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Google earth Engine PK, Gilgit (2025)

03/09/2025

🌍 Soil Erosion Risk Mapping in Gilgit (USPED Model – 2024 Season)

This study integrates Sentinel-2 (NDVI), SRTM DEM, and the USPED (Unit Stream Power Erosion Deposition) model within Google Earth Engine (GEE) to evaluate erosion risks in Gilgit, Pakistan.

🔎 Workflow Highlights

1️⃣ NDVI → C Factor: Vegetation cover translated into soil protection levels.

2️⃣ DEM → LS Factor: Terrain slope and flow accumulation quantified topographic influence.

3️⃣ USPED Erosion: Combined rainfall erosivity (R), soil erodibility (K), C and LS factors.

4️⃣ Classification: Results categorized into 6 erosion risk levels (No, Low, Moderate, High, Very High, Extreme).

5️⃣ Visualization: Multi-map layout showing LS Factor, Erosion, Erosion Classes, and C Factor with legends.

6️⃣ Quantification: Area statistics calculated and displayed via bar chart.

📊 Key Insights

Areas with steep slopes & low vegetation show high to extreme erosion risks.

Vegetated zones (high NDVI) contribute to lower erosion rates.

Results highlight priority regions for soil conservation and watershed management.

🛰️ Tools: Google Earth Engine + Sentinel-2 + SRTM DEM

📍 Region: Gilgit, Pakistan

📅 Season: May – September 2024

✨ These results support sustainable land management and can guide erosion control practices in fragile mountain ecosystems.

02/09/2025

🌍 Soil Erosion Risk Mapping in Swat Valley using RUSLE (Revised Universal Soil Loss Equation)
This work applies the RUSLE model in Google Earth Engine to estimate potential annual soil loss (t/ha/yr) across the Swat region. Soil erosion is a major environmental challenge in mountainous areas like Swat, where steep slopes, rainfall intensity, and land-use practices accelerate land degradation.
🔎 Model Inputs
R-Factor (Rainfall erosivity): Derived from CHIRPS daily rainfall data (2000–2024).
K-Factor (Soil erodibility): Extracted from SoilGrids silt content.
LS-Factor (Slope & Topography): Computed from SRTM DEM, combining slope steepness and flow accumulation.
C-Factor (Cover management): Estimated from MODIS NDVI (2023) representing vegetation cover.
P-Factor (Conservation practices): Based on ESA WorldCover land-use classes and their corresponding management factors.
🖥️ Outputs
Spatial distribution maps for each factor (R, K, LS, C, P).
Final Soil Loss (A-Factor) map showing estimated erosion risk across Swat.
Interactive histograms and statistics summarizing soil loss trends.
📊 Key Insights
High erosion risk areas are strongly linked to steep slopes, sparse vegetation, and high rainfall zones.
Agricultural and bare soil regions contribute significantly to soil loss, highlighting the need for conservation practices.
Mean annual soil loss (t/ha/yr) was computed for the region, providing a quantitative benchmark for policy makers.
🌱 Why It Matters?
Soil erosion not only reduces agricultural productivity but also causes siltation in rivers, landslides, and ecological imbalance. Mapping erosion hotspots helps in planning sustainable land management, reforestation, and soil conservation strategies in fragile mountain ecosystems like Swat.

28/08/2025

🌍 District-wise Carbon Stock Estimation in Punjab, Pakistan (2024–2025)

This analysis uses Sentinel-2 imagery (Nov 2024 – Mar 2025) to estimate vegetation carbon stocks across Punjab at the district level.

✅ Workflow Highlights:

NDVI derived from Sentinel-2 bands (B8, B4).

Converted NDVI → Above Ground Biomass (AGB) (tons/ha).

Estimated Carbon Stock = AGB × 0.5.

Classified into Carbon Zones:

🌱 Low (0–25 tons/ha)

🌿 Medium (25–50 tons/ha)

🌳 High (>50 tons/ha)

✅ Outputs:

Spatial maps of NDVI, Carbon Stock, and Carbon Zones.

District-level statistics of mean carbon stock.

Bar chart visualization for easy comparison.

Highlighted districts with high carbon reserves (>50 tons/ha).

📊 This approach helps in:

Monitoring vegetation health & biomass.

Supporting climate change mitigation and carbon credit programs.

Guiding policy decisions for sustainable forest/agro-ecosystem management in Punjab.

25/08/2025

🗺️ Landslide Detection using Google Earth Engine (Gilgit Region, 2022)

This workflow integrates multi-source remote sensing data to detect potential landslide-affected areas.

