Browsing by Subject "Remote sensing"
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Item A two-branch multi-scale residual attention network for single image super-resolution in remote sensing imagery(IEEE, 2024) Patnaik, Allen; Bhuyan, Manas K.; MacDorman, Karl F.High-resolution remote sensing imagery finds applications in diverse fields, such as land-use mapping, crop planning, and disaster surveillance. To offer detailed and precise insights, reconstructing edges, textures, and other features is crucial. Despite recent advances in detail enhancement through deep learning, disparities between original and reconstructed images persist. To address this challenge, we propose a two-branch multiscale residual attention network for single-image super-resolution reconstruction. The network gathers complex information about input images from two branches with convolution layers of different kernel sizes. The two branches extract both low-level and high-level features from the input image. The network incorporates multiscale efficient channel attention and spatial attention blocks to capture channel and spatial dependencies in the feature maps. This results in more discriminative features and more accurate predictions. Moreover, residual modules with skip connections can help to overcome the vanishing gradient problem. We trained the proposed model on the WHU-RS19 dataset, collated from Google Earth satellite imagery, and validated it on the UC Merced, RSSCN7, AID, and real-world satellite datasets. The experimental results show that our network uses features at different levels of detail more effectively than state-of-the-art models.Item Designing and experimenting with e-DTS 3.0(2014-08-29) Phadke, Aboli Manas; Raje, Rajeev; Tuceryan, Mihran; Liang, YaoWith the advances in embedded technology and the omnipresence of smartphones, tracking systems do not need to be confined to a specific tracking environment. By introducing mobile devices into a tracking system, we can leverage their mobility and the availability of multiple sensors such as camera, Wi-Fi, Bluetooth and Inertial sensors. This thesis proposes to improve the existing tracking systems, enhanced Distributed Tracking System (e-DTS 2.0) [19] and enhanced Distributed Object Tracking System (eDOTS)[26], in the form of e-DTS 3.0 and provides an empirical analysis of these improvements. The enhancements proposed are to introduce Android-based mobile devices into the tracking system, to use multiple sensors on the mobile devices such as the camera, the Wi-Fi and Bluetooth sensors and inertial sensors and to utilize possible resources that may be available in the environment to make the tracking opportunistic. This thesis empirically validates the proposed enhancements through the experiments carried out on a prototype of e-DTS 3.0.Item Exploring the Utility of High Resolution Imagery for Determining Wetland Signatures(2012-07-03) DeLury, Judith Ann; Wilson, Jeffrey S. (Jeffrey Scott), 1967-; Ottensmann, John R.; Tedesco, Lenore P.Wetland habitats are characterized by periodic inundation and saturation by water creating anaerobic conditions that generate hydric soils and support hydrophytic vegetation. Wetland habitats provide important ecological functions including breeding grounds for fish, other wildlife, water purification, reduction in flooding, species diversity, recreation, food production, aesthetic value, and transformation of nutrients (Tiner, 1999). The multiple benefits of wetlands make them an important resource to monitor. A literature review suggests a combination of geospatial variables and methods should be tested for appropriateness in wetland delineation within local settings. Advancements in geospatial data technology and ease of accessing new, higher resolution geospatial data make study at local levels easier and more feasible (Barrette et al, 2000). The purpose of the current study is to evaluate new sources of geospatial data as potential variables to improve wetland identification and delineation. High resolution multispectral digital imagery, topographic data, and soils information are used to derive and evaluate independent variables. Regression analysis was used to analyze the data.Item A Habitat Suitability Model for Ricord’s Iguana in the Dominican Republic(2009-06-23T20:28:12Z) Dine, James; Wilson, Jeffrey S. (Jeffrey Scott), 1967-; Banerjee, Aniruddha; Ramer, JanThe 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.Item How satellites can help control the spread of diseases such as Zika(The Conversation US, Inc., 2016-02-15) Moreno-Madriñán, Max Jacobo; Epidemiology, Richard M. Fairbanks School of Public HealthItem Improvement and use of radiative transfer models to assess lunar space weathering and mechanisms for swirl formation(2015-06-15) Liu, Dawei; Li, Lin; Jacinthe, Pierre-André; Wang, Lixin; Cheng, Ruihua; Johnson, DanielThis