M. Lindner, M. Maroschek, S. Netherer, A. Kremer, A. Barbati et al., Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems, Forest Ecology and Management, vol.259, issue.4, pp.698-709, 2010.
DOI : 10.1016/j.foreco.2009.09.023

C. D. Allen, A. K. Macalady, H. Chenchouni, D. Bachelet, N. Mcdowell et al., A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests, Forest Ecology and Management, vol.259, issue.4, pp.660-684, 2010.
DOI : 10.1016/j.foreco.2009.09.001

URL : https://hal.archives-ouvertes.fr/hal-00457602

M. E. Harmon, A. J. Larson, J. M. Smith, and A. H. Taylor, Widespread increase of tree mortality rates in the western united states, Science, vol.323, pp.521-524, 2009.

G. B. Bonan, Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests, Science, vol.309, issue.5734, pp.1444-1449, 2008.
DOI : 10.1126/science.1111772

J. Verbesselt, R. Hyndman, G. Newnham, and D. Culvenor, Detecting trend and seasonal changes in satellite image time series, Remote Sensing of Environment, vol.114, issue.1, pp.106-115, 2010.
DOI : 10.1016/j.rse.2009.08.014

M. A. White, K. M. De-beurs, K. Didan, D. W. Inouye, A. D. Richardson et al., Intercomparison, interpretation, and assessment of spring phenology in north america estimated from remote sensing for, 1982.

J. Verbesselt, R. Hyndman, A. Zeileis, and D. Culvenor, Phenological change detection while accounting for abrupt and gradual trends in satellite image time series, Remote Sensing of Environment, vol.114, issue.12, pp.2970-2980, 2010.
DOI : 10.1016/j.rse.2010.08.003

B. N. Holben, Characteristics of maximum-value composite images from temporal AVHRR data, International Journal of Remote Sensing, vol.7, issue.11, pp.1417-1434, 1986.
DOI : 10.1016/0034-4257(83)90053-6

E. Glenn, A. Huete, P. Nagler, and S. Nelson, Relationship Between Remotely-sensed Vegetation Indices, Canopy Attributes and Plant Physiological Processes: What Vegetation Indices Can and Cannot Tell Us About the Landscape, Sensors, vol.188, issue.213, pp.2136-2160, 2008.
DOI : 10.1016/j.ecolmodel.2005.01.057

URL : http://doi.org/10.3390/s8042136

P. M. Teillet, K. Staenz, and D. J. William, Effects of spectral, spatial, and radiometric characteristics on remote sensing vegetation indices of forested regions, Remote Sensing of Environment, vol.61, issue.1, pp.61-139, 1997.
DOI : 10.1016/S0034-4257(96)00248-9

T. Hlásny, I. Barka, Z. Sitková, T. Bucha, M. Konôpka et al., MODIS-based vegetation index has sufficient sensitivity to indicate stand-level intra-seasonal climatic stress in oak and beech forests, Annals of Forest Science, vol.27, issue.1, pp.109-125, 2015.
DOI : 10.1007/s00468-012-0786-4

V. Chéret, J. P. Denux, J. P. Ortisset, and C. Gacherieu, Utilisation de séries temporelles d'images satellitales pour cartographier le dépérissement des boisements résineux du sud massif central. Rendez-Vous Tech, pp.31-55, 2011.

J. Lambert, C. Drénou, J. P. Denux, G. Balent, and V. Chéret, Monitoring forest decline through remote sensing time series analysis. GISci. Remote Sens, pp.437-457, 2013.

J. Lambert, Evaluation des Baisses de Vitalité des Peuplements Forestiers à Partir de Séries Temporelles D'images Satellitaires?Application aux Résineux du sud du Massif Central et à la Sapinière Pyrénéenne, 2014.

X. Zhan, R. A. Sohlberg, J. R. Townshend, C. Dimiceli, M. L. Carroll et al., Detection of land cover changes using MODIS 250 m data, Remote Sensing of Environment, vol.83, issue.1-2, pp.336-350, 2002.
DOI : 10.1016/S0034-4257(02)00081-0

R. H. Fraser, A. Abuelgasim, and R. Latifovic, A method for detecting large-scale forest cover change using coarse spatial resolution imagery, Remote Sensing of Environment, vol.95, issue.4, pp.414-427, 2005.
DOI : 10.1016/j.rse.2004.12.014

T. Bucha and H. Stibig, Analysis of MODIS imagery for detection of clear cuts in the boreal forest in north-west Russia, Remote Sensing of Environment, vol.112, issue.5, pp.2416-2429, 2008.
DOI : 10.1016/j.rse.2007.11.008

S. Jin and S. A. Sader, MODIS time-series imagery for forest disturbance detection and quantification of patch size effects, Remote Sensing of Environment, vol.99, issue.4, pp.462-470, 2005.
DOI : 10.1016/j.rse.2005.09.017

