The study of changes in phenology and, in particular, land surface phenology (LSP) provides an important approach to detecting responses to climate change in terrestrial ecosystems. LSP has been studied primarily through analysis of time series of vegetation indices retrieved from passive optical sensors, such as the series of AVHRRs on polar-orbiting satellites and the pair of MODIS sensors on the Terra and Aqua platforms that provide higher spatial, spectral, and radiometric resolution. Most broad-scale vegetation studies use normalized difference vegetation index (NDVI) data. Here, we provide an overview of the LSP of the northern polar and high-latitude regions (§60uN) based on MODIS data at climate modeling grid (0.05u) resolution. We demonstrate the relationship between three onset-of-greening measures and snow cover and accumulated growing degree-days.We show that the Arctic Oscillation index is significantly correlated with the peak timing of the growing seasons since 2000 for a range of ecoregions, and we demonstrate that there were more than three times as many negative NDVI changes since 2000 as positive changes (25.3% versus 7.3%) based on all land area above 60uN. We reveal that these changes are predominantly driven by minimum temperature changes.
- de Beurs, K.M., Henebry, K.M. 2010.A land surface phenology assessment of the northern polar regions using MODIS reflectance time series.Canadian Journal of Remote Sensing, Volume 36, Supplement S1, S87-110.
- de Beurs, K.M., Henebry, G.M., 2008.Northern Annular Mode effects on the Land Surface Phenologies of Northern Eurasia.Journal of Climate, 21:4257-4279
Monitoring and understanding plant phenology is becoming an increasingly important way to identify and model global changes in vegetation life cycle events. High elevation biomes cover twenty percent of the Earth’s land surface and provide essential natural resources. These areas experience limited resource availability for plant growth, development, and reproduction, and are one of the first ecosystems to reflect the harmful impact of climate change. Despite this, the phenology of mountain ecosystems has historically been understudied due to the rough and variable terrain and inaccessibility of the area. In addition, although numerous studies have used synoptically sensed data to study phenological patterns at the continental and global scale, relatively few have focused on characterizing the land surface phenology in mountainous areas. Here we use the MODIS/Terra+Aqua satellite 8-day 500m Nadir BRDF Adjusted Reflectance product to quantify the land surface phenology. We relate independent data for elevation, slope, aspect, solar radiation, and temperature as well as longitude and latitude with the derived phenology estimates. We present that satellite derived SOS can be predicted based on topographic and weather variables with a significant R² between 0.56 and 0.62 for the entire western mountain range. Elevation and latitude exhibit the most significant influences on the timing of SOS throughout our study area. When examined at both the local and regional scale, as well as when accounting for aspect and temperature, SOS follows closely with Hopkins’ Bioclimatic Law with respect to elevation and latitude.
** Hudson-Dunn, A., de Beurs, K.M., 2011. Land Surface Phenology of North American Mountain Environments Using Moderate Resolution Imaging Spectroradiometer data. Remote Sensing of Environment, vol 115(5), p1220-1233.
Variations in agricultural production due to rainfall and temperature fluctuations are a primary cause of food insecurity on the continent in Africa. Analysis of changes in phenology can provide quantitative impact of climate variability on growing seasons in agricultural regions. Using a robust statistical methodology, we describe the relationship between phenology metrics derived from the 26 year AVHRR NDVI record and the North Atlantic Oscillation index (NAO), the Indian Ocean Dipole (IOD), the Pacific Decadal Oscillation (PDO), and the Multivariate ENSO Index (MEI). We map the most significant positive and negative correlation for the four climate indices in Eastern, Western and Southern Africa between two phenological metrics and the climate indices. Our objective is to provide evidence of whether climate variability captured in the four indices has had a significant impact on the vegetative productivity of Africa during the past quarter century. We found that the start of season and cumulative NDVI were significantly affected by large scale variations in climate. The particular climate index and the timing showing highest correlation depended heavily on the region examined. In Western Africa the cumulative NDVI correlates with PDO in September-November. In Eastern Africa the start of the June-October season strongly correlates with PDO in March-May, while the PDO in December-February correlates with the start of the February-June season. The cumulative NDVI over this last season relates to the MEI of March-May. For Southern Africa, high correlations exist between SOS and NAO of September-November, and cumulative NDVI and MEI of March-May. The research shows that climate indices can be used to anticipate late start and variable vigor in the growing season of sensitive agricultural regions in Africa.
Brown, M.E., de Beurs, K.M., Vrieling, A. 2010. The response of African land surface phenology to large scale climate oscillations. Remote Sensing of Environment, vol 114(10), p2286-2296