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Soil moisture estimates from space on a continuous spatial domain could afford researchers with insight about agricultural productivity, flood vulnerability, and biological processes. To evaluate satellite soil moisture estimates, the SMAPVEX-16 experiment was one of a suite of verification data collection campaigns for NASA’s Soil Moisture Active Passive satellite. Soil moisture and its role in rainfall partitioning are of great interest to researchers at the Iowa Flood Center [IFC], which was founded in Iowa City, Iowa after a devastating flood event in 2008. A network of two dual-pol capable X-band radar units owned by the IFC, as well as five tipping bucket rain gauges, complemented by 15 from the USDA’s Agricultural Research Service were deployed in Central Iowa from May to August 2016 to monitor precipitation on a fine spatiotemporal domain. The data from this particular experiment was analyzed. Several radar-rainfall algorithms were assembled with a focus on assimilating multivariate radar data. Different variables allow researchers to overcome problems due to signal attenuation by X-band radars, and process radar observations into rainfall accumulations by several methods popular in the literature. Special techniques for accumulating instantaneous rainfall rates at discrete observation intervals were employed to account for the movement of storms. The rain totals between the observation points were estimated and the accumulations were compared to the rain gauge totals. Methods of rain rate calculation that assimilate many sources of data, such as radar reflectivity, differential reflectivity, and specific differential phase shift yielded the best results.

Icings, also known as aufeis, are groundwater fed sheet-layered ice bodies that normally forms in local depression or more often in low angled, shallow river beds. Understanding their distribution in the Mackenzie Valley corridor (N.W.T.) and adjacent Yukon (618,430 km2) provided important insights to groundwater discharge and recharge. This study aimed at; i) creating the first extensive map of icings in Northwestern Canada, using over 500 late-winter scene Landsat 5 and 7; and ii) assessing hydrographic parameters (streamflow, baseflow and winter contribution) and terrain factors (slope, permafrost, geology) on icing distribution at the watershed level. Results show that; 1) icings are likely to develop close to geological faults on carbonate foothills and mountainous terrain, where continuous permafrost is present and on slopes of less than 5 degrees; 2) in the continuous permafrost zone, the cumulative surface area of icings, winter discharge and winter contribution to total annual discharge have significant positive relations with watershed extents. Icings located at the southern boundary of continuous permafrost are more sensitive to degrading permafrost and the predicted increase in groundwater discharge which may lead to a later icing accretion and earlier ablation during the year.

This research investigates land use change and the area of occurrence of an introduced primate species, Chlorocebus sabaeus, from 1940 until the present. Research into the importation and subsequent release of these monkeys has revealed that they were released from a failed tourist attraction in 1947. The attraction was located southeast of the Hollywood International Airport in Fort Lauderdale, Florida. Remote sensing techniques were utilized to examine land use change over time, create a land classification map, and create a canopy model. These data were used to better understand the area of occurrence of an introduced primate species by examining anthropogenic changes through time and measuring changes in available forest habitat. Corridors, and their transformation through the decades, were evaluated to better understand potential dispersal routes and connectivity to natural areas for colonization. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

