Use of Hyperspectral Remote Sensing to Estimate Water Quality

Approximating and forecasting water variables like phosphorus, nitrogen, chlorophyll, dissolved organic matter, and turbidity are of supreme importance due to their strong influence on water resource quality. This chapter is aimed at showing the practicability of merging water quality observations from remote sensing with water quality modeling for efficient and effective monitoring of water quality. We examine the spatial dynamics of water quality with hyperspectral remote sensing and present approaches that can be used to estimate water quality using hyperspectral images. The methods presented here have been embraced because the blue-green and green algae peak wavelengths reflectance are close together and make their distinction more challenging. It has also been established that hyperspectral imagers permit an improved recognition of chlorophyll and hereafter algae, due to acquired narrow spectral bands between 450 nm and 600 nm. We start by describing the practical application of hyperspectral remote sensing data in water quality modeling. The surface inherent optical properties of absorption and backscattering of chlorophyll a, colored dissolved organic matter (CDOM), and turbidity are estimated, and a detailed approach on analyzing ARCHER data for water quality estimation is presented

Habitat Suitability Analysis for Mountain Lions (Puma Concolor) Recolonization/ Reintroduction in Minnesota

The mountain lion range once extended throughout the state of Minnesota. The breeding population has been greatly reduced with time, new roads, and timber harvesting, which have broken large tracts of contiguous forest into isolated patches that are too small and no longer suitable for the breeding mountain lion population. The objective of this study is to use suitability analysis to determine the most suitable habitat to conserve mountain lion populations threatened by habitat fragmentation. To attain our objective, we created three sub models that contribute to the overarching goal of the suitability model. A habitat sub model was developed for finding the best habitat, a food sub model for access to the maximum amount of food needed, and a security sub model focusing on the distance from houses, roads, and urban development. Using the Weighted Sum tool, the three sub models were combined to produce a suitability surface based on the trade-off of the preferences of the goals represented by each sub model. Our suitability model shows large areas of high-quality mountain lion habitat in the northern and north-eastern sections of the state. These areas contain favourable locations for mountain lion habitat, such as forested land cover, low-density populations, steep slopes, short distances to streams, and area unimpeded by major roads. The southern and western parts of the state are characterized by lower slopes, more agricultural land, grassland, developed land, and higher population density, which results in lower quality habitat. The twin cities have the worst mountain lion habitat

Spatiotemporal Characteristics of Atmospheric Dust Sources on the Southern High Plains and Eastern New Mexico Using Modis Imagery from 2001 to 2009

The Southern High Plains of Texas and eastern New Mexico are one of the major sources of dust in North America. Using MODIS satellite imagery, this study has identified locations, climatic factors and landform characteristics for 676 dust plumes from 2000 to 2009. Meteorological data were used to model an index of potential erosion (Ew) over a 37 county area. The analyses show that blowing dust is not uniform over the source area region. The Ew index did not correlate with the frequency of dust sources and explains less than 10% of dust occurrence in the study area, suggesting that annual climate variations did not affect dust sources. However, there is a strong relationship between dust sources and geology. Almost 80% of all dust sources occurred on landforms composed of Quaternary aeolian sand sheets. Furthermore, farmlands within these flat and sandy land areas tend to produce the majority of the dust sources since soil cultivation reduces vegetation cover and soil resistance to wind erosion. Within the Southern High Plains region, climate variables are not the major factor controlling the spatial patterns of dust emission over a 10 year period of observation, wind only provides the energy that drives the process. However, atmospheric dust sources are strongly correlated with the presence of flat plains, sandy soils (i.e., aolian sand sheets and playas), and agricultural lands in the Southern High Plains

Optimization of Airborne Real-time Cueing Hyperspectral Enhanced Reconnaissance (ARCHER) imagery, in situ data with chemometrics to evaluate nutrients in the Shenandoah River, Virginia

