Mapping tropical forest canopy diversity using high‐fidelity imaging spectroscopy.

Area of Science:

• Ecology
• Remote Sensing
• Conservation Biology

Background:

• Operational biodiversity mapping is essential for assessing climate change and human impacts on ecosystems.
• Quantifying alpha (within-habitat) and beta (between-habitat) diversity is key to understanding ecosystem health.
• Tropical forests harbor immense biodiversity, making them critical areas for conservation efforts.

Purpose of the Study:

• To present an original method for estimating alpha and beta diversity in tropical forests using high-fidelity imaging spectroscopy.
• To develop an unsupervised approach for mapping biodiversity metrics.
• To validate remotely sensed diversity estimates with extensive field data.

Main Methods:

• Acquisition of airborne imaging spectroscopy data over Amazonian tropical forests in Peru.
• Development of an unsupervised method to estimate the Shannon index (H') for alpha diversity.
• Estimation of species composition variations using Bray-Curtis dissimilarity (BC) and nonmetric multidimensional scaling (NMDS) for beta diversity.

Main Results:

• Airborne maps of Shannon index (H') showed high correlation with field-derived alpha diversity estimates (r = 0.86).
• Remotely sensed estimates of Bray-Curtis dissimilarity (BC) demonstrated demonstrable accuracy (r = 0.61–0.76).
• The study provides the first direct, spatially explicit, remotely sensed estimates of alpha and beta diversity for humid tropical forests.

Conclusions:

• The developed imaging spectroscopy method offers a powerful tool for operational biodiversity mapping.
• These findings enable new applications for airborne and space-based remote sensing in tropical forest ecosystems.
• Spatially explicit biodiversity data are vital for effective conservation and management strategies in the face of environmental change.