🔹 1. AOI (Gilgit) – A shapefile is loaded and centered for analysis.
🔹 2. Timeframes – Pre- and post-event periods are defined for both Sentinel-1 SAR and Sentinel-2 optical datasets.
🔹 3. Terrain Data – SRTM DEM is used to calculate slope, as steep terrain is more landslide-prone.
🔹 4. Sentinel-1 Analysis – VV backscatter difference highlights sudden surface changes (soil/rock displacement).
🔹 5. Sentinel-2 Analysis – NDVI difference indicates vegetation loss due to slope failure.
🔹 6. Risk Masking – A threshold-based mask combines:
• VV drop (< -0.7)
• NDVI drop (< -0.03)
• Slope > 10°
Result = Potential landslide zones 🧱
🔹 7. Visualization – Maps show input layers, masks, and final detected landslides.
🔹 8. Export – Detected landslide zones are exported as GeoTIFF.
🔹 9. Ground Truth Sampling – Random landslide (1) and non-landslide (0) points generated for validation.
🔹 10. Validation – Confusion matrix used to assess classification accuracy.
🔹 11. Output – Ground truth samples exported as CSV.
🔹 12. Legend – A custom interactive legend added for better interpretation.

⚡ Key Insight: By combining radar (Sentinel-1), optical (Sentinel-2), and DEM slope information, this approach provides a reliable method to detect landslides in mountainous regions like Gilgit.

🌍 This can support disaster risk management, hazard mapping, and resilience planning.

21/08/2025

🌍 Punjab, Pakistan – Temperature Trend Analysis (2020–2025) 🌡️

This visualization highlights monthly mean temperature variations across Punjab, Pakistan, derived from the ERA5-Land Monthly Aggregated dataset (ECMWF).

✅ Data & Methodology

Source: ERA5-Land (ECMWF) – global reanalysis climate dataset.

Time Period: January 2020 – July 2025.

Region of Interest (ROI): Punjab province, Pakistan, extracted from FAO GAUL administrative boundaries.

Conversion: Temperatures were converted from Kelvin → Celsius for easier interpretation.

Analysis: For each month, the average temperature across Punjab was calculated and visualized.

✅ Key Insights

Punjab exhibits seasonal fluctuations, with peaks in summer (above 35°C) and lows in winter (around 10–15°C).

The mean temperature map shows spatial distribution, while the trend line chart captures temporal variation.

This type of analysis is crucial for climate monitoring, agriculture planning, water management, and heatwave risk assessment.

📊 The line chart clearly shows year-to-year variations, making it easier to track warming or cooling anomalies.

18/08/2025

🌍 Gilgit Flood 2024 – Interactive Dashboard 🌊

This interactive map visualizes flood risk and impact in Gilgit during July 2024 using multiple datasets:

Elevation & Low-Lying Areas: SRTM DEM highlights areas below 2000m at higher risk. 🟥

Flood Detection: Sentinel-1 VV imagery identifies flooded zones in blue. 💧

Rainfall: CHIRPS daily data shows precipitation trends and total rainfall, linked to flooding patterns. 📊

Land Cover: MODIS land cover provides context on forests, agriculture, built-up areas, and water bodies. 🌳🏘️💦

Overlay Maps & Dashboard: Combine terrain, rainfall, flood zones, and land cover to identify high-risk areas and support disaster monitoring.

A comprehensive tool for flood assessment, planning, and community awareness.

11/08/2025

🌾 Mapping Cultivable Land in District Nagar using Sentinel-2 & NDVI 🌱

This analysis uses Sentinel-2 SR Harmonized imagery for the 2024 growing season (May–September) to identify and visualize cultivable land in District Nagar.

1️⃣ NDVI Calculation → We used the Normalized Difference Vegetation Index (NDVI) formula:

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NDVI=
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where B8 is near-infrared and B4 is red.

2️⃣ Cultivable Land Mask → Pixels with NDVI > 0.4 are considered healthy vegetation, likely cultivated crops.

3️⃣ Area Estimation → Calculated in m², hectares, and acres using pixel area statistics.

4️⃣ Visualization Layers →

Green: Cultivable land (NDVI > 0.4)

Black outline: District Nagar boundary

Gradient green: NDVI values (white = low, dark green = high)

5️⃣ Charts for Insights →

NDVI Histogram shows the distribution of vegetation health.

NDVI Time Series tracks changes in average NDVI over the growing season, revealing crop growth trends.

This workflow combines remote sensing and Google Earth Engine processing power to help with agricultural monitoring, land management, and planning in District Nagar.

📍 Data source: Copernicus Sentinel-2
🛰 Processing: Google Earth Engine
📅 Period: May–September 2024
📊 Key threshold: NDVI > 0.4 = cultivable

07/08/2025

🌍 Vegetation & Surface Analysis of Gilgit Region (May–September 2024)

Using high-resolution Sentinel-2 imagery (Harmonized Surface Reflectance), this panel-based visualization showcases six critical spectral indices for the Gilgit region, providing insights into land cover, vegetation health, water content, and built-up areas.

📊 Indices Displayed (from left to right, top to bottom):

NDVI (Normalized Difference Vegetation Index) – Highlights vegetation vigor. Higher values indicate dense, healthy green cover.

NDWI (Normalized Difference Water Index) – Emphasizes surface water features and moisture content.

NDSI (Normalized Difference Snow Index) – Useful for detecting snow and ice cover, especially in mountainous terrains.

NDBI (Normalized Difference Built-up Index) – Helps detect urban and built-up structures.

