01072nas a2200133   4500008004100000245005700041210005700098260002500155300001200180520064200192100001800834700001700852856006900869       2013                        eng d00aPhenologies of North American Grasslands and Grasses0 aPhenologies of North American Grasslands and Grasses  bSpringer Netherlands  a197-2103 a<p>Inquiry into the phenologies of grasslands and grasses in North America has progressed substantially in the past decade. Four themes of the recent phenological research are surveyed: (1) the role of exotic and invasive species in affecting grasslands phenology; (2) the role of water and belowground dynamics on phenologies; (3) how experimental manipulations of grasslands have affected constitutive phenologies; and (4) advances in the remote sensing of grasslands. The phyllochron concept used in ontogenetic studies of grass species is discussed in light of grasslands phenology and its link between daylength and thermal time</p>1 aHenebry, G.M.1 aSchwartz, MD  uhttps://link.springer.com/chapter/10.1007%2F978-94-007-6925-0_1101659nas a2200241   4500008004100000245011100041210006900152300001300221490000600234520090700240100002001147700002101167700002001188700001701208700001701225700001901242700001701261700002001278700001801298700001601316700001401332856007101346       2009                        eng d00aTracking the rhythm of the seasons in the face of global change: phenological research in the 21st century0 aTracking the rhythm of the seasons in the face of global change   a253 -2600 v73 a<p>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&ndash;phenology relationships, and making land-management decisions.</p>1 aMorisette, J.T.1 aRichardson, A.D.1 aKnapp, Alan, K.1 aFisher, J.I.1 aGraham, E.A.1 aAbatzoglou, J.1 aWilson, B.E.1 aBreshears, D.D.1 aHenebry, G.M.1 aHanes, J.M.1 aLiang, L.  uhttps://esajournals.onlinelibrary.wiley.com/doi/abs/10.1890/07021702752nas a2200145   4500008004100000245012200041210006900163300001300232490000700245520218800252100001702440700001202457700001802469856011902487       2004                        eng d00aThe effect of solar zenith angle and sensor view angle on observed patterns of spatial structure in tallgrass prairie0 aeffect of solar zenith angle and sensor view angle on observed p  a154 -1650 v423 aWhile it has long been recognized that the anisotropic reflectance properties of a natural surface affect the intensity and spectral distribution of radiance received by a remote sensing instrument, the effects of canopy reflectance geometry on the observed spatial structure of canopy reflectance have not adequately been evaluated. In this paper, near-surface spectrometers were used as part of two experiments to evaluate the systematic variations in the sun-target-sensor geometry on semivariogram metrics (range, sill+nugget variance) summarizing the spatial structure observed in a tallgrass prairie canopy. In the first experiment, reflectance measurements and normalized difference vegetation index (NDVI) values were collected at five sensor viewing angles (-50°, -25°, 0°, 25°, and 50°) from six measurement grids representing three burn treatments and two slope/aspect situations. In the second experiment, data were collected at 2-h intervals, beginning at ≈0800 LST from the same grids with the radiometer at nadir, allowing the spatial structure of reflectance and NDVI to be observed under naturally changing illumination. Results of the geostatistical analysis show that both the range and sill+nugget variance values change with viewing angle. These effects were consistent across all treatments and slope/aspect combinations. However, when viewed from nadir, the sill+nugget variance values of the canopy changed with solar illumination angle and the range values remained nearly constant. These relationships were also observed across all treatments and slope/aspect combinations. The results suggest that sill+nugget values for the same surface may not be directly comparable if not acquired under very similar view angle and illumination conditions. Range values are comparable if the nadir view is used, but not under off-nadir viewing conditions. The implications of these findings point to the need for caution in interpreting spatial structure derived from close-range radiometry or from satellite/aircraft instruments with cross-track or off-nadir pointing capabilities, and in the comparison of images acquired under