K.M. de Beurs

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 in...

Northern Annular Mode Effects on the Land Surface Phenologies of Northern Eurasia

Abstract 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 investig...

Trend analysis of the Pathfinder AVHRR Land (PAL) NDVI data for the deserts of central Asia

We analyzed spatially averaged normalized difference vegetation index (NDVI) time series from the Pathfinder Advanced Very High Resolution Radiometer (AVHRR) Land (PAL) dataset of 11 desert and semidesert ecoregions in central Asia using standard statistical tests for discontinuities and trends. Results from the test for discontinuities reveal that seven ecoregions display significant differences in the data acquired by the AVHRRs on the National Oceanic and Atmospheric Administration satellite 11 (NOAA-11) versus the data acquired by AVHRR on other NOAA satellites (NOAA-7, NOAA-9, and NOAA-14). Across the more than 2/spl times/10/sup 6/ km/sup 2/ of deserts and semideserts in the selected central Asian ecoregions, a significant upward trend in NDVI is evident during the tenure of NOAA-11 (1989-1994). This trend is not found during any other period. We argue that the data from the PAL NDVI dataset for NOAA-11 will pose problems for land surface change analyses, if these significant sensor-related artifacts are ignored. We do not find these artifacts in data from the other three satellites (NOAA-7, NOAA-9, and NOAA-14). We suggest that the comparison of data from any combination of these three AVHRRs can be used for land surface change analyses, but that the inclusion of NOAA-11 AVHRR NDVI data in trend analyses may result in the detection of spurious trends.

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.

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.

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 Afghanistans 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.

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.

Land surface dynamics in Kazakhstan: Dynamic baselines and change detection

Analysis of the spatio-temporal structure of NDVI in the Pathfinder AVHRR Land data set for Kazakhstan from 1981-1999 reveals significant changes in the distributions of the scale of fluctuation (SOF) before and after 1992 in some ecoregions at certain phases of the growing season. These differences are likely due to actual influences on the land surface and not changes in sensor characteristics. Further analysis is required to identify and quantify these influences.

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.