Module 1: Introduction to Time Series Analysis of Satellite Imagery

• What is time series analysis in remote sensing?
• Importance of temporal dynamics in environmental monitoring and change detection.
• Key concepts: Frequency, seasonality, trend, anomaly.
• Overview of common applications of time series analysis (e.g., deforestation, urban growth, crop monitoring).
• Introduction to the software and platforms used for time series analysis (e.g., Google Earth Engine, R/Python).
• Practical session: Exploring and visualizing a long-term satellite image archive for a region of interest.

Module 2: Satellite Missions and Data for Time Series Analysis

• Overview of satellite missions providing suitable data for time series (e.g., Landsat, Sentinel, MODIS, NOAA AVHRR).
• Understanding data characteristics: Spatial, spectral, and temporal resolution trade-offs.
• Accessing and downloading multi-temporal satellite imagery from various sources.
• Data continuity and consistency across different sensor generations.
• Leveraging cloud-based platforms for large-scale time series data access.
• Practical session: Acquiring multi-temporal satellite imagery for a specific area and time period from a cloud platform.

Module 3: Time Series Data Pre-processing and Harmonization

• Importance of consistent pre-processing for time series analysis.
• Atmospheric correction and cloud masking techniques for multi-temporal data.
• Radiometric and geometric harmonization across different dates and sensors.
• Gap filling and smoothing techniques for noisy time series data.
• Creating consistent time series stacks for analysis.
• Practical session: Applying atmospheric correction, cloud masking, and radiometric normalization to a satellite image time series.

Module 4: Visualizing and Exploring Satellite Image Time Series

• Techniques for visualizing temporal changes (e.g., animations, temporal profiles).
• Creating temporal signatures for different land cover types.
• Exploring seasonal patterns and inter-annual variability.
• Interactive visualization tools for time series data.
• Identifying initial trends and anomalies through visual inspection.
• Practical session: Generating temporal profiles for selected land cover features and creating an animation of land cover change over time.

Module 5: Vegetation and Environmental Indices for Time Series Analysis

• Review of common spectral indices (e.g., NDVI, EVI, NDWI).
• Calculating and interpreting time series of various indices.
• Using indices to monitor vegetation health, water stress, and urban development.
• Understanding the phenological cycle of different vegetation types through indices.
• Customizing indices for specific application needs.
• Practical session: Calculating and analyzing time series of NDVI and NDWI for different land cover types (e.g., forest, agriculture, water).

Module 6: Detecting Anomalies and Breakpoints in Time Series

• Definition and importance of anomalies and breakpoints in time series.
• Statistical methods for anomaly detection (e.g., Z-score, moving averages).
• Algorithms for detecting abrupt changes or breakpoints (e.g., BFAST, CCDC (Continuous Change Detection and Classification) concepts).
• Identifying disturbance events (e.g., deforestation, fire, flood impacts).
• Interpreting breakpoint characteristics (magnitude, direction, timing of change).
• Practical session: Applying a simple anomaly detection method to a time series of a vegetation index to identify significant drops or increases.

Module 7: Seasonal and Trend Decomposition of Time Series

• Understanding components of a time series: Trend, seasonality, and residual.
• Methods for decomposing time series (e.g., STL (Seasonal-Trend decomposition using Loess)).
• Separating long-term trends from seasonal variations.
• Analyzing the residual component for unusual events.
• Applications in understanding long-term environmental processes.
• Practical session: Decomposing a long-term time series of an environmental variable (e.g., temperature, vegetation index) into its trend and seasonal components.

Module 8: Machine Learning for Time Series Analysis of Satellite Data

• Introduction to machine learning concepts for temporal data.
• Supervised learning for time series classification (e.g., classifying crop types based on their phenological signature).
• Unsupervised learning for grouping similar temporal patterns.
• Time series feature engineering for machine learning models.
• Deep learning approaches for sequence modeling in remote sensing.
• Practical session: Training a simple machine learning model (e.g., Random Forest) to classify land cover based on temporal features.

Module 9: Applications of Time Series Analysis in Remote Sensing

• Forest disturbance monitoring: Deforestation, degradation, regrowth.
• Agricultural monitoring: Crop type mapping, yield forecasting, phenology tracking.
• Urban expansion and dynamics: Analyzing urban growth patterns.
• Water resource management: Monitoring lake levels, flood extent, drought conditions.
• Climate change impact assessment: Tracking environmental responses to climate shifts.
• Practical session: Applying time series analysis to a specific application, such as mapping forest disturbance over several years.

Module 10: Project Work and Advanced Topics

• Review of all time series analysis techniques and workflows.
• Guided individual or group project work on a comprehensive time series problem using satellite imagery.
• Designing a complete time series analysis project from data selection to final product generation.
• Introduction to advanced concepts: Spatio-temporal data cubes, Big Data for time series analysis (e.g., using Google Earth Engine at scale).
• Best practices for documenting, validating, and presenting time series analysis results.
• Practical session: Completing an end-to-end time series analysis project on a new dataset, including data preparation, analysis, and interpretation of temporal patterns.

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