NDVI dynamics of the taiga zone in connection with modern climate changes

Abstract

ABSTRACT:This research is dedicated to the investigation of the relations between the XXI century climate changes and Normalized Difference Vegetation Index (NDVI) variability of the taiga zone. For this purposes was used the observations of vegetation variability on the test area located nearby Syktyvkar city (Komi Republic, Russia), 16-day averages of NDVI data derived from TERRA/MODIS space imagery (spatial resolution is about 250 meters), and the air temperature and precipitation observations from Syktyvkar meteorological station. The research results confirmed the statistically significant positive correlation between NDVI and air temperature for all vegetation types of the test area, for both spring and autumn seasons. The weakest correlation was found for coniferous forest, namely, pine forest on poor soils, and the strongest correlation was found for meadows and bogs. Additionally the map of NDVI trends of the test area shows that the sectors of greatest positive trend located on the territories with non-forest cover, and as a result, the positive trend of air temperature is indicated most brightly on vegetation of non-forest lands. Thereby these lands can serve as climate changes indicator in the investigated region.

Mapping of the Land Cover Spatiotemporal Characteristics in Northern Russia Caused by Climate Change

“…et al, 2010). Previous stages of our study (Bobkov A. et al, 2015;Tsepelev V. et al, 2015) demonstrated that this relationship appears more clearly for shrub vegetation cover during the spring and summer growing seasons. Obtained result was verified at the ground test plots.…”

“…Currently, we have used Version 6 of the MODIS NDVI data (K. Didan, 2015) instead of previously used Version 5. The seasonal NDVI graphs that were produced according to new data version differ slightly from graphs that were produced in our previous studies (Bobkov A. et al, 2015). Analysis of these graphs shows that autumn season temperature trend data coincide with NDVI trend data and could be also applicable for the study of climate change (Table 1).…”

“…We allocated the growing season frames in each year in the period of 2000-2015. The first group of the graphs was produced accordingly to our previous studies (Bobkov A. et al, 2015;Tsepelev V. et al, 2015) where the frames of the growing seasons were allocated as their mean calendar dates (currently, for the 2000-2015 period). Mean calendar framing dates for the growing seasons were computed in this case basing on surface air temperature values observed at Syktyvkar meteorological station.…”

Changes of Grassland Rain Use Efficiency and NDVI in Northwestern China from 1982 to 2013 and Its Response to Climate Change

“…Changes in vegetation productivity are mainly determined by climate change [62,63]. Bobkov et al [64] reported that there was a statistically positive correlation between NDVI and spring and autumn temperatures of various vegetation types in the experimental area. Coniferous forests, that is, pine forests on poor soils, have the weakest correlation, and grassland and marsh are the most relevant.…”