Browsing by Subject "Remote sensing"

Now showing 1 - 12 of 12
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    Alternative methods for monitoring polar bears in the North American arctic
    (2013-12) Stapleton, Seth
    Because polar bears (Ursus maritimus) are dependent on sea ice, climate change poses a significant threat to their long-term existence. The forecasted impacts of sea ice loss are circumpolar, but to date, effects have been documented in only a few, well-studied populations. Data demonstrating the impacts of climate change are less conclusive or simply lacking elsewhere. In general, current inventory regimes do not enable monitoring with enough regularity to meet the information needs of decision-makers. This reality, combined with pressures from northern communities to reform invasive research techniques (i.e., capture and marking), provided the backdrop for my dissertation. My objective was to implement and evaluate novel, efficient and broadly applicable methods for monitoring polar bears. I first conducted comprehensive aerial (helicopter) surveys of the Foxe Basin population in Nunavut, Canada during the summer, ice-free season. This work demonstrated the utility of the method for estimating the abundance of polar bear populations on land and provided a model for applications in other seasonally ice-free populations. I applied this framework to a neighboring population (Western Hudson Bay) and compared the result to an estimate obtained from physical mark-recapture. This comparison suggested negative bias in the mark-recapture estimate due to spatially limited sampling and resultant capture heterogeneity. Next, I assessed the potential for employing aerial surveys on sea ice in springtime. Although results suggest that detection can be estimated with adequate precision, logistical constraints may hinder the ability to obtain a representative density estimate during springtime. Monitoring programs based on aerial surveys can be designed with sufficient power (>0.8) to detect declines of 40% and 50% over 15- and 30-year periods, with costs comparable to mark-recapture. Costs may be significantly diminished and safety concerns alleviated, however, if bears could be monitored with satellite imagery. I evaluated this technique in a low topography, ice-free setting. Results indicate that bears were reliably identified on imagery, and an estimate of abundance was highly consistent with an independent aerial survey.
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    Constraints On Global And Regional Sources Of Atmospheric Organic Compounds From Space-Based Measurements.
    (2020-03) Chaliyakunnel, Sreelekha
    Emission changes over the tropics and developing regions of the world are causing major adverse effects on human health and air quality. For many of these regions, in-situ measurements are sparse or non-existent; consequently, satellite measurements provide a valuable tool for understanding and predicting atmospheric composition. In this research, I have used the GEOS-Chem chemical transport model to interpret space-based observations of key trace gases such as formic acid (HCOOH) and formaldehyde (HCHO) from multiple satellite instruments in terms of the constraints they provide on volatile organic compound (VOC) emission sources, with a particular focus on Africa and the Indian subcontinent. I demonstrate that current models severely underestimate the abundance of atmospheric formic acid. This discrepancy is most prominent over tropical burning regions, suggesting a major missing source of organic acids from fires. Next, I developed a new modeling framework to analyze formaldehyde observations from two satellite sensors and better quantify regional VOC emissions over the Indian subcontinent. Inverse analyses based on the satellite data reveal that biogenic VOC emissions in the prior bottom-up inventory are overestimated (by ~30-60%) for the Indian subcontinent. The satellite-derived anthropogenic VOC emissions are slightly higher (13-16%) than the prior bottom-up estimate, with some larger regional and seasonal discrepancies. Our analysis reveals that terrestrial vegetation represents the largest VOC source type over the Indian subcontinent (47-53% of the total flux). Anthropogenic emissions account for 37-50% of the annual regional VOC flux and fires provide only a minor fraction (<7%) of the total. Finally, I quantify the decadal (2005-2016) trends in HCHO columns over the Indian subcontinent. After correcting for variability driven by the temperature dependence of biogenic emissions, I interpret the resulting changes in terms of changing anthropogenic and fire VOC emissions in this region.
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    Data and code for remote spectral detection of biodiversity effects on forest biomass
    (2020-08-26) Williams, Laura J; Cavender-Bares, Jeannine; Townsend, Philip A; Couture, John J; Wang, Zhihui; Stefanski, Artur; Messier, Christian; Reich, Peter B; will3972@umn.edu; Williams, Laura J
    Quantifying how biodiversity affects ecosystem functions through time over large spatial extents is needed to meet global biodiversity goals yet is infeasible with field-based approaches alone. Imaging spectroscopy is a tool with potential to help address this challenge. In this study, we demonstrated a spectral approach to assess biodiversity effects in young forests that provides insight into its underlying drivers and could potentially be applied at large spatial scales. Using airborne imaging (NASA AVIRIS-NG) of a tree diversity experiment (IDENT-Cloquet in Cloquet, MN), spectral differences among plots enabled us to quantify net biodiversity effects on stem biomass and canopy nitrogen. In this repository, we present the spectral data and field data along with spectral model coefficients and example code for fitting and applying spectral models to calculate spectral biodiversity effects.
