Examining aboveground biomass and tree cover change in the region of Cantabria, Spain using multiple satellite sensors and machine learning tools in ArcGIS Pro.

This talk will discuss techniques used to map aboveground biomass in the region of Cantabria, Spain, and examine tree cover change over time. Using the biomass workflow outlined by Esri's Hong Xu we leverage machine learning tools in ArcGIS Pro, altimeter sensor data, optical satellite imagery, and DEM data to gain foundational insights into land cover and forest density. By combining trajectory point data from GEDI, which contains aboveground biomass information, with surface reflectance bands from Landsat collection 2, and DEM data we examine forest density, and coverage in the Cantabria region. These results are compared to data analyzed from previous decades using deep learning techniques to assess forest change in the region. Using ArcGIS Pro and AWS we can integrate remote sensing datasets from multiple satellite sensors and utilize machine learning tools to train and run a regression model providing us with an estimation of the above-ground biomass for the region of Cantabria, Spain in the form of a raster layer. This data can then be used in conjunction with results from tree cover analysis over time using deep learning and displayed in an interactive web application. This multifaceted analysis can provide researchers, policymakers, and stakeholders with key insights into progress and prioritization and aid in addressing local challenges related to forest health.