Indiana University-Purdue University Indianapolis (IUPUI) / This thesis presents the results of an analysis of predicting phytoplankton pigment concentrations (chlorophyll a and phycocyanin) from remotely sensed imagery. Hyperspectral airborne and hand-held reflectance spectra were acquired on three reservoirs (Geist, Morse and Eagle Creek) in Central Indiana, USA. Concurrent with the reflectance acquisition, in situ samples were collected and analyzed in laboratories to quantify the pigment concentration and other water quality parameters. The resultant concentration was then linked to Airborne Imaging Spectrometer for Applications (AISA) reflectance spectra for the sampling stations to develop predictive models. AISA reflectance spectra were extracted from the imagery which had been processed for radiometric calibration and geometric correction. Several previously published algorithms were examined for the estimation of pigment concentration from the spectra. High coefficients of determination were achieved for predicting chlorophyll a in two of the three reservoirs (Geist R2 = 0.712, Morse R2 = 0.895 and Eagle Creek Reservoir R2 = 0.392). This situation was similar for PC prediction, where two of the three reservoirs had high coefficients of determination between pigment concentration and reflectance (Geist R2 = 0.805, Morse R2 = 0.878 and Eagle Creek Reservoir R2 = 0.316). The results of this study show that reflectance spectra collected with an airborne hyperspectral imager are statistically significant, p < 0.03, in predicting chlorophyll a and phycocyanin pigment concentration in all three reservoirs in this study without the consideration of other parameters. The algorithms were then applied to the AISA image to generate high spatial resolution (1 m2) maps of Chlorophyll a and Phycocyanin distribution for each reservoir.

Indiana University-Purdue University Indianapolis (IUPUI) / This thesis applies several different remote sensing techniques to data collected from 2005 to 2007 on central Indiana reservoirs to determine the best performing algorithms in estimating the cyanobacterial pigments chlorophyll a and phycocyanin. This thesis is a set of three scientific papers either in press or review at the time this thesis is published. The first paper describes using a curve fitting model as a novel approach to estimating cyanobacterial pigments from field spectra. The second paper compares the previous method with additional methods, band ratio and semi-empirical algorithms, commonly used in remote sensing. The third paper describes using a partial least squares (PLS) method as a novel approach to estimate cyanobacterial pigments from field spectra. While the three papers had different methodologies and cannot be directly compared, the results from all three studies suggest that no type of algorithm greatly outperformed another in estimating chlorophyll a on central Indiana reservoirs. However, algorithms that account for increased complexity, such as the stepwise regression band ratio (also known as 3-band tuning), curve fitting, and PLS, were able to predict phycocyanin with greater confidence.

<p> </p> <p>Leaf Area Index (LAI) is commonly defined as the total area of a leaf per unit area of the ground. LAI is an important variable for characterizing plant canopy related to the interception of solar radiation. Direct measurement of LAI by destructive sampling is tedious, time-consuming, and labor-intensive. With the advance of remote sensing, studies have explored multispectral and hyperspectral remote sensing image data and LiDAR point clouds as individual sources to estimate LAI indirectly. This study investigates the estimation of LAI for maize row crops over the growing season based on features derived from high resolution LiDAR and hyperspectral data acquired simultaneously from a UAV platform. Support Vector Regression (SVR) models are developed using cross validation and evaluated relative to the contribution of the multi-modality remote sensing data. The study is based on data acquired for experiments in plant breeding and evaluation of nitrogen management practice trials conducted at the Agronomy Center for Research and Education (ACRE) in 2021 and 2022, respectively. Reference data for the models were collected using a LI-COR® LAI-2200-C Plant Canopy Analyzer. Including both LiDAR and hyperspectral data sources in the SVR model improved the 𝑅_ref^2 (relative to 1:1 comparison line), RMSE and Relative RMSE (rRMSE) values for both the plant breeding and nitrogen management practice experiments, although incremental gains were small overall. More importantly, it was observed that the contributions of the LiDAR vs hyperspectral inputs to the models also varied throughout the growing season. </p>