dissertation focuses on quantification of submicroscopic iron of different sizes, mineral abundance and grain size of lunar soils using Hapke's radiative transfer model. The main objective is to explore implications of these results for assessing the relative importance of solar wind implantation versus micrometeorite impacts for lunar space weathering as well as three hypotheses (solar wind deflection, comet impact and dust transport) for swirl formation on the Moon. Results from this study can help to make connections between ordinary chondritic meteorites and asteroids, and put physical and chemical constraints on heating processes in the early solar system.Item Invasion of Spartina alterniflora in the coastal zone of mainland China: Control achievements from 2015 to 2020 towards the Sustainable Development Goals(Elsevier, 2022-12-01) Li, Huiying; Mao, Dehua; Wang, Zongming; Huang, Xiao; Li, Lin; Jia, Mingming; Earth and Environmental Sciences, School of ScienceThe Sustainable Development Goals (SDGs) and the Convention on Biological Diversity's 15th Conference of the Parties (CBD COP15) both emphasized the urgency of protecting biological diversity. Spartina alterniflora (S. alterniflora), as an invasive species in China, has posed severe biodiversity challenges, demanding nationwide control and management. This study aims to assess the effectiveness of S. alterniflora management during China's SDGs implementation from 2015 to 2020. Landsat images acquired in 2015 (the beginning year of SDGs), 2018, and 2020 (the end year of SDGs' targets 6.6, 14.2, 14.5, and 15.8 related to alien invasion) were applied to quantify the spatiotemporal dynamics of S. alterniflora extent. The results revealed a consistent shrinkage of S. alterniflora, with a net areal reduction of 2610 ha from 2015 to 2020, implying the effectiveness of control measures on S. alterniflora invasion. Provinces including Zhejiang, Jiangsu, and Shanghai have succeeded in controlling S. alterniflora, evidenced by the sharp reduction in S. alterniflora area by 4908 ha, 2176 ha, and 1034 ha, respectively, from 2015 to 2020. However, better management of S. alterniflora is needed in regions with more severe S. alterniflora invasion, e.g., Shandong, Fujian, and Guangdong provinces. Our results suggest that relevant policies, regulations, and ecological restoration projects implemented by national or local governments in China received satisfactory results in S. alterniflora control. Nevertheless, S. alterniflora potential utilities and its governance effectiveness should be objectively evaluated and weighed to obtain the greatest ecological benefits and promote sustainable coastal ecosystems. The results of this study are expected to provide important baseline information benefitting the formulation of coastal protection and restoration strategies in China.Item Leveraging the NEON Airborne Observation Platform for socio-environmental systems research(Wiley, 2021) Ordway, Elsa M.; Elmore, Andrew J.; Kolstoe, Sonja; Quinn, John E.; Swanwick, Rachel; Cattau, Megan; Taillie, Dylan; Guinn, Steven M.; Chadwick, K. Dana; Atkins, Jeff W.; Blake, Rachael E.; Chapman, Melissa; Cobourn, Kelly; Goulden, Tristan; Helmus, Matthew R.; Hondula, Kelly; Hritz, Carrie; Jensen, Jennifer; Julian, Jason P.; Kuwayama, Yusuke; Lulla, Vijay; O’Leary, Donal; Nelson, Donald R.; Ocón, Jonathan P.; Pau, Stephanie; Ponce-Campos, Guillermo E.; Portillo-Quintero, Carlos; Pricope, Narcisa G.; Rivero, Rosanna G.; Schneider, Laura; Steele, Meredith; Tulbure, Mirela G.; Williamson, Matthew A.; Wilson, Cyril; Geography, School of Liberal ArtsDuring the 21st century, human–environment interactions will increasingly expose both systems to risks, but also yield opportunities for improvement as we gain insight into these complex, coupled systems. Human–environment interactions operate over multiple spatial and temporal scales, requiring large data volumes of multi-resolution information for analysis. Climate change, land-use change, urbanization, and wildfires, for example, can affect regions differently depending on ecological and socioeconomic structures. The relative scarcity of data on both humans and natural systems at the relevant extent can be prohibitive when pursuing inquiries into these complex relationships. We explore the value of multitemporal, high-density, and high-resolution LiDAR, imaging spectroscopy, and digital camera data from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) for Socio-Environmental Systems (SES) research. In addition to providing an overview of NEON AOP datasets and outlining specific applications for addressing SES questions, we highlight current challenges and provide recommendations for the SES research community to improve and expand its use of this platform for SES research. The coordinated, nationwide AOP remote sensing