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-prairie00431nas a2200133 4500008004100000245005100041210005100092300000900143490000800152653002200160100001700182700001900199856007900218 1997 eng d00aModelling impact of bison on tallgrass prairie0 aModelling impact of bison on tallgrass prairie a3 -90 v10010atallgrass prairie1 aNellis, M.D.1 aBriggs, J., M. uhttp://lter.konza.ksu.edu/content/modelling-impact-bison-tallgrass-prairie01697nas a2200181 4500008004100000245006700041210006700108300001100175490000800186520112600194100001901320700001601339700001701355700001801372700001701390700001701407856009101424 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://lter.konza.ksu.edu/content/spatial-and-temporal-patterns-vegetation-flint-hills02140nas a2200205 4500008004100000245007900041210006900120300001300189490000600202520153300208653001201741653001901753653000801772653002301780653002201803100001701825700001901842700001701861856005601878 1994 eng d00aExpansion of gallery forest on Konza Prairie Research Natural Area, Kansas0 aExpansion of gallery forest on Konza Prairie Research Natural Ar a117 -1250 v93 aTo determine the dynamics of the spatial extent of gallery forest on Konza Prairie Research Natural Area (KPRNA), aerial photographs taken over a 46 year time frame were digitized into an ARC-INFO Geographic Information System (GIS). A Global Positioning System (GPS) was used to collect ground control points to co-register the photographs for each year. Gallery forest areas for the three major drainage boundaries (Kings Creek, Shane Creek, and White Pasture) were analyzed to assess the uniformity of change in the landscape system. Results indicated that the total gallery forest area on KPRNA has increased in area from 157 ha in 1939 to over 241 ha in 1985. During this time, there was an increase in the total number of patches and a decrease in the mean size of forest patches. However, the rate of increase was not consistent over this time period, nor was it uniform from one drainage basin or stream order to another. Detailed spatial analysis of the forested area with a geomorphology and digital elevation model of Konza Prairie showed that in 1985, 58% of the forest was on alluvial/colluvial soil, yet only 15% of that soil type was forested. In addition, over 70% of the forest was on the 0–15% slope interval, but only 15–20% of that slope interval was forested. These results may be attributed to a variety of factors such as changing management practices (i.e., frequency of fires and herbicide spraying) and the temporal constraints on extent to which the gallery forest can expand across the landscape.10aburning10agallery forest10aGIS10ahistorical landuse10atallgrass prairie1 aKnight, C.L.1 aBriggs, J., M.1 aNellis, M.D. uhttp://link.springer.com/article/10.1007/BF0012437800433nas a2200121 4500008004100000245005900041210005900100300001300159100001700172700001400189700001900203856008900222 1994 eng d00aSpace shuttle photography for monitoring global change0 aSpace shuttle photography for monitoring global change a532 -5341 aNellis, M.D.1 aLulla, K.1 aBriggs, J., M. uhttp://lter.konza.ksu.edu/content/space-shuttle-photography-monitoring-global-change00552nas a2200145 4500008004100000245008600041210006900127300001300196490000600209653002400215100001700239700001900256700001900275856011200294 1992 eng d00aGeographic information systems for modeling bison impact on Konza Prairie, Kansas0 aGeographic information systems for modeling bison impact on Konz a618 -6230 v210aInformation Systems1 aNellis, M.D.1 aBathgate, J.D.1 aBriggs, J., M. uhttp://lter.konza.ksu.edu/content/geographic-information-systems-modeling-bison-impact-konza-prairie-kansas01463nas a2200157 4500008004100000245010400041210006900145300001500214490000700229520086900236653002401105100001701129700001901146700001701165856012301182 1992 eng d00aGrowth and transition: Remote sensing and geographic information systems at Kansas State University0 aGrowth and transition Remote sensing and geographic information  a1159 -11610 v583 a

Remote sensing and geographic information systems education and research at Kansas State University have grown tremendously in the past decade. Dramatic improvements in hardware and software have enhanced offerings in a wide range of spatial analysis (including remote sensing) courses. Success in extramural funding through such agencies as the U.S. Agency for International Development and the National Science Foundation have created a major research thrust in Kansas State University's Geography and Biology programs relative to the application of remote sensing and geographic information systems for natural resource assessment and landscape ecology. The State of Kansas initatives have also enhanced Kansas State University's role in providing a significant contribution in remote sensing/geographic information systems research and education

