I.; Puillat, I.; Millot, C. & Raimbault, P. (2003). Biological response to

Volpe, G.; Santoleri, R.; Vellucci, V.; d'Alcala, M.R.; Marullo, S. & D'Ortenzio, F. (2007). The

Wilson, C. & Adamec, D. (2001). Correlations between surface chlorophyll and sea surface

Wilson, C. & Adamec, D. (2002). A global view of bio-physical coupling from SeaWiFS and

event. *Journal of Geophysical Research*, 106 (12), 31175-31188.

sensing applications. *Limnology and Oceanography*, 34(8), 1545– 1562.

model of the north Atlantic Ocean. *Nature*, 394 (6690), 266-269.

Mediterranean Sea. *Journal of Marine Systems*, 65(1-4), 190-211.

sensing at local and regional scales. *Science*, 241, 1613– 1620.

years of altimetric data. *Journal of Marine Systems*, 58 (3-4), 121-142.

*Journal of Geophysical Research*, 108(C9), doi:10.1029/2002JC001636.

eddies in the Sargasso Sea. *Deep-Sea Research Part II*, 55 (1), 218–1,230. Siegel, D.A.; Fields, E. & McGillicuddy, D.J. (1999). Mesoscale motions, satellite altimetry

production of the euphotic layer: Relationship reinvestigated in view of remote-

the consistency of products derived from various ocean color sensors in open ocean (case 1) waters in the perspective of a multisensor approach. *Remote Sensing of the* 

capabilities of multi-satellite altimeter missions: First results with real data in the

MERIS – A review of the instrument and its mission. *International Journal of Remote* 

to-year variability of the phytoplankton bloom in the southern Adriatic Sea (1998- 2000): Sea-viewing Wide Field-of-view Sensor observations and modeling study.

Satellite and in situ observations of the bio-optical signatures of two mesoscale

and new production in the Sargasso Sea. *Journal of Geophysical Research*, 104 (13),

surface topography observations. *Journal of Geophysical Research*, 102 (9), 20987-

mesoscale eddies in the Algerian Basin. *Journal of Geophysical Research*, 108 (C8),

colour of the Mediterranean Sea: Global versus regional bio-optical algorithms evaluation and implication for satellite chlorophyll estimates. *Remote Sensing of the* 

height in the tropical Pacific during the 1997-1999 El Nino-Southern Oscillation

TOPEX satellite data, 1997-2001. *Geophysical Research Letters*, 29 (8),


Gordon, H.R.; Clark, D.K.; Brown, J.W.; Brown, O.B.; Evans, R.H. & Broenkow, W.W. (1983).

Hooker, S.B. & McClain, C.R. (2000). The calibration and validation of SeaWiFS data.

Jaquet, J.M.; Tassan, S.; Barale, V. & Sarbaji, M. (1999). Bathymetric and bottom effects on

Jordi, A. & Wang, D.-P. (2009). Mean dynamic topography and eddy kinetic energy in the

Jordi, A.; Basterretxea, G. & Anglès, S. (2009). Influence of ocean circulation on

Kishino, M.; Ishizaka, J.; Saitoh, S.; Senga, Y. & Utashima, M. (1997). Verification plan of

Larnicol, G.; Ayoub, N. & Le Traon, P.Y. (2002). Major changes in Mediterranean Sea level

Le Traon, P.Y. & Dibarboure, G. (1999). Mesoscale mapping capabilities of multiple-

Le Traon, P.Y.; Nadal, F. & Ducet, N. (1998). An improved mapping method of

Lehahn, Y.; d'Ovidio, F.; Lévy, M. & Heifetz, E. (2007). Stirring of the northeast Atlantic

Leterme, S.C. & Pingree, R.D. (2008). The Gulf Stream rings and North Atlantic eddy

Maritorena, S.; d'Andon, O.H.F.; Mangin, A. & Siegel, D.A. (2010). Merged satellite ocean

McClain, C.R. (2009). A decade of satellite ocean color observations. *Annual Review of Marine* 

McGillicuddy, D.J.; Robinson, A.R.; Siegel, D.A.; Jannasch, H.W.; Johnson, R.; Dickeys, T.;

Millot, C.; Taupier-Letage, I. (2005). Additional evidence of LIW entrainment across the

new production in the Sargasso Sea. *Nature*, 394 (6690), 263-266.

ship determinationos and CZCS estimates. *Applied Optics*, 22, 20-36

(Tunisia). *International Journal of Remote Sensing*, 20(7), 1343−1362.

*Geophysical Research*, 114 (C11005), doi:10.1029/2009JC005301.

system. *Journal of Geophysical Research*, 102, 17197-17207.

