Geoffrey M. Henebry

Tracking the rhythm of the seasons in the face of global change: phenological research in the 21st century

Phenology is the study of recurring life-cycle events, classic examples being the flowering of plants and animal migration. Phenological responses are increasingly relevant for addressing applied environmental issues. Yet, challenges remain with respect to spanning scales of observation, integrating observations across taxa, and modeling phenological sequences to enable ecological forecasts in light of future climate change. Recent advances that are helping to address these questions include refined landscape-scale phenology estimates from satellite data, advanced, instrument-based approaches for field measurements, and new cyberinfrastructure for archiving and distribution of products. These breakthroughs are improving our understanding in diverse areas, including modeling land-surface exchange, evaluating climate–phenology relationships, and making land-management decisions.

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...

Spatio-Temporal Statistical Methods for Modelling Land Surface Phenology

This chapter surveys 12 different spatio-temporal statistical methods to determine the start and end of the growing season using a time series of satellite images. In the first section of the chapter, we divided the methods into four categories: thresholds, derivatives, smoothing functions, and fitted models. The general use, advantages, and potential limitations of each method are discussed. In the second section of the chapter, a case study is presented to highlight one method from each category. The four study areas range from the Northwest Territories in Canada to the winter wheat areas in south-central Kansas. We concluded the case study with a discussion of the differences in results for the four methods. The chapter is finished with a synopsis discussing the use of nomenclature, the problems with a lack of statistical error structure from most methods, and the perennial issue of oversmoothing.

Inferring Process from Pattern in Natural CommunitiesCan we understand what we see

basic assumption of scientific investigation is that observed phenomena have underlying physical causes (Beck 1982). Understanding causes-the biotic and abiotic processes of nature-can explain why the world is the way it appears and how it can change. Biologists use the word pattern to describe the observable traits of a system and their configuration. Pattern is what is seen, whether using an electron microscope or a satellite imaging system. Biologists and other scientists apply logic, experience, and statistical analysis to explain a pattern in terms of the processes believed to underlie it. But to

Detecting change in grasslands using measures of spatial dependence with Landsat TM data

Abstract Spatial dependence is a fundamental structure of spatial data that can be readily measured. Change in landscapes can be monitored effectively using measures of spatial dependence. Scale of fluctuation analysis estimates the dimensional extent to which data are significantly autocorrelated by observing the behavior of sample variance under extended local averaging. Algorithms to estimate the one-dimensional scale of fluctuation (correlation length) and two-dimensional scale fluctuation (correlation area) in image data are described. The approach is demonstrated with an analysis of a 9-year TM image series of a tallgrass prairie preserve, Konza Prairie Research Natural Area, to assess the impact of bison on the spatial patterning of vegetation.

Fractal signature and lacunarity in the measurement of the texture of trabecular bone in clinical CT images

Fractal analysis is a method of characterizing complex shapes such as the trabecular structure of bone. Numerous algorithms for estimating fractal dimension have been described, but the Fourier power spectrum method is particularly applicable to self-affine fractals, and facilitates corrections for the effects of noise and blurring in an image. We found that it provided accurate estimates of fractal dimension for synthesized fractal images. For natural texture images fractality is limited to a range of scales, and the fractal dimension as a function of spatial frequency presents as a fractal signature. We found that the fractal signature was more successful at discriminating between these textures than either the global fractal dimension or other metrics such as the mean width and root-mean-square width of the spectral density plots. Different natural textures were also readily distinguishable using lacunarity plots, which explicitly characterize the average size and spatial organization of structural sub-units within an image. The fractal signatures of small regions of interest (32x32 pixels), computed in the frequency domain after corrections for imaging system noise and MTF, were able to characterize the texture of vertebral trabecular bone in CT images. Even small differences in texture due to acquisition slice thickness resulted in measurably different fractal signatures. These differences were also readily apparent in lacunarity plots, which indicated that a slice thickness of 1 mm or less is necessary if essential architectural information is not to be lost. Since lacunarity measures gap size and is not predicated on fractality, it may be particularly useful for characterizing the texture of trabecular bone.

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.

Lacunarity as a texture measure for SAR imagery

Abstract Lacunarity analysis is a simple technique for characterizing texture in binary images. Lacunarity quantifies deviation from translational invariance by describing the distribution of gaps within the image at multiple scales: the more lacunar an image, the more heterogeneous the spatial arrangement of gaps. For grey-level data, a series of binary images are formed through slicing the image histogram by quantiles. Characteristic decays of lacunarity as a function of window size permit scene object texture to be distinguished from speckle. Using a series of ERS-1 SAR images of the Brazilian Pantanal, we demonstrate how lacunarity functions can link image phenomenology with scene dynamics.

A technique for monitoring ecological disturbance in tallgrass prairie using seasonal NDVI trajectories and a discriminant function mixture model

Abstract Natural and anthropogenic disturbance in tallgrass prairie communities can induce changes in plant species composition, including shifts in the relative abundance of C3 and C4 lifeforms. The asynchronous seasonality in greenness exhibited by C3 and C4 species enables monitoring of their relative abundance using temporal trajectories of sensor-derived vegetation indices, such as NDVI. We use close-range measurements made over 22 experimental plots at the Konza Prairie Research Natural Area (KPRNA) to evaluate seasonal trajectories in NDVI as a function of C 3 C 4 ratio. The NDVI data were collected from each plot at approximately 10-day intervals throughout the 1995 growing season. Metrics for summarizing the temporal behavior of NDVI trajectories were derived from two transformations of the data: 1) a conventional approach plotting NDVI against day of year and 2) an alternative approach plotting normalized cumulative integrated NDVI against growing degree day. Discriminant function mixture models derived from each set of inetrics were used together with species composition data from the experimental plots to derive relative C 3 C 4 abundances. Results show that both methods can classify the majority of cases into their correct C 3 C 4 abundance category; however, the transformation using normalized integrated NDVI against growing degree day was a significantly better discriminator (p=0.0102). The techniques presented are effective for monitoring relative C 3 C 4 abundance in tallgrass prairie; however, more investigation is needed to assess their performance at different spatial resolutions and in different geographic settings.