Expected Learning outcomes
By the end of this course the participants are expected to:
- Appreciate the value of remote sensing in insurance Organizations
- Be able to apply Remote Sensing to Delimit the total area cropped by a farmer
- Understand the image resolutions appropriate for Agricultural remote sensing
- Understand the fundamentals of spectroscopy and their applications in land auditing
- Be able to extract geometric properties of an image and use them to validate insurance claims made by clients
- Monitor compliance i.e. irrigation and fertilization of crops
- Be able to extract the total area lost to (pest infestations, drought, fire among others)
| Contents | |
| Part I: Fundamentals of Remote Sensing
1. Remote Sensing Overview 1.1 Definition 1.2 Remote Sensing Concept 1.3 Components in Remote Sensing 1.4 Types of Remote Sensing 1.5 Multistage Remote Sensing Data Collection 1.7 Types and Uses of Satellites
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4. Spectroscopy ( Hyper-spectral remote sensing)
4.1 Fundamentals of spectroscopy 4.2 Sources of spectroscopic data 4.3 Hyper-spectral image analysis
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| 2. Remote Sensing Data Acquisition
2.1 Electromagnetic Waves Used in Remote Sensing 2.2 Properties of Electromagnetic Waves 2.3 Spectral Reflectance and Earth Surface Interaction 2.4 Multi-spectral Remote Sensing Data (Image) 2.5 Spectral Properties and Principal Applications 2.6 Spectral Reflectance to DN (Digital Number) 2.7 Structure of Remote Sensing Data 2.8 Resolutions in Remote Sensing
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5. Remote Sensing Data for Agricultural applications
5.1 Land Cover Classification 5.2 Vegetation monitoring 5.3 Crop condition assessment 5.4 Crop identification 5.5 Soil moisture mapping 5.6 Burnt area mapping 5.7 property, boundary and cropped area delineation 5.8 Land and Environmental auditing 5.9 Vegetation stress monitoring using red edge shifts
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| 3. Remote Sensing Data Processing and Analysis
3.1 Remote Sensing Data Pre-processing 3.2 Visual Interpretation (Band combination) 3.3 Apply Algorithms 3.4 Multi-spectral Classification |
6. Practical applications of RS in Agricultural applications
6.1 Digital image processing (Sentinel 2 and landsat8) 6.2 Image classification 6.3 Spectral resampling for sentinel 2 MSI 6.4 NDVI calculations 6.5 Above ground biomass calculation 6.6 Extracting spectral properties of an image |
Assumed knowledge
Basic digital literacy is needed to complete the practicals
Requirements of the course
Dual core laptops or desktop computers
Internet connection to download satellite images
GIS software’s
Nb: Some open software’s will be provided together with this tutorial
Software’s
Integrated Land and Water Information System (ILWIS)
Quantum Geographic Information System (QGIS)
Snap
Sen2core
Nb: All these are open source software i.e. they are distributed for free
Datasets
Landsat 8 Operational Land imager
Sentinel 2 satellite image
Suggested Reading materials
ISRS Proceeding Papers of Sort Interactive Session ISPRS TC VIII International Symposium on “Operational Remote Sensing Applications: Opportunities, Progress and Challenges”, Hyderabad, India, December 9 – 12, 2014