Satellite‐based mapping of the growing season and bioclimatic zones in Fennoscandia

Abstract

Aim To test whether satellite-derived NDVI values obtained during the growing season as delimited by the onset of phenological phases can be used to map bioclimatically a large region such as Fennoscandia.Location Fennoscandia north of about 58 ° N and neighbouring parts of NW Russia.Methods Phenology data on birch from 15 research stations and the half-monthly GIMMS-NDVI data set with 8 × 8 km 2 resolution from the period 1982-2002 were used to characterize the growing season. To link surface phenology with NDVI data, new algorithms on a pixel-by-pixel basis that show high correlation with phenophases on birch were developed. Then, time-integrated values (TI NDVI) during the phenologically defined growing season were computed to produce a bioclimatological map of Fennoscandia, which was tested and correlated with growing degree days (GDD) obtained from 20 meteorological stations. The map was also compared vs. traditional bioclimatic maps, and analysed for error factors distorting NDVI values. ResultsThe correlation between GDD and TI NDVI data during the phenologically defined growing season was very high. Therefore, the TI NDVI map could be presented as a bioclimatic map reflecting GDD. However, several major areas have interfering factors distorting NDVI values, such as the pixel heterogeneity caused by the altitudinal mosaic in western Norway, the mosaic of lakes in southeastern Finland, and the agriculture-dominated areas in southern Fennoscandia.Main conclusions TI NDVI data from the phenologically defined growing season during 1982-2002 in Fennoscandia can be processed as a bioclimatic map reflecting GDD, except for the areas distorting NDVI values by their strong ground-cover heterogeneity.

Relating seasonal dynamics of enhanced vegetation index to the recycling of water in two endorheic river basins in north-west China

“…Some authors use single arbitrary thresholds, e.g. 0.17 (Fischer, 1994), 0.09 (Markon et al, 1995), and 0.099 (Lloyd, 1990), whereas some use threshold specifiers like the long-term average (Karlsen et al, 2006) or % peak amplitude of vegetation indices (Jonsson and Eklundh, 2002).…”

“…The values of vegetation index are plotted against time of year. The time when the threshold value is passed in the upward direction is identified as the start of the growing period and when the same value is passed in the downward direction, the time is identified as the end of the growing period (Karlsen et al, 2006; e.g. Fig.…”

Effects of precipitation, temperature and topographic parameters on evergreen vegetation greenery in the Western Ghats, India

“…These studies and several others, conclude that there is a strong relationship between climate variability and fluctuations in satellite-derived vegetation indices at local, regional and continental scales Tateishi and Kajiwara, 1992;Myneni et al, 1997;Paruelo and Lauenroth, 1998;Schwartz and Reed, 1999;Ichii et al, 2002;Nemani et al, 2003;Jolly and Running, 2004;Tateishi and Ebata, 2004;Zhou et al, 2003;Karlsen et al, 2006;Suzuki et al, 2006).…”

Long-Term Detection of Global Vegetation Phenology from Satellite Instruments

“…The commonly used methods are the threshold-based technique which is divided into absolute VI threshold (e.g., Lloyd, 1990;Fischer, 1994;Myneni et al, 1997;Zhou et al, 2001) and relative threshold (e.g., White et al, 1997;Jonsson and Eklundh, 2002;Delbart et al, 2005;Karlsen et al, 2006;Dash et al, 2010), moving average (Reed et al, 1994), spectral analysis (Jakubauskas et al, 2001;Moody and Johnson, 2001), and inflection point estimation in the time series of vegetation indices (Moulin et al 1997;Zhang et al 2003;Tan et al, 2011). Various approaches in detecting phenological timing, particularly the greenup onset, are compared using the same dataset (de Beurs and Henebry, 2010;White et al, 2009).…”