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Articles 31 - 45 of 45
Full-Text Articles in Remote Sensing
An Evaluation Of Unmanned Aircraft Systems' Ability To Assess Stripe Rust In Large Wheat Breeding Nursies, Jamison T. Murry
An Evaluation Of Unmanned Aircraft Systems' Ability To Assess Stripe Rust In Large Wheat Breeding Nursies, Jamison T. Murry
Graduate Theses and Dissertations
Stripe Rust (Puccinia striiformis f. sp. tritici) is a foliar disease that significantly impacts global wheat production, and resistant cultivars provide the most efficient method of control. High-throughput phenotyping using unmanned aircraft systems (UAS) offers a potentially more efficient method for field-based phenotyping compared to visual assessment. Here we tested the ability of remote sensing to predict stripe rust severity in a diverse population of 594 soft red winter wheat lines, planted in single-rows, and evaluated them by visually rating stripe rust intensity and remotely using the dark green color index (DGCI), normalized difference vegetation index (NDVI) and blue NDVI. …
Is It Possible To Discern Striga Weed (Striga Hermonthica) Infestation Levels In Maize Agro-Ecological Systems Using In-Situ Spectroscopy?, Bester Tawona Mudereri, Timothy Dube, Saliou Niassy, Emily Kimathi, Tobias Landmann, Zeyaur Khan, Elfatih M. Abdel-Rahman
Is It Possible To Discern Striga Weed (Striga Hermonthica) Infestation Levels In Maize Agro-Ecological Systems Using In-Situ Spectroscopy?, Bester Tawona Mudereri, Timothy Dube, Saliou Niassy, Emily Kimathi, Tobias Landmann, Zeyaur Khan, Elfatih M. Abdel-Rahman
All Peer-Reviewed Publications
The invasion by Striga in most cereal crop fields in Africa has posed a significant threat to food security and has caused substantial socioeconomic losses. Hyperspectral remote sensing is an effective means to discriminate plant species, providing possibilities to track such weed invasions and improve precision agriculture. However, essential baseline information using remotely sensed data is missing, specifically for the Striga weed in Africa. In this study, we investigated the spectral uniqueness of Striga compared to other co-occurring maize crops and weeds. We used the in-situ FieldSpec® Handheld 2™ analytical spectral device (ASD), hyperspectral data and their respective narrow-band indices …
A Comparative Analysis Of Planetscope And Sentinel Sentinel-2 Space-Borne Sensors In Mapping Striga Weed Using Guided Regularised Random Forest Classification Ensemble, B. T. Mudereri, T. Dube, E. M. Adel-Rahman, S. Niassy, E. Kimathi, Z. Khan, T. Landmann
A Comparative Analysis Of Planetscope And Sentinel Sentinel-2 Space-Borne Sensors In Mapping Striga Weed Using Guided Regularised Random Forest Classification Ensemble, B. T. Mudereri, T. Dube, E. M. Adel-Rahman, S. Niassy, E. Kimathi, Z. Khan, T. Landmann
All Peer-Reviewed Publications
Weeds are one of the major restrictions to sustaining crop productivity. Weeds often outcompete crops for nutrients, soil moisture, solar radiation, space and provide platforms for breeding of pests and diseases. The ever-growing global food insecurity triggers the need for spatially explicit innovative geospatial technologies that can deliver timely detection of weeds within agro-ecological systems. This will help pinpoint maize fields to be prioritized for weed control. Satellite remote sensing offers incomparable opportunities for precision agriculture, ecological applications and vegetation characterisation, with vast socioeconomic benefits. This work compares and evaluates the strength of Sentinel-2 (S2) satellite with the constellation of …
Characterizing Spatiotemporal Patterns Of White Mold In Soybean Across South Dakota Using Remote Sensing, Confiance L. Mfuka
Characterizing Spatiotemporal Patterns Of White Mold In Soybean Across South Dakota Using Remote Sensing, Confiance L. Mfuka
Electronic Theses and Dissertations
Soybean is among the most important crops, cultivated primarily for beans, which are used for food, feed, and biofuel. According to FAO, the United States was the biggest soybeans producer in 2016. The main soybean producing regions in the United States are the Corn Belt and the lower Mississippi Valley. Despite its importance, soybean production is reduced by several diseases, among which Sclerotinia stem rot, also known as white mold, a fungal disease that is caused by the fungus Sclerotinia sclerotiorum is among the top 10 soybean diseases. The disease may attack several plants and considerably reduce yield. According to …
