Engineering Commons^™

Enhancing Urban Water Quality Through Biological-Chemical Treatment: Aquatic Macroinvertebrate Community And Temporal Chlorophyll-A Response, Matthew Chaffee

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

With a growing human population, urbanization is impeding a plethora of natural waterways. Of these, urban ponds play a vital role in nutrient sequestration, flood prevention, and habitat sanctuaries. However, nutrient loading can reduce habitat effectiveness and promote harmful algae blooms. To reduce internal nutrient loads, a biological-chemical treatment strategy consisting of floating treatment wetlands (FTWs) and lanthanum were applied to two urban retention ponds, Densmore and Wilderness Ridge Ponds. To measure effectiveness, chlorophyll-a samples were collected and correlated with Sentinel-2. A novel band algorithm termed 3BR1 produced a strong correlation (R² = 0.72) to physical chlorophyll-a …

Characterization Of Physical And Biochemical Traits In Wheat And Corn Plants Using High Throughput Image Analysis, Kantilata Thapa

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Plant phenotyping has been recognized as a rapidly growing field of research due to the labor-intensive, destructive, and time-consuming nature of traditional phenotyping methods. These phenotyping bottlenecks can be addressed by advancements in image-based phenotyping like RGB and hyperspectral imaging for the assessment of plant traits important for breeding purposes. This study aims (1) to characterize the physical and biochemical traits of wheat and corn plants using RGB and hyperspectral imaging in the greenhouse, and (2) to estimate leaf nitrogen (N), phosphorus (P), and potassium (K) content using hyperspectral imaging and an analytical spectral device (ASD spectrometer) and compare the …

Classifying Reflectance Targets Under Ambient Light Conditions Using Passive Spectral Measurements, Ali Hamidisepehr, Michael P. Sama, Joseph S. Dvorak, Ole O. Wendroth, Michael D. Montross

Biosystems and Agricultural Engineering Faculty Publications

Collecting remotely sensed spectral data under varying ambient light conditions is challenging. The objective of this study was to test the ability to classify grayscale targets observed by portable spectrometers under varying ambient light conditions. Two sets of spectrometers covering ultraviolet (UV), visible (VIS), and near−infrared (NIR) wavelengths were instrumented using an embedded computer. One set was uncalibrated and used to measure the raw intensity of light reflected from a target. The other set was calibrated and used to measure downwelling irradiance. Three ambient−light compensation methods that successively built upon each other were investigated. The default method used a variable …

A Decade Of Unmanned Aerial Systems In Irrigated Agriculture In The Western U.S., Jose L. Chavez, Alfonso F. Torres-Rua, Wayne E. Woldt, Huihui Zhang, Christopher Robertson, Gary W. Marek, Dong Wang, Derek M. Heeren, Saleh Taghvaeian, Christopher M. U. Neale

Biological Systems Engineering: Papers and Publications

Several research institutes, laboratories, academic programs, and service companies around the United States have been developing programs to utilize small unmanned aerial systems (sUAS) as an instrument to improve the efficiency of in-field water and agronomical management. This article describes a decade of efforts on research and development efforts focused on UAS technologies and methodologies developed for irrigation management, including the evolution of aircraft and sensors in contrast to data from satellites. Federal Aviation Administration (FAA) regulations for UAS operation in agriculture have been synthesized along with proposed modifications to enhance UAS contributions to irrigated agriculture. Although it is feasible …

Unmanned Aircraft System (Uas) Technology And Applications In Agriculture, Samuel C. Hassler, Fulya Baysal-Gurel

Agricultural and Environmental Sciences Faculty Research

Numerous sensors have been developed over time for precision agriculture; though, only recently have these sensors been incorporated into the new realm of unmanned aircraft systems (UAS). This UAS technology has allowed for a more integrated and optimized approach to various farming tasks such as field mapping, plant stress detection, biomass estimation, weed management, inventory counting, and chemical spraying, among others. These systems can be highly specialized depending on the particular goals of the researcher or farmer, yet many aspects of UAS are similar. All systems require an underlying platform—or unmanned aerial vehicle (UAV)—and one or more peripherals and sensing …

Variable Rate Irrigation Of Maize And Soybean In West-Central Nebraska Under Full And Deficit Irrigation, J Burdette Barker, Sandeep Bhatti, Derek M. Heeren, Christopher M.U. Neale, Daran Rudnick

