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🌍 Welcome to – GIS, Remote Sensing & Google Earth Engine Freelancers Hub 🌍
We offer top-notch GIS, Remote Sensing, and Google Earth Engine services, including mapping, spatial analysis, image processing, and cloud-based geospatial data processing.

29/07/2026

🌧️ Visualizing 25 Years of Rainfall Patterns Across Pakistan (2000–2025)

Using CHIRPS precipitation data via Google Earth Engine, I built this animated map tracking annual total rainfall across Pakistan from 2000 to 2025.

A few things stand out:
β†’ Northern and northeastern regions consistently receive the highest precipitation
β†’ Southern and southwestern areas remain persistently arid year-round
β†’ Year-to-year variability is clearly visible β€” useful for spotting drought/flood-prone years at a glance

This kind of geospatial time-series visualization is valuable for climate research, agricultural planning, and water resource management.

Note: Legend scale (0–1000mm) is an illustrative approximation for visual clarity, not an exact reproduction of raw data values.

Built with Google Earth Engine + Python.

Mapping the May 2026 Margalla Hills Wildfire: A Burn Severity AnalysisUsing Sentinel-2 satellite imagery and Google Eart...
27/07/2026

Mapping the May 2026 Margalla Hills Wildfire: A Burn Severity Analysis

Using Sentinel-2 satellite imagery and Google Earth Engine, I mapped the burn severity of the wildfire that swept through Margalla Hills National Park, Islamabad in May 2026.

By comparing pre- and post-fire NDVI and NBR (Normalized Burn Ratio) values, I calculated the dNBR to classify burn severity across the park β€” following the standard USGS classification scheme (Unburned β†’ Low β†’ Moderate-Low β†’ Moderate-High β†’ High Severity).

Key findings:
πŸ”₯ Peak dNBR of 0.807 falls within the "High Severity" class
🌱 Post-fire NBR values dropped sharply, reflecting significant vegetation loss
πŸ“ Burn severity was concentrated on the central and southern slopes, while northern ridgelines remained largely unburned

This kind of remote sensing analysis helps quantify wildfire impact objectively and can support restoration planning and future fire-risk monitoring for protected areas like Margalla Hills.

Independent GIS analysis using open Sentinel-2 data via Google Earth Engine β€” not an official government assessment.

A few weeks ago I asked myself a simple question: how bad is drought really getting in Punjab, Pakistan β€” and can I actu...
23/07/2026

A few weeks ago I asked myself a simple question: how bad is drought really getting in Punjab, Pakistan β€” and can I actually prove it with data instead of just going by news headlines?

So I pulled 43 years of climate data (1982–2024) from ERA5-Land satellite reanalysis, and calculated two drought indices β€” SPI and SPEI β€” across the whole province at multiple timescales.

The results were more interesting than I expected.

The worst drought in the entire 43-year record turned out to be 1999–2002 β€” a slow-building, multi-year disaster that peer-reviewed research had already documented. Good, that matched.

But then December 2024 showed something unusual: a sudden, sharp drought signal that barely showed up when I looked at rainfall alone, but became "extreme" the moment I factored in temperature. Turns out October and November 2024 were unusually hot, which quietly increased evaporation and dried things out β€” even in areas where rainfall wasn't dramatically low. I checked, and Pakistan's own Meteorological Department had flagged the exact same period and the exact same districts in their December 2024 drought bulletin.

That was a good feeling β€” building something from scratch and then watching it line up with what officials on the ground were independently reporting.

Here's what that divergence actually looks like on the ground β€” 2002 (the worst year on record) next to December 2024, across two different drought indices.

A few things I took away from this:

Drought isn't one thing. A 3-month view and a 12-month view of the same place can tell completely different stories β€” one can look fine while the other is screaming "extreme."

Temperature matters as much as rainfall. Ignore it, and you'll miss droughts that are quietly building from heat, not just from a lack of rain.

Always check your work against something real. It's easy to make a good-looking map. It's harder β€” and far more valuable β€” to make one that's actually true.

Built using Google Earth Engine, Python, and ArcGIS Pro.

🌱 NDVI vs SAVI: Which Vegetation Index Performs Better?Vegetation indices are essential tools in remote sensing for moni...
21/07/2026

🌱 NDVI vs SAVI: Which Vegetation Index Performs Better?