Uas-Based Remote Sensing For Weed Identification And Cover Crop Termination Determination, Shailaja Vemula
Uas-Based Remote Sensing For Weed Identification And Cover Crop Termination Determination, Shailaja Vemula
Student Theses and Dissertations
This project aimed at demonstrating the utility of using unmanned aerial system (UAS) based remote sensing to assess percentage weed coverage and cash crop vegetative coverage development corresponding to cover crop (cereal rye) termination at different growth stages. A UAS equipped with an RGB (visible bands) camera was used to acquire aerial imagery of cover crop integrated soybean plots. The specific objectives were: 1) to discriminate crop and weed vegetation based on (a) spectral information and (b) location relative to the crop rows; (2) to verify optimum timing for cover crop termination using vegetative cover development based on visible spectroscopy …
Exploring Spatial And Temporal Variability Of Soil And Crop Processes For Irrigation Management, Javier Reyes
Exploring Spatial And Temporal Variability Of Soil And Crop Processes For Irrigation Management, Javier Reyes
Theses and Dissertations--Plant and Soil Sciences
Irrigation needs to be applied to soils in relatively humid regions such as western Kentucky to supply water for crop uptake to optimize and stabilize yields. Characterization of soil and crop variability at the field scale is needed to apply site specific management and to optimize water application. The objective of this work is to propose a characterization and modeling of soil and crop processes to improve irrigation management. Through an analysis of spatial and temporal behavior of soil and crop variables the variability in the field was identified. Integrative analysis of soil, crop, proximal and remote sensing data was …
Synergistic Use Of Remote Sensing And Modeling To Assess An Anomalously High Chlorophyll-A Event During Summer 2015 In The South Central Red Sea, Wenzhao Li, Hesham El-Askary, K. P. Manikandan, Mohamed A. Qurban, Michael J. Garay, Olga V. Kalishnikova
Synergistic Use Of Remote Sensing And Modeling To Assess An Anomalously High Chlorophyll-A Event During Summer 2015 In The South Central Red Sea, Wenzhao Li, Hesham El-Askary, K. P. Manikandan, Mohamed A. Qurban, Michael J. Garay, Olga V. Kalishnikova
Mathematics, Physics, and Computer Science Faculty Articles and Research
An anomalously high chlorophyll-a (Chl-a) event (>2 mg/m3) during June 2015 in the South Central Red Sea (17.5° to 22°N, 37° to 42°E) was observed using Moderate Resolution Imaging Spectroradiometer (MODIS) data from the Terra and Aqua satellite platforms. This differs from the low Chl-a values (<0.5 mg/m3) usually encountered over the same region during summertime. To assess this anomaly and possible causes, we used a wide range of oceanographical and meteorological datasets, including Chl-a concentrations, sea surface temperature (SST), sea surface height (SSH), mixed layer depth (MLD), ocean current velocity and aerosol optical depth (AOD) obtained from different sensors and models. Findings confirmed this anomalous behavior in the spatial domain using Hovmöller data analysis techniques, while a time series analysis addressed monthly and daily variability. Our analysis suggests that a combination of factors controlling nutrient supply contributed to the anomalous phytoplankton growth. These factors include horizontal transfer of upwelling water through eddy circulation and possible mineral fertilization from atmospheric dust deposition. Coral reefs might have provided extra nutrient supply, yet this is out of the scope of our analysis. We thought that dust deposition from a coastal dust jet event in late June, coinciding with the phytoplankton blooms in the area under investigation, might have also contributed as shown by our AOD findings. However, a lag cross correlation showed a two- month lag between strong dust outbreak and the high Chl-a anomaly. The high Chl-a concentration at the edge of the eddy emphasizes the importance of horizontal advection in fertilizing oligotrophic (nutrient poor) Red Sea waters.