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    Glaciers in the Earth system: an evaluation of the causes and effects of glacier change in southern Patagonia and beyond
    (2022-04) Van Wyk de Vries, Maximillian
    Glaciers do not exist in isolation: they interact with the surrounding Earth System across a wide range of temporal and spatial scales. Global glacier recession driven by anthropogenically forced global warming has both intensified many of the interactions between glaciers and their surroundings and highlighted their importance. Glacier-ocean interactions are one well-known and globally important example, considering that unstable marine-terminating portions of Greenland and West Antarctica hold several metres of potential sea-level rise. However, other important interactions, such as between glaciers and ice-contact lakes, remain largely unexplored. Likewise, a wide suite of connections between glaciers and the surrounding Earth System can influence, and are impacted by glacier change. This thesis explores these interactions with the objective of contributing to a new integrated view of glaciers in the Earth System. The twenty-first century has both highlighted the need to understand glacier retreat and provided scientists with powerful new tools to analyze our cryosphere. In this thesis, I assess the drivers of glacier change by combining high-resolution satellite imagery, lake sediment cores, and field data with numerical modelling. Southern Patagonia is an ideal natural laboratory to examine the interactions between glaciers and the surrounding Earth System, due to its extensive glaciation, high relief, large proglacial lake systems, and extreme climate gradients. We also include two other study locations, the Northern Andes and Uttarakhand Himalaya, in which glacier change has created new geohazards and water security challenges for nearby populations. We find that large ice-contact lakes preserve an high-resolution record of climatic and glacial changes. We identify annual-resolution sediment layering ('varves') in Lago Argentino, the world's largest ice-contact lake, and use this to investigate the dominant controls on sedimentation across the lake. We show that, at Lago Argentino, varves are formed due to seasonal variations in glacial and fluvial sediment fluxes along with a seasonal cycle in lake mixing. In addition, we demonstrate that the dominant climatic controls on sedimentation are summer temperature and wind speed. On a larger scale, we use Lago Argentino's sedimentary record to identify a high late-Holocene eruption rate at the nearby Andean Austral Volcanic Zone, and dominant 200, 150, and 85 year periodicities in the high-latitude Southern Hemisphere's dominant mode of climatic variability, the Southern Annular Mode. Next, we investigate the interactions between glaciers and two major natural hazards: landslides and volcanic eruptions. For the former, we show that landslides can induce extensive and long-lasting effects on glaciers, with a 0.25 km^3 landslide reversing the multi-decadal retreat trend of a Patagonian tidewater glacier. For the latter, we identify a ~75% greater eruption rate in times of low ice volume in Southern Patagonia, due to deglaciation-induced tensional upper-crustal stresses. This process may affect other ice-clad volcanoes around the world. Glaciers can also contribute to major disasters, of which the deadly Chamoli rock-ice avalanche in February 2021 is a devastating example. We combine several advanced remote sensing methods to investigate the pre-collapse conditions of this avalanche, and show that the landslide was mobile more than five years prior to its February 2021 collapse. We also use a newly developed glacier velocity-based method to calculate the thickness and volume of all glaciers in the Northern Andes, where volcano-ice interactions initiated the worst volcanic disaster of the past 100 years. These ice-thickness maps will enable future research to identify zones most vulnerable to glacier-related hazards, and map out future water-resource vulnerabilities. Overall, our results highlight the importance of integrative and collaborative studies for forecasting the future of the world's glaciers, and their impacts on nearby populations.
