⚡ SPACE LAB NOTES DAY 1⚡

Prepared by Yash Varshney

✍️ Handwritten by Yash Varshney
day1
Page 01
GIS
Layers Stack

Chapter 01: GIS Basics (Start here!)

  • 💡
    QGIS = Google Docs for Maps (Create, edit, analyze and visualize maps on your computer)
  • 💡
    Google Earth Engine = Google Cloud for Satellite Data (Process millions of satellite images without downloading them)

📍 What is GIS?

Before learning QGIS or GEE, you need to know GIS.

💡 GIS = Geographic Information System
It is a system that stores, manages and analyzes data that has a location on Earth.

📊 Example: Imagine a spreadsheet like this:

School Students Latitude Longitude
IPEC 3000 28.673 77.452
MIET 5000 28.985 77.712
  • Normal Excel sees only numbers.
  • GIS understands: "These numbers represent locations on Earth."
  • Now you can display them on a map.

❓ Why GIS?

Suppose government wants to know:

  • 📍 Which villages lack hospitals?
  • 📍 Which roads flood every year?
  • 📍 Where should a new metro station be built?
  • 📍 Which forests are disappearing?
  • 📍 How much crop area is affected by drought?

⭐⭐ All of these are GIS problems! ⭐⭐

⚙️ Components of GIS

DATA PIPELINE
Real World
↓
Collect Data
↓
Store Data
↓
Analyze
↓
Display on Map
Hospitals, Roads, Rainfall... GIS Decision Making Fig 1.1: Simplified GIS Decision Flow
📝 EXAM TIP

Always state that GIS is NOT just software. It's an entire ecosystem of data, hardware, analysis algorithms, and the human decisions that come from it!

Page 02
Info

📂 Types of GIS Data

There are only two major data types you will work with:

1. Vector Data

Made using points, lines, and polygons.

Point (Hospital/ATM)
Line (Road/River)
Polygon (District/Forest)

Vector = Precise boundaries.

2. Raster Data

Made of pixels. Exactly like a digital photo. Satellite images are raster.

30
29
31
35
28
0.8
0.4
0.1
Each pixel stores a numerical value (Temp, Vegetation)

Continuous data like temperature or land cover.

💡 REAL-WORLD EXAMPLE (Google Maps):
  • Roads & Labels ➜ Vector Data
  • Satellite Map View ➜ Raster Data

💻 What is QGIS?

QGIS is a free, open-source Geographic Information System.

It helps you: Create, Edit, Analyze maps, measure distances, calculate areas, overlay datasets, and build layout exports. Everything runs locally on your PC!

💡 MEMORY TRICK

💥 Photoshop edits images ➜ QGIS edits maps!

🚀 What can QGIS actually do? (Examples):

  1. Load village boundaries + schools ➜ Find villages lacking education infrastructure.
  2. Load roads + accident locations ➜ Find critical accident hotspots.
  3. Load satellite images ➜ Measure exact forest canopy area over time.
  4. Create detailed election voting visual maps.
  5. Analyze flood models and inundated zone coverages.

🖥️ The QGIS Interface Layout

When you boot up QGIS, you will see this functional workspace:

TOOLBARS (File, Edit, Vector, Raster Tools)
LAYERS PANEL
  • ☑️ Roads (Vec)
  • ☑️ Rivers (Vec)
  • ☑️ Schools (Vec)
  • ☑️ Satellite (Ras)
MAP CANVAS
Coords: 77.452, 28.673 Scale: 1:50,000 EPSG:4326

Fig 1.2: QGIS UI Panel Layout & Map Canvas

Page 03
Layers Stack

🥞 Layers: The Core Concept

Every dataset in GIS is stacked as a separate Layer.