SAVI (Soil-Adjusted Vegetation Index) – An improved NDVI for areas with sparse vegetation by minimizing soil brightness effects.

EVI (Enhanced Vegetation Index) – Offers better sensitivity in high-biomass regions and reduces atmospheric distortion.

📅 Time Period: May 1 – September 30, 2024
📍 Region: Gilgit, Pakistan
🛰️ Data Source: Sentinel-2 SR (via Google Earth Engine)
🎨 Visualization: 2-row by 3-column grid layout with synchronized legends and map titles for comparison.

This multi-index layout supports integrated environmental assessment, monitoring of climate impacts, and planning for agriculture, forestry, hydrology, and urban development in the Gilgit-Baltistan region.

06/08/2025

🗺️ Just finished this automatic lithology map of the Gilgit region using satellite data and machine learning! 🔍
With help from Sentinel-2 imagery and Google Earth Engine, I applied unsupervised kMeans clustering to group different rock and surface types — no manual labels needed!

📷 The map shows 9 lithology classes like Slate, Granite, Gabbro, Soil, and more — all color-coded with a custom legend.

✅ Features:

Sentinel-2 imagery (2024)

AI-based lithology classification

Interactive map + legend

Total area stats per class (in hectares!)

This is a great example of how remote sensing + cloud computing can support geology, land analysis, and even mineral exploration 🌍💻

Drop a comment if you’d like to try something similar or want the GEE code! 👇

05/08/2025

🌱 Mapping Carbon Stock in Punjab, Pakistan Using Google Earth Engine 🌍

I’m proud to share my latest geospatial analysis using Google Earth Engine (GEE) to estimate and visualize above-ground carbon stock across all districts of Punjab, Pakistan. This work leverages Sentinel-2 imagery and NDVI-based modeling to support climate-smart decision-making and land management.

🔬 Methodology Highlights:

🛰️ Processed cloud-free Sentinel-2 SR imagery (Nov 2024 – Mar 2025)

🌿 Calculated NDVI to assess vegetation health

📈 Applied a biomass model to derive Above Ground Biomass (AGB) and Carbon Stock (tons C/ha)

🗺️ Classified carbon into three zones: Low (0–25), Medium (25–50), High (>50)

🧠 Computed district-level statistics to identify carbon hotspots

📊 Visualized insights using interactive maps, legends, and district-wise carbon bar charts

🔍 Key Findings:

Several districts in central and northern Punjab show high carbon stock values, making them key areas for conservation and carbon offset programs.

The map layers and color-coded zones help simplify complex spatial data for policy-makers and environmental planners.

💡 This project showcases the potential of remote sensing and cloud-based geospatial platforms to deliver scalable, transparent, and data-driven insights for environmental monitoring, carbon accounting, and climate adaptation planning in Pakistan.

04/08/2025

🌿 Vegetation Health Assessment in Gilgit, Pakistan (May 2024) 🌿

This visualization showcases seven key vegetation indices derived from Sentinel-2 imagery between May 1st and June 1st, 2024, for the Gilgit region. These indices provide valuable insights into vegetation vigor, moisture content, and canopy structure — essential for monitoring agriculture, forest health, and land cover dynamics.

📍 Study Area: Gilgit, Northern Pakistan
🛰 Sensor: Sentinel-2 SR (Surface Reflectance)
📆 Date Range: May 1 – June 1, 2024
☁️ Cloud Cover Filter:

31/07/2025

🌍 GeoDashboard: Climate & Land Risk Assessment for Gilgit-Baltistan (2024)

This Earth Engine-powered dashboard provides a spatial analysis of environmental risks and land health across Gilgit-Baltistan, Pakistan, using satellite data for the year 2024. It visualizes 12 key climate and sustainability indicators in a clean 2×6 grid format:

🔸 Climate Stressors

🔥 Land Surface Temperature (°C) from MODIS

🌧 Annual Rainfall (mm) from CHIRPS

⬇️ Low Elevation Areas (500mm)

🌊 Combined Flood Risk Zones (low elevation + high rain)

🔸 Agricultural Sustainability

🌿 High NDVI Areas indicating vegetative productivity

NDVI metrics from both MODIS and Sentinel-2 sensors for comparison

🔸 Land Degradation Risks

🧂 Salinity-affected zones (low NDVI < 0.2)

🌲 Recent Tree Loss (2001–2024) using Hansen’s global forest data

⛰ Slope-induced Erosion Risk (areas >30° slope)

Each thumbnail gives a snapshot over a consistent Area of Interest (AOI) and enables side-by-side comparison of environmental indicators across seasons and datasets.

🔧 Powered by Google Earth Engine (JavaScript API)

📍 AOI: Gilgit-Baltistan, Northern Pakistan

📊 Satellite Sources: MODIS, CHIRPS, Sentinel-2, Hansen Global Forest, SRTM DEM

👉 This dashboard can support local climate resilience planning, sustainable agriculture, disaster risk reduction, and land degradation monitoring at regional scale.

Google earth Engine PK

03/09/2025

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