The use of Landsat and MODIS data to remotely estimate Russia’s sown area

The intensity of crop management is one of the most important management decisions that affect soil carbon stocks in croplands. In this study, we use satellite data at two spatial resolutions (30m Landsat and 500m MODIS) and field observations to determine arable lands in a portion of the Russian grain belt. Once arable lands are established we map cropping intensity between 2002 and 2009 to get a better understanding of the activity occurring on arable lands. Our arable land estimates compare favourably with the 2006 all Russian agricultural census. We also compare three global datasets that quantify croplands against the census data. Finally, we show that our cropping intensity map compares very well to the available regional statistical data on cropping intensity. Our crop intensity map reveals that  areas in the southern part of our study region are successfully cropped  during fewer years than more central areas of the study region.

The use of Landsat and MODIS data to remotely estimate Russia’s sown area. Journal of Land Use Science. In press.

Cropland (intensity) data created in this project.

Cropland data Russia oblasts.

This project is funded by NASA’s Land Cover Land Use Change Program.

Dual scale trend analysis

Dual scale trend analysis for evaluating climatic and anthropogenic effects on the vegetated land surface in Russia and Kazakstan

We present a dual scale trend analysis for characterizing and comparing two contrasting areas of change in Russia and Kazakhstan that lie less than 800 km apart. We selected a global NASA MODIS (moderate resolution imaging spectroradiometer) product (MCD43C4 and MCD43A4) at a 0.05◦ (∼5.6 km) and 500 m spatial resolution and a 16-day temporal resolution from 2000 to 2008. We applied a refinement of the seasonal Kendall trend method to the normalized difference vegetation index (NDVI) image series at both scales. We only incorporated composites during the vegetative growing season which was delineated by start of season and end of season estimates based on analysis of normalized difference infrared index data. Trend patterns on two scales pointed to drought as the proximal cause of significant declines in NDVI in Kazakhstan. In contrast, the area of increasing NDVI trend in Russia was linked through the dual scale analysis with agricultural land cover change. The coarser scale analysis was relevant to atmospheric boundary layer processes, while the finer scale data revealed trends that were more relevant to human decision-making and regional economics.

 

 

References

de Beurs, K.M., Wright. C.K., Henebry, G.M. 2009. Dual scale trend analysis distinguishes climatic from anthropogenic effects on the vegetated land surface.Environmental Research Letters4 045012.

 

This project is funded by NASA’s Land Cover Land Use Change Program.

Land Change in Russia

Russia’s population is projected to shrink by a staggering 29% by 2050. Differential dynamics among rural populations are correlated with ethnicity and constitute a key driver in the spatial disintegration of rural Russia. Currently, Russia is slowly transitioning into a country with an internal ‘archipelago’ of islands of productive agriculture around cities set within a matrix of much less productive and abandoned croplands. This heterogeneous spatial pattern is mainly driven by depopulation of the least favorable parts of the countryside, where ‘least favorable’ is some function of lower fertility of land, higher remoteness from urban markets, or both.
This project investigates potential sustainable productivity of remaining croplands under climatic and demographic changes. Our aim is to improve current understanding of the interactions of climate change and the spatio-temporal impacts of agricultural reform in European Russia. We propose to model future land abandonment based on (1) past abandonment estimates retrieved from satellite imagery, (2) age-structured population models, and (3) spatially structured metapopulation models using socio-demographic data, distance to major population centers, and bioclimatic potential derived from a combination of current temperature and moisture regimes retrieved from spaceborne sensors and predicted future regimes from IPCC AR4 models. We will investigate three scenarios: A1FI and B1 project drastic decreases in the Russian population by 2050, but A2 projects minor changes.

 

Field photographs
Moscow, Samara oblast and Kostroma oblast (Summer 2010)
Moscow, Chuvash Republic (Fall 2011)

This project is funded by NASA’s Land Cover Land Use Change Program.