Skip to main content

SPOT HRV and HRVIR

SPOT, which stands for "Satellite Pour l'Observation de la Terre" in French, is a system of commercial Earth imaging satellites. These satellites are equipped with high-resolution optical cameras to capture pictures of the Earth.

HRV (High Resolution Visible)
and
HRVIR (High Resolution Visible IR) detectors.

SPOT 1 was launched in February 1986 and operated until November 2003 when it was taken out of orbit. It had a high-resolution optical camera onboard.

SPOT 2 was launched in January 1990 and operated until July 2009 before being deorbited. It also had a high-resolution optical camera.

SPOT 3 was launched in September 1993 and worked for three years until a malfunction in November 1996. It had a similar high-resolution optical camera.

SPOT 4 was launched in March 1998 and retired in June 2013, carrying a high-resolution optical camera.

SPOT 5 was launched in May 2002 and operated until March 2015. This satellite had advanced cameras for 3D terrain modeling.

SPOT 6, launched in September 2012, and SPOT 7, launched in June 2014, together formed a satellite constellation that continued to provide high-resolution Earth imagery until March 2023. These satellites carried the NAOMI instrument.

Comments

Popular posts from this blog

KSHEC Scholarship 2024-25

KSHEC Scholarship 2024-25 Alert! First-Year UG Students Only, Don't Miss This Golden Opportunity! πŸ’‘βœ¨ Are you a first-year undergraduate student studying in a Government or Aided College in Kerala? Do you need financial assistance to continue your education without stress? The Kerala State Higher Education Council (KSHEC) Scholarship is here to support YOU!  This scholarship is a lifeline for deserving students, helping them focus on their studies without worrying about financial burdens. If you meet the criteria, APPLY NOW and take a step towards a brighter future! 🌟 βœ… Simple Online Application – Quick & easy process!  πŸ“Œ Who Can Apply? βœ”οΈ First-year UG students ONLY βœ”οΈ Must be studying in an Arts & Science Government or Aided college in Kerala βœ”οΈ Professional Course students are not eligible  πŸ”Ή Scholarship Amounts Per Year: πŸ“Œ 1st Year FYUGP – β‚Ή12,000 πŸ“Œ 2nd Year FYUGP – β‚Ή18,000 πŸ“Œ 3rd Year FYUGP – β‚Ή24,000 πŸ“Œ 4th Year FYUGP – β‚Ή40,000 πŸ“Œ 5th Year PG – β‚Ή60,000  Great News...

Disaster Management

1. Disaster Risk Analysis β†’ Disaster Risk Reduction β†’ Disaster Management Cycle Disaster Risk Analysis is the first step in managing disasters. It involves assessing potential hazards, identifying vulnerable populations, and estimating possible impacts. Once risks are identified, Disaster Risk Reduction (DRR) strategies come into play. DRR aims to reduce risk and enhance resilience through planning, infrastructure development, and policy enforcement. The Disaster Management Cycle then ensures a structured approach by dividing actions into pre-disaster, during-disaster, and post-disaster phases . Example Connection: Imagine a coastal city prone to cyclones: Risk Analysis identifies low-lying areas and weak infrastructure. Risk Reduction includes building seawalls, enforcing strict building codes, and training residents for emergency situations. The Disaster Management Cycle ensures ongoing preparedness, immediate response during a cyclone, and long-term recovery afterw...

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...

Role of Geography in Disaster Management

Geography plays a pivotal role in disaster management by facilitating an understanding of the impact of natural disasters, guiding preparedness efforts, and supporting effective response and recovery. By analyzing geographical features, environmental conditions, and historical data, geography empowers disaster management professionals to identify risks, plan for hazards, respond to emergencies, assess damage, and monitor recovery. Geographic Information Systems (GIS) serve as crucial tools, providing critical spatial data for informed decision-making throughout the disaster management cycle. Key Concepts, Terminologies, and Examples 1. Identifying Risk: Concept: Risk identification involves analyzing geographical areas to understand their susceptibility to specific natural disasters. By studying historical events, topography, climate patterns, and environmental factors, disaster management experts can predict which regions are most vulnerable. Terminologies: Hazard Risk: The pr...

GIS Concepts

S patial Data Components Location or Position This defines where a spatial object exists on the Earth's surface. It is represented using coordinate systems , such as: Geographic Coordinate System (GCS) – Uses latitude and longitude (e.g., WGS84). Projected Coordinate System (PCS) – Converts Earth's curved surface into a flat map using projections (e.g., UTM, Mercator). Example: The Eiffel Tower is located at 48.8584Β° N, 2.2945Β° E in the WGS84 coordinate system. Attribute Data (Descriptive Information About Location) Describes characteristics of spatial features and is stored in attribute tables . Types of attribute data: Nominal Data – Categories without a numerical value (e.g., land use type: residential, commercial). Ordinal Data – Ranked categories (e.g., soil quality: poor, moderate, good). Interval Data – Numeric values without a true zero (e.g., temperature in Β°C). Ratio Data – Numeric values with a true zero (e.g., population count, rainfall amoun...