data, collected annually over the next 30 yr, offer exciting opportunities for cross-site analyses and comparison, upscaling metrics derived from LiDAR and hyperspectral datasets across larger spatial extents, and addressing questions across diverse scales. Integrating AOP data with other SES datasets will allow researchers to investigate complex systems and provide urgently needed policy recommendations for socio-environmental challenges. We urge the SES research community to further explore questions and theories in social and economic disciplines that might leverage NEON AOP data.Item REMOTE SENSING DATA ASSIMILATION IN WATER QUALITY NUMERICAL MODELS FOR SIMULATION OF WATER COLUMN TEMPERATURE(2012-03-16) Xie, Shuangshuang; Babbar-Sebens, Meghna; Li, Lin; Zhu, LuodingNumerical models are important tools for simulating processes within complex natural systems, such as hydrodynamics and water quality processes within a water body. From decision makers’ perspectives, such models also serve as useful tools for predicting the impacts of water quality problems or develop early warning systems. However, accuracy of a numerical model developed for a specific site is dependent on multiple model parameters and variables whose values are attained via calibration processes and/or expert knowledge. Real time variations in the actual aquatic system at a site necessitate continuous monitoring of the system so that model parameters and variables are regularly updated to reflect accurate conditions. Multiple sources of observations can help adjust the model better by providing benefits of individual monitoring technology within the model updating process. For example, remote sensing data provide a spatially dense dataset of model variables at the surface of a water body, while in-situ monitoring technologies can provide data at multiple depths and at more frequent time intervals than remote sensing technologies. This research aims to present an overview of an integrated modeling and data assimilation framework that combines three-dimensional numerical model with multiple sources of observations to simulate water column temperature in a eutrophic reservoir in central Indiana. A variational data assimilation approach is investigated for incorporating spatially continuous remote sensing observations and spatially discrete in-situ observations to change initial conditions of the numerical model. This research addresses the challenge of improving the model performance by combining water temperature from multi-spectral remote sensing analysis and in-situ measurements. Results of the approach on a eutrophic reservoir in Central Indiana show that with four images of multi-spectral remote sensing data assimilated, the model results oscillate more from the in-situ measurements during the data assimilation period. For validation, the data assimilation has negative impacts on the root mean square error. According to quantitative analysis, more significant water temperature stratification leads to larger deviations. Sampling depth differences for remote sensing technology, in-situ measurements and model output are considered as possible error source.Item REMOTE SENSING OF WATER COLOR: MODEL SENSITIVITY ANALYSIS AND ESTIMATION OF PHYTOPLANKTON SIZE FRACTIONS(2013-08-14) Li, Zuchuan; Li, Lin; Babbar-Sebens, Meghna; Wilson, Jeffrey S. (Jeffrey Scott), 1967-Phytoplankton size classes (pico-plankton, nano-plankton, and micro-plankton) provide information about pelagic ocean ecosystem structure, and their spatiotemporal variation is crucial in understanding ocean ecosystem structure and global carbon cycling. Remote sensing provides an efficient approach to estimate phytoplankton size compositions on global scale. In the first part of this thesis, a global sensitivity analysis method was used to determine factors mainly controlling the variations of remote sensing reflectance and inherent optical properties inverse algorithms. To achieve these purposes, average mass-specific coefficients of particles were first calculated through Mie theory, using particle size distributions and refractive indices as input; and then a synthesis remote sensing reflectance dataset was created using Hydrolight. Based on sensitivity analysis results, an algorithm for estimating phytoplankton size composition was proposed in the second part. This algorithm uses five types of spectral features: original and normalized remote sensing reflectance, two-band ratios, continuum removed spectra, and spectral curvatures. With the spectral features, phytoplankton size compositions were regressed using support vector machine. According to validation results, this algorithm performs well with simulated and satellite Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS), indicating that it is possible to estimate phytoplankton size compositions through satellite data on global scale.