Can thermal data help improve how we monitor forests? 🌡️ In these images, recently cleared areas appear warmer than the surrounding canopy. 🌳 When trees are removed, exposed soil absorbs more sunlight and releases more heat, showing up in thermal imagery as bright, hot patches. That’s what makes thermal data so powerful. It allows analysts and algorithms to detect deforestation earlier and more precisely, even when visual changes are too small to notice. By combining thermal and visible imagery, AI models can distinguish real deforestation from natural seasonal shifts, reducing false positives and sharpening accuracy. This is what we mean when we say thermal data improves detection model accuracy. It gives machines another layer of evidence, one that’s invisible to the human eye but vital for decision-making. 🌱 From monitoring illegal clearing to supporting climate models, thermal imagery is helping turn observation into early action to protect what’s most important.
Thermal imaging can be helpful in detecting forest biotic and abiotic stresses. I just posted our latest study on this topic.
CaaS / Earth Monitoring (EM) and Geomatics / New Business Program Development
1mo#data_fusion .. imagine ...