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    Hydro-meteorological inverse problems via sparse regularization: advanced frameworks for rainfall spaceborne estimation
    (2013-09) Ebtehaj, Mohammad
    The past decades have witnessed a remarkable emergence of new spaceborne and ground-based sources of multiscale remotely sensed geophysical data. Apart from applications related to the study of short-term climatic shifts, availability of these sources of information has improved dramatically our real-time hydro-meteorological forecast skills. Obtaining improved estimates of hydro-meteorological states from a single or multiple low-resolution observations and assimilating them into the background knowledge of a prognostic model have been a subject of growing research in the past decades. In this thesis, with particular emphasis on precipitation data, statistical structure of rainfall images have been thoroughly studied in transform domains (i.e., Fourier and Wavelet). It is mainly found that despite different underlying physical structure of storm events, there are general statistical signatures that can be robustly characterized and exploited as a prior knowledge for solving hydro-meteorological inverse problems such rainfall downscaling, data fusion, retrieval and data assimilation. In particular, it is observed that in the wavelet domain or derivative space, rainfall images are sparse. In other words, a large number of the rainfall expansion coefficients are very close to zero and only a small number of them are significantly non-zero, a manifestation of the non-Gaussian probabilistic structure of rainfall data. To explain this signature, relevant family of probability models including Generalized Gaussian Density (GGD) and a specific class of conditionally linear Gaussian Scale Mixtures (GSM) are studied. Capitalizing on this important but overlooked property of precipitation, new methodologies are proposed to optimally integrate and improve resolution of spaceborne and ground-based precipitation data. In particular, a unified framework is proposed that ties together the problems of downscaling, data fusion and data assimilation via a regularized variational approach, while taking into account the underlying sparsity in an appropriately chosen transform domain. This framework seeks solutions beyond the paradigm of the classic least squares by imposing a proper regularization. The results suggest that sparsity-promoting regularization can reduce uncertainty of estimation in hydro-meteorological inverse problems of downscaling, data fusion, and data assimilation. In continuation of the proposed methodologies, we also introduce a new data driven framework for multisensor spaceborne rainfall retrieval problem and present some preliminary and promising results.
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    Modeling the phenological response to climate change and its impact on carbon cycle in Northeastern U.S. forests
    (2015-03) Xu, Hong
    By controlling the timing of leaf activities, vegetation phenology plays an important role in regulating photosynthesis and other ecosystem processes. As driven by environmental variables, vegetation phenology has been shifting in response to climate change. The shift in vegetation phenology, in turn, exerts various feedbacks to affect the climate system. The magnitude of phenological change and the feedbacks has yet been well understood. The goal of this dissertation is to use phenological model with remote sensing and climate data to quantify historical and future trends in leaf onset and offset in northeastern U.S. forests, and use a dynamic ecosystem model, Agro-IBIS, to quantify the impact of phenological change on terrestrial carbon balance. This dissertation has three major parts. First, six phenological metrics based on remotely sensed vegetation index were evaluated with ground phenological observation in Agro-IBIS. Second, a modified phenological metric was used to parameterize a set of phenological models at regional scale; one model for each of leaf onset and offset were selected to examine historical trends; Agro-IBIS simulations were run to quantify the impact of phenological trends on ecosystem productivities. Finally, downscaled climate projections from global climate models under two emission scenarios were used to drive phenological models to predict the trends in leaf onset and offset in the 21st century; and the impact of photoperiod on leaf onset were particularly examined. The results of this study suggest that remotely sensed phenological metrics can be used to improve phenological models with evaluation and adjustment; advancement of leaf onset and delay of leaf offset in the past have increased productivities and could potentially mitigate the warming temperature in the future; lack of physiological understanding of the driving factors of phenology such as photoperiod could result in large uncertainties in phenological projections.
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    Pipeline right-of-way encroachment in Arepo, Nigeria
    (Journal of Transport and Land Use, 2017) Oyinloye, Michael Ajide; Olamiju, Isaac Oluwadare; Oladosu, Benjamin Lanre
    Encroachment by host communities on pipeline right-of-way (PROW) constitutes a major problem for the oil and gas sector of the economy. This paper uses remote sensing and geographic information system (GIS) technologies to assess the level of vulnerability of people living along the PROW in Arepo, Ogun State, Nigeria. A satellite imagery of the community was acquired and processed using ArcGIS computer software. A GIS buffering operation was performed on the PROW using 15 m, 30 m, 60 m, and 90 m distances, respectively. Three hundred and forty buildings were identified in the buffered zones, out of which 200 (60%) were randomly selected for the study. A structured questionnaire was administered to household heads in the sampled buildings. Empirical analysis shows that 140 buildings (70%) observed less than a 30 m setback to the pipeline. Also, residents benefit from incidents of oil spillage and see these as an avenue to vandalize the pipeline, making them more vulnerable. GIS analysis shows that more than 30% of respondents are highly vulnerable to the hazard of pipeline explosion incidents. Enforcement of setback regulations by the Town Planning Authority and public education and awareness of risks associated with encroachment on the PROW are canvassed among others.
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    Remote sensing of particulate emissions from heavy-duty vehicles: final report on phase I
    (1994-09) Chen, Guoguang; Prochnau, Timothy J.
