Artificial Intelligence for Mangrove Mapping and Monitoring Using Remote Sensing Imagery and Extreme Gradient Boosting (XGBoost) Algorithm with Python

Mangroves are one of the important blue carbon ecosystems that provide numerous benefits for humans and the surrounding environment. The mapping and monitoring of mangrove forests are important information to know the conditions of the mangrove forests. In recent years, artificial intelligence has gained significant attention in the field of remote sensing due to its effectiveness and accuracy for the classification and regression tasks that use remote sensing data. Remote sensing satellite imagery offers advantages such as large area coverage and temporal information which make it suitable for mangrove mapping and monitoring. Extreme Gradient Boosting (XGBoost) is one of the supervised machine learning algorithms based on decision trees which utilizes gradient boosting to iteratively build a stronger model by combining multiple weak decision tree models. This talk will cover the application of the XGBoost algorithm for mangrove mapping and monitoring using Sentinel-2 imagery within a specific study area using Python. Attendees will be exposed to the latest developments in artificial intelligence applications for mangrove studies using remote sensing data and will also learn how to map and monitor mangrove areas using the XGBoost algorithm, including calculations of vegetation and mangrove spectral indices as input features, evaluation assessment, and the analysis of changes in mangrove area.