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Using Local Knowledge And Remote Sensing To Map Known And Potential Prairie-Chicken Distribution In Kansas, Michael E. Houts, Randy D. Rodgers, Roger D. Applegate, William H. Busby
Using Local Knowledge And Remote Sensing To Map Known And Potential Prairie-Chicken Distribution In Kansas, Michael E. Houts, Randy D. Rodgers, Roger D. Applegate, William H. Busby
The Prairie Naturalist
The greater prairie-chicken (Tympanuchus cupido) and lesser prairie-chicken (Tympanuchus pallidicinctus) have experienced considerable fluctuations in their range and distribution over time. Having current range maps would help wildlife managers and policy makers with decisions regarding prairie-chicken habitat. To create an updated and accurate map of the Kansas prairie-chicken range, a two-pronged approach was implemented. First, a map of potential habitat was created by using known habitat preferences and avoidance factors. Second, a preliminary map showing the distribution of greater and lesser prairie-chickens was created and mailed to regional experts for comments and edits. The returned edits …
Sensor Ranging Technique For Determining Corn Plant Population, Joe D. Luck, Santosh Pitla, Scott A. Shearer
Sensor Ranging Technique For Determining Corn Plant Population, Joe D. Luck, Santosh Pitla, Scott A. Shearer
Department of Biological Systems Engineering: Conference Presentations and White Papers
Mapping of corn plant population can provide useful information for making field management decisions. This research focused on using low cost infra-red sensors to count plants. The voltage output data from the sensors were processed using an algorithm developed to extract plant populations. Preliminary investigations were conducted using sensors mounted on a stationary track for laboratory testing and on a row crop tractor for field testing. Repeated measurements were taken on a manually counted corn row. Visual inspection of the data from the field test indicated the potential to identify corn stalks based on approximate physical widths of the stalks. …
Synoptic Monitoring Of Gross Primary Productivity Of Maize Using Landsat Data, Anatoly A. Gitelson, Andrés Viña, Jeffrey G. Masek, Shashi Verma, Andrew E. Suyker
Synoptic Monitoring Of Gross Primary Productivity Of Maize Using Landsat Data, Anatoly A. Gitelson, Andrés Viña, Jeffrey G. Masek, Shashi Verma, Andrew E. Suyker
School of Natural Resources: Faculty Publications
There is a growing interest in monitoring the gross primary productivity (GPP) of crops due mostly to their carbon sequestration potential. Both within- and between-field variability are important components of crop GPP monitoring, particularly for the estimation of carbon budgets. In this letter, we present a new technique for daytime GPP estimation in maize based on the close and consistent relationship between GPP and crop chlorophyll content, and entirely on remotely sensed data. A recently proposed chlorophyll index (CI), which involves green and near-infrared spectral bands, was used to retrieve daytime GPP from Landsat Enhanced Thematic Mapper Plus (ETM+) data. …
Multi-Angle Remote Sensing Of Forest Light Use Efficiency By Observing Pri Variation With Canopy Shadow Fraction, Forrest G. Hall, Thomas Hilker, Nicholas C. Coops, Alexei Lyapustin, Karl F. Huemmrich, Elizabeth M. Middleton, Hank Margolis, Guillaume Drolet, T. Andrew Black
Multi-Angle Remote Sensing Of Forest Light Use Efficiency By Observing Pri Variation With Canopy Shadow Fraction, Forrest G. Hall, Thomas Hilker, Nicholas C. Coops, Alexei Lyapustin, Karl F. Huemmrich, Elizabeth M. Middleton, Hank Margolis, Guillaume Drolet, T. Andrew Black
United States National Aeronautics and Space Administration: Publications
We show that observed co-variations at sub-hourly time scales between the photochemical reflectance index (PRI) and canopy light use efficiency (LUE) over a Douglas-fir forest result directly from sub-hourly leaf reflectance changes in a 531 nm spectral window roughly 50 nm wide. We conclude then, that over a forest stand we are observing the direct effects of photosynthetic down-regulation on leaf-level reflectance at 531 nm. Key to our conclusion is our ability to simultaneously measure the LUE and reflectance of the Douglas-fir stand as a function of shadow fraction from the “hot spot” to the "dark spot" and a new …
