Allen Hope

Variability of the Seasonally Integrated Normalized Difference Vegetation Index Across the North Slope of Alaska in the 1990s

The interannual variability and trend of above-ground photosynthetic activity of Arctic tundra vegetation in the 1990s is examined for the north slope region of Alaska, based on the seasonally integrated normalized difference vegetation index (SINDVI) derived from local area coverage (LAC) National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data. Smaller SINDVI values occurred during the three years (1992-1994) following the volcanic eruption of Mt Pinatubo. Even after implementing corrections for this stratospheric aerosol effect and adjusting for changes in radiometric calibration coefficients, an apparent increasing trend of SINDVI in the 1990s is evident for the entire north slope. The most pronounced increase was observed for the foothills physiographical province.

The relationship between tussock tundra spectral reflectance properties and biomass and vegetation composition

Abstract Frequent cloud cover and logistical constraints hamper biophysical remote sensing studies in arctic locations, resulting in a general lack of information regarding relationships between biophysical quantities and the spectral reflectance of arctic vegetation communities. An experiment was conducted on the north slope of Alaska to characterize relationships between the spectral reflectance of three tussock tundra communities (moist tussock, dry heath and water track) and the above ground biomass and vegetation composition of each community. Hand-held radiometric and ground reference data were collected three times during the 1989 growing season. The normalized difference vegetation index (NDVI) was regressed on above ground photosynthetic and non-photosynthetic biomass quantities and individual blue, green red and near-infrared spectral reflectances and the NDVI were regressed on vegetation cover type fractions. Up to 51 per cent of the variance in the NDVI was explained by the amount of photosynt...

Interannual growth dynamics of vegetation in the Kuparuk River watershed, Alaska based on the Normalized Difference Vegetation Index

Interannual above-ground production patterns are characterized for three tundra ecosystems in the Kuparuk River watershed of Alaska using NOAA-AVHRR Normalized Difference Vegetation Index (NDVI) data. NDVI values integrated over each growing season (SINDVI) were used to represent seasonal production patterns between 1989 and 1996. Spatial differences in ecosystem production were expected to follow north-south climatic and soil gradients, while interannual differences in production were expected to vary with variations in seasonal precipitation and temperature. It was hypothesized that the increased vegetation growth in high latitudes between 1981 and 1991 previously reported would continue through the period of investigation for the study watershed. Zonal differences in vegetation production were confirmed but interannual variations did not covary with seasonal precipitation or temperature totals. A sharp reduction in the SINDVI in 1992 followed by a consistent increase up to 1996 led to a further hypothesis that the interannual variations in SINDVI were associated with variations in stratospheric optical depth. Using published stratospheric optical depth values derived from the SAGE and SAGE-II satellites, it is demonstrated that variations in these depths are likely the primary cause of SINDVI interannual variability.

