Remote Sensing - fundamentals and applications
What you'll learn
- Remote Sensing Background
- Types of Remote Sensing
- Applications of Remote Sensing data
- Multispectral Satellite data Microwave (Radar) Satellite data Hyperspectral, Satellite data Applications in Land Cover Applications in Agriculture
- Applications in Forestry, Applications in Geology, Applications in Hydrology, Applications in Sea-ice, Applications in Oceans and Coastal
- no recqurements
Remote sensing is the process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation at a distance (typically from satellite or aircraft). Special cameras collect remotely sensed images, which help researchers "sense" things about the Earth. Some examples are:
Cameras on satellites and airplanes take images of large areas on the Earth's surface, allowing us to see much more than we can see when standing on the ground.
Sonar systems on ships can be used to create images of the ocean floor without needing to travel to the bottom of the ocean.
Cameras on satellites can be used to make images of temperature changes in the oceans.
Some specific uses of remotely sensed images of the Earth include:
Large forest fires can be mapped from space, allowing rangers to see a much larger area than from the ground.
Tracking clouds to help predict the weather or watching erupting volcanoes, and help watching for dust storms.
Tracking the growth of a city and changes in farmland or forests over several years or decades.
Discovery and mapping of the rugged topography of the ocean floor (e.g., huge mountain ranges, deep canyons, and the “magnetic striping” on the ocean floor).
Section-2: Remote Sensing Background
Principle of Remote Sensing
Important concepts for Principle of Remote Sensing
History of Remote sensing
Section-3: Types of Remote Sensing
Sensor based types of Remote Sensing
Resolution based types and characteristics of Remote Sensing
Purpose based types of Remote Sensing
Section-4: Applications of Remote Sensing data
Multispectral Satellite data
Microwave (Radar) Satellite da ta
Hyperspectral Satellite data
Applications in Land Cover
Applications in Agriculture
Applications in Forestry
Applications in Geology
Applications in Hydrology
Applications in Sea-ice
Applications in Oceans and Coastal
Who this course is for:
- geology proffesionals
- GIS users
- land science proffesionals
- earth science students
- social studies teachers
We choose the best courses and make them available to new audiences.
Our training offer covers the entire spectrum of data intelligence:
Art - Capture - Modeling - Design - Construction - Operation. Using technological development and process improvement as a transverse thread.
The creators of courses with which we have decided to work have been carefully selected, to offer a complementary set of knowledge. We firmly believe that today people do not seek courses to fill their walls with diplomas; but to make their abilities more productive.
Waleed is a Ph.D. student in Geography at HKBU, Hong Kong. He has been working in the Remote Sensing domain for the last four years focusing on Google Earth Engine, Geospatial Data Science, and Machine/Deep Learning. His research interests include but are not limited to 1) Hazard Risk-Resilience studies, 2) Monitoring rapid urbanization/urban sprawl patterns, 3) Forecasting future land use and land surface temperature patterns, 4) Applications of Google Earth Engine in ecology, Paleoclimatology, and Microclimate (UHI).
His research activities primarily revolve around the use of GEE, RS workflow automation through GEE, Python, ArcGIS Pro, QGIS, TerrSet, PowerBi, and Python-based Geospatial Modules.
He’s focusing on regional/national/global scale studies involving GEE and is also available for working and collaboration opportunities in the aforementioned domains.