Quantitative Change Analysis of Undisturbed Lands in Eastern South Dakota: 2012-2021

The actual rate of loss of undisturbed prairie and woodland in eastern South Dakota is unknown, and the landscape composition of the region continues to change. Undisturbed land is land with no proven prior cropping or other disturbance history. Agriculture, development, recreation, and other land use practices create disturbances resulting in the further conversion of undisturbed prairies and woodlands. Previous work by South Dakota State University (SDSU) quantified the remaining undisturbed land in eastern South Dakota as of 2012 (Bauman et al 2016). Farm Service Agency (FSA) common land unit (CLU) and National Agricultural Imagery Program (NAIP) imagery were the primary data used by SDSU to quantify undisturbed land as of 2012. Analysis was then conducted utilizing South Dakota Natural Resource Conservation Service (NRCS)derived Light Detecting and Ranging (LiDAR) imagery to determine additional areas of disturbance not previously detected with other methods. The objective of our study was to quantify the rate of conversion of Potentially Undisturbed Land between 2012-2021, using the SDSU Potentially Undisturbed Land results of the 2012 analysis as a baseline. Undisturbed land is defined as not being cultivated or mechanically disrupted (Bauman et al. 2016). Our analysis revisited previously designated polygons where LiDAR indicated a change in land use. Images containing land use change detected by LiDAR were contrasted with National Agricultural Imagery Program (NAIP) imagery to determine if the conversion of the land was prior or post 2012. Any LiDAR-indicated land conversion prior to 2012 was not included in our analysis. Once we determined the date of conversion for the LiDAR data, we then analyzed the remaining undisturbed land tracts to determine if additional conversion occurred after 2012. The total land area in these counties is 9,164,826 hectares (22,646,780 acres), of which 1,946,936 hectares (4,810,985 acres) or 21% was considered potentially undisturbed as of 2012. Our analysis concluded that an additional 56,561 hectares (139,766 acres) of previously undisturbed land in eastern South Dakota was converted between 2012 and 2021. Undisturbed prairies are complex ecosystems with a myriad of above and below ground biotic and abiotic components and are believed to be irrecoverable once they have been converted to other land use. Conversion of undisturbed lands in eastern South Dakota is, therefore, irreversible. For perspective, our data suggests an average rate of conversion of over 1,214 hectares (3,000 acres) per county over this 9-year period, or roughly 134 hectares (333 acres) per county per year

Liquid oxygen/liquid hydrogen boost/vane pump for the advanced orbit transfer vehicles auxiliary propulsion system

A rotating, positive displacement vane pump with an integral boost stage was designed to pump saturated liquid oxygen and liquid hydrogen for auxiliary propulsion system of orbit transfer vehicle. This unit is designed to ingest 10% vapor by volume, contamination free liquid oxygen and liquid hydrogen. The final pump configuration and the predicted performance are included

Left Ventricular Outflow Tract Obstruction Defined by Active Three-Dimensional Echocardiography Using Rotational Transthoracic Acquisition

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75675/1/j.1540-8175.1994.tb01105.x.pd

Newly formed cystic lesions for the development of pneumomediastinum in Pneumocystis jirovecii pneumonia

<p>Abstract</p> <p>Background</p> <p><it>Pneumocystis jirovecii</it>, formerly named <it>Pneumocystis carinii</it>, is one of the most common opportunistic infections in human immunodeficiency virus (HIV)-infected patients.</p> <p>Case presentations</p> <p>We encountered two cases of spontaneous pneumomediastinum with subcutaneous emphysema in HIV-infected patients being treated for <it>Pneumocystis jirovecii </it>pneumonia with trimethoprim/sulfamethoxazole.</p> <p>Conclusion</p> <p>Clinicians should be aware that cystic lesions and bronchiectasis can develop in spite of trimethoprim/sulfamethoxazole treatment for <it>P. jirovecii </it>pneumonia. The newly formed bronchiectasis and cyst formation that were noted in follow up high resolution computed tomography (HRCT) but were not visible on HRCT at admission could be risk factors for the development of pneumothorax or pneumomediastinum with subcutaneous emphysema in HIV-patients.</p

Effective modeling and teaching of reading strategies to secondary content area teachers

This Capstone examines research on how to train secondary teachers to use reading comprehension strategies in their content area classrooms, with the goal being to increase students\u27 reading comprehension. It uses the four-step process of explicit instruction to train teachers in the use of before reading, during reading and after reading strategies. The teachers in turn used the same process to teach their students how to use the strategies with their textbooks. The appendix includes master copies of five before reading strategies, nine during reading strategies, and three after reading strategies for classroom use. It also includes some helpful tools for teacher training

Validation of a CNN classifier for RADAR in real and simulation domain, exclusively trained in simulated RSI model

The current developments in driver assistance systems enabled OEMs to offer vehicles, ready for highly automatic driving (Level 3). A key factor for this development is the environmental perception, based on sensor-systems. Currently, LIght Detection And Ranging (LIDAR), Cameras and RAnge Detection And Ranging (RADAR) are the mainly used sensor-systems, to generate a perception of the vehicular environment. Due to different used spectrums, the performance and reliability of a sensor depends on environmental conditions, like darkness, rain or fog. RADAR is known as a robust sensor, working well also in rain or light conditions. A key feature for an object, surrounding the own car, is a classification, independently of a certain sensor-system. It enables the adjustment of the own trajectory to specific situations, defined by the surrounded objects. Nevertheless, it is challenging to classify an object with RADAR. Common sophisticated automotive classification algorithms rely on camera. But a precise classification of objects, detected by RADAR, gained on importance due to the reliable characteristics of RADAR mentioned before. In addition to real world domain, simulation software provides tools for RADAR measurements and deployment. Even physical sensor models, near to reality, are implemented in software, enabling the improvement of signal processing techniques in a reproducible, convenient and fast manner. RADAR-specific effects, like multipath or clutter, are also implemented. The effects cause so-called "Ghost-Targets", pretending fake objects. This thesis will make a contribution to the question how the simulation of RADAR can be used to classify objects in real world. The classification task is taken over by a CNN classifier. Due to the fact that it is a supervised learning method, the classifier gets trained. Current published papers used real but quit static data for the training. This thesis uses data out of IPG Carmaker 8 simulation model. The proposed methods cluster the data with a state of the art cluster algorithm for RADAR and label, based on ground truth, with a novel approach. A multi-class CNN, implemented in Tensorflow, is enabled to differ between a car, bicycle or pedestrian. The evaluation is concentrated on a confusion matrix. Beside simulation data, also real world data is used to evaluate the classifier. Therefore, a scenario in real world is created. The measurement is performed with INRAS RadarLog, specialized for research and raw data analyze. Both domains, simulation and real world, are scaled on a common frame size and amplitude. The comparison should indicate the potential of a classifier to classify objects with a certain accuracy. The appropriate classifier is exclusively trained in simulation domain and gets applied in the real world and simulation domain. Weather phenomenons like rain, fog or extreme temperatures are excluded in the simulation

Hypostratotype (reference section) of Lower Permian (Wolfcamp) shallow shelf carbonates in Hueco Mountains, El Paso and Hudspeth Counties, Texas

Abstract not availabl