Biological Systems Engineering: Papers and Publications

Variable rate irrigation (VRI) may improve center pivot irrigation management, including deficit irrigation. A remote-sensing-based evapotranspiration model was implemented with Landsat imagery to manage irrigations for a VRI equipped center pivot irrigated field located in West-Central Nebraska planted to maize in 2017 and soybean in 2018. In 2017, the study included VRI using the model, and uniform irrigation using neutron attenuation for full irrigation with no intended water stress (VRI-Full and Uniform-Full treatments, respectively). In 2018, two deficit irrigation treatments were added (VRI-Deficit and Uniform-Deficit, respectively) and the model was modified in an attempt to reduce water balance drift; model …

Automated Tree-Level Forest Quantification Using Airborne Lidar, Hamid Hamraz

Theses and Dissertations--Computer Science

Traditional forest management relies on a small field sample and interpretation of aerial photography that not only are costly to execute but also yield inaccurate estimates of the entire forest in question. Airborne light detection and ranging (LiDAR) is a remote sensing technology that records point clouds representing the 3D structure of a forest canopy and the terrain underneath. We present a method for segmenting individual trees from the LiDAR point clouds without making prior assumptions about tree crown shapes and sizes. We then present a method that vertically stratifies the point cloud to an overstory and multiple understory tree …

Using Remote Sensing To Estimate Crop Water Use To Improve Irrigation Water Management, Arturo Reyes-Gonzalez

Electronic Theses and Dissertations

Irrigation water is scarce. Hence, accurate estimation of crop water use is necessary for proper irrigation managements and water conservation. Satellite-based remote sensing is a tool that can estimate crop water use efficiently. Several models have been developed to estimate crop water requirement or actual evapotranspiration (ETa) using remote sensing. One of them is the Mapping EvapoTranspiration at High Resolution using Internalized Calibration (METRIC) model. This model has been compared with other methods for ET estimations including weighing lysimeters, pan evaporation, Bowen Ratio Energy Balance System (BREBS), Eddy Covariance (EC), and sap flow. However, comparison of METRIC model outputs to …

A Method For Reflectance Index Wavelength Selection From Moisture-Controlled Soil And Crop Residue Samples, Ali Hamidisepehr, Michael P. Sama, Aaron P. Turner, Ole O. Wendroth

Biosystems and Agricultural Engineering Faculty Publications

Reflectance indices are a method for reducing the dimensionality of spectral measurements used to quantify material properties. Choosing the optimal wavelengths for developing an index based on a given material and property of interest is made difficult by the large number of wavelengths typically available to choose from and the lack of homogeneity when remotely sensing agricultural materials. This study aimed to determine the feasibility of using a low-cost method for sensing the moisture content of background materials in traditional crop remote sensing. Moisture-controlled soil and wheat stalk residue samples were measured at varying heights using a reflectance probe connected …

Assessing The Limitations And Capabilities Of Lidar And Landsat 8 To Estimate The Aboveground Vegetation Biomass And Cover In A Rangeland Ecosystem Using A Machine Learning Algorithm, Shital Dhakal

Boise State University Theses and Dissertations

Remote sensing based quantification of semiarid rangeland vegetation provides the large scale observations required for monitoring native plant distribution, estimating fuel loads, modeling climate and hydrological dynamics, and measuring carbon storage. Fine scale 3-dimensional vertical structural information from airborne lidar and improved signal to noise ratio and radiometric resolution of recent satellite imagery provide opportunities for refined measurements of vegetation structure.

In this study, we leverage a large number of time series Landsat 8 vegetation indices and lidar point cloud - based vegetation metrics with ground validation for scaling aboveground shrub and herb biomass and cover from small scale plot …

Use Of Remote Sensing To Support Forest And Wetlands Policies In The Usa, Audrey L. Mayer, Ricardo D. Lopez

College of Forest Resources and Environmental Science Publications

The use of remote sensing for environmental policy development is now quite common and well-documented, as images from remote sensing platforms are often used to focus attention on emerging environmental issues and spur debate on potential policy solutions. However, its use in policy implementation and evaluation has not been examined in much detail. Here we examine the use of remote sensing to support the implementation and enforcement of policies regarding the conservation of forests and wetlands in the USA. Specifically, we focus on the “Roadless Rule” and “Travel Management Rules” as enforced by the US Department of Agriculture Forest Service …

