ForestSAT AI : Preventing Devastating Large Forest Fires
At ForestSAT we are leveraging satellite earth observation data and artificial intelligence (AI) to predict and prevent large forest fires that are devastating our planet’s lungs with increasing frequency and ferocity due to climate change-induced stresses and extremities.
ForestSAT ML algorithms are trained to process many years of satellite imagery and remote sensing data on weather, soil, forest vegetation, tree height & health change, infestations, biomass, carbon stock & hydrology to measure fuel buildup and areas at risk.
- ForestSAT procures and processes satellite imagery and remote sensing data from sensors at different temporal, spatial and spectral resolutions from Landsat Sentinel, Terra satellites of NASA, European Space Agency, German, Japanese, Indian and commercial satellites going back more than 20 years.
- ForestSAT Machine and Deep Learning for Earth Observation extract valuable knowledge on forest vegetation to estimate forest carbon storage over large regions.
- Our algorithms measure forest fuel build-up, drought and climate impact on vegetation to estimate areas at risk of a forest fire. Many forests are already in “tinderbox” conditions and could explode anytime thus requiring the urgent intervention of controlled burns, defensive civil works, or mechanical fuel removal.
- We estimate the risk of fire by tracing and measuring fuel to forests, grasslands, croplands, communities and assets.
- Our algorithms calculate carbon emission risk from wildfire and/or deforestation and predict the impact of carbon sequestration from standing forest and/or regenerated forest.
- Deep learning change analysis provides insights into forest health and lays the foundation for science-driven preventive and regenerative actions. A direct relationship is established between carbon emission risk and sequestration potential through forest regeneration to channel carbon finance into restoration, resilience and reforestation.
ForestSAT AI app delivers forest managers with deep forest fire risk profiles, forest-human interfaces & assets at risk polygons as a decision support for fire risk mitigation, fire prevention and forest regeneration.
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