Abstract
The integration of cutting-edge remote sensing technologies, biophysical principles, and advanced spatial statistics enables innovative landscape analysis across various spatial and temporal scales. Traditional approaches relied on classification methods and indices derived from multi-spectral imagery to assess landscape degradation. However, modern techniques can extract biophysical indices like leaf area index and canopy chemistry from satellite imagery. Long-term remote sensing archives (e.g., Landsat, AVHRR) facilitate retrospective studies of landscape changes and trajectories. Recent advancements in sensors and analysis techniques, such as sub-pixel classifications and continuous fields, have improved the accuracy of variable retrieval (e.g., Albedo, chlorophyll concentration). These developments enable powerful monitoring tools for land use/cover change detection, leading to a better understanding of landscape dynamics and the mapping of previously unexplored features. However, a trade-off exists between high spatial and high temporal resolution depending on the platform used.
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