00741nas a2200217 4500008004100000245007500041210006900116260003800185300001300223653002200236100001900258700001700277700001700294700001800311700001100329700002000340700001900360700001900379700002400398856010100422 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://lter.konza.ksu.edu/content/landscape-perspective-patterns-and-processes-tallgrass-prairie01458nas a2200169 4500008004100000245012000041210006900161300001500230520080800245653002201053100001101075700001901086700002001105700002001125700001801145856012501163 1996 eng d00aDetecting spatial and temporal patterns of aboveground production in a tallgrass prairie using remotely-sensed data0 aDetecting spatial and temporal patterns of aboveground productio a2361 -23653 aSpatial and temporal patterns of aboveground production in a tallgrass prairie ecosystem constitute one of the important spatial components associated with ecological processes and biophysical resources (e.g., water and nutrients). This study addresses the effects of disturbance, topography, and climate on the spatial and temporal patterns of North American tallgrass prairie at a landscape level by using high resolution satellite data. Spatial heterogeneity (SH) derived from the satellite data was related to the impacts of the disturbance of fire and grazing, topographical gradient, and amount of precipitation during the growing season. The result suggests that ecological processes and biophysical resources can be quantified with high resolution satellite data for tallgrass prairie management10atallgrass prairie1 aSu, H.1 aBriggs, J., M.1 aKnapp, Alan, K.1 aBlair, John, M.1 aKrummel, J.R. uhttp://lter.konza.ksu.edu/content/detecting-spatial-and-temporal-patterns-aboveground-production-tallgrass-prairie-using00515nas a2200121 4500008004100000245009900041210006900140300001300209100001800222700001100240700001900251856012300270 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://lter.konza.ksu.edu/content/observing-spatial-structure-flint-hills-using-avhrr-biweekly-composites-maximum-ndvi00620nas a2200157 4500008004100000245009700041210006900138260003500207300001200242100001900254700001100273700001900284700001600303700001900319856012400338 1994 eng d00aDevelopment and refinement of the Konza Prairie LTER Research Information Management Program0 aDevelopment and refinement of the Konza Prairie LTER Research In aLondonbTaylor and Francis Ltd a87 -1001 aBriggs, J., M.1 aSu, H.1 aMichener, W.K.1 aBrunt, J.W.1 aStafford, S.G. uhttp://lter.konza.ksu.edu/content/development-and-refinement-konza-prairie-lter-research-information-management-program00634nas a2200145 4500008004100000245011200041210006900153260005700222300001300279653002200292100001100314700002000325700001900345856012400364 1994 eng d00aEffects of topography and fire on spatial and temporal distribution of soil moisture in a tallgrass prairie0 aEffects of topography and fire on spatial and temporal distribut bUnited States Department of Interior Bureau of Mines a154 -16210atallgrass prairie1 aSu, H.1 aKnapp, Alan, K.1 aBriggs, J., M. uhttp://lter.konza.ksu.edu/content/effects-topography-and-fire-spatial-and-temporal-distribution-soil-moisture-tallgrass01634nas a2200133 4500008004100000245008400041210006900125300001500194490000700209520114700216100001801363700001101381856010801392 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://lter.konza.ksu.edu/content/using-landscape-trajectories-assess-effects-radiometric-rectification01573nas a2200169 4500008004100000245007300041210006900114300001100183490000700194520102100201653002201222100001101244700001901255700001701274700001301291856009901304 1990 eng d00aSeparability of soils in a tallgrass prairie using SPOT and DEM Data0 aSeparability of soils in a tallgrass prairie using SPOT and DEM a10 -170 v323 a
A sufficient transformation technique is important in using computer-aided soil pattern recognition to interpret soils in soil surveys. This study was conducted using the Systeme Probatoire d'Observation de la Terre (SPOT) satellite images and Digital Elevation Model (DEM) data to seperate five major sils in a tallgrass prairie near Manhattan, Kansas. The high resolution SPOT satellite im;ages were integrated with DEM data. the soils were sampled and classified using conventional soil survey procedures to extract the important soil features from SPOT and DEM data. A pairwise transformed divergence analysis was also conducted to evaluate the several aanonical variables in order to sufficiently separate the soils. Consequently, two canonical variables derived from training samples were selected. Our results suggest that canonically transformed data were superior to combined SPOT and DEM data. High resolution SPOT images and DEM data can be used to aid secondd- order soil surveys in grasslands
10atallgrass prairie1 aSu, H.1 aKanemasu, E.T.1 aRansom, M.D.1 aYang, S. uhttp://lter.konza.ksu.edu/content/separability-soils-tallgrass-prairie-using-spot-and-dem-data01893nas a2200145 4500008004100000245009200041210006900133300001500202490000700217520135500224100001101579700001701590700001901607856012101626 1989 eng d00aDetecting soil information on a native prairie using Landsat TM and SPOT satellite data0 aDetecting soil information on a native prairie using Landsat TM a1479 -14830 v533 aComputer pattern recoginiton techniques were used to discriminate soil information from the Landsat Thematic Mapper (TM) and the French Systeme Probatoire d'Observation de le Terre (SPOT) satellite data on a native prairie near Manahttan, KS. Digital Elevation Model (DEM) data were merged to Landsat TM and SPOT data to delineate soil mapping units within the study area. Soil mapping units from a conventional soil survey were compared with a classified soil spectral map obtained from Landsat TM or SPOT, and DEM derived elevation, slope, and aspect data, using an overall accuracy assessment. The overall accuracy of soil spectral classesfrom TM and SPOT data were merged. A higher average accuracy for soil mapping units was obtained with a low frequency filtering transformation of the data. For Landsat TM data, Band 5 (middle infrared, 1.55-1.75 æm) was most useful for soil information extraction, and a higher overall accuracy was obtained in the dormant season compared with the accuracy in the growing season. The overall accuracy (about 57%) from SPOT data was slightly higher than the accuracy (about 53%) from Landsat TM at a similar wavelength range. Our results indicate that high resolution Landsat TM and SPOT satellite data can be used to aid second-order soil surveys in areas where the dominant land use is rangeland
1 aSu, H.1 aRansom, M.D.1 aKanemasu, E.T. uhttp://lter.konza.ksu.edu/content/detecting-soil-information-native-prairie-using-landsat-tm-and-spot-satellite-data00516nas a2200121 4500008004100000245008900041210006900130260004300199300001100242490001400253100001100267856011600278 1988 eng d00aDetecting soil information on the Konza Prairie using high resolution satellite data0 aDetecting soil information on the Konza Prairie using high resol aManhattan, KSbKansas State University a1 -1090 vMS Thesis1 aSu, H. uhttp://lter.konza.ksu.edu/content/detecting-soil-information-konza-prairie-using-high-resolution-satellite-data