Design Of Laser Multi-Beam Generator For Plant Discrimination, Sreten Askraba, Arie Paap, Kamal Alameh, John Rowe

Research outputs 2011

Optimisation of the optical signal from the laser multi-spot beam generator employed in a photonic based real-time plant discrimination sensor for use in selective herbicide spraying systems is presented. The plant detection sensor uses a three-wavelength laser diode module that sequentially emits identically-polarized laser light beams through a common aperture, along one optical path. Each laser beam enters a multi-spot beam generator which produces 15 parallel laser beams over a 240mm span. The intensity of the reflected light from each spot is detected by a high-speed line scan image sensor. Plant discrimination is based on calculating the slope of the …

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 …

Narrow-Band And Derivative-Based Vegetation Indices For Hyperspectral Data, Kelly Robert Thorp, Lei Tian, Haibo Yao, Lie Tang

Lie Tang

Hyperspectral remote sensing imagery was collected over a soybean field in central Illinois in mid-June 2001 before canopy closure. Estimates of percent vegetation cover were generated through the processing of RGB (red, green, blue) digital images collected on the ground with an automated crop mapping system. A comparative study was completed to test the ability of broad-band, narrow-band, and derivative-based vegetation indices to predict percent soybean cover at levels less than 70%. Though remote sensing imagery is commonly analyzed using reference data collected at random points over a scene of interest, the analysis of the hyperspectral imagery in this research …

Soil Moisture Estimation From Remotely Sensed Hyperspectral Data, Amy L. Kaleita, Lei F. Tian, Haibo Yao

Amy L. Kaleita

A methodology for mapping surface soil moisture content across an agricultural field from optical remote sensing and ground sampling is developed. This study uses both ground-based and remotely sensed spectral measurements of soil reflectance in visible and near-infrared wavelengths and concurrent measurements of volumetric soil moisture within the top 6 cm. After determining appropriate wavelengths for soil moisture estimation from spectral reflectance, a cokriging scheme was used to generate soil moisture maps. Results indicate that combining reflectance and ground measurements can yield more detailed maps of soil moisture than ground measurement alone.

Soil Moisture Estimation From Remotely Sensed Hyperspectral Data, Amy L. Kaleita, Lei Tian, Haibo Yao

Amy L. Kaleita

A methodology for mapping surface soil moisture content across an agricultural field from optical remote sensing and ground sampling is developed. This study uses both ground-based and remotely sensed spectral measurements of soil reflectance in visible and near-infrared wavelengths and concurrent measurements of volumetric soil moisture within the top 6 cm. After determining appropriate wavelengths for soil moisture estimation from spectral reflectance, a cokriging scheme was used to generate soil moisture maps. Results indicate that combining reflectance and ground measurements can yield more detailed maps of soil moisture than ground measurement alone.

Remote Sensing Of Site-Specific Soil Characteristics For Precision Farming, Amy L. Kaleita, Lei F. Tian

Amy L. Kaleita

A methodology for assessing distributed surface soil moisture content from optical remote sensing is developed. This study uses both ground-based and remotely sensed spectral measurements of soil reflectance in visible and near-infrared wavelengths and concurrent measurements of volumetric soil moisture within the top 6 cm to establish a relationship between spectral response and moisture. Various approaches, including principal component analyses and regression techniques are investigated to determine the potential for quantifying soil moisture from the spectral reflection data. Preliminary investigations have yielded R 2 values as high as 0.62 when comparing predictions to actual moisture values. Investigation of predicting soil …

Remote Sensing Of Site-Specific Soil Characteristics For Precision Farming, Amy L. Kaleita, Lei Tian

Amy L. Kaleita

A methodology for assessing distributed surface soil moisture content from optical remote sensing is developed. This study uses both ground-based and remotely sensed spectral measurements of soil reflectance in visible and near-infrared wavelengths and concurrent measurements of volumetric soil moisture within the top 6 cm to establish a relationship between spectral response and moisture. Various approaches, including principal component analyses and regression techniques are investigated to determine the potential for quantifying soil moisture from the spectral reflection data. Preliminary investigations have yielded R 2 values as high as 0.62 when comparing predictions to actual moisture values. Investigation of predicting soil …

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 …