Possible impacts of climate change and economic globalization on the grain production trends in Russia and its neighbors

The recent volatility of world agricultural prices indicates that the global food balance and livelihoods are highly vulnerable to the existing economic instability and climate variability and change. Increasing the production of grain is central to meeting food demands of the growing population of the world, both to provide sufficient food grain and to meet the demand for animal feed. Climate and agro-ecological models project that the grain production in Russia and other countries of the former USSR is likely to increase due to a combination of winter temperature increase, extension of the growing season, and elevated levels of carbon dioxide (CO2). Russias wheat production and exports have been growing during the past decade and are projected to equal those of the United States. The future of this regions grain production is likely to have a very significant impact on the global and regional food security over the next decades. This paper analyses the recent land-use changes, climatic trends and variability, and grain production trends in the countries of the former USSR, climate change and agro-ecological scenarios, as well as economic and institutional trends in this region

War, drought, and phenology : Changes in the land surface phenology of Afghanistan since 1982

War and resulting institutional changes can be important drivers of land use and land cover change. We explore how war, its consequences, and drought have affected the land surface phenology (LSP) of Afghanistan. Afghanistan offers a unique case of a semi-arid country with multiple institutional changes during the past two decades. Long image time series are able to characterize the seasonal development of Afghanistan's vegetated land surface. We apply a statistical framework to four governance periods and compare the average AVHRR NDVI 8 km data across periods, and calculate trends within study periods. We focus on significant changes in LSP in the region around Qandahar. Finally, we assess changes in LSP between 2001 (a drought year) and 2003 (a year with sufficient precipitation) using MODIS NDVI 1km data. Results reveal the strengths and limitations of LSP modeling in an environment characterized by high interannual and spatial variability as well as by socio-economic turbulence

A statistical framework for the analysis of long image time series

Coarse spatial resolution satellites are capable of observing large swaths of the planetary surface in each overpass resulting in image time series with high temporal resolution. Many change-detection strategies commonly used in remote sensing studies were developed in an era of image scarcity and thus focus on comparing just a few scenes. However, change analysis methods applicable to images with sparse temporal sampling are not necessarily efficient and effective when applied to long image time series. We present a statistical framework that gathers together: (1) robust methods for multiple comparisons; (2) seasonally corrected Mann-Kendall trend tests; (3) a testing sequence for quadratic models of land surface phenology. This framework can be applied to long image time series to partition sources of variation and to assess the significance of detected changes. Using a standard image time series, the Pathfinder AVHRR Land (PAL) NDVI data, we apply the framework to address the question of whether the institutional changes accompanying the collapse of the Soviet Union resulted in significant changes in land surface phenologies across the ecoregions of Kazakhstan

Northern annular mode effects on the land surface phenologies of northern Eurasia

Land surface phenology (LSP) is the spatiotemporal development of the vegetated land surface as revealed by synoptic sensors. Modeling LSP across northern Eurasia reveals the magnitude, significance, and spatial pattern of the influence of the northern annular mode. Here the authors fit simple LSP models to two normalized difference vegetation index (NDVI) datasets and calculate the Spearman rank correlations to link the start of the observed growing season (SOS) and the timing of the peak NDVI with the North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) indices. The relationships between the northern annular mode and weather station data, accumulated precipitation derived from the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) dataset, accumulated growing degree-days (AGDDs) derived from the NCEP-Department of Energy Atmospheric Model Intercomparison Project (AMIP-II) reanalysis, and the number of snow days from the National Snow and Ice Data Center are investigated. Tne analyses confirm strong relationships between the temporal behavior of temperature and precipitation and large-scale climatic variability across Eurasia. The authors find widespread influence of the northern annular mode (NAM) on the land surface phenologies across northern Eurasia affecting 200-300 Mha. The tundra ecoregions were especially impacted with significant results for about a quarter of the biome. The influence of the AO was also extensive (>130 Mha) for the boreal forests. The AO appears to affect the Asian part of northern Eurasia more strongly than the NAO, especially for the NDVI peak position as a function of AGDD. Significant responses of vegetation timing to NAO and AO in northeastern Russia have not been as well documented as the seasonal advancement in Europe. The two Advanced Very High Resolution Radiometer NDVI datasets yield fields of LSP model parameter estimates that are more similar in dates of peak position than in dates for SOS and more similar for AO than for NAO. As a result, the authors conclude that peak position appears to be a more robust characteristic of land surface phenology than SOS to link vegetation dynamics to variability and change in regional and global climates

A land surface phenology assessment of the northern polar regions using MODIS reflectance time series

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 (≥60°N) based on MODIS data at climate modeling grid (0.05°) 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 60°N. We reveal that these changes are predominantly driven by minimum temperature changes

