Improving the accuracy of honey bee forage class mapping using ensemble learning and multi-source satellite data in Google Earth Engine #sciencefather #researcher #honeybee

 🌾 Mapping Bee Forage in Semi-Arid Africa: A Smarter Way to Support Beekeepers

In the semi-arid agro-pastoral regions of Africa, beekeeping is more than just a sweet business—it’s a lifeline. It boosts food security, enhances income, and helps conserve biodiversity. But for beekeepers to thrive, they must place or move their hives where floral resources (bee forage) are abundant. Knowing when and where to position hives can significantly impact honey yields. That’s where technology comes in.

🔍 The Problem: Where Are the Best Bee Forage Zones?

Identifying and mapping suitable forage areas for honey bees, particularly Apis mellifera subspecies, is a major challenge. The landscapes are vast, varied, and often remote. Traditional field surveys are time-consuming and expensive. However, satellite data and machine learning offer powerful tools to detect these valuable floral resources from space.

🌐 Our Approach: Using Google Earth Engine (GEE) and Smart Algorithms

A recent study conducted in a semi-arid region of Ethiopia explored how different satellite datasets and machine learning models can be used in Google Earth Engine (GEE) to accurately map honey bee forage classes.

Researchers used multiple data sources:

• 🌍 PlanetScope imagery (P) – high-resolution optical data

• 🛰️ Sentinel-1 (S1) – radar imagery

• 🌈 Sentinel-2 (S2) – multispectral imagery

• 🗺️ SRTM DEM – topographic elevation data

These inputs were optimized using Forward Feature Selection (FFS) to choose the most relevant variables for mapping.

🧠 Machine Learning Models Compared

The study tested and compared four widely-used classifiers:

• Gradient Tree Boost (GTB)

• Random Forest (RF)

• Classification and Regression Trees (CART)

• Support Vector Machine (SVM)

Each model was run separately and then combined using an Ensemble Learning Approach (ELA)—a method that merges predictions from multiple models to improve performance.

📊 Key Results: Ensemble Learning Takes the Crown

• GTB alone had the highest single-model accuracy at 90.9%

• RF, CART, and SVM followed with 88.2%, 85.5%, and 79.9% respectively

• But when combined in the Ensemble Learning Approach, accuracy soared to 94.7%

This ensemble approach improved classification accuracy by up to 14.8% compared to individual models.

🐝 Why This Matters for Beekeeping

Better maps mean better decisions for beekeepers:

• Optimal hive placement = better honey yields

• Timely colony movement = stronger bee health

• Sustainable rangeland use = enhanced biodiversity

By leveraging machine learning and open-source tools like GEE, even resource-limited communities can gain advanced insight into their local ecosystems.

💡 Conclusion: Tech Meets Tradition
This study highlights how emerging technologies can empower traditional livelihoods. With smarter tools and more accurate data, African beekeepers can better manage their colonies, boost productivity, and protect fragile ecosystems in the face of climate and land-use changes.

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