The effects of scale influence all aspects of spatial analysis and should be expressly considered early in research planning. Remotely sensed images provide unique landscape perspectives and possess several features amenable to dealing with scale. In particular, images can be segmented into image objects representative of landscape features and structured as nested hierarchies for evaluating landscape patterns across a range of scales. The objectives of this research are to evaluate methods for: 1) characterizing candidate image objects to inform the selection of user-supplied segmentation parameters and 2) exploring the multi-scale structure of landscape patterns for defining and describing potentially important scales for conducting subsequent geospatial and ecological investigations. I followed a recursive strategy to develop an image hierarchy using a corrected version of the normalized difference vegetation index (NDVIc) derived from a Landsat ETM+ satellite image over a complex, forested landscape at Lava Cast Forest (LCF), Oregon. At each scale level, I calculated an objective function based on within-object variance and spatial autocorrelation to distinguish between alternative image objects created with the region-merging segmentation algorithm available in the Definiens Developer 7 software. Segmentation quality was considered highest for results exhibiting the lowest overall within-object variance and between-object spatial autocorrelation. I then applied geographical variance analysis to calculate the independent contribution and relative variability of each level in the hierarchy to evaluate the scene's spatial structure across scales. My results reveal overall trends in image object spatial variance consistent with scaling theory, but suggest judging image object quality without sampling the entire range of segmentation parameters is insufficient. Statistical limitations of the spatial autocorrelation coefficient at small sample sizes constrained the number of possible hierarchy levels within the image spatial extent, preventing identification of larger-scale landscape patterns. Geographical variance analysis results show patterns in vegetation conditions at LCF possess a multi-scaled structure. Three levels exhibiting high variance relative to the entire hierarchy coincide with abrupt transitions in the slopes of within-object variance and spatial autocorrelation trends, which I interpreted as scale thresholds potentially important for relating landscape patterns and processes. These methods provide an objective, object-oriented approach for addressing scale issues within heterogeneous landscapes using remote sensing.

The seafloor properties are of high importance for many applications such as marine biology, oil and gas exploration, laying cables, dredging operations and off-shore construction. Several approaches exist to classify the properties of the seabed. These include taking direct samples of the seabed (e.g., coring), however, these methods are costly and slow. Underwater acoustic remote sensing techniques are of interest because they are lower cost and faster. The information about the seabed properties can be extracted by studying the energy of single beam echo sounders (SBES). This can be done by either phenomenological or numerical methods [1], [2]. This research investigates a numerical, model-data fitting method using a high frequency backscattering model developed by Jackson et al [3]. In this "inversion modeling" method, the matching process between the model and average echo envelope provides information about the sediment parameters, namely the sediment mean grain size (Mz) as the indicator of the seabed type, spectral parameter (W2) as the indicator of seabed roughness and normalized sediment volume parameter σ2 as the indicator of the scattering due to sediment inhomogeneities.

Thesis (M.Sc. (Agriculture)) --University of Limpopo, 2009 / The purpose of the study was to assess nitrogen variability in tomato using the Remote Sensing Technique. The assessment was carried out through three growth stages (seedling, 50% flowering, and 50% fruiting stage). The GreenSeeker optical sensor unit that records NDVI values and total leafy nitrogen analyzer, “The Primacssn Nitrogen Analyzer,” was used in this study for data collection. Fertilizers were applied to the soil (Urea - 46% N, Superphosphate) every two weeks in the pots only for the treated experiment, and no nitrogen application for the untreated experiment. Tomato cultivars Flora Dade and Roma VF were used during the experimentation. The mean NDVI values for cultivars Flora Dade and Roma VF were 0.83 with N application. This value was 0.81 without N-application. The mean N-content for cultivars Flora Dade and Roma VF were 3.30 g/plant with N application. This value was 2.94 g/plant without N-application. The cultivar Flora Dade with N applied had higher N-content (3.38 g/plant) than the cultivar Roma VF with 3.22 g/plant when no N is applied across the three growth stages. The number of fruits’ means values at 50% fruiting stage for cultivars Flora Dade and Roma VF were 8.9 fruit per plant with N application. These mean values were 5 fruit per plant without N application. It was also evident that plants likely to have lower N content (untreated) had delayed maturation unlike those with nitrogen applied (treated), which had rapid/early maturation. Untreated plants took an average of 120 days till maturity, whereas the treated plants took an average of 100 days till maturity. Ground measurement of NDVI by the GreenSeeker sensor in this study showed potential for assessing nitrogen variability in tomato. / National Research Foundation