Vegetation indices are essential tools in remote sensing for monitoring crop health, vegetation density, and land conditions. In this comparison, I analyzed Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) over Bahawalpur, Punjab, Pakistan, using Sentinel-2 (10 m) imagery acquired in May 2025.

Although both indices identify similar vegetation patterns, SAVI incorporates a soil adjustment factor (L = 0.5), which minimizes the influence of exposed soil. This makes SAVI particularly useful in semi-arid agricultural regions like Bahawalpur, where bare soil and sparse vegetation are common.

Key Observations

βœ”οΈ Both NDVI and SAVI successfully mapped agricultural vegetation.
βœ”οΈ NDVI produced slightly higher vegetation values.
βœ”οΈ SAVI reduced the influence of soil background, resulting in more stable vegetation estimates in areas with exposed soil.
βœ”οΈ The overall spatial patterns remained consistent, confirming the reliability of both indices for vegetation monitoring.

This comparison highlights how selecting the appropriate vegetation index depends on landscape characteristics and study objectives rather than relying on a single universal approach.

Study Area: Bahawalpur, Punjab, Pakistan
Satellite: Sentinel-2 (10 m Spatial Resolution)
Period: May 2025
Platform: Google Earth Engine

🌿 NDVI vs EVI: Comparing Vegetation Indices over Kashmir using Google Earth EngineUnderstanding vegetation health begins...
14/07/2026

🌿 NDVI vs EVI: Comparing Vegetation Indices over Kashmir using Google Earth Engine

Understanding vegetation health begins with choosing the right vegetation index. In this project, I compared the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) over Kashmir using Sentinel-2 Surface Reflectance imagery processed in Google Earth Engine (GEE).

Study Details
Satellite: Sentinel-2 SR Harmonized
Platform: Google Earth Engine
Study Area: Kashmir
Spatial Resolution: 10 m
Key Findings

βœ… NDVI effectively highlights vegetation greenness and is widely used for crop monitoring, drought assessment, and land cover analysis.

βœ… EVI minimizes atmospheric effects and soil background influence, providing improved sensitivity in densely vegetated areas such as forests.

Although both indices show similar vegetation patterns across Kashmir, EVI is designed to better capture variations in high-biomass regions, while NDVI remains a simple and reliable indicator for general vegetation monitoring.

This comparison demonstrates how selecting the appropriate vegetation index depends on the study objective and landscape characteristics.

I always enjoy exploring remote sensing techniques that support better environmental monitoring and geospatial decision-making.

πŸ’¬ Which vegetation index do you prefer for your projectsβ€”NDVI or EVI? I'd love to hear your experience.

🌍 Land Use/Land Cover (LULC) Mapping of Swat District – 2025I'm pleased to share my latest Land Use/Land Cover (LULC) Ma...
01/07/2026

🌍 Land Use/Land Cover (LULC) Mapping of Swat District – 2025

I'm pleased to share my latest Land Use/Land Cover (LULC) Map of Swat District (2025), developed using Google Earth Engine (GEE) and satellite imagery. This map illustrates the spatial distribution of major land cover classes across the district, providing valuable insights into the region's environmental and landscape characteristics.

The classified land cover includes:
βœ… Water Bodies
βœ… Trees/Forest
βœ… Grassland
βœ… Flooded Vegetation
βœ… Croplands
βœ… Shrub & Scrub
βœ… Built-up Areas
βœ… Bare Land
βœ… Snow & Ice

This work demonstrates the application of Remote Sensing and GIS techniques for environmental monitoring, natural resource management, urban planning, and sustainable development. Accurate LULC mapping plays a vital role in understanding land dynamics and supports informed decision-making for researchers, planners, and policymakers.

Tools & Technologies Used:
πŸ›°οΈ Google Earth Engine (GEE)
🌍 Sentinel-2 Satellite Imagery
πŸ—ΊοΈ ArcGIS Pro (Map Layout & Cartography)
πŸ“Š Remote Sensing & GIS Analysis

I welcome your feedback and suggestions. Let's connect and collaborate on innovative geospatial solutions!

29/06/2026

Annual Evapotranspiration (ET) in the Potohar Plateau, Pakistan (2015–2025)

I developed an evapotranspiration analysis using Google Earth Engine and the MODIS MOD16A2GF dataset to examine changes in annual ET across the Potohar Plateau for 2015, 2020, and 2025.

