Ph.D. or M.Sc. Assistantship in Hyperspectral Remote Sensing of Biodiversity Oklahoma State University

Description: We invite applications for a Ph.D. or M.Sc. position in the field of remote sensing at Oklahoma State University. The successful candidate will use airborne and spaceborne hyperspectral data (from DLR's DESIS sensor), as well as in-situ measurements (functional traits and species diversity) to (1) map grassland diversity and (2) detect the spread of an invasive alien species in the Joseph H. Williams Tallgrass Prairie Preserve. The position will be housed in the Department of Geography at Oklahoma State University. The target start date is August 2021.

Qualifications: Applicants with a masters' degree or bachelor's degree (at the time of appointment) in physical sciences (remote sensing, ecology, environmental science, geography, plant biology), engineering (environmental, optical), or other related fields with relevant research or work experience (e.g., hyperspectral remote sensing, landscape ecology, spatial modeling) are encouraged to apply. Programming is expected to be the core for remote sensing data analysis. Therefore, having previous experience and knowledge on how to code (e.g., MATLAB, Python, R) is ideal. Familiarity with shell scripting, Linux command line tools, and high performance computing for image processing will be ideal (but not necessary). Ability to work independently and excellent written and oral communication skills will be desirable.

To Apply: If interested in the positon or for more information, please contact Dr. Hamed Gholizadeh (hamed.gholizadeh@okstate.edu) and include your CV and a brief cover letter explaining your research and career interests. Review of the applications will begin immediately and will continue until position is filled. A full application to the Graduate College will be required for an official offer to be made. More information can be found at https://geog.okstate.edu/programs/graduate-program/application-procedures

Oklahoma State University, as an equal opportunity employer, complies with all applicable federal and state laws regarding non-discrimination and affirmative action. Oklahoma State University is committed to a policy of equal opportunity for all individuals and does not discriminate based on race, religion, age, sex, color, national origin, marital status, sexual orientation, gender identity/expression, disability, or veteran status with regard to employment, educational programs and activities, and/or admissions. For more information, visit https://eeo.okstate.edu.

Vineesh V
Assistant Professor of Geography,
Directorate of Education,
Government of Kerala.
https://www.facebook.com/Applied.Geography
http://geogisgeo.blogspot.com

Comments

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...

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...

Supervised Classification

In the context of Remote Sensing (RS) and Digital Image Processing (DIP) , supervised classification is the process where an analyst defines "training sites" (Areas of Interest or ROIs) representing known land cover classes (e.g., Water, Forest, Urban). The computer then uses these training samples to teach an algorithm how to classify the rest of the image pixels. The algorithms used to classify these pixels are generally divided into two broad categories: Parametric and Nonparametric decision rules. Parametric Decision Rules These algorithms assume that the pixel values in the training data follow a specific statistical distribution—almost always the Gaussian (Normal) distribution (the "Bell Curve"). Key Concept: They model the data using statistical parameters: the Mean vector ( $\mu$ ) and the Covariance matrix ( $\Sigma$ ) . Analogy: Imagine trying to fit a smooth hill over your data points. If a new point lands high up on the hill, it belongs to that cl...

History of GIS

The history of Geographic Information Systems (GIS) is rooted in early efforts to understand spatial relationships and patterns, long before the advent of digital computers. While modern GIS emerged in the mid-20th century with advances in computing, its conceptual foundations lie in cartography, spatial analysis, and thematic mapping. Early Roots of Spatial Analysis (Pre-1960s) One of the earliest documented applications of spatial analysis dates back to 1832 , when Charles Picquet , a French geographer and cartographer, produced a cholera mortality map of Paris. In his report Rapport sur la marche et les effets du choléra dans Paris et le département de la Seine , Picquet used graduated color shading to represent cholera deaths per 1,000 inhabitants across 48 districts. This work is widely regarded as an early example of choropleth mapping and thematic cartography applied to epidemiology. A landmark moment in the history of spatial analysis occurred in 1854 , when John Snow inv...

Representation of Spatial and Temporal Relationships

In GIS, spatial and temporal relationships allow the integration of location (the "where") and time (the "when") to analyze phenomena across space and time. This combination is fundamental to studying dynamic processes such as urban growth, land-use changes, or natural disasters. Key Concepts and Terminologies Geographic Coordinates : Define the position of features on Earth using latitude, longitude, or other coordinate systems. Example: A building's location can be represented as (11.6994° N, 76.0773° E). Timestamp : Represents the temporal aspect of data, such as the date or time a phenomenon was observed. Example: A landslide occurrence recorded on 30/07/2024 . Spatial and Temporal Relationships : Describes how features relate in space and time. These relationships can be: Spatial : Topological (e.g., "intersects"), directional (e.g., "north of"), or proximity-based (e.g., "near"). Temporal : Sequential (e....

Vineesh V, Geography

Search This Blog