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Water resources play an important role in society and fulfill various functions such as providing drinking water, supporting industrial production and enhancing the overall landscape. Water bodies, such as rivers and lakes, are particularly important in this context. However, as societies and economies develop, the demand for water increases significantly. This also leads to the release of domestic, agricultural and industrial wastewater, which often exceeds the self-purification capacity of water bodies. Consequently, rivers and lakes are getting more and more polluted, endangering the safety of drinking water and causing ecological damage, affecting human health and biodiversity.  Water quality monitoring plays a crucial role in evaluating the state of water bodies. Traditional monitoring methods involve labor-intensive field sampling and expensive construction and maintenance of automatic stations. Although these methods provide accurate results, they are limited to specific sampling points and struggle to meet the demands of monitoring water quality across entire surfaces of rivers and lakes. This degree project aim at developing a method that can predict absorbance in water with the aim of remote sensing. Along with multispectral imaging and machine learning this work proves that this is possible. The result from multivariate analysis is an optimal model that can predict absorbance at 420 nm with RSQ of 0,996 and RMSE of 0,00081.

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Indiana University-Purdue University Indianapolis (IUPUI) / Nuisance blue-green algal blooms contribute to aesthetic degradation of water resources and produce toxins that can have serious adverse human health effects. Current field-based methods for detecting blooms are costly and time consuming, delaying management decisions. Remote sensing techniques which utilize the optical properties of blue-green algal pigments (chlorophyll a and phycocyanin) can provide rapid detection of blue-green algal distribution. Coupled with physical and chemical data from lakes, remote sensing can provide an efficient method for tracking cyanobacteria bloom occurrence and toxin production potential to inform long-term management strategies. In-situ field reflectance spectra were collected at 54 sampling sites on two turbid, productive Indianapolis reservoirs using ASD Fieldspec (UV/VNIR) spectroradiometers. Groundtruth samples were analyzed for in-vitro pigment concentrations and other physical and chemical water quality parameters. Empirical algorithms by Gitelson et al. (1986, 1994), Mittenzwey et al. (1991), Dekker (1993), and Schalles et al. (1998), were applied using a combined dataset divided into a calibration and validation set. Modified semi-empirical algorithms by Simis et al. (2005) were applied to all field spectra to predict phycocyanin concentrations. Algorithm accuracy was tested through a least-squares regression and residual analysis. Results show that for prediction of chlorophyll a concentrations within the range of 18 to 170 ppb, empirical algorithms yielded coefficients of determination as high as 0.71, RMSE 17.59 ppb, for an aggregated dataset (n=54, p<0.0001). The Schalles et al. (2000) empirical algorithm for estimation of phycocyanin concentrations within the range of 2 to 160 ppb resulted in an r2 value of 0.70, RMSE 23.97 ppb (n=48, p<0.0001). The Simis et al. (2005) semi-empirical algorithm for estimation of chlorophyll a and phycocyanin concentrations yielded coefficients of determination of 0.69, RMSE 20.51 ppb (n=54, p<0.0001) and 0.85, RMSE 24.61 pbb (n=49, p<0.0001), respectively. Results suggest the Simis et al. (2005) algorithm is robust, where error is highest in water with phycocyanin concentrations of less than 10 ppb and in water where chlorophyll a dominates (Chl:PC>2). A strong correlation between measured phycocyanin concentrations and blue-green algal biovolume measurements was also observed (r2=0.95, p<0.0001).

Indiana University-Purdue University Indianapolis (IUPUI) / The West Indian iguanas of the genus Cyclura are the most endangered group of lizards in the world (Burton & Bloxam, 2002). The Ricord’s iguana, Cyclura ricordii, is listed as critically endangered by the International Union for Conservation of Nature (IUCN) (Ramer, 2004). This species is endemic to the island of Hispaniola (Figure 1), and can only be found in limited geographic areas (Burton & Bloxam, 2002). The range of this species is estimated to be only 60% of historical levels, with most areas being affected by some level of disturbance (Ottenwalder, 1996). The most recent population estimation is between 2,000 and 4,000 individuals (Burton & Bloxam, 2002). Information on potentially suitable habitat can help the conservation efforts for Ricord’s iguana. However, intensive ground surveys are not always feasible or cost effective, and cannot easily provide continuous coverage over a large area. This paper presents results from a pilot study that evaluated variables extracted from satellite imagery and digitally mapped data layers to map the probability of suitable Ricord’s iguana habitat. Bayesian methods were used to determine the probability that each pixel in the study areas is suitable habitat for Ricord’s iguanas by evaluating relevant environmental attributes. This model predicts the probability that an area is suitable habitat based on the values of the environmental attributes including landscape biophysical characteristics, terrain data, and bioclimatic variables.

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Located on a ridgetop in central Kentucky, the Singer-Hieronymus Site Complex consists of at least four Native American villages. The Native Americans who lived there are called the “Fort Ancient” by archaeologists. This study examined relationships between these villages, both spatially and temporally, to build a more complete history of site occupation. To do this, aerial imagery analysis, geophysical survey, and archaeological investigations were conducted. This research determined there were differences among villages in terms of their size, however other characteristics—internal village organization, village shape, radiometric dates, and material culture—overlapped significantly. Additionally, landscape-scale geophysical survey identified at least three potentially new villages. It has been suggested that Fort Ancient groups abandoned villages every 10 to 30 years due to environmental degradation, but these results suggest that native peoples did not abandon villages at Singer-Hieronymus. Current thought surrounding Fort Ancient village abandonment and reoccupation must therefore be reconsidered.