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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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...

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...

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...

Pre During and Post Disaster

Disaster management is a structured approach aimed at reducing risks, responding effectively, and ensuring a swift recovery from disasters. It consists of three main phases: Pre-Disaster (Mitigation & Preparedness), During Disaster (Response), and Post-Disaster (Recovery). These phases involve various strategies, policies, and actions to protect lives, property, and the environment. Below is a breakdown of each phase with key concepts, terminologies, and examples. 1. Pre-Disaster Phase (Mitigation and Preparedness) Mitigation: This phase focuses on reducing the severity of a disaster by minimizing risks and vulnerabilities. It involves structural and non-structural measures. Hazard Identification: Recognizing potential natural and human-made hazards (e.g., earthquakes, floods, industrial accidents). Risk Assessment: Evaluating the probability and consequences of disasters using GIS, remote sensing, and historical data. Vulnerability Analysis: Identifying areas and p...

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...

Vineesh V, Geography

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