A three dimensional sampling technique was used to compare field understory conditions in Southeastern Louisiana using a laser range finder at three height levels (0.5m, 1.0m, and 1.5m) to LiDAR generated understory conditions to determine if a relationship existed. A similar comparison was made between densitometer crown closure measurements and understory LiDAR vegetation counts. A comparison between overstory LiDAR counts and understory LiDAR counts was also performed. LiDAR and understory counts exhibited a significant linear relationship but were poorly correlated at each sample level (Level-1 R2 = 0.34 ? 0.38, Level-2 R2 = 0.36 ? 0.43). The Level-3 LiDAR slope coefficient was non-significant. The crown closure versus understory linear model did not produce any significant results. The overstory LiDAR versus understory LiDAR model produced a moderate correlation (R2 = 0.5226) and was significant. The process of relating LiDAR points to understory conditions was not repeatable, even in the same geographic region.

No description available.

The overall goal of this thesis is to better understand changes in the spatial pattern of urban growth and its impact on landscape configuration by conducting a case study in Greater Chengdu, an inland megacity in China. The objectives are as follows: 1) Quantifying changes in the spatial pattern of the study area between 2003 and 2013; 2) Evaluating the degree of urban sprawl over that period; 3) Evaluating urban expansion dynamics; and 4) Examining and defining the types of urban growth. Satellite imagery was employed to distinguish and identify different land surface categories. Integrated remote sensing and GIS (Geographic Information System) technique was used to analyse both qualitative and quantitative perspectives regarding the objectives. The results indicate that the urban area of Greater Chengdu doubled from 525.5 km2 to 1191.85 km2 during 2003 to 2013. The geographic footprint demonstrates that the distribution of the built-up area was dispersed and continues to grow more dispersed. The dominant type of urban growth is outward expansion, by which the city grew within a 10 km to 25 km radius surrounding the city center. A substantial infill phenomenon exists between a 5 km and 10 km radius from the city center. The urban core boundary expanded outward by 5 km, while the fringe of suburban area expanded outward by 10 km during the time period, which both indicate a substantial outward expansion over the city. The significant contribution of this study could benefit to many aspects such as comparative studies between cities or continuous studies relevant to urban growth.

This master thesis in geography investigates how remote sens- ing can be used in Transnational Corporations (TNC) global Corporate Social Responsibility (CSR) initiatives. The study aims to delineate an accurate method in remote sensing to be used to monitor deforestation in global value chains. Research questions asked are 1) What are the current monitoring practises used by TNCs to monitor global value chains? 2) Which is the most user-friendly and accurate remote sensing technique to map deforestation? 3) How can remote sensing successfully be implemented in TNCs CSR-initiatives? The study is approached from two perspectives, building on theories of value chains, and qualitative methods to answer the first research question. While the second question is a method study, investigating how well a spectral approach versus a contextual approach can map deforest- ation in Landsat scenes. The results are compared with Global Forest Watch (GFW), and the highest accuracy were acquired from the WICS (Window Indipendent Context Segmentation) technique. Conclusions includes that remote sensing can be used in CSR initiatives, to establish a baseline level or as a fifth dimen- sion in a score sheet approach. However, inconclusive mapping of value chains are a big hinder today.

ITC/USA 2008 Conference Proceedings / The Forty-Fourth Annual International Telemetering Conference and Technical Exhibition / October 27-30, 2008 / Town and Country Resort & Convention Center, San Diego, California / In this work, a scalable perceptual JPEG2000 encoder that exploits properties of the human visual system (HVS) is presented. The algorithm modifies the final three stages of a conventional JPEG2000 encoder. In the first stage, the quantization step size for each subband is chosen to be the inverse of the contrast sensitivity function (CSF). In bit-plane coding, two masking effects are considered during distortion calculation. In the final bitstream formation step, quality layers are formed corresponding to desired perceptual distortion thresholds. This modified encoder exhibits superior visual performance for remote sensing images compared to conventional JPEG2000 encoders. Additionally, it is completely JPEG2000 Part-1 compliant, and therefore can be decoded by any JPEG2000 decoder.