10aInformation Systems1 aNellis, M.D.1 aBriggs, J., M.1 aSeyler, H.L. uhttp://lter.konza.ksu.edu/content/growth-and-transition-remote-sensing-and-geographic-information-systems-kansas-state00530nas a2200121 4500008004100000245011800041210006900159300001100228490000700239100001700246700001900263856012600282 1992 eng d00aTransformed vegetation index for measuring spatial variation in drought impacted biomass on Konza Prairie, Kansas0 aTransformed vegetation index for measuring spatial variation in  a93 -990 v951 aNellis, M.D.1 aBriggs, J., M. uhttp://lter.konza.ksu.edu/content/transformed-vegetation-index-measuring-spatial-variation-drought-impacted-biomass-konza01474nas a2200145 4500008004100000245010600041210006900147300001300216490000700229520090300236653002201139100001901161700001701180856013101197 1991 eng d00aSeasonal variation of heterogeneity in tallgrass prairie: a quantitative measure using remote sensing0 aSeasonal variation of heterogeneity in tallgrass prairie a quant a407 -4110 v573 a

Remote sensing may be a tool to quantitatively measure the change in heterogeneity that takes place in a landscape over a growing season. Seven different SPOT satellite scenes of the Konza Prairie Research Natural Area were analyzed to assess the potential of using textural algorithms as a quantitative measure of seasonal variation in heterogeneity. Unburned watersheds usually have greater heterogeneity than annually burned watersheds. However, the greatest amount of heterogeneity as measured by textural analysis occurs in those area with a mixture of forest and upland tallgrass prairie. Results suggest that remote sensing textural algorithms, in combination with normalized vegetation difference indices, can provide insight regarding both temporal changes that occur seasonally and the influences of periodic spring fires and management practices on the tallgrass prairie ecosystem

10atallgrass prairie1 aBriggs, J., M.1 aNellis, M.D. uhttp://lter.konza.ksu.edu/content/seasonal-variation-heterogeneity-tallgrass-prairie-quantitative-measure-using-remote-sensing01591nas a2200145 4500008004100000245009100041210006900132300001200201490000600213520105300219653002201272100001701294700001901311856011501330 1989 eng d00aThe effects of spatial scale on Konza landscape classification using textural analysis0 aeffects of spatial scale on Konza landscape classification using a93 -1000 v23 a

Spatial scale is inherent in the definition of landscape hererogeneity and diversity. For example, a landscape may appear heterogenous at one scale but not quite homogeneous at another scale. In assessing the impact of burning and grazing on the Konza Prairie Research Natural Area (a tallgrass prairie), spatial scale is extremely important. Textural contrast algorithms were applied to various scales of remote sensing data and related to landscape units for assessment of heterogeneity under a variety of burning treatments. Acquired data sets included Landsat multispectral scanner (MSS), with 80 m resolution, Landsat thematic mapper (TM), with 30 m resolution, and high resolution density sliced aerial photography (with a 5 m resolution). Results suggest that heterogeneous areas of dense patchiness (e.g., unburned areas) must be analyzed at a finer scale than more homogeneous areas which are burned at least every four years. Key words: spatial scale, textural analysis, tallgrass prairie, remote sensing, landscape ecology, Kansas

10atallgrass prairie1 aNellis, M.D.1 aBriggs, J., M. uhttp://lter.konza.ksu.edu/content/effects-spatial-scale-konza-landscape-classification-using-textural-analysis01864nas a2200157 4500008004100000245009200041210006900133260004500202300001200247520126600259100001701525700001701542700001601559700001401575856011701589 1989 eng d00aIntergrating field experiences with remote sensing for understanding vegetation indices0 aIntergrating field experiences with remote sensing for understan aHong KongbGeocarto International Centre a93 -1003 a

Students of the geographic field research techniques course at Kansas State University were presented with the problem of integrating data collected from field experiences with Landsat MSS digital data to better understand the influence of physical and cultural factors on the condition of vegetation. As part of a National Science Foundation sponsored program, Konza Prairie Natural Area in the Kansas Flint Hills, has developed a diversity of rangeland management strategies based on watershed units. During the summer of 1987, students were asked to gather information at the Konza site on field conditions along transects for selected watersheds. Students noted dominant vegetation types, density of vegatitive cover, and relative vegetetive greenness. Landsat MSS digital data sets for a date similat to the field data were then analyzed by the students using a microcomputer-based digital image processing system. Mean brightness values for each MSS band by selected watersheds were calculated and entered into two algorithms that estimated soil brightness and green vegetation index. Students then compared field data with the derived soil brightness and green vegetation indices, and determined reasons for variation in indices between watersheds

