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- School of Natural Resources: Faculty Publications (2)
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Articles 1 - 6 of 6
Full-Text Articles in Life Sciences
Extreme Fire As A Management Tool To Combat Regime Shifts In The Range Of The Endangered American Burying Beetle, Alison K. Ludwig, Daniel R. Uden, Dirac Twidwell
Extreme Fire As A Management Tool To Combat Regime Shifts In The Range Of The Endangered American Burying Beetle, Alison K. Ludwig, Daniel R. Uden, Dirac Twidwell
Department of Agronomy and Horticulture: Dissertations, Theses, and Student Research
This study is focused on the population of federally-endangered American burying beetles in south-central Nebraska. It is focused on changes in land cover over time and at several levels of spatial scale, and how management efforts are impacting both the beetle and a changing landscape. Our findings are applicable to a large portion of the Great Plains, which is undergoing the same shift from grassland to woodland, and to areas where the beetle is still found.
A Review Of Vegetation Phenological Metrics Extraction Using Time-Series, Multispectral Satellite Data, Linglin Zeng, Brian D. Wardlow, Daxiang Xiang, Shun Hu, Deren Li
A Review Of Vegetation Phenological Metrics Extraction Using Time-Series, Multispectral Satellite Data, Linglin Zeng, Brian D. Wardlow, Daxiang Xiang, Shun Hu, Deren Li
School of Natural Resources: Faculty Publications
Vegetation dynamics and phenology play an important role in inter-annual vegetation changes in terrestrial ecosystems and are key indicators of climate-vegetation interactions, land use/land cover changes, and variation in year-to-year vegetation productivity. Satellite remote sensing data have been widely used for vegetation phenology monitoring over large geographic domains using various types of observations and methods over the past several decades. The goal of this paper is to present a detailed review of existing methods for phenology detection and emerging new techniques based on the analysis of time-series, multispectral remote sensing imagery. This paper summarizes the objective and applications of detecting …
Improving On Modis Mcd64a1 Burned Area Estimates In Grassland Systems: A Case Study In Kansas Flint Hills Tall Grass Prairie, Rheinhardt Scholtz, Jayson Prentice, Yao Tang, Dirac Twidwell
Improving On Modis Mcd64a1 Burned Area Estimates In Grassland Systems: A Case Study In Kansas Flint Hills Tall Grass Prairie, Rheinhardt Scholtz, Jayson Prentice, Yao Tang, Dirac Twidwell
Department of Agronomy and Horticulture: Faculty Publications
Uncertainty in satellite-derived burned area estimates are especially high in grassland systems, which are some of the most frequently burned ecosystems in the world. In this study, we compare differences in predicted burned area estimates for a region with the highest fire activity in North America, the Flint Hills of Kansas, USA, using the moderate resolution imaging spectroradiometer (MODIS) MCD64A1 burned area product and a customization of the MODIS MCD64A1 product using a major ground-truthing effort by the Kansas Department of Health and Environment (KDHE-MODIS customization). Local-scale ground-truthing and the KDHE-MODIS product suggests MODIS burned area estimates under predicted fire …
Improving The Accessibility And Transferability Of Machine Learning Algorithms For Identification Of Animals In Camera Trap Images: Mlwic2, Michael A. Tabak, Mohammad S. Norouzzadeh, David W. Wolfson, Erica J. Newton, Raoul K. Boughton, Jacob S. Ivan, Eric Odell, Eric S. Newkirk, Reesa Y. Conrey, Jennifer Stenglein, Fabiola Iannarilli, John Erb, Ryan K. Brook, Amy J. Davis, Jesse Lewis, Daniel P. Walsh, James C. Beasley, Kurt C. Vercauteren, Jeff Clune, Ryan S. Miller
Improving The Accessibility And Transferability Of Machine Learning Algorithms For Identification Of Animals In Camera Trap Images: Mlwic2, Michael A. Tabak, Mohammad S. Norouzzadeh, David W. Wolfson, Erica J. Newton, Raoul K. Boughton, Jacob S. Ivan, Eric Odell, Eric S. Newkirk, Reesa Y. Conrey, Jennifer Stenglein, Fabiola Iannarilli, John Erb, Ryan K. Brook, Amy J. Davis, Jesse Lewis, Daniel P. Walsh, James C. Beasley, Kurt C. Vercauteren, Jeff Clune, Ryan S. Miller
USDA Wildlife Services: Staff Publications
Motion-activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter-out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera …
A Decade Of Unmanned Aerial Systems In Irrigated Agriculture In The Western U.S., Jose L. Chavez, Alfonso F. Torres-Rua, Wayne E. Woldt, Huihui Zhang, Christopher Robertson, Gary W. Marek, Dong Wang, Derek M. Heeren, Saleh Taghvaeian, Christopher M. U. Neale
A Decade Of Unmanned Aerial Systems In Irrigated Agriculture In The Western U.S., Jose L. Chavez, Alfonso F. Torres-Rua, Wayne E. Woldt, Huihui Zhang, Christopher Robertson, Gary W. Marek, Dong Wang, Derek M. Heeren, Saleh Taghvaeian, Christopher M. U. Neale
Department of Biological Systems Engineering: Papers and Publications
Several research institutes, laboratories, academic programs, and service companies around the United States have been developing programs to utilize small unmanned aerial systems (sUAS) as an instrument to improve the efficiency of in-field water and agronomical management. This article describes a decade of efforts on research and development efforts focused on UAS technologies and methodologies developed for irrigation management, including the evolution of aircraft and sensors in contrast to data from satellites. Federal Aviation Administration (FAA) regulations for UAS operation in agriculture have been synthesized along with proposed modifications to enhance UAS contributions to irrigated agriculture. Although it is feasible …
The Role Of Topography, Soil, And Remotely Sensed Vegetation Condition Towards Predicting Crop Yield, Trenton E. Franz, Sayli Pokal, Justin P. Gibson, Yuzhen Zhou, Hamed Gholizadeh, Fatima Amor Tenorio, Daran Rudnick, Derek M. Heeren, Matthew F. Mccabe, Matteo Ziliani, Zhenong Jin, Kaiyu Guan, Ming Pan, John Gates, Brian Wardlow
The Role Of Topography, Soil, And Remotely Sensed Vegetation Condition Towards Predicting Crop Yield, Trenton E. Franz, Sayli Pokal, Justin P. Gibson, Yuzhen Zhou, Hamed Gholizadeh, Fatima Amor Tenorio, Daran Rudnick, Derek M. Heeren, Matthew F. Mccabe, Matteo Ziliani, Zhenong Jin, Kaiyu Guan, Ming Pan, John Gates, Brian Wardlow
School of Natural Resources: Faculty Publications
Foreknowledge of the spatiotemporal drivers of crop yield would provide a valuable source of information to optimize on-farm inputs and maximize profitability. In recent years, an abundance of spatial data providing information on soils, topography, and vegetation condition have become available from both proximal and remote sensing platforms. Given the wide range of data costs (between USD $0−50/ha), it is important to understand where often limited financial resources should be directed to optimize field production. Two key questions arise. First, will these data actually aid in better fine-resolution yield prediction to help optimize crop management and farm economics? Second, what …