Master in Environmental & Social Change (MSc, MA, MEnv) @ The University of Winnipeg. Fully funded scholarship and fellowship

The new Master in Environmental & Social Change (MSc, MA, MEnv) program offered through the Department of Geography and Department of Environmental Studies and Sciences at The University of Winnipeg has 14 fully funded positions for excellent students in the following areas:

Catchment Biogeochemistry (MEnv or MSc)

Impacts of climate change on carbon and nutrient cycling in boreal forested catchments

Soil Science (MEnv or MSc)

Selection of amendments to immobilize potentially toxic trace elements released from intermittently flooded agricultural soils

Soil amendments to reduce phosphorus losses from flooded soils in Manitoba

Environmental Governance (MEnv or MA)

A First Nation community-university partnership for capacity enhancement in forest land governance

Climate learning and adaptation for northern development

Harnessing economic, social and political changes forced by COVID-19 to advance adaptation: Rerouting institutional pathways for improved social-ecological resilience

Forest Ecology (MEnv or MSc)

Susceptibility of protected black ash stands to potential regulation of spring lake water level, northern Quebec: a biodiversity conservation issue

Planetary Science (MEnv or MSc)

Searching for biosignatures on Mars

Exploring the Moon for in-situ resources

Analysis of returned asteroid samples: insights into prebiotic chemistry

Oil sands: clays and environmental effects

Climate Science Communication (MA, MEnv, MSc)

Climate change communications: the science of storytelling

Indigenous ways of knowing and climate change

Applications are now open for a Fall 2021 start date.

Dr. Ryan Bullock

Canada Research Chair in Human-Environment Interactions

Co-Chair, Master in Environmental and Social Change (MESC) program

Associate Professor, Environmental Studies & Sciences

The University of Winnipeg

P: 204.988.7594 ; E: r.bullock@uwinnipeg.ca

https://esrg.uwinnipeg.ca/

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

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Photogrammetry – Types of Photographs

In photogrammetry, aerial photographs are categorized based on camera orientation , coverage , and spectral sensitivity . Below is a breakdown of the major types: 1️⃣ Based on Camera Axis Orientation Type Description Key Feature Vertical Photo Taken with the camera axis pointing directly downward (within 3° of vertical). Used for maps and measurements Oblique Photo Taken with the camera axis tilted away from vertical. Covers more area but with distortions Low Oblique: Horizon not visible High Oblique: Horizon visible 2️⃣ Based on Number of Photos Taken Type Description Single Photo One image taken of an area Stereoscopic Pair Two overlapping photos for 3D viewing and depth analysis Strip or Mosaic Series of overlapping photos covering a long area, useful in mapping large regions 3️⃣ Based on Spectral Sensitivity Type Description Application Panchromatic Captures images in black and white General mapping Infrared (IR) Sensitive to infrared radiation Veget...

Photogrammetry – Geometry of a Vertical Photograph

Photogrammetry is the science of making measurements from photographs, especially for mapping and surveying. When the camera axis is perpendicular (vertical) to the ground, the photo is called a vertical photograph , and its geometry is central to accurate mapping. Elements of Vertical Photo Geometry In a vertical aerial photograph , the geometry is governed by the central projection principle. Here's how it works: 1. Principal Point (P) The point on the photo where the optical axis of the camera intersects the photo plane. It's the geometric center of the photo. 2. Nadir Point (N) The point on the ground directly below the camera at the time of exposure. Ideally, in a perfect vertical photo, the nadir and principal point coincide. 3. Photo Center (C) Usually coincides with the principal point in a vertical photo. 4. Ground Coordinates (X, Y, Z) Real-world (map) coordinates of objects photographed. 5. Flying Height (H) He...

Raster Data Structure

Raster Data Raster data is like a digital photo made up of small squares called cells or pixels . Each cell shows something about that spot — like how high it is (elevation), how hot it is (temperature), or what kind of land it is (forest, water, etc.). Think of it like a graph paper where each box is colored to show what's there. Key Points What's in the cell? Each cell stores information — for example, "water" or "forest." Where is the cell? The cell's location comes from its place in the grid (like row 3, column 5). We don't need to store its exact coordinates. How Do We Decide a Cell's Value? Sometimes, one cell covers more than one thing (like part forest and part water). To choose one value , we can: Center Point: Use whatever feature is in the middle. Most Area: Use the feature that takes up the most space in the cell. Most Important: Use the most important feature (like a road or well), even if it...

Photogrammetry

Photogrammetry is the science of taking measurements from photographs —especially to create maps, models, or 3D images of objects, land, or buildings. Imagine you take two pictures of a mountain from slightly different angles. Photogrammetry uses those photos to figure out the shape, size, and position of the mountain—just like our eyes do when we see in 3D! Concepts and Terminologies 1. Photograph A picture captured by a camera , either from the ground (terrestrial) or from above (aerial or drone). 2. Stereo Pair Two overlapping photos taken from different angles. When seen together, they help create a 3D effect —just like how two human eyes work. 3. Overlap To get a 3D model, photos must overlap each other: Forward overlap : Between two photos in a flight line (usually 60–70%) Side overlap : Between adjacent flight lines (usually 30–40%) 4. Scale The ratio of the photo size to real-world size. Example: A 1:10,000 scale photo means 1 cm on the photo...

Logical Data Model in GIS

In GIS, a logical data model defines how data is structured and interrelated—independent of how it is physically stored or implemented. It serves as a blueprint for designing databases, focusing on the organization of entities, their attributes, and relationships, without tying them to a specific database technology. Key Features Abstraction : The logical model operates at an abstract level, emphasizing the conceptual structure of data rather than the technical details of storage or implementation. Entity-Attribute Relationships : It identifies key entities (objects or concepts) and their attributes (properties), as well as the logical relationships between them. Business Rules : Business logic is embedded in the model to enforce rules, constraints, and conditions that ensure data consistency and accuracy. Technology Independence : The logical model is platform-agnostic—it is not tied to any specific database system or storage format. Visual Representat...

Vineesh V, Geography

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