The State Of Tobacco : A Remote Sensing Approach To Understanding Tobacco Crop Production In Kentucky., Laura Krauser
The State Of Tobacco : A Remote Sensing Approach To Understanding Tobacco Crop Production In Kentucky., Laura Krauser
College of Arts & Sciences Senior Theses
Agricultural policy allows for governing bodies to better control the landscape, economy, and security of resources. Because of this power, it is essential for policy and its effects to be thoroughly understood. This study examines the Tobacco Transition Payment Program (TTPP, “tobacco buyout”), in effect from 2005 to 2014, using a mixed methods approach. The TTPP lifted the existing geographic restrictions of tobacco production and deregulated market prices formerly controlled by the government. Kentucky’s economic, social, and agricultural landscapes changed significantly in the wake of this legislation. To explore these changes, this study employs semi-structured interviews and remote sensing analyses …
Use Of Remote Imagery And Object-Based Image Methods To Count Plants In An Open-Field Container Nursery, Josue Nahun Leiva
Use Of Remote Imagery And Object-Based Image Methods To Count Plants In An Open-Field Container Nursery, Josue Nahun Leiva
Graduate Theses and Dissertations
In general, the nursery industry lacks an automated inventory control system. Object-based image analysis (OBIA) software and aerial images could be used to count plants in nurseries. The objectives of this research were: 1) to evaluate the effect of an unmanned aerial vehicle (UAV) flight altitude and plant canopy separation of container-grown plants on count accuracy using aerial images and 2) to evaluate the effect of plant canopy shape, presence of flowers, and plant status (living and dead) on counting accuracy of container-grown plants using remote sensing images. Images were analyzed using Feature Analyst® (FA) and an algorithm trained using …
Automatic Extraction Of Plots From Geo-Registered Uas Imagery Of Crop Fields With Complex Planting Schemes, Anthony A. Hearst
Automatic Extraction Of Plots From Geo-Registered Uas Imagery Of Crop Fields With Complex Planting Schemes, Anthony A. Hearst
Open Access Theses
Complex planting schemes are common in experimental crop fields and can make it difficult to extract plots of interest from high-resolution imagery of the fields gathered by Unmanned Aircraft Systems (UAS). This prevents UAS imagery from being applied in High-Throughput Precision Phenotyping and other areas of agricultural research. If the imagery is accurately geo-registered, then it may be possible to extract plots from the imagery based on their map coordinates. To test this approach, a UAS was used to acquire visual imagery of 5 ha of soybean fields containing 6.0 m2 plots in a complex planting scheme. Sixteen artificial targets …
Remote Sensing Of Green Leaf Area Index In Maize And Soybean: From Close-Range To Satellite, Anthony L. Nguy-Robertson
Remote Sensing Of Green Leaf Area Index In Maize And Soybean: From Close-Range To Satellite, Anthony L. Nguy-Robertson
School of Natural Resources: Dissertations, Theses, and Student Research
This dissertation seeks to explore alternative methodologies for estimating green leaf area index (LAI) and crop developmental stages. Specifically this research [1] developed an approach for creating a Moderate Resolution Imaging Spectroradiometer (MODIS) high spatial resolution product for estimating green LAI on the base of data collected using two different close-range sensors. It was determined that the vegetation indices (VIs) Wide Dynamic Range Vegetation Index (WDRVI) and Enhanced Vegetation Index 2 (EVI2) were capable of accurate estimation of green LAI from MODIS 250 m data using models developed from hyperspectral (RMSE < 0.69 m2 m-2; CV < 33%) or multispectral sensors (RMSE < 0.69 m2 m-2; …
Proximal Sensing As A Means Of Characterizing Phragmites Australis, Travis Yeik
