Module 1: Fundamentals of Remote Sensing

• Definition and principles of remote sensing.
• Electromagnetic Spectrum: Understanding energy interactions with Earth's surface.
• Spectral signatures of common land cover types (vegetation, water, soil).
• Types of remote sensing: Passive vs. Active.
• Overview of different remote sensing platforms (satellites, aircraft, drones).
• Practical session: Exploring spectral signatures of various land cover features using spectral profile tools in software.

Module 2: Remote Sensing Data Acquisition and Sensors

• Characteristics of remote sensing data: Spatial, spectral, temporal, radiometric resolution.
• Common optical satellite sensors: Landsat, Sentinel, MODIS, Planet.
• Understanding sensor types: Multispectral, Hyperspectral, Panchromatic.
• Data access and download platforms (e.g., USGS Earth Explorer, Copernicus Open Access Hub).
• Introduction to drone-based remote sensing data.
• Practical session: Downloading satellite imagery from a public repository and examining its metadata.

Module 3: Image Pre-processing Techniques

• Importance of image pre-processing for accurate analysis.
• Radiometric correction: Correcting for sensor errors and atmospheric effects.
• Geometric correction/Registration: Aligning images to a coordinate system.
• Image mosaicking: Combining multiple images into a seamless composite.
• Subsetting and clipping images for specific areas of interest.
• Practical session: Performing geometric correction/registration of a raw satellite image to a base map.

Module 4: Image Enhancement and Transformation

• Contrast Enhancement: Linear, histogram equalization, Gaussian stretch.
• Spatial Filtering: Smoothing (low-pass) and edge detection (high-pass) filters.
• Band Combinations: Creating true color and false color composites for visualization.
• Image Transforms: Principal Component Analysis (PCA) for dimensionality reduction.
• Tasseled Cap Transformation for vegetation studies.
• Practical session: Applying various contrast enhancements, spatial filters, and creating custom band combinations.

Module 5: Image Classification: Supervised Methods

• Introduction to image classification: Pixel-based vs. Object-based.
• Defining training areas for different land cover classes.
• Common supervised classification algorithms: Maximum Likelihood, Support Vector Machine (SVM), Random Forest.
• Performing supervised classification and generating a classified map.
• Accuracy assessment of classified images: Confusion matrix, Kappa coefficient.
• Practical session: Performing a supervised classification on an image, defining training areas, and generating a land cover map.

Module 6: Image Classification: Unsupervised Methods and Advanced Techniques

• Unsupervised Classification: K-Means clustering, ISODATA.
• Advantages and disadvantages of supervised vs. unsupervised methods.
• Object-Based Image Analysis (OBIA) concepts.
• Introduction to machine learning and deep learning for image classification.
• Post-classification processing: Smoothing, majority filtering.
• Practical session: Performing an unsupervised classification and comparing the results with a supervised classification.

Module 7: Change Detection Analysis

• Understanding change detection: Methods and applications.
• Types of change detection techniques: Image differencing, image ratioing, post-classification comparison.
• Multi-temporal image co-registration.
• Quantifying and mapping land cover changes over time.
• Case studies of change detection (e.g., deforestation, urban growth, water body changes).
• Practical session: Performing post-classification change detection between two time periods and mapping areas of change.

Module 8: Introduction to Radar Remote Sensing

• Principles of radar remote sensing: Active vs. Passive.
• Advantages of radar: All-weather capability, penetration through clouds.
• Understanding SAR (Synthetic Aperture Radar) data.
• Basic radar image interpretation for land cover, flood mapping, and biomass estimation.
• Introduction to radar interferometry (InSAR) for deformation monitoring.
• Practical session: Loading and visualizing a radar image, performing basic filtering.

Module 9: Applications of Remote Sensing

• Environmental monitoring: Deforestation, land degradation, water quality.
• Agriculture: Crop health assessment, yield prediction, drought monitoring.
• Urban planning: Urban sprawl analysis, impervious surface mapping.
• Disaster management: Flood mapping, damage assessment, wildfire tracking.
• Geology and natural resource exploration.
• Practical session: Applying learned techniques to a specific application-focused dataset (e.g., mapping agricultural fields or urban expansion).

Module 10: Project Work and Advanced Topics

• Review of all image processing and analysis techniques.
• Guided individual or group project work on a selected remote sensing problem.
• Designing a complete remote sensing workflow from data acquisition to final product.
• Introduction to advanced topics: Hyperspectral imaging, LiDAR data processing, Google Earth Engine.
• Best practices for documenting and presenting remote sensing results.
• Practical session: Completing an end-to-end remote sensing project, including data processing, analysis, and map generation.

Requirements:

·       Participants should be reasonably proficient in English.

·       Applicants must live up to Armstrong Global Institute admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588