varying illumination conditions.1 aGoodin, D.G.1 aGao, J.1 aHenebry, G.M.  uhttp://knz.lternet.edu/content/effect-solar-zenith-angle-and-sensor-view-angle-observed-patterns-spatial-structure01097nas a2200181   4500008004100000245005000041210005000091260002100141300001300162520055300175653001300728653001700741653002200758653003200780100001800812700001900830856006600849       2003                        eng d00aGrasslands of the North American Great Plains0 aGrasslands of the North American Great Plains  aNew YorkbKluwer  a157 -1743 aThe study of appearances of growth, development, and senescence in grassland communities is not an enterprise commonly pursued. Phenology of mid-latitude grasslands is, nevertheless, too large and diverse a collection of phenomena to cover within a single chapter, as it ought to embrace at once the vast Kazakh steppe, the chalk grasslands of southern England, and myriad other grassy landscapes. Thus, the view here shall be on the grasslands of the North American Great Plains, with particular reference to the tallgrass prairie as a type model.10aAnthesis10aGreat Plains10atallgrass prairie10atransient maxima hypothesis1 aHenebry, G.M.1 aSchwartz, M.D.  uhttp://link.springer.com/chapter/10.1007/978-94-007-0632-3_1101720nas a2200133   4500008004100000245011000041210006900151300001500220490000700235520118100242100001701423700001801440856012801458       2002                        eng d00aThe effect of rescaling on fine spatial resolution NDVI data: a test using multi-resolution aircraft data0 aeffect of rescaling on fine spatial resolution NDVI data a test   a3865 -38710 v233 aSimulation studies have suggested that pixel aggregation can rescale accurately both Normal Difference Vegetation Index (NDVI) data and fields of biogeophysical values inferred from NDVI. There is, however, a dearth of empirical tests of this hypothesis. This study uses nested NDVI observations acquired from an aircraft-mounted digital camera to assess the spatial properties of measured and rescaled image data. Imagery was collected at four altitudes, yielding spatial resolutions varying from 0.625 m to 3.125 m. The common area from these four images was extracted. The finest spatial resolution image was then rescaled by aggregating pixels to synthesize three rescaled images matching the spatial resolution of the measured images. The spatial properties of these measured and rescaled images were compared, using metrics derived from the variogram. Results demonstrate that the spatial patterns of rescaled data do not correspond to those of the directly observed data. The results suggest that rescaling by averaging blocks of pixels may not adequately preserve spatial properties of very high spatial resolution images and consequent inferred biogeophysical fields.1 aGoodin, D.G.1 aHenebry, G.M.  uhttp://knz.lternet.edu/content/effect-rescaling-fine-spatial-resolution-ndvi-data-test-using-multi-resolution-aircraft-data00738nas a2200217   4500008004100000245007500041210006900116260003800185300001300223653002200236100001900258700001700277700001700294700001800311700001100329700002000340700001900360700001900379700002400398856009800422       1998                        eng d00aA landscape perspective of patterns and processes in tallgrass prairie0 alandscape perspective of patterns and processes in tallgrass pra  aNew YorkbOxford University Press  a265 -27910atallgrass prairie1 aBriggs, J., M.1 aNellis, M.D.1 aTurner, C.L.1 aHenebry, G.M.1 aSu, H.1 aKnapp, Alan, K.1 aBriggs, J., M.1 aHartnett, D.C.1 aCollins, Scott., L.  uhttp://knz.lternet.edu/content/landscape-perspective-patterns-and-processes-tallgrass-prairie01675nas a2200145   4500008004100000245011300041210006900154300001500223490000700238520110000245653002201345100001701367700001801384856012701402       1998                        eng d00aSeasonality of finely-resolved spatial structure of NDVI and its component reflectances in tallgrass prairie0 aSeasonality of finelyresolved spatial structure of NDVI and its   a3213 -32200 v193 aGrass canopies exhibit distinct seasonality in reflectance and spatial patterns in reflectance result from landscape heterogeneity. To investigate the spatial structure of NDVI and its two component reflectances at fine resolution in burned and unburned tallgrass prairie, we use range and relative nugget effect two indicators of spatial structure derived from semivariograms. Results