Proximal Sensing As A Means Of Characterizing Phragmites Australis, Travis Yeik
Department of Geography: Dissertations, Theses, and Student Research
Phragmites australis is an invasive wetland weed found throughout much of the United States. Documenting and mapping the growth and spread of this emergent macrophyte can be an important step in developing and implementing successful management strategies. Characterizing the phenology of a vegetation species with a sensor capable of hyperspectral resolution, positioned at close proximity to the canopy of interest, is often a first step necessary for understanding the basic species-specific reflectance patterns, and for quantifying the manner in which light interacts with the plants comprising particular communities. Spectral data over a P. australis canopy were collected during 22 …
Seasonal Adaptation Of Vegetation Color In Satellite Images, Srinivas Jakkula, Vamsi K.R. Mantena, Ramu Pedada, Yuzhong Shen, Jiang Li, Hamid R. Arabnia (Ed.)
Seasonal Adaptation Of Vegetation Color In Satellite Images, Srinivas Jakkula, Vamsi K.R. Mantena, Ramu Pedada, Yuzhong Shen, Jiang Li, Hamid R. Arabnia (Ed.)
Electrical & Computer Engineering Faculty Publications
Remote sensing techniques like NDVI (Normal Difference vegetative Index) when applied to phenological variations in aerial images, ascertained the seasonal rise and decline of photosynthetic activity in different seasons, resulting in different color tones of vegetation. The rise and fall of NDVI values decide the biological response, either the green up or brown down [1]. Vegetation in green up period appears with more vegetative vigor and during brown down period it has a dry appearance. This paper proposes a novel method that identifies vegetative patterns in satellite images and then alters vegetation color to simulate seasonal changes based on training …
Biomass Estimation And Classification Of Secondary Succession Using Radar And Optical Remote Sensing Data Based On Textural And Spectral Analysis In Amazonia, Ping Jiang
All-Inclusive List of Electronic Theses and Dissertations
Deforestation that happened during past decades in the Amazon Basin has been potentially affecting the global climate and carbon-cycle by contributing a vast amount of carbon into the atmosphere. Above-ground biomass (AGB) estimation of secondary successional and mature forests using remote sensing technology has been attracting scientists' attention. Optical sensor data are sensitive to forest canopies only, and therefore have limitations in AGB estimation, especially for tropical forests with complex structures and abundant species. Radar signals have the ability to penetrate canopies to detect the sub-layers of forests, and therefore potentially have a better performance in AGB estimation. Based on …
Manipulation Of High Spatial Resolution Aircraft Remote Sensing Data For Use In Site-Specific Farming, Gabriel B. Senay, Andrew D. Ward, John G. Lyon, Norman R. Fausey, Sue E. Nokes
Manipulation Of High Spatial Resolution Aircraft Remote Sensing Data For Use In Site-Specific Farming, Gabriel B. Senay, Andrew D. Ward, John G. Lyon, Norman R. Fausey, Sue E. Nokes
Biosystems and Agricultural Engineering Faculty Publications
Three spatial data sets consisting of high spatial resolution (1 m) remote sensing images acquired in 12 spectral bands, an on-the-go yield map, and a Digital Elevation Model were co-registered and evaluated for spatial variability studies in a Geographic Information Systems environment. Separate on-the-go yield maps were developed for 3, 5, and 12 statistically significant mean yield classes. For each yield class, the corresponding mean spectral and elevation data were extracted. The relationship between mean spectral and yield data was strongly linear (r = 0.99). Also, a strong linear relationship between mean yield and elevation data (r = 0.92) was …