https://medium.com/@gis.remotesensingeducation

GIS and Remote Sensing Education, Dhaka (2026)

11/07/2026

15 𝐨𝐜𝐞𝐚𝐧 𝐝𝐚𝐭𝐚 𝐬𝐨𝐮𝐫𝐜𝐞𝐬 by Ocean Science Analytics worth bookmarking:

1. NASA - National Aeronautics and Space Administration Earthdata – Ocean color, sea surface temperature, sea level, winds, and many other Earth observation datasets.
https://lnkd.in/gDG53zV

2. Copernicus Marine Service (CMEMS) – Global and regional physical, biogeochemical, and ocean forecast products.
https://lnkd.in/e7rQ_gCV

3. Global Ocean Observing System (GOOS) – International coordination of ocean observing systems and data networks.
https://goosocean.org/

4. Argo Program – Autonomous profiling floats measuring temperature, salinity, oxygen, and more.
https://argo.ucsd.edu/

5. Ocean Observatories Initiative (OOI) – Open-access mooring, glider, cabled array, and hydrophone observations.
https://lnkd.in/eHeJbAQ

6. Integrated Marine Observing System (IMOS) – Australia's national ocean observing network, providing biological, physical, acoustic, and biogeochemical observations.
https://imos.org.au/

7. GBIF: The Global Biodiversity Information Facility – Global biodiversity occurrence records, including marine species.
https://www.gbif.org/

8. Ocean Networks Canada (ONC) – Continuous ocean observations from cabled observatories, including hydrophone recordings.
https://lnkd.in/dWxd2K_y

9. JAMSTEC: Japan Agency for Marine-Earth Science and Technology Data Portal – Oceanographic, climate, deep-sea, and marine geology datasets from Japan.
https://lnkd.in/g62HUvsG

10. EMODnet – European Marine Observation and Data Network covering bathymetry, biology, chemistry, habitats, geology, and human activities.
https://lnkd.in/eWbcfadm

11. GEBCO - General Bathymetric Chart of the Oceans – The world's most comprehensive publicly available bathymetry.
https://www.gebco.net/

12. OBIS-SEAMAP – Marine mammal, seabird, and sea turtle observations.
https://lnkd.in/gEwwiV95

13. NOAA National Centers for Environmental Information (NCEI) – One of the world's largest repositories of oceanographic, fisheries, climate, and bathymetric datasets.
https://www.ncei.noaa.gov/

14. SeaDataNet – A pan-European infrastructure providing standardized access to marine environmental data from over 100 organizations.
https://lnkd.in/gw-Fdh4v

15. British Oceanographic Data Centre (BODC) – Long-term marine observations, cruises, moorings, and oceanographic datasets from the UK and beyond.
https://www.bodc.ac.uk/

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19/06/2026

▶️▶️Weighted Overlay Analysis in GIS⏬

Weighted Overlay Analysis is a multi-criteria decision-making (MCDM) technique widely used in Geographic Information Systems (GIS) to identify the most suitable locations for a specific purpose. The method integrates multiple raster-based criteria, such as elevation, slope, rainfall, land use, soil type, proximity to roads, or groundwater potential, into a single suitability map.

The process begins by preparing and standardizing individual raster layers. Each layer is then reclassified into a common suitability scale, typically ranging from low to high suitability. Based on their relative importance, weights are assigned to each criterion, ensuring that the total weight equals 100%.

The weighted overlay operation combines all reclassified layers using their assigned weights to calculate a final suitability score for every raster cell. Areas with higher scores represent locations that better satisfy the defined criteria, while lower scores indicate less suitable areas.

This analytical approach is extensively applied in land suitability assessment, groundwater potential mapping, agricultural planning, urban development, environmental conservation, disaster risk assessment, renewable energy site selection, transportation planning, and natural resource management.

**Key Components of Weighted Overlay Analysis:**

▶️ Raster-based thematic layers
▶️Reclassification of criteria
▶️Weight assignment based on importance
▶️Overlay and suitability computation
▶️Generation of final suitability maps
▶️Spatial decision support for planning and management

By transforming complex spatial datasets into actionable information, Weighted Overlay Analysis enables researchers, planners, and decision-makers to make objective, transparent, and scientifically supported decisions.

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29/05/2026

🌧️ Flood Mitigation Starts Before the Monsoon

Flooding is not caused by rainfall alone.
Terrain, drainage conditions, land use changes, urban expansion, and runoff characteristics all play a major role in determining flood risk.

Using GIS, Remote Sensing, and Satellite Data, Flood Susceptibility Mapping helps identify:

✅ Water logging hotspots
✅ Flood-prone zones
✅ Poor drainage regions
✅ High runoff areas
✅ Vulnerable urban locations

before heavy rainfall events occur.

By integrating DEM analysis, rainfall datasets, hydrology, drainage networks, and land use mapping, geospatial technologies can support smarter disaster preparedness and climate-resilient planning.

🛰️ GIS + Hydrology + Satellite Intelligence can transform flood management from reactive response to proactive planning.