*Geophysical Research*, 112 (8) doi:10.1029/2006JC003927.

*Remote Sensing of the Environment*, 114, 1791–1804.

*Progress in Oceanography*, 66(2-4), 231-250.

circulation model. *Ocean Modelling*, 29 (2), 137-146.

*Progress in Oceanography*, 45, 427–465.

*Systems*, 33, 63-89.

1208–1223.

522-534.

69 (3-4) 177-190.

*Sciences*, 1, 19–42.

Phytoplankton pigment concentrations in the Middle Atlantic Bight: comparison of

CZCS chlorophyll-like pigment estimation: data from the Kerkennah shelf

Mediterranean Sea: Comparison between altimetry and a 1/16 degree ocean

phytoplankton biomass distribution in the Balearic Sea: Study based on Seaviewing Wide Field-of-view Sensor and altimetry satellite data. *Journal of* 

OCTS atmospheric correction and phytoplankton pigment by moored optical buoy

variability from 7 years of TOPEX/Poseidon and ERS-1/2 data. *Journal of Marine* 

satellite altimeter missions. *Journal of Atmospheric and Oceanic Technology*, 16,

multisatellite altimeter data. *Journal of Atmospheric and Oceanic Technology*, 15 (2),

spring bloom: A Lagrangian analysis based on multisatellite data. *Journal of* 

structures from remote sensing (Altimeter and SeaWiFS). *Journal of Marine Systems*,

color data products using a bio-optical model: Characteristics, benefits and issues.

McNeil, J.; Michaels, A.F. & Knap, A.H. (1998). Influence of mesoscale eddies on

Algerian subbasin by mesoscale eddies and not by a permanent westward flow.


**Part 3** 

**Fires** 


**Part 3** 

140 Remote Sensing of Biomass – Principles and Applications

Wunsch, C. (1996). *The Ocean Circulation Inverse Problem*, Cambridge University Press, ISBN

Yong, S.-S.; Shim, H.-S.; Heo, H.-P.; Cho, Y.-M.; Oh, K.-H.; Woo, S.-H. & Paik, H.-Y. (1999).

The ground checkout test of OSMI on KOMPSAT-1. *Journal of Korean Society of* 

0-521-48090-6, New York, USA.

*Remote Sensing*, 15, 297–305.\*

**Fires** 

**7** 

*1Greece 2Lebanon 3Belgium 4USA* 

Ioannis Gitas1, George Mitri2,

*Faculty of Science, University of Balamand,* 

*3Department of Geography, Ghent University, Ghent* 

**Advances in Remote Sensing of Post-Fire** 

Sander Veraverbeke3,4 and Anastasia Polychronaki1 *1Laboratory of Forest Management and Remote Sensing, Aristotle University of Thessaloniki, Thessaloniki, 2Biodiversity Program, Institute of the Environment,* 

*University of Balamand and Department of Environmental Sciences,* 

*4Jet Propulsion Laboratory, California Institute of Technology,Pasadena, CA,* 

Accurate information relating to the impact of fire on the environment and the way it is distributed throughout the burned area is a key factor in quantifying the impact of fires on landscapes (van Wagtendonk et al. 2004), selecting and prioritizing treatments applied on site (Patterson and Yool 1998), planning and monitoring restoration and recovery activities (Jakubauskas 1988; Jakubauskas et al. 1990; Gitas 1999) and, finally, providing baseline

In order to assess economic losses and ecological effects, post-fire impact assessment requires precise information on extent, type and severity of fire (short-term impact assessment) as well as on forest regeneration and vegetation recovery (long-term impact assessment). Assessing the short-term impact is related to the study of fire behaviour, fire suppression and fire effects while the long-term impact assessment of fires is needed in order to establish post-fire monitoring management and introduce restoration and recovery

As fire sizes increase and time becomes a constraining factor, traditional methods to assess post-fire impact on vegetation have become costly and labour-intensive (Bertolette and Spotskey 2001; Mitri and Gitas 2008). Given the extremely broad spatial expanse and often limited accessibility of the areas affected by fire, satellite remote sensing is an essential technology for gathering post-fire related information in a cost-effective and time-saving manner (Smith and Woodgate 1985; Chuvieco and Congalton 1988; Jakubauskas et al. 1990; White et al. 1996; Patterson and Yool 1998; Beaty and Taylor 2001;

information for future monitoring (Brewer et al. 2005).

**1. Introduction** 

activities.

Escuin et al. 2002).

**Vegetation Recovery Monitoring – A Review** 