Photoshop Layers
Foreground / Text
↓
Overlay Graphics / Logo
↓
Background Image
➔
GIS Layers
Hospitals (Points)
↓
Road Network (Lines)
↓
District Boundary (Polygon)
↓
Satellite Image (Raster)

🌍 Coordinate System & Projections

Every spot on Earth needs to be mapped. Example: Delhi ➜ Lat: 28.6139, Lon: 77.2090

QGIS reads these coordinates to place everything perfectly.

🌐 Projection Concept:

"Earth is a round sphere. Maps are flat sheets. Projection converts 3D Sphere ➜ 2D Flat Map."

🌟 Common Standard: WGS84 (EPSG:4326)
— This is the global default GPS standard coordinate system!

💾 Core File Formats in QGIS

Format / Extension Purpose & Data Type
Shapefile (.shp) Standard format for Vector geographic data
GeoJSON Lightweight coordinate storage used in Web mapping
KML Keyhole Markup Language used by Google Earth
GeoTIFF Raster satellite images embedded with coordinate data
CSV Spreadsheets storing columns of Lat/Lon values

🔄 Typical QGIS Workflow:

schools.csv ➔ Import into QGIS ➔ Convert to Points ➔ Display on Map ➔ Measure distance ➔ Export final map
Page 04
Warning!

Chapter 02: Google Earth Engine (GEE)

Google Earth Engine is a cloud-based platform for processing and analyzing massive collections of geospatial data, especially satellite imagery.

Instead of downloading terabytes of satellite imagery to your machine, you write code and Google's high-performance servers do the heavy lifting!

❌ Traditional Workflow (Without GEE)
1. Download Terabytes of data ↓ 2. Store locally on hard drives ↓ 3. Process on standard laptop ↓ Wait hours / system crashes!
✅ Cloud Workflow (With GEE)
1. Write a few lines of code ↓ 2. Google's cloud servers process ↓ Get final results in seconds!

❓ Why was GEE created?

NASA and the USGS have satellite archive imagery spanning decades:

1984 ➜ 1985 ➜ 1986 ➜ ... ➜ 2026

This equals thousands of terabytes of data. No single standard laptop can store or compute this efficiently.

How it works: Google stores all satellite collections on their data centers. You simply run a query:
"Show Delhi satellite image between 2018 and 2024" ➜ Google processes and returns the output image instantly.

💻 Programming in GEE

You primarily write code using:

  • JavaScript — Most common, easiest for beginners using the online Code Editor interface.
  • Python — Excellent for backend automation and advanced pipeline integration.
GEE API Script // Load Landsat 8 image collection
var image = ee.ImageCollection("LANDSAT/LC08/C02/T1");
💡 MEMORY TRICK / ADVICE

Don't stress over complex syntax early! Focus on mastering spatial concepts first. Code templates can always be referenced as you learn.

Page 05
Satellite

🌟 Main Features of GEE

  • 📡 Access global satellite imagery
  • 🌳 Map & analyze forests
  • 🌊 Detect regional flood zones
  • 🌾 Monitor crop health & growth
  • 🏙️ Track urban development
  • 💧 Measure changes in water bodies
  • 🔥 Detect active wildfires
  • 📈 Analyze long-term climate trends
  • 🎞️ Generate stunning map timelapses

📦 Core Datasets Stored in GEE

GEE contains pre-loaded, cleaned datasets from major space agencies:

  • NASA satellite archives
  • USGS Landsat imagery
  • European Space Agency (ESA) Sentinel imagery
  • Global weather and climate datasets
  • Digital Elevation Models (DEMs)
  • Land cover classification maps

🛠️ Common Use Case Workflows

1. Forest Loss Monitoring:
2015 Satellite Image ➔ 2025 Satellite Image ➔ Compare Diff ➔ Forest Lost Area
2. Flood Mapping:
Before Storm Image ➔ After Storm Image ➔ Overlay & Subtract ➔ Inundated Area
3. Crop Monitoring & Urban expansion:
  • Uses NDVI (Normalized Difference Vegetation Index) to analyze plant health.
  • Higher values = Healthier, denser vegetation.
  • Air Pollution: Tracks greenhouse and toxic gases in the atmosphere.
  • Urban growth: Compares satellite scans between 2000 and 2025 to locate urban spread.
⚠️ COMMON MISTAKE

Do not confuse NDVI values! They range from -1.0 to 1.0. Water/Snow are close to 0 or negative, while lush green forests score high (0.6 to 0.8+).