1 aNellis, M.D.1 aNellis, M.D.1 aLougeay, R.1 aLulla, K. uhttp://lter.konza.ksu.edu/content/intergrating-field-experiences-remote-sensing-understanding-vegetation-indices02223nas a2200169 4500008004100000245010900041210006900150260004600219300001100265520155800276653002201834100001901856700001701875700001601892700002101908856012401929 1989 eng d00aLandsat thematic mapper digital data for predicting aboveground biomass in a tallgrass prairie ecosystem0 aLandsat thematic mapper digital data for predicting aboveground  aLincoln, NEbUniversity of Nebraska Press a53 -553 a

Landstat thematic mapper digital data was found to offer an excellent potential for regular monitoring of the tallgrass prairie ecosystem by providing estimates of aboveground biomass production. Data from several channels of a May thematic mapper scene were analyzed individually and in various combinations using stepwise regression in Statistical Analysis System (SAS). These procedures were used to determine the most appropriate multiple regression equation for estimating production of 1) total live aboveground biomass, 2) grasses, 3) forbs, 4) previous years dead, and 5) current years dead. Regression equations were based on satellite-derived estimates relative to ground level biomass values for watersheds on Konza Prairie Research Natural Area under a variety of burning treatments. Results suggest that multiple channel equations were most appropriate for measuring production of forbs and total live aboveground biomass. Channel one (0.45 to 0.52 æmeters) and channel four (0.76 to 0.90 æmeters) were applicable to estimate production of grass and levels of previous years litter, respectively. However, none of the channels were accurate in predicting current years dead. Further plans involve using thematic mapper data to estimate aboveground biomass over an entire growing season on Konza and exploring the potential of using satellite data to monitor grassland production across the Great Plains. Key Words: tallgrass prairie, remote sensing, aboveground biomass, Landstat, thematic mapper, monitoring, Konza Prairie, Kansas

10atallgrass prairie1 aBriggs, J., M.1 aNellis, M.D.1 aBragg, T.B.1 aStubbendieck, J. uhttp://lter.konza.ksu.edu/content/landsat-thematic-mapper-digital-data-predicting-aboveground-biomass-tallgrass-prairie01551nas a2200145 4500008004100000245012700041210006900168300001100237490000700248520096300255653002201218100001701240700001901257856012901276 1987 eng d00aMicro-based Landsat TM data processing for tallgrass prairie monitoring in the Konza Prairie Research Natural Area, Kansas0 aMicrobased Landsat TM data processing for tallgrass prairie moni a76 -800 v103 a

Despite the large amount of evidence supporting the important role that burning and frequency of burning have on grassland resources, information on the spatial extent of burning in the Flint Hills of Kansas has not been developed. This is primarily due to the massive data requirements needed to access burning on a regional scale. This study assesses the regional potential for monitoring burning practices in the Flint Hills of Kansas using satellite data. The specific objective of this study was to ascertain the utility of Landsat thematic mapper digital data for delineating the spatial characteristics of burning in the Konza Prairie Research Natural Area. The Konza Prairie is an area exceeding 8,000 acres owned by Nature Conservancy and leased to Kansas State University for long-term ecological research. The management objectives of the research area are based on the efforts of periodic fire and grazing on a tall grass prairie ecosystem

10atallgrass prairie1 aNellis, M.D.1 aBriggs, J., M. uhttp://lter.konza.ksu.edu/content/micro-based-landsat-tm-data-processing-tallgrass-prairie-monitoring-konza-prairie-research01224nas a2200145 4500008004100000245009200041210006900133300001300202490000700215520069100222653001400913653001900927100001700946856011500963 1986 eng d00aRemote sensing for monitoring rangeland management strategies in the Kansas Flint Hills0 aRemote sensing for monitoring rangeland management strategies in a370 -3750 v263 a

The Konza Prairie Research Natural Area represents an effort by the Nature Conservancy to preserve natural diversity in the Flint Hills of Kansas. The management objectives within the Konza Prairie are based on a comparison of the natural prairie ecosystem with manipulated systems. The effects of fire and grazing on the prairie ecosystem are of particular concern. Qualitative examiniations using color infrared photography and digital enhanced photography of study zones within the Konza Prairie yielded positive results for delineation of varying mangement strategies. The study illustrates the utility of using color infrared for regular monitoring of the Flint Hills Region

10arangeland10aremote sensing1 aNellis, M.D. uhttp://lter.konza.ksu.edu/content/remote-sensing-monitoring-rangeland-management-strategies-kansas-flint-hills