D. C. Morton, R. S. Defries, Y. E. Shimabukuro, L. O. Anderson, F. D. Espirito-santo et al., Rapid Assessment of Annual Deforestation in the Brazilian Amazon Using MODIS Data, Earth Interactions, vol.9, issue.8, 2005.
DOI : 10.1175/EI139.1

S. Bontemps, P. Bogaert, N. Titeux, and P. Defourny, An object-based change detection method accounting for temporal dependences in time series with medium to coarse spatial resolution, Remote Sensing of Environment, vol.112, issue.6, pp.3181-3191, 2008.
DOI : 10.1016/j.rse.2008.03.013

M. C. Hansen, R. S. Defries, J. R. Townshend, R. Sohlberg, C. Dimiceli et al., Towards an operational MODIS continuous field of percent tree cover algorithm: examples using AVHRR and MODIS data, Remote Sensing of Environment, vol.83, issue.1-2, pp.303-319, 2002.
DOI : 10.1016/S0034-4257(02)00079-2

B. Desclée, P. Bogaert, and P. Defourny, Forest change detection by statistical object-based method, Remote Sensing of Environment, vol.102, issue.1-2, pp.1-11, 2006.
DOI : 10.1016/j.rse.2006.01.013

M. C. Hansen, D. P. Roy, E. Lindquist, B. Adusei, C. O. Justice et al., A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin, Remote Sensing of Environment, vol.112, issue.5, pp.2495-2513, 2008.
DOI : 10.1016/j.rse.2007.11.012

P. Potapov, M. C. Hansen, S. V. Stehman, T. R. Loveland, and K. Pittman, Combining MODIS and Landsat imagery to estimate and map boreal forest cover loss, Remote Sensing of Environment, vol.112, issue.9, pp.3708-3719, 2008.
DOI : 10.1016/j.rse.2008.05.006

T. Hilker, M. A. Wulder, N. C. Coops, J. Linke, G. Mcdermid et al., A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS, Remote Sensing of Environment, vol.113, issue.8, pp.1613-1627, 2009.
DOI : 10.1016/j.rse.2009.03.007

R. S. Lunetta, J. F. Knight, J. Ediriwickrema, J. G. Lyon, and L. D. Worthy, Land-cover change detection using multi-temporal MODIS NDVI data, Remote Sensing of Environment, vol.105, issue.2, pp.142-154, 2006.
DOI : 10.1016/j.rse.2006.06.018

J. E. Vogelmann, G. Xian, C. Homer, and B. Tolk, Monitoring gradual ecosystem change using Landsat time series analyses: Case studies in selected forest and rangeland ecosystems, Remote Sensing of Environment, vol.122, pp.92-105, 2012.
DOI : 10.1016/j.rse.2011.06.027

F. Gao, J. Masek, M. Schwaller, and F. Hall, On the blending of the Landsat and MODIS surface reflectance: Predicting daily landsat surface reflectance, IEEE Trans. Geosci. Remote Sens, vol.44, pp.2207-2218, 2006.

D. Alcaraz-segura, E. Liras, S. Tabik, J. Paruelo, J. Cabello et al., Evaluating the Consistency of the 1982???1999 NDVI Trends in the Iberian Peninsula across Four Time-series Derived from the AVHRR Sensor: LTDR, GIMMS, FASIR, and PAL-II, Sensors, vol.74, issue.2, pp.1291-1314, 2010.
DOI : 10.2307/1939924

D. Jong, R. De-bruin, S. De-wit, A. Schaepman, M. E. Dent et al., Analysis of monotonic greening and browning trends from global NDVI time-series, Remote Sensing of Environment, vol.115, issue.2, pp.692-702, 2011.
DOI : 10.1016/j.rse.2010.10.011

A. Jacquin, D. Sheeren, and J. Lacombe, Vegetation cover degradation assessment in Madagascar savanna based on trend analysis of MODIS NDVI time series, International Journal of Applied Earth Observation and Geoinformation, vol.12, pp.3-10, 2010.
DOI : 10.1016/j.jag.2009.11.004

J. Tüshaus, O. Dubovyk, A. Khamzina, and G. Menz, Comparison of Medium Spatial Resolution ENVISAT-MERIS and Terra-MODIS Time Series for Vegetation Decline Analysis: A Case Study in Central Asia, Remote Sensing, vol.121, issue.93, pp.5238-5256, 2014.
DOI : 10.1016/j.rse.2012.01.017

D. Jong, R. Verbesselt, J. Schaepman, M. E. De-bruin, and S. , Trend changes in global greening and browning: contribution of short-term trends to longer-term change, Global Change Biology, vol.106, issue.2, pp.642-655, 2012.
DOI : 10.1029/2000JD000115

A. Huete, K. Didan, T. Miura, E. P. Rodriguez, X. Gao et al., Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sensing of Environment, vol.83, issue.1-2, pp.195-213, 2002.
DOI : 10.1016/S0034-4257(02)00096-2

P. Jönsson and L. Eklundh, TIMESAT???a program for analyzing time-series of satellite sensor data, Computers & Geosciences, vol.30, issue.8, pp.833-845, 2004.
DOI : 10.1016/j.cageo.2004.05.006

M. Roderick, R. Smith, and S. Cridland, The precision of the NDVI derived from AVHRR observations. Remote Sens. Environ, pp.57-65, 1996.