Land surface phenology along urban to rural gradients in the U.S. Great Plains

The elevated surface and air temperatures of urban environments can influence the timing of vegetation growth dynamics within and across city boundaries. We examined patterns of land surface phenology (LSP) throughout the U.S. Great Plains region, which contains diverse metropolitan areas embedded within a predominately agricultural landscape. We assembled a time series (2002-2012) of Moderate Resolution Imaging Spectroradiometer (MODIS) Nadir BRDF-Adjusted Reflectance (NBAR) data and land surface temperature data at 500m and 1000m spatial resolution, respectively. We derived measures of the vegetated land surface and the thermal regime of the growing season at 8-day intervals using the Normalized Difference Vegetation Index (NDVI) and Accumulated Growing Degree-Days (AGDD). Fitting the convex quadratic LSP model of NDVI as a function of AGDD yielded two phenometrics - Peak NDVI and Thermal Time to Peak - and one model fit metric - the adjusted coefficient of determination (r2adj) - for each pixel per growing season. We linked the phenometrics with impervious surface area (ISA) data extracted from the U.S. Geological Survey National Land Cover Dataset (NLCD) to characterize differences in timing and amplitude of peak greenness between urban areas and their surrounding landscapes. Our results reveal the broad control of climatic conditions and moisture availability on phenological patterns across urban to rural gradients, with drier, southern cities displaying more varied responses of peak greenness timing and amplitude to urban intensity

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

We present a dual scale trend analysis for characterizing and comparing two contrasting areas of change in Russia and Kazakhstan that lie less than 800km apart. We selected a global NASA MODIS (moderate resolution imaging spectroradiometer) product (MCD43C4 and MCD43A4) at a 0.05° (∼5.6km) and 500m 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

Spatial and temporal heterogeneity of agricultural fires in the central United States in relation to land cover and land use

Agricultural burning is an important land use practice in the central U. S. but has received little attention in the literature, whereas most of the focus has been on wildfires in forested areas. Given the effects that agricultural burning can have on biodiversity and emissions of greenhouse gasses, there is a need to quantify the spatial and temporal patterns of fire in agricultural landscapes of the central U. S. Three years (2006-2008) of the MODIS 1 km daily active fire product generated from the MODIS Terra and Aqua satellite data were used. The 2007 Cropland Data Layer developed by the U. S. Department of Agriculture was used to examine fire distribution by land cover/land use (LCLU) type. Global ordinary least square (OLS) models and local geographically weighted regression (GWR) analyses were used to explore spatial variability in relationships between fire detection density and LCLU classes. The monthly total number of fire detections peaked in April and the density of fire detections (number of fires/km2/3 years) was generally higher in areas dominated by agriculture than areas dominated by forest. Fire seasonality varied among areas dominated by different types of agriculture and land use. The effects of LCLU classes on fire detection density varied spatially, with grassland being the primary correlate of fire detection density in eastern Kansas; whereas wheat cropping was important in central Kansas, northeast North Dakota, and northwest Minnesota.

Reanalysis data underestimate significant changes in growing season weather in Kazakhstan

We present time series analyses of recently compiled climate station data which allowed us to assess contemporary trends in growing season weather across Kazakhstan as drivers of a significant decline in growing season normalized difference vegetation index (NDVI) recently observed by satellite remote sensing across much of Central Asia. We used a robust nonparametric time series analysis method, the seasonal Kendall trend test to analyze georeferenced time series of accumulated growing season precipitation (APPT) and accumulated growing degree-days (AGDD). Over the period 2000-2006 we found geographically extensive, statistically significant (p<0.05) decreasing trends in APPT and increasing trends in AGDD. The temperature trends were especially apparent during the warm season and coincided with precipitation decreases in northwest Kazakhstan, indicating that pervasive drought conditions and higher temperature excursions were the likely drivers of NDVI declines observed in Kazakhstan over the same period. We also compared the APPT and AGDD trends at individual stations with results from trend analysis of gridded monthly precipitation data from the Global Precipitation Climatology Centre (GPCC) Full Data Reanalysis v4 and gridded daily near surface air temperature from the National Centers for Climate Prediction Reanalysis v2 (NCEP R2). We found substantial deviation between the station and the reanalysis trends, suggesting that GPCC and NCEP data substantially underestimate the geographic extent of recent drought in Kazakhstan. Although gridded climate products offer many advantages in ease of use and complete coverage, our findings for Kazakhstan should serve as a caveat against uncritical use of GPCC and NCEP reanalysis data and demonstrate the importance of compiling and standardizing daily climate data from data-sparse regions like Central Asia

The false spring of 2012, earliest in North American record

Phenology—the study of recurring plant and animal life cycle stages, especially their timing and relationships with weather and climate—is becoming an essential tool for documenting, communicating, and anticipating the consequences of climate variability and change. For example, March 2012 broke numerous records for warm temperatures and early flowering in the United States [Karl et al., 2012; Elwood et al., 2013]. Many regions experienced a “false spring,” a period of weather in late winter or early spring sufficiently mild and long to bring vegetation out of dormancy prematurely, rendering it vulnerable to late frost and drought