Phosphorus and nitrogen have a strong influence on water resource and remote sensing technology has demonstrated that water quality monitoring over a greater range of temporal and spatial scales can be used to overcome these constraints. This research was designed to demonstrate the feasibility of combining remotely-sensed water quality observation and chemometric techniques to estimate water quality in the Shenandoah River. We used Airborne Real-time Cueing Hyperspectral Enhanced Reconnaissance (ARCHER) imagery, combined with a partial least squares analysis to characterize the spatial distribution of nutrients in the Sheanadoah river. ARCHER retrievals for phosphorous with cross-validation show high sensitivity in estimating water quality in the Shenandoah River with the Bentonville in the South Fork, with an R2 of 0.93 sensitivity. Using the significance level of 0.05, data from the summer of 2014 showed that the p-value was 0.00 for both nitrogen and phosphorous. Results show retrieval method is transferable

Habitat Suitability Analysis for Mountain Lions (Puma Concolor) Recolonization/ Reintroduction in Minnesota

The mountain lion range once extended throughout the state of Minnesota. The breeding population has been greatly reduced with time, new roads, and timber harvesting, which have broken large tracts of contiguous forest into isolated patches that are too small and no longer suitable for the breeding mountain lion population. The objective of this study is to use suitability analysis to determine the most suitable habitat to conserve mountain lion populations threatened by habitat fragmentation. To attain our objective, we created three sub models that contribute to the overarching goal of the suitability model. A habitat sub model was developed for finding the best habitat, a food sub model for access to the maximum amount of food needed, and a security sub model focusing on the distance from houses, roads, and urban development. Using the Weighted Sum tool, the three sub models were combined to produce a suitability surface based on the trade-off of the preferences of the goals represented by each sub model. Our suitability model shows large areas of high-quality mountain lion habitat in the northern and north-eastern sections of the state. These areas contain favourable locations for mountain lion habitat, such as forested land cover, low-density populations, steep slopes, short distances to streams, and area unimpeded by major roads. The southern and western parts of the state are characterized by lower slopes, more agricultural land, grassland, developed land, and higher population density, which results in lower quality habitat. The twin cities have the worst mountain lion habitat

Spatiotemporal analysis of urban heat island intensification in the city of Minneapolis-St. Paul and Chicago metropolitan areas using Landsat data from 1984 to 2016

Most major cities worldwide are affected Urban Heat Islands – a condition of relatively higher temperatures being observed in one area compared to another that can be caused by a decrease in greenspace. One of the major reasons attributed to this increase in the warming of urban landscapes is the decrease in green space. This concept has received a lot of attention due to the destruction of vegetation for urban development and has prompted long-term spatial-temporal studies of Urban Heat Islands to understanding local climates. The objective of this study is to use Landsat data to examine the temporal intensification of UHIs and their variability from 1984–2016 for the cities of Chicago and Minneapolis-St. Paul. Landsat L4-5 TM), L7 ETM+), OLI and TIRS from 1984 to 2016 was used to examine land surface temperature (LST). Firstly, we converted the digital number (DN) to spectral radiance (L) and to temperature in Kelvin and from kelvin to Celsius and a conversion from Radiance to Top of the Atmosphere Reflectance and estimation of land surface emissivity. Finally, LST was estimated and Urban Heat Island retrieval and anomalies computed to help examine inconsistencies in our data. Our analysis showed year-to-year fluctuations in surface temperature, intensification of UHIs for both metro areas. Using a defined deductive index to identify environmentally critical areas, estimates of UHIs based on LST showed that both metropolitan areas are UHIs with LST > µ + 0.5 × δ. Higher intensification values were observed in 1988 and 2010 for Chicago and 1984, 1999 and 2016 for Minneapolis-St. Paul from analysis. While both areas have the similar climatic conditions, our analysis show differences in UHIs intensification as observed in their urban growth patterns. Chicago experiences a higher UHI intensity compared to Minneapolis-St. Paul and this could be explained by higher number of tall buildings than Minneapolis-St. Paul