Abstract
Excessive tree mortality rates prevail in many regions of the world. Understanding tree mortality dynamics remains elusive as this multifaceted phenomenon is influenced by an interplay of abiotic and biotic factors including, but not limited to, global warming, climate extremes, pests, pathogens, and other environmental stressors. Earth observation satellites, coupled with machine learning, present a promising avenue to unravel map standing dead trees and lay the foundation for explaining the underlying dynamics.However, the lack of globally comprehensive, georeferenced training data spanning various biomes and forest types has hindered the development of a unified global product detailing tree mortality patterns. Present ground-based observations, eg, sourced from national inventories, are often sparse, lack standardization, and spatial specificity. Alternatively, aerial imagery captured via drones or airplanes …
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