show that spatial dependence of the three spectral measures are similar in the early season when the litter layer has been removed by burning and again in the later season when the canopy is senescent. However, as the canopy develops, the spatial dependence of NDVI deviates from that of its component reflectances. In the unburned canopy, red reflectance appears to strongly influence the spatial pattern of NDVI. In the burned canopy, neither component reflectance strictly determines the spatial pattern of NDVI. Maximum divergence in spatial pattern between NDVI and its components coincides with minimum available moisture, suggesting a relation between moisture stress and spatio-spectral heterogeneity.10atallgrass prairie1 aGoodin, D.G.1 aHenebry, G.M.  uhttp://knz.lternet.edu/content/seasonality-finely-resolved-spatial-structure-ndvi-and-its-component-reflectances-tallgrass00446nas a2200109   4500008004100000245009800041210006900139260003600208300001300244100001800257856006100275       1997                        eng d00aAdvantages of principal components analysis for land cover segmentation from SAR image series0 aAdvantages of principal components analysis for land cover segme  aNoordwijk, The NetherlandsbESA  a175 -1781 aHenebry, G.M.  uhttp://earth.esa.int/symposia/papers/henebry3/index.html01694nas a2200181   4500008004100000245006700041210006700108300001100175490000800186520112600194100001901320700001601339700001701355700001801372700001701390700001701407856008801424       1997                        eng d00aSpatial and temporal patterns of vegetation in the Flint Hills0 aSpatial and temporal patterns of vegetation in the Flint Hills  a10 -200 v1003 aIn tallgrass prairie, complex interactions among multiple limiting resources in combination with a variety of land use practices can lead to a heterogeneous landscape. Remote-sensing data (AVHRR) were coupled with abiotic factors to explore spatial and temporal vegetation patterns of the Flint Hills in Kansas and Oklahoma. This information should enable the detection of both natural (e.g., interannual variation in precipitation and temperature) and anthropogenic (e.g., climate change, over-grazing, land-use practices) stresses on this grassland ecosystem. Shifts in the spatial and temporal patterns of vegetation (as measured from NDVI by AVHRR) have been correlated with meteorological data (from 117 weather stations) to identify key abiotic variables that determined vegetation patterns across this region. In 4 years, the combination of annual precipitation and growing degree days was useful to detect spatial and temporal vegetation patterns of the Flint Hills. However, it is imperative that land-use patterns are known in order to assess adequately spatial and temporal patterns of vegetation in this area.1 aBriggs, J., M.1 aRieck, D.R.1 aTurner, C.L.1 aHenebry, G.M.1 aGoodin, D.G.1 aNellis, M.D.  uhttp://knz.lternet.edu/content/spatial-and-temporal-patterns-vegetation-flint-hills02320nas a2200145   4500008004100000245015000041210006900191300001300260490000700273520172000280653002202000100001702022700001802039856011702057       1997                        eng d00aA technique for monitoring ecological disturbance in tallgrass prairie using seasonal NDVI trajectories and a discriminant function mixture model0 atechnique for monitoring ecological disturbance in tallgrass pra  a270 -2780 v613 aNatural 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 Full-size image (<1 K) 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 Full-size image (<1 K) abundances. Results show that both methods can classify the majority of cases into their correct Full-size image (<1 K) 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 Full-size image (<1 K) abundance in tallgrass prairie; however, more investigation is needed to assess their performance at different spatial resolutions and in different geographic settings.10atallgrass prairie1 aGoodin, D.G.1 aHenebry, G.M.  uhttp://knz.lternet.edu/content/technique-monitoring-ecological-disturbance-tallgrass-prairie-using-seasonal-ndvi01627nas a2200181   4500008004100000245006000041210005600101300001100157490000700168520107900175653002101254653001601275653002301291653001401314100001801328700001801346856008101364       1996                        eng d00aThe effect of density dependence on community structure0 aeffect of density dependence on community structure  a33 -420 