The University of Oregon is pleased to announce that we are offering full research and teaching fellowships for new students admitted to our PhD in Planning and Public Affairs.

The University of Oregon is pleased to announce that we are offering full research and teaching fellowships for new students admitted to our PhD in Planning and Public Affairs. Students pursuing the PhD enroll in one of three disciplinary tracks:

Community and Regional Planning

Public Administration / Public Policy

Nonprofit Management

We are admitting a small cohort for Fall 2021 in one or more of the following research areas:

Sustainable Transportation and Cities Research Group (led by Dr. Marc Schlossberg)

Access and Equity Research Group (led by Dr. Gerardo Sandoval)

Nonprofit, Philanthropic and Social Enterprise Research Group (led by Dr. Renee Irvin)

We expect to admit two to three students, who will be fully supported through research and/or teaching fellowships. This support includes fully paid tuition, health insurance, and a monthly stipend. You can find more detailed information about this program in the attached flyer. Prospective students can also learn more about the program at: https://pppm.uoregon.edu/grad/phd

The application process for the PhD program is online. See more information about our program and faculty on our web page. If you have questions please contact:

Bob Choquette, Graduate Program Coordinator: choquett@uoregon.edu 541-346-3851

Rich Margerum, PhD Program Director: rdm@uoregon.edu 541-346-2526

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

Comments

Raster Data Model

A raster data model represents geographic space as a grid of cells (called pixels ). Think of it like a chessboard covering the Earth. Each square = cell / pixel Each cell contains a value That value represents information about that location Example: Elevation = 245 meters Temperature = 32°C Land use = Forest The grid is arranged in: Rows Columns This structure is called a matrix . GRID Model (Cell-Based Matrix Model) 🔹 Concept The GRID model is the most common raster structure used in GIS for spatial analysis . It is mainly used for: Continuous data (data that changes gradually) Sometimes discrete/thematic data 🔹 Structure A 2D matrix (rows × columns) Each cell stores one numeric value Integer (whole number) Float (decimal number) 🔹 Key Terminologies Cell Resolution → Size of each pixel (e.g., 30m × 30m) Spatial Resolution → Level of detail DEM (Digital Elevation Model) → Elevation grid Raster Calculator → Tool for mathematical operations Overlay Analysis → Combining mu...

Atmospheric Window

The atmospheric window in remote sensing refers to specific wavelength ranges within the electromagnetic spectrum that can pass through the Earth's atmosphere relatively unimpeded. These windows are crucial for remote sensing applications because they allow us to observe the Earth's surface and atmosphere without significant interference from the atmosphere's constituents. Key facts and concepts about atmospheric windows: Visible and Near-Infrared (VNIR) window: This window encompasses wavelengths from approximately 0. 4 to 1. 0 micrometers. It is ideal for observing vegetation, water bodies, and land cover types. Shortwave Infrared (SWIR) window: This window covers wavelengths from approximately 1. 0 to 3. 0 micrometers. It is particularly useful for detecting minerals, water content, and vegetation health. Mid-Infrared (MIR) window: This window spans wavelengths from approximately 3. 0 to 8. 0 micrometers. It is valuable for identifying various materials, incl...

REMOTE SENSING INDICES

Remote sensing indices are band ratios designed to highlight specific surface features (vegetation, soil, water, urban areas, snow, burned areas, etc.) using the spectral reflectance properties of the Earth's surface. They improve classification accuracy and environmental monitoring. 1. Vegetation Indices NDVI – Normalized Difference Vegetation Index Formula: (NIR – RED) / (NIR + RED) Concept: Vegetation reflects strongly in NIR and absorbs in RED due to chlorophyll. Measures: Vegetation greenness & health Uses: Agriculture, drought monitoring, biomass estimation EVI – Enhanced Vegetation Index Formula: G × (NIR – RED) / (NIR + C1×RED – C2×BLUE + L) Concept: Corrects for soil and atmospheric noise. Measures: Vegetation vigor in dense canopies Uses: Tropical rainforest mapping, high biomass regions GNDVI – Green Normalized Difference Vegetation Index Formula: (NIR – GREEN) / (NIR + GREEN) Concept: Uses Green instead of Red ...

Spectral Signature vs. Spectral Reflectance Curve

Spectral Signature A spectral signature is the unique pattern in which an object: absorbs energy reflects energy emits energy across different wavelengths of the electromagnetic spectrum. ✔ Key Points Every natural and man-made object on Earth interacts with sunlight differently. These interactions produce a distinct pattern , just like a "fingerprint". Sensors on satellites record these patterns as digital numbers (DN values) . These patterns help to identify and differentiate objects such as vegetation, soil, water, snow, buildings, minerals, etc. ✔ Examples of Spectral Signatures Healthy vegetation → High reflectance in NIR , strong absorption in red Water → Strong absorption in NIR and SWIR , low reflectance Dry soil → Gradual increase in reflectance from visible to NIR Snow → High reflectance in visible , low in SWIR ✔ Why Spectral Signature Matters It allows: Land cover classification Chan...

Remote Sensing Technology

Remote sensing is a rapidly evolving geospatial technology used to collect information about the Earth's surface and atmosphere without direct physical contact . It involves detecting and measuring electromagnetic radiation (EMR) reflected or emitted from objects using sensors mounted on satellites, aircraft, or drones. Remote sensing systems are fundamentally classified based on (1) the energy source used for illumination and (2) the region of the electromagnetic spectrum utilized for sensing . 1. Types of Remote Sensing Based on Energy Source Remote sensing systems are commonly categorized according to whether the sensor generates its own energy or relies on naturally available radiation . Passive Remote Sensing Principle: Passive remote sensing relies on natural sources of electromagnetic energy , primarily solar radiation reflected from the Earth's surface or thermal radiation emitted by objects. Operation: Most passive sensors operate during daylight when sunlight is av...

Vineesh V, Geography

Search This Blog