Advanced Applications of Remote Sensing and GIS Technologies for Mapping and Monitoring in Agriculture and Climate Change in Nairobi, Kenya

The course is useful for professionals in the field of agriculture and climate change who use spatial data as part of their work. Additionally, it caters to those who need to make decisions from such data. Such professionals, including M&E practitioners, agricultural projects and programs managers, researchers, consultants, and students, among others, will find this course beneficial.

By the end of the course the learner should be able to:

• Appreciate the role of spatial data infrastructure (SDI) in agriculture and climate change setup
• Gain knowledge on how to acquire and process spatial data through optical and SAR satellite images
• Conduct agriculture and climate change modeling using GIS
• Collect data using Mobile data gathering tools
• Know how to analysis and understand SAR satellite data
• Integrate GIS within new and existing activities in agriculture and climate change.

5 days

Module 1: Introduction to remote sensing and GIS technologies

• Characteristics of different optical and SAR satellite images.
• Properties of satellite images using GIS products.
• Monitoring climate change patterns using remote sensing and GIS technologies.

Module 2: Advanced analysis and processing of Satellite images

• Advanced and automated processing steps of both Optical and SAR satellite images.
• Collecting and processing of Unmanned Aerial Vehicle (UAV) imageries.
• Development of different Machine Learning models using satellite images and other geospatial data.

Module 3: Machine Learning for Land Use/Land Cover Classification

• Land Use/Land Cover classification and accuracy assessment using Machine Learning techniques (Unsupervised and supervised classifications)
• Change detection of different land surface features spatially and temporally.
• Incorporating different indices generated from Satellite images in Land Use/Land Cover classification to improve the accuracy and spatial representation of the classification output.

Module 4: Analysis and understanding of different SAR satellite data

• Basics theoretical and practical concepts on the SAR signatures from different satellite products.
• Applying the time-series SAR satellite images (Sentinel-1 and other SAR products with different bands) for monitoring of key concepts in agriculture and climate change.
• Processing and analyzing the SAR satellite images using Python and GIS products.

Module 5: Big Geo-data processing and storage

• General Big Geo-data tools and technologies.
• Storing, accessing, analyzing, and sharing Big Geo-data using popular techniques.
• Developing of machine learning workflows to handle and analyze Big Geo-data for supporting various decision-making processes.