Marine predators such as seabirds, cetaceans, turtles, pinnipeds, sharks and large teleost fish are essential components of healthy, biologically diverse marine ecosystems. However, intense anthropogenic pressure on the global ocean is causing rapid and widespread change, and many predator populations are in decline. Conservation solutions are urgently required, yet only recently have we begun to comprehend how these animals interact with the vast and dynamic oceans that they inhabit. A better understanding of the mechanisms that underlie habitat selection at sea is critical to our knowledge of marine ecosystem functioning, and to ecologically-sensitive marine spatial planning. The collection of studies presented in this thesis aims to elucidate the influence of biophysical coupling at oceanographic fronts – physical interfaces at the transitions between water masses – on habitat selection by marine predators. High-resolution composite front mapping via Earth Observation remote sensing is used to provide oceanographic context to several biologging datasets describing the movements and behaviours of animals at sea. A series of species-habitat models reveal the influence of mesoscale (10s to 100s of kilometres) thermal and chlorophyll-a fronts on habitat selection by taxonomically diverse species inhabiting contrasting ocean regions; northern gannets (Morus bassanus; Celtic Sea), basking sharks (Cetorhinus maximus; north-east Atlantic), loggerhead turtles (Caretta caretta; Canary Current), and grey-headed albatrosses (Thalassarche chrysostoma; Southern Ocean). Original aspects of this work include an exploration of quantitative approaches to understanding habitat selection using remotely-sensed front metrics; and explicit investigation of how the biophysical properties of fronts and species-specific foraging ecology interact to influence associations. Main findings indicate that front metrics, particularly seasonal indices, are useful predictors of habitat preference across taxa. Moreover, frontal persistence and spatiotemporal predictability appear to mediate the use of front-associated foraging habitats, both in shelf seas and in the open oceans. These findings have implications for marine spatial planning and the design of protected area networks, and may prove useful in the development of tools supporting spatially dynamic ocean management.

Amazonian tropical forests account for 20-50% of global primary productivity and up to 40% of carbon stored in terrestrial biomass (Phillips et al., 1998). The Amazon is also home to the Earth’s largest river system, accounting for approximately 20% of the world’s total river discharge (Richey et al., 1989). Despite the clear global significance of the Amazon basin, substantial uncertainties remain in terms of both aboveground wood biomass and carbon storage within its extensive forests (Houghton et al., 2001), and the functioning of its river systems, particularly in terms of floodplain inundation (Wilson et al., 2007). This thesis addresses the aforementioned uncertainties through providing new insight into the interaction between fluvial processes and Amazonian floodplain varzea forests, for the Beni floodplain in north east Bolivia. Flood inundation dynamics for the Beni floodplain are quantified through application of a 1D-2D hydraulic model code, with topographical forcing provided through bare earth DEMs derived from the SRTM global elevation dataset (Farr and Kobrick, 2000). Subsequently, in the final part of the thesis, aboveground wood biomass estimates are generated for the Beni floodplain, through extrapolation of plot scale inventory measurements with respect to spatially distributed remote sensing datasets. These estimates are subsequently integrated with modelled flood inundation and maps depicting Beni river channel migration, in order to explore the influence which fluvial processes exert upon aboveground wood biomass storage in varzea forest stands. Overall, results presented within this thesis quantitatively demonstrate that fluvial processes, specifically flood inundation and lateral channel migration, exert significant impacts upon aboveground biomass storage within Beni floodplain forests. Furthermore, as a result of these influences, aboveground wood biomass storage within Beni floodplain forests is substantially lower than would be expected based upon published estimates for varzea forests across the Amazon (Baker et al., 2004; Saatchi et al., 2007). This implies that systematic overestimation of aboveground wood biomass storage for Amazonian varzea forests may constitute a significant source of uncertainty in basin scale biomass estimates.