v933 aThe effect of population density controls on the relationship between stability and species richness was explored by constructing Monte Carlo community models with density dependent population controls. Simulated communities with more initial species exhibit proportionally fewer extinctions. This form of stability is greatest at low interaction strengths and intermediate connectivities. Stability appeared to be related to the fact that species with more interactions were less likely to be driven to extinction by negative interactions. Species with mutualistic (+ / +) and commensal (0/ +) interactions survive at greater than expected proportions, while those with relatively more competitive (− / −) and amensal (0 / −) interactions tend toward extinction. Incorporation of density dependent population control thus leads to features that previous community models have not exhibited. It is suggested that communities with many of the populations controlled by abiotic factors in a density dependent fashion should exhibit increased stability with more species.10aCommunity matrix10aCompetition10adensity dependence10amutualism1 aDodds, W., K.1 aHenebry, G.M.  uhttp://knz.lternet.edu/content/effect-density-dependence-community-structure02129nas a2200193   4500008004100000245005700041210005700098300001300155490000700168520154600175653002201721100001801743700002001761700001801781700002001799700001801819700001501837856008301852       1996                        eng d00aNitrogen transport from tallgrass prairie watersheds0 aNitrogen transport from tallgrass prairie watersheds  a973 -9810 v253 aDischarge and N content of surface water flowing from four Karst watersheds on Konza Prairie Research Natural Area, Kansas, managed with different burn frequencies, were monitored from 1986 to 1992. The goal was to establish the influence of natural processes (climate, fire, and bison grazing) on N transport and concentration in streams. Streams were characterized by variable flow, under conditions that included an extreme flood and a drought during which all channels were dry for over a year. The estimated groundwater/stream water discharge ratio varied between 0.15 to 6.41. Annual N transport by streams, averaged across all watersheds and years, was 0.16 kg N ha−1 yr−1. Annual N transport per unit area also increased as the watershed area increased and as precipitation increased. Total annual transport of N from the prairie via streams ranged from 0.01 to 6.0% of the N input from precipitation. Nitrate and total N concentrations in surface water decreased (P < 0.001, r values ranged from 0.14–0.26) as length of time since last fire increased. Increased watershed area was correlated negatively (P < 0.0001) to stream water concentrations of NO−3N and total N (r values = −0.43 and −0.20, respectively). Low N concentration is typical of these streams, with NH+4-N concentrations below 1.0 µg L−1, NO−3-N ranging from below 1.4 to 392 µg L−1, and total N from 3.0 to 714 µg L−1. These data provide an important baseline for evaluating N transport and stream water quality from unfertilized grasslands.10atallgrass prairie1 aDodds, W., K.1 aBlair, John, M.1 aHenebry, G.M.1 aKoelliker, J.K.1 aRamundo, R.A.1 aTate, C.M.  uhttp://knz.lternet.edu/content/nitrogen-transport-tallgrass-prairie-watersheds01754nas a2200145   4500008004100000245012100041210006900162260002500231300001300256520116100269653002201430100001701452700001801469856012101487       1996                        eng d00aSeasonal NDVI trajectories in response to disturbance: toward a spectral-temporal mixing model for tallgrass prairie0 aSeasonal NDVI trajectories in response to disturbance toward a s  aPiscataway, NJbIEEE  a215 -2173 aNatural and anthropogenic disturbance in tallgrass prairie communities can induce changes in plant species composition, including shifts in abundance of C3 vs. C4 lifeforms. The asynchronous seasonalities in greenness that C3 and C4 species exhibit should enable monitoring of their relative abundance using sensor derived vegetation indices, such as NDVI. The authors used close-range measurements made over 22 experimental. Plots at the Konza Prairie Research Natural Area (KPRNA) to evaluate seasonal trajectories in canopy greenness as a function of C3/C4 ratio. NDVI data were collected from each plot at approximately ten day intervals throughout the 1995 growing season. Temporal trajectories were