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

Radar Remote Sensing SAR

1. Radar and Radar Remote Sensing RADAR stands for Radio Detection and Ranging . It is an active remote sensing system that transmits microwave energy toward the Earth's surface and records the energy that is returned to the sensor as an echo or backscatter . Unlike passive optical remote sensing, radar does not depend on sunlight. Therefore, it can operate day and night and, at suitable wavelengths, can acquire data through clouds, haze and light rain. Hence, radar is widely described as an all-weather, day-and-night remote sensing technology . Basic principle Microwave pulse → transmission → interaction with surface → backscatter/echo → antenna receives signal → signal processing → radar image 2. Microwave Energy Radar systems use microwave electromagnetic radiation , generally in wavelength ranges from approximately 1 mm to 1 m . Important radar bands include: Band Approx. wavelength Common applications ...

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...

Lidar

LiDAR (Light Detection and Ranging) is an active remote sensing technology that measures distances by illuminating a target with laser pulses and analyzing the time it takes for the reflected light to return. Unlike passive systems (e.g., cameras, multispectral sensors), LiDAR provides its own energy source (laser), allowing it to operate both day and night and even penetrate through vegetation canopies . 🔹 How LiDAR Works (Step-by-Step Process) Laser Pulse Emission The system emits rapid, short pulses of laser light (commonly in the near-infrared wavelength, 1064 nm ). Some systems emit up to hundreds of thousands of pulses per second . Interaction with Target Surface The laser beam strikes objects such as vegetation, buildings, or bare ground. Depending on the object's structure, part of the pulse may scatter or reflect. Return Signal Detection The sensor records multiple returns : First Return → typically vegetation canopy tops. ...

Image Transformation

In remote sensing, image transformation is used to create new images by manipulating existing ones, enhancing the information extracted from each pixel. Multi-image manipulation and other transformations allow analysts to highlight specific features, compare time-series data, or reduce data dimensions for better interpretation. 1. Multi-Image Manipulation This technique involves applying mathematical operations to multiple images, typically from different bands, dates, or sensors, to highlight specific features or changes. Common methods include: a. Image Addition Purpose : Highlights areas of similarity or enhances features when the same objects appear in multiple images. How it Works : By adding pixel values from two images, the output image emphasizes areas where brightness is high in both. Example : Enhancing overall brightness or detecting seasonal changes by adding images from different times. b. Image Subtraction Purpose : Identifies differences between images, useful for chan...

Isodata clustering

Iso Cluster Classification in Unsupervised Image Classification Iso Cluster Classification is a common unsupervised classification technique used in remote sensing. The "Iso Cluster" algorithm groups pixels with similar spectral characteristics into clusters, or spectral classes, based solely on the data's statistical properties. Unlike supervised classification, Iso Cluster classification doesn't require the analyst to predefine classes or training areas; instead, the algorithm analyzes the image data to find natural groupings of pixels. The analyst interprets these groups afterward to label them with meaningful information classes (e.g., water, forest, urban). How Iso Cluster Classification Works The Iso Cluster algorithm follows several steps to group pixels: Initial Data Analysis : The algorithm examines the entire dataset to understand the spectral distribution of the pixels across the spectral bands. Clustering Process : - The algorithm starts by divid...

Vineesh V, Geography

Search This Blog