Page 06

📊 QGIS vs Google Earth Engine

Comparison table to keep your concept clear:

Feature QGIS (Desktop) GEE (Cloud)
Runs On Your local PC / Desktop hardware Google high-performance cloud servers
Coding Needed? Mostly optional (GUI Point-and-Click) Mostly required (JS or Python API)
Best For Creating, styling, and finalized static maps Massive scale, fast multi-temporal raster analysis
Data Storage Uses your local files (Shapefiles, GeoTIFFs) Accesses Google's petabyte scale online catalog
Computation Speed Depends directly on your system CPU/RAM Utilizes distributed Google infrastructure
Internet Not required after downloading datasets Strong, continuous internet required

🤝 How QGIS & GEE Work Together

They are not competitors; they complement each other perfectly!

Satellite Data Google Earth Engine (Heavy calc) GeoTIFF export QGIS (Final Styling/Map) Fig 1.3: Collaborative Geospatial Analysis Pipeline

🔄 Step-by-Step Example:
1. Use GEE to calculate vegetation health index (NDVI) over a country scale.
2. Export resulting processed raster layer as a GeoTIFF.
3. Open that GeoTIFF locally in QGIS.
4. Overlay precise vector datasets like local roads, schools, district labels.
5. Output a stunning, publication-ready map layout for presentations!

🌍 Real-World Fields & Applications

  • 🌾 Agriculture (Crop health tracking)
  • 🌋 Disaster Response (Flood/Wildfires)
  • 🏙️ Smart City Development
  • 🐯 Wildlife Conservation
  • 💧 Water Resource Systems
  • 🛣️ Transport & Highway Planning
  • ⛏️ Resource Extraction & Mining
  • 🌳 Climate Change Studies
  • 🪖 Defense, Security, Intelligence
Page 07
Space Lab

📅 A Beginner's 7-Day learning Roadmap

Follow this step-by-step program to build a strong baseline:

Day Core Focus Concept
Day 1 Master basic GIS theory, raster vs vector data, layers, coordinates.
Day 2 Install QGIS, navigate interface, load custom shapefiles and local CSV.
Day 3 Learn map styling presets, text labeling, measurements, create basic layouts.
Day 4 Understand projections (CRS), learn georeferencing, perform basic analysis.
Day 5 Create GEE account, explore layout of Code Editor, run test script commands.
Day 6 Query/filter image collections, display satellite imagery on base interactive map.
Day 7 Build basic analysis script (e.g. water detection/NDVI), export results to QGIS!

🔑 Key Takeaways

  • 🌟 GIS is the entire foundational science of location-based data.
  • 🌟 QGIS is local desktop software for styling, editing, and publishing.
  • 🌟 GEE is a global cloud playground processing massive remote sensing catalogs.
  • 🤝 Synergy: GEE executes raw heavy calculations ➜ QGIS styles final presentation maps!

🚀 Next Concepts to Learn for Your Space Lab Journey:

  1. Satellite Imagery Fundamentals (how satellites capture Earth data).
  2. Remote Sensing Basics (spectral bands & electromagnetic spectrum).
  3. Common scientific indices: NDVI (Veg), NDWI (Water), NDBI (Urban).
  4. A Hands-on Project: Combine GEE + QGIS (e.g., monitor vegetation health or urban growth around Ghaziabad).
    "These steps build a bulletproof roadmap for spatial work in any space program!"
✏️ Designed for quick study and reference in the Space Lab. Good Luck!