PHYSIOLOGICAL MODELS FOR SCALING PLOT MEASUREMENTS OF CO2 FLUX ACROSS AN ARCTIC TUNDRA LANDSCAPE

Regional estimates of arctic ecosystem CO2 exchange are required because of the large soil carbon stocks located in arctic regions, the potentially large global-scale feedbacks associated with climate-change-induced alterations in arctic ecosystem C sequestration, and the substantial small-scale (1–10 m2) heterogeneity of arctic vegetation and hydrology. Because the majority of CO2 flux data for arctic ecosystems are derived from plot-scale studies, a scaling routine that can provide reliable estimates of regional CO2 flux is required. This study combined data collected from chamber measurements of CO2 exchange, meteorology, hydrology, and surface reflectance with simple physiological models to quantify the diurnal and seasonal dynamics of whole-ecosystem respiration (R), gross primary production (GPP), and net CO2 exchange (F) of wet- and moist-sedge tundra ecosystems of arctic Alaska. Diurnal fluctuations in R were expressed as exponential functions of air temperature, whereas diurnal fluctuations in GPP were described as hyperbolic functions of diurnal photosynthetic photon flux density (PPFD). Daily integrated rates of R were expressed as an exponential function of average daily water table depth and temperature, whereas daily fluctuations in GPP were described as a hyperbolic function of average daily PPFD and a sigmoidal function of the normalized difference vegetation index (NDVI) calculated from satellite imagery. These models described, on average, 75–97% of the variance in diurnal R and GPP, and 78–95% of the variance in total daily R and GPP. Model results suggest that diurnal F can be reliably predicted from meteorology (radiation and temperature), but over seasonal time scales, information on hydrology and phenology is required to constrain the response of GPP and R to variations in temperature and radiation. Using these physiological relationships and information about the spatial variance in surface features across the landscape, measurements of CO2 exchange in 0.5-m2 plots were extrapolated to the hectare scale. Compared to direct measurements of hectare-scale F made using eddy covariance, the scaled estimate of seasonally integrated F was within 20% of the observed value. With a minimum of input data, these models allowed plot measurements of arctic ecosystem CO2 exchange to be confidently scaled in space and time.

Post-fire recovery of leaf area index in California chaparral: a remote sensing-chronosequence approach

Fire is a major driver of land surface transformation in California Mediterranean-type shrublands (i.e. chaparral). The re-growth of leaves following fire impacts a wide variety of ecosystem processes and information on the post-fire recovery of leaf area index (LAI) is often required in eco-hydrologic modelling studies. A few studies have reported LAI values for chaparral, but none have tracked LAI dynamics over the entire post-fire recovery sequence. In this study we used a chronosequence approach with satellite imagery to determine the post-fire development sequence of LAI for chaparral shrublands in central California. Moreover, we explored how LAI varied with differences in annual antecedent precipitation conditions (APC) and physical site factors. LAI recovery following fire was most rapid during the first 15 years, after which it remained relatively constant with increasing stand age. For a given stand age, LAI varied nonlinearly with annual APC, while spatial variations in LAI were associated with differences in topographic aspect and landscape wetness potential. However, a better understanding of the nature and interaction of these controls on LAI is needed if realistic post-fire LAI trajectories (for historic, present and future periods) for eco-hydrological modelling studies in chaparral catchments are to be developed in the future.

Greenness trends of Arctic tundra vegetation in the 1990s: comparison of two NDVI data sets from NOAA AVHRR systems

The primary objective of this study was to compare the sensitivity of two different normalized difference vegetation index (NDVI) time series derived from Local Area Coverage (LAC) and Global Areal Coverage (GAC) data sets of the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) satellite system. This comparison was conducted in the context of analysing spatiotemporal patterns of Arctic tundra vegetation greenness change in the 1990s within the North Slope of Alaska. A second objective was to examine patterns of greenness change with respect to the distribution of vegetation association types. An 8 km spatial resolution NDVI series was produced by the Global Inventory Modeling and Mapping Studies (GIMMS) group at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center based on a GAC data set and corrected for stratospheric aerosol effects from the eruption of Mt Pinatubo. The LAC (1 km spatial resolution) NDVI time series was generated through recalibration and fine‐tuning of image registration of a twice‐monthly time series produced by the US Geological Survey, and was cross‐calibrated with the GIMMS data set to reduce stratospheric aerosol effects from the Mt Pinatubo eruption. While the general patterns of pixels exhibiting significant increase in seasonally integrated NDVI over the 1990s were similar from both data sets, many of the more localized areas of more rapidly increasing greenness (i.e. ‘hotspots’) between 1990 and 1999 were lost with the product from the GIMMS data set. The majority of the ‘hotspots’ of greenness increase within the North Slope region are located in the southern portions of the foothills physiographic province and within vegetation units composed primarily of prostrate or dwarf shrubs with a mixture of graminoid species. Notably fewer hotspots of greenness increase were detected in Arctic tundra areas of the Seward Peninsula and none in the Chukotka Peninsula of the Russian Far East, an area that had not experienced the same warming trend in the 1990s and preceding decades as the Alaskan Arctic.

