[115] Analyzing Geospatial Forest Data with Geopandas and Rasterio (Laya Zeinali Yadegari)

Join our Meetup group:
https://www.meetup.com/data-umbrella

About the Event
In this session, we will explore how to model forest structure and quantify ecosystem services using Python and Earth observation data. Leveraging GEDI, Sentinel-1, Sentinel-2, and SRTM datasets, we’ll walk through data preprocessing, machine learning techniques (e.g., Random Forest), and interpretation using SHAP for understanding feature importance.

Overview of forest structure, aboveground biomass (AGB), and ecosystem service indicators
Preprocessing spatial and tabular data using Python (geopandas, rasterio, pandas)
Machine learning approaches for forest modeling: Random Forest, feature selection, and model evaluation

Resources
slides: will be added

Timestamps
00:00 Data Umbrella introduction
03:50 Laya begins presentation


About the Speaker
As a Ph.D. candidate at Tarbiat Modares University, Tehran, Laya's research interests lie in forest remote sensing and the assessment of forest ecosystem services. She is currently investigating the application of machine learning techniques to analyze multi-sensor remote sensing data, including LiDAR, radar, and optical imagery, for the estimation of forest carbon stocks, biomass, and the provision of ecosystem services. Her research specifically utilizes GEDI (Global Ecosystem Dynamics Investigation)

LinkedIn:   / laya-zeinali-2667171ba  
GitHub: https://github.com/layazeinali

#Python #forestmodeling #MachineLearning