used to develop discriminant functions to model relative C3/C4 abundances. The discriminant model yielded values of Kendall's τb and Cohen's κ statistic of 0.794 and 0.781, indicating strong agreement between classes and an overall classification significantly better than random assignment. Results suggest the possibility of applying spectral-temporal mixture models derived from close-range sensing to larger scale monitoring of tallgrass prairie10atallgrass prairie1 aGoodin, D.G.1 aHenebry, G.M.  uhttp://knz.lternet.edu/content/seasonal-ndvi-trajectories-response-disturbance-toward-spectral-temporal-mixing-model02052nas a2200145   4500008004100000245012100041210006900162260002500231300001300256520154600269653002201815100001801837700002001855856003101875       1996                        eng d00aSpatio-temporal dynamics of soil moisture in tallgrass prairie using ERS-1 SAR image time series: first year results0 aSpatiotemporal dynamics of soil moisture in tallgrass prairie us  aPiscataway, NJbIEEE  a166 -1683 aRadar remote sensing of soil moisture has been demonstrated in croplands and rangelands. The authors are interested in synoptic estimates of soil moisture to aid long term ecological investigation of tallgrass prairie and have initiated a three year study to develop this technology. The authors' study area, Konza Prairie Research Natural Area (KPRNA), exhibits a variety of factors to complicate soil moisture estimation via synthetic aperture radar (SAR) including significant terrain variation, heterogeneous soil types and depths, and watershed-level experimental treatments including prescribed burning, grazing by large ungulates, and water supplementation. Several indicators of the moisture status of soil-water-vegetation continuum were gathered during the 1995 growing season including soil moisture via neutron probe and TDR, xylem pressure,potential values, and aboveground biomass (wet and dry weights). Due to orbital track overlap, eleven ERS-1 SAR images of KPRNA were acquired from mid-April to early October. Spring through mid-summer were unusually cool and wet, thereby delaying phenological development and attenuating the water stress typical of the later growing season. Backscattering values show some consistent agreement with ground data. Watersheds burned in April showed markedly higher backscattering at the end of the growing season due to a lack of litter layer from previous seasons' growth. Grazed units that were also burned showed the highest backscattering caused by no litter layer and more exposed soil10atallgrass prairie1 aHenebry, G.M.1 aKnapp, Alan, K.  u10.1109/IGARSS.1996.51627900512nas a2200121   4500008004100000245009900041210006900140300001300209100001800222700001100240700001900251856012000270       1995                        eng d00aObserving spatial structure in the Flint Hills using AVHRR biweekly composites of maximum NDVI0 aObserving spatial structure in the Flint Hills using AVHRR biwee  a143 -1511 aHenebry, G.M.1 aSu, H.1 aHartnett, D.C.  uhttp://knz.lternet.edu/content/observing-spatial-structure-flint-hills-using-avhrr-biweekly-composites-maximum-ndvi02085nas a2200193   4500008004100000245010300041210006900144300001100213490000700224520141600231653001601647653001801663653001401681653001301695653002501708100001801733700001801751856012201769       1995                        eng d00aSimulation of responses of community structure to species interactions driven by phenotypic change0 aSimulation of responses of community structure to species intera  a85 -940 v793 aWe developed a community model which considers the effect of phenotype on species interaction in order to elucidate general relationships between phenotype, species interactions and community structure. The model consists of community matrices with density-dependent species interactions and a limit on the total population of each species. A general natural pattern forms the basis of the model: organisms which are most similar compete, those which are less similar may enter into a predator-prey relationship, and those which are most different may be mutualistic. In this model, contrary to some previously published studies, increasing the number of species increased stability of community matrices. The rate of change in phenotype was varied to assess the response of community characteristics. At the highest rate of random phenotypic change there were the most