A new model for the estimation of quantities redundantly measured as noisy outer products is presented and applied to a previously intractable problem in hyperspectral signal processing. We propose the rank-one outer-product model for a nearly rank-one data set. The estimator, when applied to a matrix of data which approximates this model, allows robust and efficient factorization (or decomposition) of the matrix into its vector factors. This method takes advantage of the inherent plaid structure of the data matrix and, using singular value decomposition, separates the data into left and right spanning sets from which the estimates of the outer-product factors are obtained. This model enjoys freedom from ground truth during estimation and we fit the measurement data to the model without regression. The singular values are used to determine quality of fit of the estimates. This estimator is used to solve simultaneously for depth and diffuse attenuation coefficient from airborne hyperspectral image data of the ocean without use of ground truth / acase@tulane.edu

Modeling of surface energy fluxes and evapotranspiration (ET) requires the understanding of the interaction between land and atmosphere as well as the appropriate representation of the associated spatial and temporal variability and heterogeneity. This dissertation provides new methodology showing how to rationally and properly incorporate surface features characteristics/properties, including the leaf area index, fraction of cover, vegetation height, and temperature, using different representations as well as identify the related effects on energy balance flux estimates including ET. The main research objectives were addressed in Chapters 2 through 4 with each presented in a separate paper format with Chapter 1 presenting an introduction and Chapter 5 providing summary and recommendations. Chapter 2 discusses a new approach of incorporating temporal and spatial variability of surface features. We coupled a remote sensing-based energy balance model with a traditional water balance method to provide improved estimates of ET. This approach was tested over rainfed agricultural fields ~ 10 km by 30 km in Ames, Iowa. Before coupling, we modified the water balance method by incorporating a remote sensing-based estimate for one of its parameters to ameliorate its performance on a spatial basis. Promising results were obtained with indications of improved estimates of ET and soil moisture in the root zone. The effects of surface features heterogeneity on measurements of turbulence were investigated in Chapter 3. Scintillometer-based measurements/estimates of sensible heat flux (H) were obtained over the riparian zone of the Cibola National Wildlife Refuge (CNWR), California. Surface roughness including canopy height (hc), roughness length, and zero-plane displacement height were incorporated in different ways, to improve estimates of H. High resolution, 1-m maps of ground surface digital elevation model and canopy height, hc, were derived from airborne LiDAR sensor data to support the analysis. The effects of using different pixel resolutions to account for surface feature variability on modeling energy fluxes, e.g., net radiation, soil, sensible, and latent heat, were studied in Chapter 4. Two different modeling approaches were applied to estimate energy fluxes and ET using high and low pixel resolution datasets obtained from airborne and Landsat sensors, respectively, provided over the riparian zone of the CNWR, California. Enhanced LiDAR-based hc maps were also used to support the modeling process. The related effects were described relative to leaf area index, fraction of cover, hc, soil moisture status at root zone, groundwater table level, and vegetation stress conditions.

The Middle Rio Grande River (MRGR) is the main source of fresh water for the state of New Mexico. Located in an arid area with scarce local water resources, this has led to extensive diversions of river water to supply the high demand from municipalities and irrigated agricultural activities. The extensive water diversions over the last few decades have affected the composition of the native riparian vegetation by decreasing the area of cottonwood and coyote willow and increasing the spread of invasive species such as Tamarisk and Russian Olives, harmful to the river system, due to their high transpiration rates, which affect the river aquatic system. The need to study the river hydrological processes and their relation with its health is important to preserve the river ecosystem. To be able to do that a detailed vegetation map was produced using a Utah State University airborne remote sensing system for 286 km of river reach. Also a groundwater model was built in ArcGIS environment which has the ability to estimate soil water potential in the root zone and above the modeled water table. The Modified Penman- Monteith empirical equation was used in the ArcGIS environment to estimate riparian vegetation ET, taking advantage of the detailed vegetation map and spatial soil water potential layers. Vegetation water use per linear river reach was estimated to help decision makers to better manage and release the amount of water that keeps a sound river ecosystem and to support agricultural activities.