Responsive In-Season Nitrogen Management For Cereals, J.F. Shanahan, N. R. Kitchen, W. R. Raun, James S. Schepers
Responsive In-Season Nitrogen Management For Cereals, J.F. Shanahan, N. R. Kitchen, W. R. Raun, James S. Schepers
United States Department of Agriculture-Agricultural Research Service / University of Nebraska-Lincoln: Faculty Publications
Current nitrogen (N) management strategies for worldwide cereal production systems are characterized by low N use efficiency (NUE), environmental contamination, and considerable ongoing debate regarding what can be done to improve N fertilizer management. Development of innovative strategies that improve NUE and minimize off-field losses is crucial to sustaining cereal-based farming. In this paper, we review the major managerial causes for low NUE, including (1) poor synchrony between fertilizer N and crop demand, (2) uniform field applications to spatially variable landscapes that commonly vary in crop N need, and (3) failure to account for temporally variable influences on crop N …
Use Of Spectral Vegetation Indices Derived From Airborne Hyperspectral Imagery For Detection Of European Corn Borer Infestation In Iowa Corn Plots, Matthew W. Carroll, John A. Glaser, Richard L. Hellmich, Thomas E. Hunt, Thomas W. Sappington, Dennis Calvin, Ken Copenhaver, Jon Fridgen
Use Of Spectral Vegetation Indices Derived From Airborne Hyperspectral Imagery For Detection Of European Corn Borer Infestation In Iowa Corn Plots, Matthew W. Carroll, John A. Glaser, Richard L. Hellmich, Thomas E. Hunt, Thomas W. Sappington, Dennis Calvin, Ken Copenhaver, Jon Fridgen
Department of Entomology: Faculty Publications
Eleven spectral vegetation indices that emphasize foliar plant pigments were calculated using airborne hyperspectral imagery and evaluated in 2004 and 2005 for their ability to detect experimental plots of corn manually inoculated with Ostrinia nubilalis (Hübner) neonate larvae. Manual inoculations were timed to simulate infestation of corn, Zea mays L., by first and second fiights of adult O. nubilalis. The ability of spectral vegetation indices to detect O. nubilalis-inoculated plots improved as the growing season progressed, with multiple spectral vegetation indices able to identify infested plots in late August and early September. Our findings also indicate that for …
Sensor Ranging Technique For Determining Corn Plant Population, Joe D. Luck, Santosh Pitla, Scott A. Shearer
Sensor Ranging Technique For Determining Corn Plant Population, Joe D. Luck, Santosh Pitla, Scott A. Shearer
Department of Animal Science: Faculty Publications
Mapping of corn plant population can provide useful information for making field management decisions. This research focused on using low cost infra-red sensors to count plants. The voltage output data from the sensors were processed using an algorithm developed to extract plant populations. Preliminary investigations were conducted using sensors mounted on a stationary track for laboratory testing and on a row crop tractor for field testing. Repeated measurements were taken on a manually counted corn row. Visual inspection of the data from the field test indicated the potential to identify corn stalks based on approximate physical widths of the stalks. …
An Automatic Bridge Detection Technique For Multispectral Images, D. Chaudhuri, Ashok Samal
An Automatic Bridge Detection Technique For Multispectral Images, D. Chaudhuri, Ashok Samal
CSE Conference and Workshop Papers
Extraction of features from images has been a goal of researchers since the early days of remote sensing. While significant progress has been made in several applications, much remains to be done in the area of accurate identification of high-level features such as buildings and roads. This paper presents an approach for detecting bridges over water bodies from multispectral imagery. The multispectral image is first classified into eight land-cover types using a majority-must-be-granted logic based on the multiseed supervised classification technique. The classified image is then categorized into a trilevel image: water, concrete, and background. Bridges are then recognized in …