DETECTION OF TREE PHENOSTAGES USING DJI PHANTOM DRONE ORTHOMOSAIC

Vol 20, 2023. - 156096
Anais / Proceedings XX SBSR
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Abstract

Evergreen tropical forest leaf demography drives seasonality of photosynthesis and may also drive other critical seasonal processes, such as wood growth, herbivore avoidance and water use efficiency. Here we assess the separability of fine-scale canopy elements that comprise different crown phenostages, which must be successfully classified as part of the workflow for inferring landscape scale leaf demography. We used a drone-derived orthomosaic covering 4.5 ha from which training and validation sets of pixel classes were extracted. Pixels classes were chosen to represent three foliar phenostages at the crown scale, needed to derived leaf demography -- recently flushed crowns, bare crowns, and crowns with mature to old leaves. Linear Discriminant Analysis (LDA) identified the corresponding pixel classes with high user´s accuracy (> 99%).

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Institutions
  • 1 Departamento de Dinâmica Ambiental, Instituto Nacional de Pesquisas da Amazônia
  • 2 Biogeochemical Processes Department, Max Planck Institute for Biogeochemistry
Track
  • 35. UAVs, videography and high spatial resolution
Keywords
Amazon
leaf tree phenology
green-up
spectral separation
Unmanned Aerial Vehicle