Rio Conference, Rio+5 and the Rio+10

The Rio Conference, also known as the United Nations Conference on Environment and Development (UNCED), was held in Rio de Janeiro, Brazil, in June 1992. It was a landmark event that brought together world leaders, policymakers, and representatives from various sectors to address pressing environmental and development issues. The conference aimed to reconcile economic development with environmental protection, leading to the concept of sustainable development.

During the Rio Conference, several important agreements were adopted:

1. Rio Declaration on Environment and Development: This declaration outlined the principles for sustainable development, emphasizing the integration of environmental protection and socio-economic development. It recognized the need for global cooperation, public participation, and intergenerational equity in achieving sustainable development.

2. Agenda 21: Agenda 21 is a comprehensive action plan for sustainable development. It covers various sectors, including poverty eradication, sustainable agriculture, biodiversity conservation, and the role of women and indigenous peoples. Agenda 21 provides guidelines for national and international action to promote sustainable development.

3. United Nations Framework Convention on Climate Change (UNFCCC): The UNFCCC was opened for signature during the Rio Conference. It aimed to stabilize greenhouse gas concentrations in the atmosphere and prevent dangerous human interference with the climate system. The UNFCCC established the basis for subsequent climate negotiations and led to the adoption of the Kyoto Protocol in 1997 and the Paris Agreement in 2015.

Rio+5 refers to the five-year follow-up to the Rio Conference. In 1997, the United Nations General Assembly held a special session called "Earth Summit +5" to review the progress made since the Rio Conference. The session focused on evaluating the implementation of Agenda 21, discussing challenges and achievements, and identifying priorities for further action.

The Rio+10, also known as the World Summit on Sustainable Development (WSSD), took place in Johannesburg, South Africa, in 2002. It aimed to review progress on sustainable development since the Rio Conference and identify new strategies and initiatives. The summit addressed key issues such as poverty eradication, access to clean water, renewable energy, biodiversity conservation, and the role of globalization in sustainable development.

The Johannesburg Summit resulted in the adoption of the Johannesburg Plan of Implementation (JPOI). The JPOI reaffirmed the commitments made in Agenda 21 and outlined specific targets and actions in various areas, including water and sanitation, energy, health, education, and sustainable consumption and production patterns.

The Rio Conference, Rio+5, and Rio+10 played pivotal roles in shaping the global sustainability agenda, promoting sustainable development principles, and encouraging international cooperation to address environmental challenges. These conferences have contributed to the development of multilateral environmental agreements and frameworks that guide global efforts towards a more sustainable and equitable future.

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Geometric Correction

When satellite or aerial images are captured, they often contain distortions (errors in shape, scale, or position) caused by many factors — like Earth's curvature, satellite motion, terrain height (relief), or the Earth's rotation . These distortions make the image not properly aligned with real-world coordinates (latitude and longitude). 👉 Geometric correction is the process of removing these distortions so that every pixel in the image correctly represents its location on the Earth's surface. After geometric correction, the image becomes geographically referenced and can be used with maps and GIS data. Types 1. Systematic Correction Systematic errors are predictable and can be modeled mathematically. They occur due to the geometry and movement of the satellite sensor or the Earth. Common systematic distortions: Scan skew – due to the motion of the sensor as it scans the Earth. Mirror velocity variation – scanning mirror moves at a va...

RADIOMETRIC CORRECTION

Radiometric correction is the process of removing sensor and environmental errors from satellite images so that the measured brightness values (Digital Numbers or DNs) truly represent the Earth's surface reflectance or radiance. In other words, it corrects for sensor defects, illumination differences, and atmospheric effects. 1. Detector Response Calibration Satellite sensors use multiple detectors to scan the Earth's surface. Sometimes, each detector responds slightly differently, causing distortions in the image. Calibration adjusts all detectors to respond uniformly. This includes: (a) De-Striping Problem: Sometimes images show light and dark vertical or horizontal stripes (banding). Caused by one or more detectors drifting away from their normal calibration — they record higher or lower values than others. Common in early Landsat MSS data. Effect: Every few lines (e.g., every 6th line) appear consistently brighter or darker. Soluti...

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...

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...

Vineesh V, Geography

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