    This report summarizes the results of initial tests of a remote sensing system capable of real-time detection of particulate emissions from heavy-duty transit vehicles under actual on-road operating conditions. The technique employs optical extinction (sometimes called opacity) to measure concentrations of CO2 and soot in the exhaust plumes from individual vehicles. Two wavelength regions (bands) are used, one of which is sensitive to soot and the other of which is used to monitor CO, levels. From these two measurements, an emissions index can be computed which normalizes the mass of soot emitted by the amount of fuel burned. The primary goal of phase I was ascertain whether the new technique held promise of sufficient sensitivity and accuracy for on-road deployment.
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    Smartphone-Based Travel Experience Sampling and Behavior Intervention among Young Adults
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2012-05) Fan, Yingling; Chen, Qian; Liao, Chen-Fu; Douma, Frank
    This research project aims to develop a data collection application that enables real-time tracking and reporting of the health-related impacts of travel behavior. Using computing, communication, and sensing capabilities of smartphones, an Android phone application—named UbiActive—was developed to collect real-time travel-related physical activity and psychological well-being data from phone users. The application was tested on multiple Android phones, among which Nexus S and HTC Magic were found to produce comparable physical activity outputs with the commercially available accelerometer. The application was further tested in a three-week field study for its viability for real-time data collection and behavior intervention against unhealthy travel behavior. Twenty-three young adults were recruited and randomized into intervention and control groups. Both groups were asked to install UbiActive on their phone and wear their phone on their right hip during all waking hours for three consecutive weeks. The intervention group was provided information on impacts of their travel behavior on physical activity and psychological well-being. No information was provided to the control group. After the field study, all participants were asked to complete a web-based exit survey that was comprised of questions about their general participation experience and specific concerns about the study design, application, compliance requirements, and privacy issues. Findings from the field study show that UbiActive has high potential in collecting travel-related physical activity and psychological experience data, but limited effectiveness in behavior intervention. Findings from the exit survey provide useful insights into potential improvement areas of the study and the UbiActive application.
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    Spectral Detection of Soybean Aphid (Hemiptera: Aphididae) and Confounding Insecticide Effects in Soybean
    (2017-01) Alves, Tavvs
    Soybean aphid, Aphis glycines (Hemiptera: Aphididae) is the primary insect pest of soybean in the northcentral United States. Soybean aphid may cause stunted plants, leaf discoloration, plant death, and decrease soybean yield by 40%. Sampling plans have been developed for supporting soybean aphid management. However, growers’ perception about time involved in direct insect counts has been contributing to a lower adoption of traditional pest scouting methods and may be associated with the use of prophylactic insecticide applications in soybean. Remote sensing of plant spectral (light-derived) responses to soybean aphid feeding is a promising alternative to estimate injury without direct insect counts and, thus, increase adoption and efficiency of scouting programs. This research explored the use of remote sensing of soybean reflectance for detection of soybean aphids and showed that foliar insecticides may have implications for subsequent use of soybean spectral reflectance for pest detection. Chapter 1 was the first publication showing that feeding from soybean aphid affects soybean spectral reflectance. Using ground-based spectroradiometers at canopy-level, it was found that soybean aphids affected plant reflectance at a narrowband wavelength within the near-infrared spectral range (800 nm), but had no effect at a red narrowband wavelength (680 nm). Soybean aphids also affected a vegetation index referred to as NDVI (i.e., normalized difference vegetation index), which combines the near-infrared and red wavelengths into a value representing photosynthetic pigment content and potential ultrastructure changes in soybean leaves. In general, soybean aphids induced similar effects on canopy- and leaf-level spectral measurements, but there were a few instances that significant effects at leaf-level were not detected by canopy-level. Chapter 2 used hyperspectral data and simulated wide-band sensors for detection of soybean aphid. While the first chapter showed that remote sensing is a promising solution based on results from two narrowband wavelengths, the second chapter provided an extensive search for band sensors that could optimize the use of soybean canopy reflectance for soybean aphid detection. Akaike’s Information Criteria (AIC) was used to rank and select sensors. Lower AIC values were considered to provide better models. The subset of narrowband wavelengths that optimized estimation of soybean aphid abundance was similar to that obtained using simulated wide-band sensors. Increasing sensor bandwidth corresponded to larger AIC values (worse models). The smallest AIC values (better models) were observed with narrow- and wide-band sensors centered around 780 nm. Chapter 3 assessed effects of foliar insecticides on spectral response of soybean plants under greenhouse and field conditions. Such effects could potentially confound measures of pest-induced spectral changes. Representatives of the two most