Remote sensing (RS) is the science of acquiring information about the Earth’s surface without physical contact, using sensors mounted on satellites, drones, or aircraft. These sensors capture data across various spectral bands—such as visible light, infrared, and microwave—to analyze different features like vegetation, water bodies, and urban areas. The spatial resolution of an image determines its detail, with smaller pixel sizes (e.g., 1m vs. 30m) revealing finer objects. Temporal resolution refers to how frequently a sensor revisits the same location, enabling consistent monitoring of changes over time. Additionally, radiometric resolution defines the sensor’s sensitivity to variations in energy intensity, influencing the image’s ability to distinguish subtle differences in reflectance or emissions. Together, these fundamental concepts form the backbone of remote sensing technology, supporting applications like environmental monitoring, agriculture, and disaster management.
- 1. Basic:
- Remote Sensing (RS): Acquiring information about an object or area without physical contact (via satellites, drones, etc.).
- Spectral Signature: Unique reflectance/emission pattern of a material across wavelengths.
- Spatial Resolution: Smallest detectable object size in an image (e.g., 10m/pixel).
- Temporal Resolution: Frequency of data capture (e.g., daily, monthly).
- Radiometric Resolution: Sensitivity to differences in energy intensity (e.g., 8-bit vs. 16-bit imagery).
2. Sensor & Platform Types
- Active Sensors: Emit energy and measure reflected signals (e.g., LiDAR, Radar).
- Passive Sensors: Record natural radiation (e.g., Multispectral, Thermal).
- Satellite RS: Landsat, Sentinel, MODIS.
- Aerial RS: UAVs/drones, airplanes.
3. Image Processing & Analysis
- NDVI (Normalized Difference Vegetation Index): Measures vegetation health.
- Classification: Categorizing pixels (e.g., Supervised vs. Unsupervised).
- PCA (Principal Component Analysis): Reduces data dimensionality.
- Image Fusion: Combines data from multiple sensors (e.g., pan-sharpening).
4. Applications
- Land Use/Land Cover (LULC): Mapping urban, forest, water bodies.
- Change Detection: Monitoring deforestation, urban expansion.
- Hyperspectral Imaging: Detailed material analysis (e.g., mineralogy).
- SAR (Synthetic Aperture Radar): Cloud-penetrating, used in disasters.
5. Advanced Terms
- Atmospheric Correction: Removing scattering/absorption effects.
- Time Series Analysis: Tracking changes over time.
- Machine Learning in RS: AI-driven classification (e.g., CNN for object detection).