The analysis visualizes the spatial variation of ET using a consistent colour scale, where lower ET values represent relatively dry, barren, or built-up areas, while higher ET values indicate vegetation-rich, agricultural, and wetter zones. Using the same ET range across all years allows a fair comparison of changing water-loss patterns over time.

This work can support studies related to:

Water-resource management
Agricultural water demand
Drought monitoring
Land Use/Land Cover change
Climate-change impact assessment
Vegetation and ecosystem health

Tools & Data Used:
β€’ Google Earth Engine
β€’ MODIS MOD16A2GF Evapotranspiration Dataset
β€’ Remote Sensing & GIS
β€’ Annual ET analysis for 2015, 2020, and 2025

🌍 Land Surface Temperature (LST) Mapping of Gilgit-Baltistan | January 2025As part of my remote sensing and geospatial a...
23/06/2026

🌍 Land Surface Temperature (LST) Mapping of Gilgit-Baltistan | January 2025

As part of my remote sensing and geospatial analysis practice, I generated a Land Surface Temperature (LST) map of Gilgit-Baltistan, Pakistan, using MODIS MOD11A2 Version 6.1 data processed in Google Earth Engine (GEE).

The visualization highlights the spatial variation in daytime surface temperature across the mountainous terrain during January 2025. Lower temperatures are observed in high-altitude snow-covered regions, while relatively warmer temperatures appear along valleys and lower elevations.

Tools & Data Used:

πŸ›°οΈ Google Earth Engine (GEE)
🌍 MODIS MOD11A2 Version 6.1 (8-Day LST Product)
πŸ—ΊοΈ Gilgit-Baltistan Administrative Boundary
πŸ“ Visualization using custom color palette and map styling

This project helped strengthen my skills in satellite data processing, geospatial visualization, and Earth observation using cloud-based GIS platforms.

18/06/2026

🌍 Tracking Air Quality Changes in Karachi (2021–2025) using Google Earth Engine & Sentinel-5P

I'm excited to share my latest geospatial visualization project, where I analyzed the annual mean Sulfur Dioxide (SOβ‚‚) concentration over Karachi, Pakistan, from 2021 to 2025 using Sentinel-5P TROPOMI satellite data processed in Google Earth Engine (GEE).

πŸŽ₯ This 5-second animation illustrates the spatial and temporal variation of atmospheric SOβ‚‚, providing insights into air quality patterns across Karachi over the five-year period.

πŸ—ΊοΈ Study Area

πŸ“ Karachi, Pakistan

πŸ›°οΈ Data Source
Sentinel-5P TROPOMI (SOβ‚‚ Column Number Density)
Platform: Google Earth Engine
🎨 Color Interpretation

πŸ”΅ Dark Blue – Low SOβ‚‚ concentration (Cleaner air)
🟦 Light Blue / Cyan – Moderate SOβ‚‚ concentration
🟒 Green – Comparatively higher SOβ‚‚ concentration across the study area

The visualization indicates relatively higher SOβ‚‚ concentrations in parts of southern Karachi, while northern areas generally exhibit lower concentrations during the study period (2021–2025).
I welcome your feedback and suggestions!

🌍 Digital Elevation Model (DEM) of Dhaka, BangladeshExcited to share one of my recent GIS and Remote Sensing outputsβ€”a D...
16/06/2026

🌍 Digital Elevation Model (DEM) of Dhaka, Bangladesh

Excited to share one of my recent GIS and Remote Sensing outputsβ€”a Digital Elevation Model (DEM) of Dhaka, Bangladesh, developed using Google Earth Engine (GEE) and processed with geospatial analysis techniques.

The DEM provides valuable information about the elevation characteristics of the study area, supporting applications such as:

πŸ“ Flood risk assessment
πŸ“ Urban planning and infrastructure development
πŸ“ Watershed and drainage analysis
πŸ“ Environmental monitoring
πŸ“ Terrain and topographic studies

This project involved:
βœ… Clipping the DEM to the Dhaka administrative boundary
βœ… Processing elevation data in Google Earth Engine
βœ… Visualizing terrain using an elevation color gradient
βœ… Exporting high-resolution outputs for GIS mapping and analysis

Working with cloud-based geospatial platforms like Google Earth Engine continues to demonstrate how efficiently large spatial datasets can be processed for real-world decision-making.

I'm continuously expanding my skills in GIS, Remote Sensing, Google Earth Engine, ArcGIS Pro, and spatial data analysis, and I look forward to applying these technologies to solve environmental and urban challenges.

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Islamabad
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