Estimating CO2 exchange at two sites in Arctic tundra ecosystems during the growing season using a spectral vegetation index

Measurements of carbon fluxes in Arctic tundra landscapes are generally obtained through intensive field work and involve the use of chamber and/or micrometeorological tower techniques. However, findings in a variety of nonArctic ecosystems have demonstrated the potential of remote sensing-based techniques (particularly spectral vegetation indices) to provide estimates of CO2 exchange in a more timely and efficient manner. As the firststep towards modelling Arctic regional and circumpolar fluxes of CO2 using remotely sensed data, we investigated the relationships between plot-level fluxes of CO2 and a vegetation spectral reflectance index derived from hand-held radiometric data at two sites. These relationships were evaluated for variations in vegetation cover type and environmental factors using data collected during the short Arctic growing season. Overall, this study demonstrated a relationship between the Normalized Difference Vegetation Index (NDVI) and measurements of mean site gross photosynthesis ...

The relationship between surface temperature and a spectral vegetation index of a tallgrass prairie: effects of burning and other landscape controls

Abstract Sile-to-site variability in the relation between remotely-sensed surface temperatures (T 5) and the normalized difference spectral vegetation index (NDVI) of a tallgrass prairie was investigated. The primary objective was to determine whether the proportion of burnt/unburnt area within a sub-scene affected the T5-NDVI regression slope (SL), Regression analyses confirmed that burn treatments, particularly on steep slopes, were responsible for most of the observed variability in SL while soil moisture content and the forested areas also had a significant effect on SL.

Characterizing post-fire vegetation recovery of California chaparral using TM/ETM+ time-series data

A time series of normalized difference vegetation index (NDVI) data derived from 11 TM/ETM+ images was used to examine the recovery characteristics of chaparral vegetation in a small watershed near Santa Barbara, California following a fire event in 1985. The NDVI recovery trajectory was compared to a generalized recovery trajectory of leaf area index (LAI) for the same region, which was established using a chronosequence approach and TM/ETM+ data. Post‐fire NDVI recovery trajectories were derived for the entire catchment and for individual vegetation types. Post‐fire NDVI spatial patterns on each image date were compared to the pre‐fire pattern to determine the extent to which the pre‐fire pattern was re‐established, and the rate of this recovery. Results indicated that the post‐fire recovery trajectory for the catchment area average NDVI was similar to the previously established regional LAI trajectory based on a chronosequence approach. The NDVI recovery was disrupted by drought stress and attained pre‐fire levels approximately 10 years after the fire. Individual vegetation types did not exhibit different rates of recovery and the recovery trajectories were only distinguished by the maximum post‐fire NDVI observed after 10 years. The post‐fire NDVI spatial pattern also showed a systematic return to pre‐fire conditions, but exhibited a more substantial disruption due to drought stress than was the case for the average NDVI recovery trajectory.

Relationship between AVHRR surface temperature and NDVI in Arctic tundra ecosystems

A negative, linear relationship between thermal emissions and a spectral vegetation index has been demonstrated for numerous mid‐latitude ecosystems. In this study, it is hypothesized that the relationship between surface temperature and the normalized difference vegetation index (NDVI) will be linear, but positive in Arctic tundra ecosystems due to the contrast between warm vegetation and the cold soil/moss background. This hypothesis is tested using Advanced Very High Resolution Radiometer (AVHRR) data collected over the North Slope of Alaska on three days during the summer of 1999. Results of the study generally provide support for this hypothesis. However, a consistent relationship observed across two contrasting physiographic provinces on one study day was shown to change the following day and could not be readily explained by differences in satellite zenith angle or observed air temperature. Surface temperatures are shown to respond directly to spatial and temporal variations in air temperature.