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

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.
-----------------------------------------------------------------
Useful links:
Code: code.earthengi...
-----------------------------------------------------------------
Global Aboveground and Belowground Biomass Carbon Density Maps: developers.goo...
MOD13Q1.061 Terra Vegetation Indices 16-Day Global 250m:
developers.goo...
MOD15A2H.061: Terra Leaf Area Index/FPAR 8-Day Global 500m
developers.goo...
MODIS Land Cover Type Yearly Global 500m: developers.goo...
Global 30 Arc-Second Elevation: developers.goo...
-----------------------------------------------------------------
💰🤝🏻Join Membership to get access to perks & Support us🤝🏻💰
/ @terraspatial
-----------------------------------------------------------------
👩‍💻 Join the Terra Spatial Community:
Engage with fellow learners, share your experiences, and get support on our dedicated community forum.
🌐 Stay connected:
📌 Subscribe to our Channel: / @terraspatial. .
📌 Facebook: / terraspatial
📌 Geosuite blog: geosuite.blogs...
---------------------------------------------------------------
👍 **Don't forget to Like, Share, and Subscribe for more insightful tutorials! 🌐✨
#googleearthengine #biomass #prediction #NASA-ORNL #modis #RandomForestModel #earthengine

[@prasanthsoul565]

Excellent explanation

[@TerraSpatial]

Glad you liked it

[@user-sm2lc1dz3s]

i support this research community

[@TerraSpatial]

Thank you, Appreciate your valuable support

[@elizabethossonar8784]

Good 👍👍👍

[@TerraSpatial]

Thank you! Cheers!

[@irinechesang4114]

This is insightful video but i have two questions in relation to the same since I am carrying almost similar research but still exploring and learning from experts , first
1. How do you quantify, which approach to quantify carbon concentration-aspect sampling, equations?
2. How can one simulate the heavy fieldwork of the above ground biomass?
3. Is it possible to do carbon estimation using Ml / can it be achieved using ML algorithms?
Thank you in advance

[@carolinrock-yn5ic]

Pretty long video but good explanation especially using regression to predict future biomass value

[@TerraSpatial]

Thanks for sharing your valuable comments

[@ahmadnawi2132]

hello sir, what can i get if i membership?

[@Greetingshive]

Why i am not getting reply even after taking your paid membership?