B. C. Reed, Trend analysis of time series phenology of north America derived from satellite data. GISci. Remote Sens, pp.1-15, 2006.

J. Lambert, A. Jacquin, J. P. Denux, and V. Chéret, Comparison of two remote sensing time series analysis methods for monitoring forest decline, 2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp), pp.12-14, 2011.
DOI : 10.1109/Multi-Temp.2011.6005056

R. Cleveland, W. Cleveland, and J. Mcrae, Terpenning, I. Stl: A seasonal-trend decomposition procedure based on Loess (with discussion), J. Off. Stat, vol.6, pp.3-73, 1990.

S. Makridakis, S. C. Wheelwright, and R. J. Hyndman, Forecasting: Methods and Applications, p.638, 1998.

T. Fawcett, An introduction to roc analysis. Pattern Recog, pp.861-874, 2006.
DOI : 10.1016/j.patrec.2005.10.010

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.646.2144

L. C. Alatorre, R. Sanchez-andres, S. Cirujano, S. Begueria, and S. Sanchez-carrillo, Identification of Mangrove Areas by Remote Sensing: The ROC Curve Technique Applied to the Northwestern Mexico Coastal Zone Using Landsat Imagery, Remote Sensing, vol.61, issue.12, pp.1568-1583, 2011.
DOI : 10.1672/0277-5212(2006)26[649:AOTBOS]2.0.CO;2

T. Sing, O. Sander, N. Beerenwinkel, T. Lengauer, and . Rocr, ROCR: visualizing classifier performance in R, Bioinformatics, vol.21, issue.20, pp.3940-3941, 2005.
DOI : 10.1093/bioinformatics/bti623

T. Hothorn, K. Hornik, and A. Zeileis, Unbiased Recursive Partitioning: A Conditional Inference Framework, Journal of Computational and Graphical Statistics, vol.15, issue.3, pp.651-674, 2006.
DOI : 10.1198/106186006X133933

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.527.2935

R. G. Congalton, A review of assessing the accuracy of classifications of remotely sensed data, Remote Sensing of Environment, vol.37, issue.1, pp.35-46, 1991.
DOI : 10.1016/0034-4257(91)90048-B

M. Rebetez, H. Mayer, O. Dupont, D. Schindler, K. Gartner et al., Heat and drought 2003 in Europe: a climate synthesis, Annals of Forest Science, vol.63, issue.6, pp.569-577, 2006.
DOI : 10.1051/forest:2006043

URL : https://hal.archives-ouvertes.fr/hal-00884013

N. Bréda, R. Huc, A. Granier, and E. Dreyer, Temperate forest trees and stands under severe drought: a review of ecophysiological responses, adaptation processes and long-term consequences, Annals of Forest Science, vol.63, issue.6
DOI : 10.1051/forest:2006042

M. Hais, M. Joná?ová, J. Langhammer, and T. Kuera, Comparison of two types of forest disturbance using multitemporal Landsat TM/ETM+ imagery and field vegetation data, Remote Sensing of Environment, vol.113, issue.4, pp.835-845, 2009.
DOI : 10.1016/j.rse.2008.12.012

A. Jiménez-valverde and J. M. Lobo, Threshold criteria for conversion of probability of species presence to either???or presence???absence, Acta Oecologica, vol.31, issue.3, pp.31-361, 2007.
DOI : 10.1016/j.actao.2007.02.001

C. Liu, P. Frazier, and L. Kumar, Comparative assessment of the measures of thematic classification accuracy. Remote Sens. Environ, pp.606-616, 2007.

R. E. Kennedy, Z. Yang, and W. B. Cohen, Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr ??? Temporal segmentation algorithms, Remote Sensing of Environment, vol.114, issue.12, pp.2897-2910, 2010.
DOI : 10.1016/j.rse.2010.07.008

A. Röder, J. Hill, B. Duguy, J. A. Alloza, and R. Vallejo, Using long time series of Landsat data to monitor fire events and post-fire dynamics and identify driving factors. A case study in the Ayora region (eastern Spain), Remote Sensing of Environment, vol.112, issue.1, pp.259-273, 2008.
DOI : 10.1016/j.rse.2007.05.001

M. Drusch, U. Del-bello, S. Carlier, O. Colin, V. Fernandez et al., Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services, Remote Sensing of Environment, vol.120, pp.25-36
DOI : 10.1016/j.rse.2011.11.026

J. L. Privette, G. Riggs, and A. H. Strahler, The moderate resolution imaging spectroradiometer (MODIS): Land remote sensing for global change research, IEEE Trans. Geosci. Remote Sens, vol.36, pp.1228-1249, 1998.