Graduation Project
This platform was developed as a graduation project, bridging academic research with practical software engineering.
Faculty of Arts
Department of Geography & GIS
مشروع تخرج - قسم المميز المساحة ونظم المعلومات الجغرافية
Under the Supervision of
Dr. Ibrahim El-Segaey
Hello, I'm
Omar Alshandidy
GIS Specialist | Backend Developer (ASP.NET)
A motivated full-stack ASP.NET Developer and GIS Specialist seeking to contribute to innovative projects by leveraging expertise in ASP.NET Core, RESTful APIs, and diverse GIS tools to build scalable, secure, and data-driven applications.
RSAnalysis
The Future of Land Analysis
A comprehensive remote sensing platform designed for geography and geology researchers. Seamlessly integrates with Google Earth Engine to provide end-to-end analysis workflow—from data acquisition and processing to advanced spectral indices calculation and interactive map visualization. Transform complex satellite imagery into actionable insights for any land surface analysis.
What is RSAnalysis?
RSAnalysis is a professional-grade remote sensing platform designed for geography and geology researchers. Powered by Google Earth Engine, it unifies the complete remote sensing workflow—from satellite data acquisition and preprocessing to advanced spectral indices computation and interactive geospatial visualization. Leveraging Sentinel-2 and Landsat 8 archives, the platform delivers high-resolution, cloud-free analysis for diverse land surface applications.
Vegetation & Land Cover Analysis
Compute NDVI, NDWI, NDBI, and NDTI indices using Sentinel-2's 10m resolution. Monitor vegetation health, water content, urban expansion, and water quality for comprehensive land surface characterization.
Thermal Analysis & LST
Utilize Landsat 8 TIRS bands to calculate precise Land Surface Temperature with emissivity correction. Essential for heat stress mapping, urban heat island studies, geological thermal anomaly detection, and climate research applications.
Intelligent Water Masking
Automatically apply JRC Global Surface Water dataset to distinguish permanent and seasonal water bodies from land surfaces. This ensures accurate LST readings by eliminating thermal interference from water pixels, critical for precise land-only temperature analysis.
Cloud-Based Processing Pipeline
Direct integration with Google Earth Engine provides instant access to petabytes of multi-temporal satellite imagery. Automated cloud masking, atmospheric correction, and parallel processing eliminate manual preprocessing— delivering analysis-ready results in minutes, not hours.
How to Use the Platform
Register & Draw Field
Create your account, then go to the interactive map and use the drawing tools to define your field boundaries, or upload a ready-made Shapefile.
Request Analysis
Select the field you drew, and press "Start New Analysis". Choose the index (e.g., NDVI) and the time period you want to analyze.
Cloud Processing
The platform handles the rest. Your request is sent to Google Earth Engine to fetch images, process them, calculate the index, and automatically remove clouds.
View & Download Results
Once complete, you'll see a color-coded map showing plant health and you can download the data as a GeoTIFF file.
Satellites Used
Sentinel-2
COPERNICUS/S2_SR_HARMONIZED
The European satellite is the backbone for most optical analyses. It provides high-resolution (10m) imagery, allowing for precise analysis of agricultural fields.
- Spatial Resolution: 10m
- Used for: NDVI, NDWI, NDBI, NDTI
Landsat 8
LANDSAT/LC08/C02/T1_L2
The famous US satellite. We use it specifically to calculate high-resolution (30m) Sea Surface Temperature (SST) using its thermal band (ST_B10).
- Spatial Resolution: 30m (thermal)
- Used for: SST (Surface Temp)
GCOM-C / NOAA OISST
JAXA/GCOM-C / NOAA/CDR
For large-scale ocean temperature (SST) analysis, we use specialized data from Japanese (GCOM-C) and US (NOAA) satellites.
- Spatial Resolution: ~1km
- Used for: SST (Ocean Temp)
JRC Global Surface Water
JRC/GSW1_4/GlobalSurfaceWater
A comprehensive global water mapping dataset from the European Commission's Joint Research Centre. We use it to mask out water bodies from our temperature and vegetation analyses.
- Spatial Resolution: 30m
- Used for: Water Masking
- Temporal Range: 1984–2021
Indices & Formulas
NDVI
Normalized Difference Vegetation Index
(B8 - B4) / (B8 + B4)
Measures the health and density of vegetation using NIR and Red bands.
NDWI
Normalized Difference Water Index
(B3 - B8) / (B3 + B8)
Used to identify water bodies using Green and NIR bands.
NDBI
Normalized Difference Built-up Index
(B11 - B8) / (B11 + B8)
Highlights urban and built-up areas using SWIR and NIR.
NDTI
Normalized Difference Turbidity Index
(B4 - B3) / (B4 + B3)
Measures water turbidity using Red and Green bands.
SST / LST
Sea/Land Surface Temperature
(ST_B10 * 0.0034) + 149 - 273.15
Direct measurement from thermal bands converted to degrees Celsius.
Technical Deep Dive: LST & Water Masking
Landsat 8 Land Surface Temperature (LST)
Landsat 8 carries the Thermal Infrared Sensor (TIRS) with two thermal bands: Band 10 (10.6–11.19 µm) and Band 11 (11.50–12.51 µm). We use these bands to calculate accurate land surface temperature.
Step 1: Convert DN to TOA Radiance
Lλ = ML × DN + AL
Step 2: Calculate Brightness Temperature (Kelvin)
BT = K2 / ln((K1 / Lλ) + 1)
Step 3: Convert to Celsius & Apply Emissivity Correction
LST = BT / [1 + (λ × BT / ρ) × ln(ε)] − 273.15
Where: K₁ and K₂ are calibration constants, λ is wavelength, ρ is Boltzmann constant (1.438×10⁻² m·K), and ε is surface emissivity (typically 0.95–0.98 for vegetation).
JRC Global Surface Water Detection
The JRC Global Surface Water Mapping dataset (European Commission's Joint Research Centre) maps the location and temporal distribution of water surfaces at the global scale over nearly four decades (1984–2021).
Dataset Details
- Resolution: 30m × 30m
- Coverage: Global (1984–2021)
- Source: 4M+ Landsat scenes
Why Water Masking Matters for LST
Water bodies have different thermal properties than land, skewing temperature averages.
Accurate land-only readings enable precise irrigation planning and heat stress detection.
Combining LST with NDVI/NDWI provides comprehensive field health assessment.