Google earth Engine PK

🌍 Mapping Soil Health & Water Quality in Punjab using GIS & Remote Sensing 🌾💧

In this study, I used Google Earth Engine (GEE) to analyze the impact of agricultural practices and land use on soil and water quality across Punjab, Pakistan.

🔎 What was done:
✔ Landsat-8 (2015–2020) for soil health indices

🌱 NDVI → Vegetation health & soil fertility

🟤 BSI → Bare soil & land degradation

🧂 SI → Soil salinity risk

✔ Sentinel-2 (2019–2021) for water quality indicators

💧 NDWI → Surface water extent

🌫 NDTI → Water turbidity

🟢 Chl-a → Algal blooms / chlorophyll concentration

🗺 Six maps were generated (NDVI, BSI, SI, NDWI, NDTI, Chl-a) in a 2×3 grid layout, each with legends for better interpretation.

📊 District-level zonal statistics were calculated, and graphs in the Console show spatial variations in these indices across Punjab’s districts.

🚜 These insights help identify hotspots of soil degradation, salinity issues, and declining water quality, which are crucial for:

Sustainable agriculture planning 🌾

Soil conservation programs 🌱

Water resource management 💧

Climate adaptation strategies 🌍

🌍 Indus Basin – Snow, Meltwater & Climate Trends (2010–2025) ❄️💧🌡️

This study analyzes the changing cryosphere and hydrology of the Indus Basin using MODIS Snow Cover and ERA5-Land climate reanalysis data.

🔹 Snow Cover Duration (SCD): Annual days under snow cover, showing variability in seasonal snowpack.
🔹 Snowmelt (mm): Annual meltwater contribution – a lifeline for agriculture and river flows.
🔹 Mean Temperature (°C): Annual averages, revealing warming trends across the basin.
🔹 Combined Trends: An integrated look at snow persistence, meltwater availability, and climate warming.

📊 Results indicate that rising temperatures are directly influencing snow cover and meltwater dynamics, with significant implications for water security, agriculture, and climate resilience in South Asia.

⚡ Data Sources:

NASA MODIS MOD10A1 (Snow Cover)

ECMWF ERA5-Land (Temperature & Snowmelt)

🔍 These insights are vital for climate adaptation, sustainable water management, and policy planning in the Indus Basin.

🌍 Vegetation Drought Monitoring in Gilgit, Pakistan (2010–2025) 🌱

Using MODIS MOD13Q1 (250m NDVI, 16-day) data and Google Earth Engine, we analyzed vegetation health and drought severity across Gilgit from 2010 to 2025.

🔎 Key steps in the analysis:
✅ Monthly NDVI composites were generated for the entire study period.
✅ Long-term climatology (mean & standard deviation) was calculated for each month.
✅ Standardized NDVI anomaly (z-score) was derived to detect vegetation stress.
✅ Drought severity was classified into No Drought, Mild, Moderate, Severe, and Extreme.
✅ Time-series analysis was performed to track NDVI and anomalies over 15 years.
✅ Final drought severity maps and statistics were exported for decision support.

📊 Applications of this work:

Monitoring drought impacts on agriculture 🌾

Supporting water and land resource management 💧

Climate change resilience planning in mountain ecosystems 🏔️

Early warning systems for food security 🍽️

This approach highlights how remote sensing and cloud computing (GEE) can provide scalable and timely insights for sustainable natural resource management.

🌍🛰️ Agricultural Land Use Mapping – Gilgit (2025)

Using Sentinel-2 imagery & Google Earth Engine (GEE), we classified and mapped agricultural land across the Gilgit region.

✅ NDVI, NDWI & NDMI indices applied
✅ Agriculture vs Non-Agriculture classification
✅ Accuracy assessment with Random Forest
✅ Area statistics + visual maps generated

📊 These results provide valuable insights for agricultural planning, land management, and sustainable resource use in Gilgit.

🌾 Drought and Vegetation Analysis in Punjab, Pakistan 🌍

In this study, I used Google Earth Engine to analyze the relationship between rainfall anomalies and vegetation health across the districts of Punjab, Pakistan.

🔹 Standardized Precipitation Index (SPI): shows wet and dry conditions, helping identify districts vulnerable to drought.

🔹 Normalized Difference Vegetation Index (NDVI): derived from MODIS, highlights vegetation health and greenness across Punjab’s agricultural lands.

🔹 District Boundaries: make it possible to assess drought and vegetation patterns at the administrative level.

🔹 Correlation Analysis: A scatterplot of SPI vs NDVI shows how rainfall variability directly affects vegetation growth, crop productivity, and ecosystem resilience.

📊 This geospatial approach can support drought monitoring, early warning systems, and sustainable agricultural planning in Punjab — one of Pakistan’s most important food-producing regions.

🌍 Mapping Landcover Diversity Across Pakistan Using ESA WorldCover 2020 & Shannon Index in Google Earth Engine 🌱

This analysis highlights the spatial distribution of landcover classes across Pakistan’s provinces and quantifies ecosystem diversity using the Shannon Diversity Index.

🔎 Key Steps:
1️⃣ Extracted provincial boundaries of Pakistan from the FAO GAUL dataset.
2️⃣ Clipped the ESA WorldCover 2020 global landcover dataset to the provinces.
3️⃣ Computed the Shannon Diversity Index for each province to measure landcover heterogeneity.
4️⃣ Visualized results with:

WorldCover Map: Distribution of forests, croplands, built-up areas, water bodies, etc.

Shannon Diversity Map (Choropleth): Provinces ranked by ecosystem landcover diversity.
5️⃣ Added interactive charts showing:

Shannon Diversity Index per province.

Landcover class areas (hectares) across Pakistan.
6️⃣ Designed a dual-panel map interface with legends, clickable province info, and comparative visualization.

📊 Findings:

Provinces with more mixed landcover types (e.g., forest, cropland, rangeland) have higher Shannon Index values, reflecting greater ecological diversity.

Provinces dominated by one or two landcover classes exhibit lower Shannon Index scores, highlighting limited landcover heterogeneity.

⚡ Why it matters?
Understanding landcover diversity is crucial for biodiversity conservation, sustainable land-use planning, and ecosystem service management. The Shannon Index provides a quantitative way to compare regions and track changes over time.

🛰️ Tools Used:

Google Earth Engine (JavaScript API)

FAO GAUL boundaries

ESA WorldCover v200 (2020)

👉 This workflow can be extended to district-level analysis, multi-year comparison, or integrated with climate/soil datasets for deeper ecological insights.

🌍 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.

🌍 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.

🌍 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.

🗺️ 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.

🌍 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.