Concept of environment. Environmental Thought. Early environmental thinking. Environmentalism.

Geographical Explanation of Concepts:

1. Environment in Geography:

- Geographical Scope: In geography, the environment refers to the physical, biological, and cultural features of a specific area. This includes natural elements like landforms, climate, vegetation, and human-made features such as urban areas and infrastructure.

- Spatial Analysis: Geographers study how these environmental components interact, shaping the landscape and influencing human activities. Spatial patterns, distribution, and environmental changes are key considerations.

2. Environmental Thought in Geography:

- Geographical Perspective: Environmental thought in geography involves examining how human societies perceive and interact with their surroundings. Geographers explore the spatial dimensions of environmental attitudes and beliefs.

- Spatial Variation: Different regions may exhibit varying environmental thoughts influenced by factors like local ecosystems, historical experiences, and economic activities.

3. Early Environmental Thinking in Geography:

- Geographical Context: Early environmental thinking in geography emerged as a response to observable changes in landscapes due to human activities, especially during the Industrial Revolution.

- Spatial Impact: Geographers study how human actions altered geographical features and ecosystems, leading to insights into the spatial distribution of environmental degradation and conservation efforts.

4. Environmentalism in Geography:

- Geographical Dynamics: Environmentalism in geography is a spatially diverse movement advocating for sustainable practices, conservation, and protection of natural resources.

- Spatial Interventions: Geographers analyze how environmentalism manifests in different regions, influencing policies, land use planning, and community initiatives to address geographical challenges such as pollution, deforestation, and climate change.

In geography, these concepts are integral to understanding the dynamic relationships between humans and their environments, offering insights into spatial patterns, variations, and interventions for sustainable geographical development.

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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...

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

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Atmospheric Correction

It is the process of removing the influence of the atmosphere from remotely sensed images so that the data accurately represent the true reflectance of Earth's surface . When a satellite sensor captures an image, the radiation reaching the sensor is affected by gases, water vapor, aerosols, and dust in the atmosphere. These factors scatter and absorb light, changing the brightness and color of the features seen in the image. Although these atmospheric effects are part of the recorded signal, they can distort surface reflectance values , especially when images are compared across different dates or sensors . Therefore, corrections are necessary to make data consistent and physically meaningful. 🔹 Why Do We Need Atmospheric Correction? To retrieve true surface reflectance – It separates the surface signal from atmospheric influence. To ensure comparability – Enables comparing images from different times, seasons, or sensors. To improve visual quality – Remo...

Vineesh V, Geography

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