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21/05/2026

🌍💧 Climate Change Is Redefining Hydrologic & Hydraulic Design

Traditional flood estimation methods are no longer sufficient in a rapidly changing climate. Future-ready water resources planning now requires integrating:

✅ Hydrological Modeling
✅ Frequency Analysis
✅ Hydraulic Modeling
✅ Climate Change Scenarios

A climate-informed workflow helps engineers and researchers evaluate how changing precipitation patterns, temperature rise, and extreme events influence runoff generation, flood peaks, inundation extent, and infrastructure resilience.

Key Components of the Framework:

🔹 Data collection & preprocessing
🔹 Hydrological model calibration and validation
🔹 Climate model downscaling & bias correction
🔹 Extreme event frequency analysis (GEV, Log-Pearson III, Gumbel)
🔹 Design discharge estimation under future scenarios
🔹 1D/2D hydraulic simulations and floodplain mapping
🔹 Risk-informed decision support for resilient infrastructure

📌 Integrating climate projections into hydrologic and hydraulic studies is essential for:
✔ Sustainable urban drainage
✔ Flood risk reduction
✔ Climate-resilient infrastructure
✔ Watershed management
✔ Early warning systems
✔ Water security planning

The future of water engineering lies in combining physics-based models, climate science, GIS, and data-driven analytics to support adaptive decision-making.

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17/05/2026

🌍 Main Known Models for Drought and Land Degradation Assessment

Understanding drought and land degradation is essential for agriculture, water management, environmental protection, and climate resilience. In GIS and remote sensing, several well-known models and indices are used to assess these risks.

1️⃣ Drought Assessment Models / Indices

These focus on water stress, rainfall anomalies, vegetation condition, and climate pressure.

SPI — Standardized Precipitation Index
Used to measure drought based on rainfall anomalies.

SPEI — Standardized Precipitation Evapotranspiration Index
Extends SPI by including both precipitation and evaporative demand, making it very useful under climate change conditions.

PDSI — Palmer Drought Severity Index
A classic drought index used to track long-term moisture deficiency.

VCI — Vegetation Condition Index
Derived from NDVI and used to monitor vegetation stress caused by drought.

VHI — Vegetation Health Index
Combines vegetation condition and thermal stress to better assess drought impacts on plants.

✅ These indices are commonly used for meteorological drought, agricultural drought, and vegetation monitoring.

2️⃣ Land Degradation Assessment Models

These focus on soil erosion, desertification, land sensitivity, and degradation processes.

RUSLE — Revised Universal Soil Loss Equation
Widely used to estimate soil erosion risk caused by rainfall and runoff.

USLE — Universal Soil Loss Equation
The classic soil erosion model and the basis for later improved versions like RUSLE.

MEDALUS — Mediterranean Desertification and Land Use model
Used to identify environmentally sensitive areas to desertification.

PESERA — Pan-European Soil Erosion Risk Assessment
Designed for large-scale soil erosion assessment across landscapes.

WEQ / RWEQ — Wind Erosion Equation / Revised Wind Erosion Equation
Used to evaluate erosion risk caused by wind, especially in dry and semi-arid regions.

✅ These models are commonly applied in soil conservation, desertification studies, watershed management, and land degradation mapping.

3️⃣ Main Difference

The key difference is simple:

💧 Drought models focus on:

* rainfall shortage
* evapotranspiration
* water stress
* vegetation response
* climate conditions

🌱 Land degradation models focus on:

* soil loss
* erosion processes
* desertification
* land sensitivity
* management impacts

4️⃣ Typical Data Inputs

Most of these models rely on one or more of the following datasets:

* Rainfall
* Temperature
* NDVI / vegetation indices
* DEM / slope
* Soil data
* Land use / land cover
* Evapotranspiration

5️⃣ Why This Matters

These models help decision-makers answer important questions such as:

* Which areas are most affected by drought?
* Where is vegetation under stress?
* Which lands are vulnerable to soil erosion?
* Where is desertification risk highest?
* What conservation measures should be prioritized?

💡 In short:
Drought assessment looks mainly at water deficit and vegetation response, while land degradation assessment focuses more on erosion, soil quality, and land sensitivity.

Together, these models are powerful tools for GIS-based environmental monitoring, sustainable land management, and climate adaptation planning.

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17/05/2026

𝗔𝘃𝗼𝗶𝗱𝗶𝗻𝗴 𝗠𝗶𝘀𝘁𝗮𝗸𝗲𝘀 𝗶𝗻 𝗚𝗜𝗦 🚨

A beautiful map means nothing if the analysis is wrong. 🗺️❌

Here are some common GIS mistakes every spatial analyst should avoid:

🔹 Using the wrong coordinate systemOne projection error can shift your entire dataset and ruin analysis accuracy.
🔹 Ignoring data quality“Garbage in, garbage out.” Always check for missing values, overlaps, gaps, and topology errors.
🔹 Poor data organizationMessy folders and unnamed layers = future headache. Organize your geodatabase properly.
🔹 Overlooking metadataIf you don’t know where the data came from, how reliable is your result?
🔹 Wrong spatial analysis methodNot every problem needs Kriging, IDW, or Buffer analysis. Choose methods based on the problem, not popularity.
🔹 Using outdated imagery or datasetsUrban areas change fast. Always confirm the date and relevance of your data.
🔹 Making maps without cartographic principlesToo many colors, poor labeling, and cluttered layouts can confuse your audience.
🔹 Forgetting field verificationGIS analysis becomes stronger when combined with ground truthing and field observations.

