Monitoring forest decline through remote sensing time series analysis

Abstract

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.

Detecting Clear-Cuts and Decreases in Forest Vitality Using MODIS NDVI Time Series

“…The interest of remote sensing image time series for vegetation monitoring has been widely demonstrated [13,37,55]. Even if new sensors will improve the spatial resolution of the time series of remote sensing images, MODIS images, because of their archive, will remain an asset to observe gradual changes of vegetation activity.…”

“…Vitality trends were calculated using the Spring Greenness phenological metric (SG), which expresses the annual level of vegetation activity during the growth period [12,13,37]. The SG indicator corresponds to the sum of NDVI calculated over a fixed period of five syntheses from the onset of spring greenness (beginning of April) to the maximum NDVI (in June) before the dry season.…”

“…where is the slope of the trend for a given pixel, is the slope of the reference trend (here equal to zero), is the number of years, σ is the standard deviation Using the test results, the trends of SG indicator values were grouped in four classes [13,38], one class for healthy vegetation and three classes of declining vegetation: An excerpt from the trend map of the study area is presented in Figure 1b. The combination of these vitality trend classes and the clear-cut probability levels is the main goal of this work, allowing clear-cuts to be distinguished from declining stands.…”

“…Vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), are frequently used to monitor vegetation thanks to their close link with the primary production of vegetation [9][10][11]. A method based on the analysis of NDVI satellite image time series was developed in a previous study [12][13][14] to evaluate the phenomenon of decline throughout the whole forest area. Furthermore, the relevance of the use of a spring greenness indicator was validated by comparison with a ground-level tree vitality indicator.…”

Examining Forest Disturbance and Recovery in the Subtropical Forest Region of Zhejiang Province Using Landsat Time-Series Data

“…For example, Meigs et al [23] examined the detection of four subtle change types: short-duration decline then recovery; shortthen long-duration decline; long-duration decline; and long-duration decline then recovery using the LandTrendr algorithm in Oregon, USA. Lambert et al [51] monitored forest decline through remote sensing time-series analysis in southwestern Massif Central Mountains, France. Chen et al [52] detected the gradual tree line movement in Tianshan Mountain, China, using spectral mixture analysis based on multitemporal Landsat multispectral images.…”