Biomass Carbon Prediction using NASA ORNL & MODIS Dataset by Random Forest Model in Earth Engine

Опубликовано: 26 Август 2024
на канале: Terra Spatial

1,303

In this video, we dive into predicting biomass carbon using the NASA ORNL and MODIS datasets with a machine learning approach in Google Earth Engine. Watch as we define our region of interest, centered over a key geographical area, and load the biomass carbon density data to analyze above-ground biomass (AGB). We utilize MODIS NDVI, EVI, LAI, and FPAR datasets to understand vegetation health and productivity, integrating these insights with land cover and elevation data for a comprehensive view.

By training a Random Forest model, we predict AGB with high accuracy, demonstrating the power of machine learning in environmental studies. See how we visualize the results and validate our model through Pearson correlation and R-squared values, ensuring robust predictions. We also take a look into the future by predicting AGB for the year 2020, highlighting how these techniques can be used for ongoing monitoring.
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Useful links:
Code: https://code.earthengine.google.com/c...
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Global Aboveground and Belowground Biomass Carbon Density Maps: https://developers.google.com/earth-e...
MOD13Q1.061 Terra Vegetation Indices 16-Day Global 250m:
https://developers.google.com/earth-e...
MOD15A2H.061: Terra Leaf Area Index/FPAR 8-Day Global 500m
https://developers.google.cn/earth-en...
MODIS Land Cover Type Yearly Global 500m: https://developers.google.com/earth-e...
Global 30 Arc-Second Elevation: https://developers.google.com/earth-e...
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#googleearthengine #biomass #prediction #NASA-ORNL #modis #RandomForestModel #earthengine