commonly used insecticides (i.e., chlorpyrifos and λ-cyhalothrin) and a novel active ingredient referred to as sulfoxaflor affected soybean leaf reflectance. λ-cyhalothrin had the least effect on spectral reflectance and only affected a few near-infrared wavelengths, but sulfoxaflor and chlorpyrifos affected leaf reflectance at several visible and near-infrared wavelengths. I speculated that foliar insecticides had immediate effects via surface residues on plants and delayed effects via morpho-physiological changes induced by the insecticides. The potential leaf surface residues had transitory effects on soybean reflectance and no consistent pattern of spectral changes was associated with the insecticides. Overall, my results hold promise to identify and characterize injury of soybean aphid using remote sensing of soybean canopy reflectance. The information provided in this research may help to design optimized sensors for soybean aphid detection and contribute to the understanding of insect- and insecticide-induced effects on plants. It may also improve the current field-wide management tactics by making decisions for pest control when plant spectral reflectance indicates that soybean aphid abundance reached its economic threshold. To incorporate remote sensing into IPM programs, this new scouting method based on plant spectral reflectance will need further research to adjust economic thresholds, application of insecticides with no or short-duration effects on plant spectral data, and better understanding of other plant-pest interactions affecting plant morpho-physiology. It will be important to distinguish spectral changes induced by soybean aphid from other confounding factors such as other herbivores, nutritional deficiencies, diseases, and water stress. Future research will be needed to determine if the ground-based effects documented in our studies can be detected from space- and air-based platforms, such as satellites and unmanned aerial systems. Moreover, advancing our results may contribute to determine where and when insecticides are needed by using the spatial location of soybean spectral responses to soybean aphid infestations. Remote sensing has the potential to expand the use of IPM practices and collaborate to the mission of feeding an increasing population that has been changing diet habits and will require more production of food.
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    Structured sparse models for classification
    (2012-11) Castrodad, Alexey
    The main focus of this thesis is the modeling and classification of high dimensional data using structured sparsity. Sparse models, where data is assumed to be well represented as a linear combination of a few elements from a dictionary, have gained considerable attention in recent years, and its use has led to state-of-the-art results in many signal and image processing tasks. The success of sparse modeling is highly due to its ability to efficiently use the redundancy of the data and find its underlying structure. On a classification setting, we capitalize on this advantage to properly model and separate the structure of the classes. We design and validate modeling solutions to challenging problems arising in computer vision and remote sensing. We propose both supervised and unsupervised schemes for the modeling of human actions from motion imagery under a wide variety of acquisition conditions. In the supervised case, the main goal is to classify the human actions in the video given a predefined set of actions to learn from. In the unsupervised case, the main goal is to analyze the spatio-temporal dynamics of the individuals in the scene without having any prior information on the actions themselves. We also propose a model for remotely sensed hysperspectral imagery, where the main goal is to perform automatic spectral source separation and mapping at the subpixel level. Finally, we present a sparse model for sensor fusion to exploit the common structure and enforce collaboration of hyperspectral with LiDAR data for better mapping capabilities. In all these scenarios, we demonstrate that these data can be expressed as a combination of atoms from a class-structured dictionary. These data representation becomes essentially a “mixture of classes,” and by directly exploiting the sparse codes, one can attain highly accurate classification performance with relatively unsophisticated classifiers.
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    Three Essays on Economic Applications Using Satellite Imageries of Nighttime Lights
    (2023-12) Maldonado Salazar, Leonardo
    This dissertation aims to unravel applications in the economic field derived from analyzing nighttime light data. The dissertation comprises three essays, each delving into distinct aspects of the relationship between nighttime lights and economic phenomena. The first essay investigates the relationship between nighttime lights and economic activity in oil-dependent countries, exploring whether that relationship differs in oil-producing and non-oil-producing regions. The main findings highlight differences in the predictive power of night light emissions by region, emphasizing the potential of light data to enhance economic growth measures. The second essay uses nighttime light imagery to estimate rural poverty rates in Venezuela from 2000 to 2020. The analysis reveals a significant increase in rural poverty rates between 2014 and 2020, shedding light on the impact of the Venezuelan economic collapse in recent years. Finally, the third essay examines regional inequality in the Andean countries using nighttime lights and population datasets, accounting for temporal and spatial dimensions (based on a multiple-stage nested Theil decomposition approach). The study identifies changes in overall inequality, driven by both between-country and within-country factors, providing insights for targeted initiatives to address inequality at the local level.