In Europe, the 2003 summer heat wave damaged forested areas. This study aims to compare two approaches of NDVI time series analysis to monitor forest decline. Both methods analyze the trend of vegetation activity from 2000 to 2011. The first method is based on a phenometric related to spring vegetation activity, calculated for each year during the 2000-2011 period. In the second method (BFAST), the trend comes from the decomposition of the NDVI time series into three additive components: trend, seasonal and remainder. The two approaches gave similar results for estimated trends. The main advantage of BFAST is its ability to detect breakpoints in the linear trend. It allowed to highlight here the impact of exceptional events, like 2003 summer drought, on the development of forest stands. In the last part of our study, we implemented a validation based on in situ observations. Health status of silver fir stands was estimated analyzing the trees architecture. Significant relationships were highlighted between the indicator of spring vitality derived from remote sensing images and the observed status of forest stands.

This paper examines the potential of MODIS-NDVI time series for detecting clear-cuts in a coniferous forest stand in the south of France. The proposed approach forms part of a survey monitoring the status of forest health and evaluating the forest decline phenomena observed over the last few decades. One of the prerequisites for this survey was that a rapid and easily reproducible method had to be developed that differentiates between forest clear-cuts and changes in forest health induced by environmental factors such as summer droughts. The proposed approach is based on analysis of the breakpoints detected within NDVI time series, using the "Break for Additive Seasonal and Trend" (BFAST) algorithm. To overcome difficulties detecting small areas on the study site, we chose a probabilistic approach based on the use of a conditional inference tree. For model calibration, clear-cut reference data were produced at MODIS resolution (250 m). According to the magnitude of the detected breakpoints, probability classes for the presence of clear-cuts were defined, from greater than 90% to less than 3% probability of a clear-cut. One of the advantages of the probabilistic model is that it allows end users to choose an acceptable level of uncertainty depending on the application. In addition, the use of BFAST allows events to be dated, thus OPEN ACCESSRemote Sens. 2015, 7 3589 making it possible to perform a retrospective analysis of decreases in forest vitality in the study area.