💡 In GIS, accuracy is everything. A small mistake in data processing can lead to huge planning, environmental, or decision-making errors.

𝗚𝗼𝗼𝗱 𝗚𝗜𝗦 𝗶𝘀𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗮𝗯𝗼𝘂𝘁 𝗺𝗮𝗸𝗶𝗻𝗴 𝗺𝗮𝗽𝘀 — 𝗶𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝗺𝗮𝗸𝗶𝗻𝗴 𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀. 🌍

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14/05/2026

🛰️ Different Types of Resolution in Remote Sensing

In remote sensing, satellite images are not only about “seeing the Earth from space.” Their quality and usefulness depend strongly on different types of resolution.

Understanding these resolutions is essential for GIS, environmental monitoring, agriculture, climate studies, and Earth observation.

1️⃣ Spectral Resolution
Spectral resolution describes the ability of a sensor to detect different wavelength bands of the electromagnetic spectrum.

In simple words:
👉 It tells us how many spectral bands a satellite can capture and how narrow those bands are.

A sensor with more and narrower bands can better distinguish between different materials such as vegetation, water, soil, urban areas, and minerals.

Examples:
- Multispectral sensors capture a limited number of broad bands
- Hyperspectral sensors capture many narrow bands with more detail

💡 Spectral resolution = wavelength detail

2️⃣ Radiometric Resolution
Radiometric resolution describes how sensitive a sensor is to small differences in reflected or emitted energy.

In simple words:
👉 It tells us how many brightness levels the sensor can record.

For example:
- 8-bit data = 256 brightness levels
- 12-bit data = 4,096 brightness levels
- 16-bit data = 65,536 brightness levels

Higher radiometric resolution helps detect subtle differences in surface conditions, such as vegetation stress, soil moisture variation, or water quality changes.

💡 Radiometric resolution = brightness sensitivity

3️⃣ Temporal Resolution
Temporal resolution describes how often a satellite revisits the same place on Earth.

In simple words:
👉 It tells us how frequently we can monitor change.

High temporal resolution is very useful for tracking dynamic phenomena such as floods, droughts, crop growth, wildfires, storms, and seasonal vegetation changes.

For example, a satellite that revisits an area every 5 days has better temporal resolution than one that revisits every 16 days.

💡 Temporal resolution = revisit frequency

✅ Why does this matter?

Choosing the right satellite data depends on your objective:

🌱 For vegetation health → spectral resolution is important
🌊 For flood monitoring → temporal resolution is critical
🌡️ For subtle surface changes → radiometric resolution matters
🗺️ For detailed mapping → spatial resolution is also important

In short:
📡 Spectral = what wavelengths are detected
🎚️ Radiometric = how much brightness detail is captured
🕒 Temporal = how often the area is observed

Remote sensing is powerful because it allows us to monitor the Earth across space, time, and spectral information.

28/04/2026

Most people use classification in remote sensing…
But very few truly understand what happens behind the pixels.
Here’s a clean visual I designed to explain Likelihood Classification 👇
Each pixel is not just “assigned” randomly.
It’s evaluated based on statistical probability using its spectral signature.
👉 The algorithm compares every pixel to known classes
👉 Then assigns it to the class with the highest likelihood
Simple idea. Powerful results.
Why it matters:
✔ More accurate land cover mapping
✔ Better decision-making in GIS projects
✔ Essential for multispectral imagery analysis
From water to urban areas , everything you see on the map is driven by data, not guesswork.

Curious: Do you still rely on basic classification methods, or are you using probabilistic approaches like this?

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25/04/2026

🌍 ABCs of Remote Sensing

J – JPEG vs GeoTIFF

Not all satellite images are the same.
Some are just pictures, while others contain real-world location data.

That’s the key difference between JPEG and GeoTIFF.

📊 What is JPEG?

A JPEG is a common image format used for viewing and sharing.

Compressed file (smaller size)

Easy to open on any device

Does not store geographic information

Image quality can reduce after compression

👉 Best for: visuals, presentations, social media

🗺 What is GeoTIFF?

A GeoTIFF is an image format designed for GIS and remote sensing.

Stores geographic coordinates

High quality with minimal data loss

Compatible with GIS software

Maintains spatial accuracy

👉 Best for: analysis, mapping, GIS projects

⚖ Key Difference

JPEG → Just an image
GeoTIFF → Image + location data

📈 Why It Matters

Accurate mapping needs spatial data

GeoTIFF supports analysis and measurements

JPEG is useful for quick sharing and visualization

Many maps shared online are in JPEG format,
but the original data is usually stored as GeoTIFF.

🌎 Example Applications

1. GIS mapping
2. Satellite image analysis
3. Web map publishing
4. Data sharing

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23/03/2026

Himalayas as seen from Space 🌍 🛰

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GIS and Remote Sensing Education

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