Testing the robustness of the Plant Phenology Index to changes in temperature

anglais. Traditional vegetation indices have long encountered a problem of saturating at high biomass levels. A newly formulated vegetation index, the Plant Phenology Index (PPI), which has a near linear relationship with canopy green leaf area index (LAI) is tested in this study against temperature at 12 sites across Sweden. For comparison, the performance of the Normal Difference Vegetation Index (NDVI) and Enhanced Vegetation Index 2 (EVI2) are also tested. This study aim was robustness of PPI, and to see how well PPI correlates to temperature. PPI was shown to correlate very well with temperature for 11 out of the 12 sites, and also demonstrated a much more linear relationship to temperature than NDVI and EVI2. These results were replicated in a test of the vegetation indices against Growing Degree Days, a measure of accumulated heat, with high correlation coefficients for PPI. This indicated that PPI was more sensitive to changes in temperature than NDVI and EVI2 and is thus an efficient tool to show the phenological stages of vegetation.

anglais. From a general perspective, remote sensing is the science of acquiring and analysing information about an object, area or phenomena from a distance, for example via satellites. Remote sensing can be used for all manners of things; including the monitoring of vegetation, particularly their phenological developments, which are the stages a plant goes through during it’s growing season. This has become a very important study area over the past 15 years as plant phenology could be used as an indicator of the long-term biological impacts of climate change on land based ecosystems. Different characteristics of plants absorb and reflect light in different parts of the electromagnetic spectrum. Chlorophyll reflects green light, which is why we see plants as green, whilst red, and blue wavelengths are absorbed. Vegetation Indices (VIs) are a remote sensing tool, primarily used to indicate the amount of green vegetation or biomass in an area. Due to the high reflectance of plants of Near-Infrared and Red light; most VIs are based around this part of the spectrum. The most commonly used VI is the Normalized Difference Vegetation Index (NDVI), and despite its extensive use, it has one major disadvantage. This disadvantage is that when an area of vegetation becomes particularly dense, or has a high Leaf Area Index (a measure of the amount of leaf material in an ecosystem), NDVI becomes less responsive and has a tendency to saturate and as such isn’t as true of a reflection of the real life conditions as needed. The Plant Phenology Index (PPI) is a new VI that attempts to negate this issue and be approximately linear to LAI. The aim of this investigation was to test how PPI responds well to changes in temperature and changing biomass, using temperature as a proxy for biomass; and to see how well temperature correlates to PPI. NDVI and another VI, EVI2 were also both tested against temperature for comparison. Twelve study sites were chosen across Sweden, using a ten-year record of climate data and satellite data used over the same period to calculated the respective VIs. From this, variables such as Growing Degree Days (GDD (a measure of accumulated heat)) were calculated for the analysis. Once calculated, a correlation analysis were carried out between temperature and the VIs, as well as two separate regression analyses; one for temperature and the VIs, and one for GDD and the VIs. Results show PPI correlated very well with temperature for 11 out of the 12 sites, and also demonstrated a much more linear relationship to temperature than NDVI and EVI2. These results were replicated in a test of the vegetation indices against Growing Degree Days, a measure of accumulated heat, with high correlation coefficients for PPI. This indicated that PPI was more sensitive to changes in temperature than NDVI and EVI2 and is thus an efficient tool to show the phenological stages of vegetation.