Skip to main content

GRADUATE RESEARCH ASSISTANTSHIP IN SPATIAL VARIABILITY – CROP YIELD RELATIONSHIPS - University of Nebraska-Lincoln

GRADUATE RESEARCH ASSISTANTSHIP IN SPATIAL VARIABILITY – CROP YIELD RELATIONSHIPS - University of Nebraska-Lincoln

The University of Nebraska-Lincoln (UNL) invites applications for an MS or PhD graduate research assistantship. The candidate in this assistantship will support work to improve an understanding of spatial variability underlying crop yields and associated producer profitability. Specifically the student will investigate the relationship of the National Commodity Crop Productivity Index (NCCPI), an index in the SSURGO database, to crop yields in Nebraska. These efforts will provide a research-based assessment of the ability of the NCCPI to predict crop  yields in Nebraska. The selected candidate will join a collaborative research team involving faculty in Spatial Sciences (Dr. Yi Qi; https://www.qispatial.com/), Cropping Systems (Dr. Andrea Basche; https://agronomy.unl.edu/basche-research), and Applied Wildlife Ecology (Dr. Andrew Little; https://wildlifeecologylab.unl.edu/).

Responsibilities for the student will include:
·                      Data collection, organization, and analysis of relevant field-scale yield data
·                      Evaluate the spatial relationship of crop yields to the National Commodity Crop Productivity Index (NCCPI)
·                      Conduct spatial analysis and quantitative data analysis to identify hotspots of marginal or less productive regions and mapping their relationship to the NCCPI
·                      Develop map products to allow for visualization and interpretation of results

Qualifications: Applicants must have completed a minimum of a Bachelor of Science degree in a field related Geographical Information Systems, Remote Sensing, and Data Analytics. Applicants should have a GPA ≥3.0. Applicants also should have strong quantitative skills (e.g., correlation analysis, regression analysis) and organizational skills, attention to detail, and excellent oral and written communication skills. Preference will be given to applicants with prior experience or training with GIS (e.g., Esri ArcGIS develop and ArcGIS online), Remote Sensing (e.g., ENVI) or similar software.

GRA Stipend: Starting salary $22,000 for M.S. or $24,000 for Ph.D.
Tuition Waiver: A tuition waiver of up to 12 credit hours per semester and 6-12 credit hours during summer sessions (depending on previous enrollment) is provided with the GRA.
Health Insurance: Students on assistantships are provided health insurance at a reduced rate. 
GRA Availability: Summer or Fall 2020

Application: To be considered for this position, please send a cover letter outlining your interests, research background, and career aspirations as they pertain to this position; a resume or curriculum vitae; copies of transcripts (unofficial); unofficial copies of GRE scores; and contact information for 3 professional references (name, email, phone, address) combined in a single PDF file with the file name formatted as lastname_firstname to Dr. Yi Qi (yi.qi@unl.edu). Review of applications will begin immediately and the position will remain open until filled.

Comments

Popular posts from this blog

Accuracy Assessment

Accuracy assessment is the process of checking how correct your classified satellite image is . 👉 After supervised classification, the satellite image is divided into classes like: Water Forest Agriculture Built-up land Barren land But classification is done using computer algorithms, so some areas may be wrongly classified . 👉 Accuracy assessment helps to answer this question: ✔ "How much of my classified map is correct compared to real ground conditions?"  Goal The main goal is to: Measure reliability of classified maps Identify classification errors Improve classification results Provide scientific validity to research 👉 Without accuracy assessment, a classified map is not considered scientifically reliable . Reference Data (Ground Truth Data) Reference data is real-world information used to check classification accuracy. It can be collected from: ✔ Field survey using GPS ✔ High-resolution satellite images (Google Earth etc.) ✔ Existing maps or survey reports 🧭 Exampl...

History of GIS

1. 1832 - Early Spatial Analysis in Epidemiology:    - Charles Picquet creates a map in Paris detailing cholera deaths per 1,000 inhabitants.    - Utilizes halftone color gradients for visual representation. 2. 1854 - John Snow's Cholera Outbreak Analysis:    - Epidemiologist John Snow identifies cholera outbreak source in London using spatial analysis.    - Maps casualties' residences and nearby water sources to pinpoint the outbreak's origin. 3. Early 20th Century - Photozincography and Layered Mapping:    - Photozincography development allows maps to be split into layers for vegetation, water, etc.    - Introduction of layers, later a key feature in GIS, for separate printing plates. 4. Mid-20th Century - Computer Facilitation of Cartography:    - Waldo Tobler's 1959 publication details using computers for cartography.    - Computer hardware development, driven by nuclear weapon research, leads to broader mapping applications by early 1960s. 5. 1960 - Canada Geograph...

Development and scope of Environmental Geography and Recent concepts in environmental Geography

Environmental Geography studies the relationship between humans and nature in a spatial (place-based) way. It combines Physical Geography (natural processes) and Human Geography (human activities). A. Early Stage 🔹 Environmental Determinism Concept: Nature controls human life. Meaning: Climate, landforms, and soil decide how people live. Example: People in deserts (like Sahara Desert) live differently from people in fertile river valleys. 🔹 Possibilism Concept: Humans can modify nature. Meaning: Environment gives options, but humans make choices. Example: In dry areas like Rajasthan, people use irrigation to grow crops. 👉 In this stage, geography was mostly descriptive (explaining what exists). B. Evolution Stage (Mid-20th Century) Environmental problems increased due to: Industrialization Urbanization Deforestation Pollution Geographers started studying: Environmental degradation Resource management Human impact on ecosystems The field became analytical and problem-solving...

Change Detection

Change detection is the process of finding differences on the Earth's surface over time by comparing satellite images of the same area taken on different dates . After supervised classification , two classified maps (e.g., Year-1 and Year-2) are compared to identify land use / land cover changes .  Goal To detect where , what , and how much change has occurred To monitor urban growth, deforestation, floods, agriculture, etc.  Basic Concept Forest → Forest = No change Forest → Urban = Change detected Key Terminologies Multi-temporal images : Images of the same area at different times Post-classification comparison : Comparing two classified maps Change matrix : Table showing class-to-class change Change / No-change : Whether land cover remains same or different Main Methods Post-classification comparison – Most common and easy Image differencing – Subtract pixel values Image ratioing – Divide pixel values Deep learning methods – Advanced AI-based detection Examples Agricult...

Supervised Classification

Image Classification in Remote Sensing Image classification in remote sensing involves categorizing pixels in an image into thematic classes to produce a map. This process is essential for land use and land cover mapping, environmental studies, and resource management. The two primary methods for classification are Supervised and Unsupervised Classification . Here's a breakdown of these methods and the key stages of image classification. 1. Types of Classification Supervised Classification In supervised classification, the analyst manually defines classes of interest (known as information classes ), such as "water," "urban," or "vegetation," and identifies training areas —sections of the image that are representative of these classes. Using these training areas, the algorithm learns the spectral characteristics of each class and applies them to classify the entire image. When to Use Supervised Classification:   - You have prior knowledge about the c...