species; the minimum number of species was found at intermediate rates of change. The models were run in the absence of predation, competition, or mutualism. The absence of mutualism led to more extinction. The absence of competition caused communities to quickly reach a population ceiling. Results suggest that it is extremely important to understand the relative occurrence of mutualism and competition in natural communities. Furthermore, the rate of phenotypic change could be an important but subtle determinant of community structure.10aCompetition10ainterspecific10amutualism10anetworks10aSpecies interactions1 aDodds, W., K.1 aHenebry, G.M.  uhttp://knz.lternet.edu/content/simulation-responses-community-structure-species-interactions-driven-phenotypic-change01560nas a2200169   4500008004100000245006300041210006300104300001100167490000700178520100800185653002001193653001901213653002101232653002501253100001801278856009401296       1995                        eng d00aSpatial model error analysis using autocorrelation indices0 aSpatial model error analysis using autocorrelation indices  a75 -910 v823 aNo standard techniques yet exist for assessing the predictive performance of models that simulate spatially-explicit processes. Spatial simulation models generate autocorrelation patterns that are a critical aspect of model prediction. These patterns can be quantified using spatial (Moran's I) and spatio-temporal (Griffith's STI) autocorrelation indices. A method of error analysis for spatial simulation models is demonstrated using autocorrelation indices to distinguish among the effects of parameter uncertainty on a stochastic spatial simulation of seed dispersal. The resulting autocorrelation patterns of state variables display a range of nonlinear, counterintuitive effects. In contrast to techniques proposed for spatial model error analysis (contagion, spatial predictability, adjacency, multiple resolution procedure, fractal dimension, interface), autocorrelation indices can be used with interval-scaled data and have well-defined sampling distributions that enable significance testing.10aAutocorrelation10aError analysis10aSpatial patterns10aUncertainty analysis1 aHenebry, G.M.  uhttp://knz.lternet.edu/content/spatial-model-error-analysis-using-autocorrelation-indices01233nas a2200121   4500008004100000245009300041210006900134300001300203490000700216520075700223100001800980856011300998       1993                        eng d00aDetecting change in grasslands using measures of spatial dependence with Landsat TM data0 aDetecting change in grasslands using measures of spatial depende  a223 -2340 v463 aSpatial 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.1 aHenebry, G.M.  uhttp://knz.lternet.edu/content/detecting-change-grasslands-using-measures-spatial-dependence-landsat-tm-data01631nas a2200133   4500008004100000245008400041210006900125300001500194490000700209520114700216100001801363700001101381856010501392       1993                        eng d00aUsing landscape trajectories to assess the effects of radiometric rectification0 aUsing landscape trajectories to assess the effects of radiometri  a2417 -24230 v143 aWe demonstrate a simple procedure, landscape trajectories, for quantifying the effects of radiometric correction on image characteristics. Eleven Landsat Thematic Mapper (TM) images of tallgrass prairie spanning 1987 and 1988 were radiometrically rectified to a common date using a ‘pseudoinvariant features in real space’ method (Schott et ai 1988). The Normalized Difference Vegetation Index (NDVI) was calculated for each raw and corrected image. Four watersheds within the study area were subset and the images were decomposed into spectral intensity (SI), spatial eterogeneity (SH), and spatial dependence (SD) components. Time-ordered vectors in SI-SH-SD space described landscape trajectories. Vector lengths along trajectories were calculated and integrated. Differences between measures on raw and corrected images may be attributable to climatic forcings, time of season, time since fire, and watershed topography. The correction procedure changed SI most and SD least. While the rectification procedure was linear with respect to the entire scene, specific watersheds exhibited nonlinear responses to radiometric rectification.1 aHenebry, G.M.1 aSu, H.  uhttp://knz.lternet.edu/content/using-landscape-trajectories-assess-effects-radiometric-rectification