Remotely sensed heat : variation and change in surface urban heat islands in a temperate eco-region of the United States.

Urban heat island (UHI) is a term used to describe increased surface and atmospheric temperatures in an urban core relative to surrounding non-urbanized areas. To examine the variability introduced into derived estimates of the surface UHI, this study constructs and compares multiple remotely sensed indicators of the surface UHI for major metropolitan cities of a temperate eco-region of the United States. The Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day, 500-meter product (MOD11A2) is the source data used to calculate six different RS-derived UHI indicators for the year 2002 to 2012. The different SUHI indicators are evaluated using the Spearmans Rho rank-order correlation statistic to assess agreeability for 2012 and consistency over time 2002 to 2012. Inconsistencies exist in monthly rankings between indicators, and the degree to which the indicators detect change over time. Results suggest that land cover based indicators are highly correlated compared to urban heat island driven indicators in terms of magnitude and change over time

Honeybee Colony Collapse Disorder in the U.S.A

Examining Honeybee Colony Collapse Disorder in The U.S.A Gabriel Stone, Buddhi Gyawali, Jeremy Sandifer In the world we live in today, and for hundreds of years beforehand, mankind has relied on honeybees to perform plant pollination. Honeybee pollination plays a vital role in producing food for humans, animals, and various types of insects. Based on this understanding in terms of the honeybee\u27s ability to perform pollination, it is very clear to fathom what our reality would be like there were suddenly no more bees. Essentially it would cause global starvation, economic disasters, and overall strife. It is frightening to realize that colony collapse disorder (the systematic breakdown /disappearance of bees), has been increased recently in many parts of the United States. This phenomenon involves the continuation of bees dying off, and hives becoming barren. Multiple factors such as pesticides, disease, parasites, and extreme weather patterns are reported to be the major culprits for CCD. This study examines the spatial trend and pattern of CCD and it\u27s relationship with factors such as temperature, precipitation, pesticide use, and land cover change. Data related to honey bee population, pesticides, temperature, precipitation, and land use change were compiled from 1990 to 2016 and spatial analysis were conducted at the state and regional level. This study reveals that temperature, and precipitation are key contributors to CCD which has a spatial pattern of disorder. Climate and extreme weather change paterns have been found to have a direct effect on the survival, and sustainability of the honey bees especially in the southern United States

DEVELOPING A LOW-COST ARDUINO WEATHER MONITORING SYSTEM

The purpose of this research was to develop a low-cost weather monitoring system that connects via Wi-Fi for data logging and visualizations for use by farmers and evaluate the reliability and validity of data collected. The developed system is a kit created by SwitchDoc Labs and includes a microprocessor based on the Arduino platform. The system uses Wi-Fi for data transmission at the cost of being a less portable unit. The system was tested during the spring and fall to determining season differences, recording temperature, precipitation, wind speed, wind direction and barometric pressure and displaying data every fifteen minutes on Weather Underground. The microprocessor was powered by a micro-usb power cord, also resulting in decreased mobility. The data collected was compared with the KY Mesonet and National Weather Service data, but no significance difference was found among these data

Evaluation of the Operational Simplified Surface Energy Balance Model for Pastureland Evapotranspiration Mapping and Drought Monitoring in North Central Kentucky

The use of remotely sensed evapotranspiration (ET) for field applications in drought monitoring and assessment is gaining momentum, but meeting this need has been hampered by the absence of extensive ground-based measurement stations for ground validation across agricultural zones and natural landscapes. This is particularly crucial for regions more prone to recurring droughts with limited ground monitoring stations. A three-year (2016–2018) flux ET dataset from a pastureland in north central Kentucky was used to validate the Operational Simplified Surface Energy Balance (SSEBop) model at monthly and annual scales. Flux and SSEBop ET track each other in a consistent manner in response to seasonal changes. The mean bias error (MBE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) were 5.47, 21.49 mm mon−1, 30.94%, and 0.87, respectively. The model consistently underestimated ET values during winter months and overestimated them during summer months. SSEBop’s monthly ET anomaly maps show spatial ET distribution and its accurate representation. This is particularly important in areas where detailed surface meteorological and hydrological data are limited. Overall, the model estimated monthly ET magnitude satisfactorily and captured it seasonally. The SSEBop’s functionality for remote ET estimation and anomaly detection, if properly coupled with ground measurements, can significantly enhance SSEBop’s ability to monitor drought occurrence and prevalence quickly and accurately