Typhoons and Temperature Impact Crime Rates: Evidence from the Philippines

This study assesses the impact of typhoons and temperature on interpersonal, property, and non-index crime rates using a novel data set from the Philippines. Distributed lag OLS regression analysis shows that in the concurrent year of increased precipitation, theft rates decrease, but in the year following high average wind-speeds, theft rates increase again. The only significant result of impacts crimes against persons is the rate of physical injury which decreases in years of higher precipitation. These results are defended by interview and focus group information. This study also shows that higher average temperatures cause significantly higher rates of murder, index crimes, and non-index crimes in the concurrent year and increased murder in the lagged year as well. These results uphold conclusions from previous research, and make substantial contributions to the existing knowledge on this subject. The conclusions drawn here have significant implications as our climate continues to change

Comparison of Methods for Modeling Fractional Cover Using Simulated Satellite Hyperspectral Imager Spectra

Remotely sensed data can be used to model the fractional cover of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil in natural and agricultural ecosystems. NPV and soil cover are difficult to estimate accurately since absorption by lignin, cellulose, and other organic molecules cannot be resolved by broadband multispectral data. A new generation of satellite hyperspectral imagers will provide contiguous narrowband coverage, enabling new, more accurate, and potentially global fractional cover products. We used six field spectroscopy datasets collected in prior experiments from sites with partial crop, grass, shrub, and low-stature resprouting tree cover to simulate satellite hyperspectral data, including sensor noise and atmospheric correction artifacts. The combined dataset was used to compare hyperspectral index-based and spectroscopic methods for estimating GV, NPV, and soil fractional cover. GV fractional cover was estimated most accurately. NPV and soil fractions were more difficult to estimate, with spectroscopic methods like partial least squares (PLS) regression, spectral feature analysis (SFA), and multiple endmember spectral mixture analysis (MESMA) typically outperforming hyperspectral indices. Using an independent validation dataset, the lowest root mean squared error (RMSE) values were 0.115 for GV using either normalized difference vegetation index (NDVI) or SFA, 0.164 for NPV using PLS, and 0.126 for soil using PLS. PLS also had the lowest RMSE averaged across all three cover types. This work highlights the need for more extensive and diverse fine spatial scale measurements of fractional cover, to improve methodologies for estimating cover in preparation for future hyperspectral global monitoring missions

Remote Sensing of Urban Climate and Vegetation in Los Angeles

In cities, microclimates are created by local mixtures of vegetation, constructed materials, vertical structure, and moisture, with significant consequences for human health, air quality, and resource use. Vegetation can moderate microclimates through evapotranspiration, however this function is dependent on local conditions so its effect may vary over space and time. This dissertation used hyperspectral and thermal remote sensing imagery to derive key observations of urban physical and biophysical properties and model urban microclimates across the megacity of Los Angeles. In Chapter 1, I used Multiple Endmember Spectral Mixture Analysis (MESMA) to map sub-pixel fractions of different vegetation types, as well as other types of urban cover, at 4 m and 18 m resolution over Santa Barbara, California (Wetherley et al., 2017). Fractional estimates correlated with validation fractions at both scales (mean R2 = 0.84 at 4 m and R2 = 0.76 at 18 m), with accuracy affected by image spatial resolution, endmember spatial resolution, and class spectral (dis)similarity. Accuracy was improved by using endmembers measured at multiple spatial resolutions, likely because they incorporated additional spectral variability that occurred across spatial scales. In Chapter 2, I applied this methodology to derive sub-pixel cover for the greater Los Angeles metropolitan area (4,466 km2) (Wetherley et al., 2018). Further improvement in quantifying sub-pixel vegetation types was achieved by modifying the MESMA shade parameter. Land surface temperature (LST), derived from thermal imagery, was used to model temperature change along vegetation fractional gradients, with slopes of LST change showing significant differences between trees and turfgrass (p < 0.001). Expected per-pixel LST was derived from these gradients based on sub-pixel composition, and when compared to measured LST was found to deviate with a standard deviation of 3.5 °C across the scene. These deviations were negatively related to irrigation and income, while building density was observed to affect tree LST more than it affected turfgrass LST. In Chapter 3, I used the map of Los Angeles landcover, along with data from LiDAR, GIS, and WRF climate variables, to parameterize an urban climate model (Surface Urban Energy and Water Balance Scheme: SUEWS) for 2,123 neighborhoods (each 1 km2) across Los Angeles. Modeled latent fluxes were correlated with remote sensing LST (R2 = 0.39) collected over a period of 5 hours, with an overall diurnal pattern modified by irrigation timing. Spatial variability across the study area was related to local landcover, with albedo and vegetation fraction strongly influencing latent and sensible fluxes. A strong regional climatic gradient was observed to affect latent fluxes based on coastal proximity. Overall, this dissertation quantifies the key drivers of urban vegetation function in a large city, and further demonstrates the potential of hyperspectral and thermal imagery for observing city scale surface and microclimate variability

Solar powered locator beacon

The following describes the process and creation of a location device to be used in the event of an emergency. When in trouble, the device is user activated, and will alert rescue authorities of the user\u27s location. The device will need to integrate several subsystems to be renewably charged, long lasting, and have a suitable transmission distance