Coral reefs provide essential social, economic, and ecological services for millions of people worldwide. Yet, climate change and local anthropogenic stressors are damaging reefs globally, compromising reef-building capacity, and therefore impacting functionality. Growth of coral reefs depends upon the production and maintenance of the reef framework when calcium carbonate production exceeds erosion, and utilization of remote sensing to scale-up estimates of reef carbonate production remains limited. This study provided a first field estimate of net carbonate production on Hawaiʻi Island, in Hōnaunau Bay, and used high-resolution benthic-cover data, derived from Global Airborne Observatory (GAO) airborne imaging spectroscopy, to scale-up estimates. Net carbonate production was, on average, 0.5 kg CaCO3 m-2 y-1 across the depth gradient, with the highest rates of approximately 2.4 kg CaCO3 m-2 y-1 at 6 m. Urchins, especially the abundant Echinometra, suppressed reef-accretion potential in the shallow reef (< 6 m) and urchin bioerosion decreased with depth. Critically, a threshold of ~26% live-coral cover is currently needed to maintain positive net production across depths. Scaling-up estimates were achieved using a 2 m resolution map of live-coral cover collected by the GAO. Overall, field measurements translate to average vertical reef growth of 0.5 mm y-1 across depths, whereas sea level is currently increasing at 3.55 mm y-1, suggesting the reef in its present status is not keeping pace with sea-level rise. This work lays the foundation to enhance monitoring of carbonate production over increased temporal and spatial scales with airborne imaging spectroscopy — to help determine where reefs are potentially keeping up with anthropogenic stressors, ocean warming, and sea-level rise — and to help inform restoration and management decisions that support resilient carbonate budgets of coral reefs.

Remote sensing, with its capacity to capture continuous, high spatial and spectral resolution data, has emerged as an invaluable tool for ecological research and addressing conservation challenges. To fully harness the potential of remote sensing, spectral ecology has emerged as a field that investigates the interactions between the electromagnetic spectrum and biological processes. This dissertation capitalizes on a model system to explore the spectral ecology of a dominant, highly polymorphic, keystone, and endemic tree species (Metrosideros polymorpha). M. polymorpha not only serves as a model organism for studying adaptive radiation and intraspecific variation but also presents a critical conservation challenge. The recent introduction of the fungal disease Ceratocystis lukuohia has resulted in millions of M. polymorpha mortalities. This dissertation employs leaf-level spectroscopy data and canopy-level imaging spectroscopy data. Imaging spectroscopy captures reflectance across the visible to short-wave infrared (VSWIR) spectrum to provide high-spectral resolution data that enable canopy trait retrievals, species classifications, disease resistance detection, and genotype differentiation. Chapter 1 serves as an introduction, framing the subsequent chapters by presenting an overview of spectral ecology, imaging spectroscopy, and M. polymorpha. Chapter 2 explores M. polymorpha trait and spectra variation across environmental gradients. This chapter concludes that intraspecific variation follows the leaf economic spectrum and that elevation is a dominant driver of M. polymorpha trait and spectral variation. In Chapter 3, leaf-level spectroscopy was able to discriminate between sympatric, conspecific varieties of M. polymorpha and their hybrids as well as individuals resistant and susceptible to Ceratocystis wilt. Together, Chapters 2 and 3 support the concept of “genetic turnover,” akin to species turnover, wherein environmental conditions filter M. polymorpha genotypes present in a given region. Chapter 4 classifies M. polymorpha across the over 10,000 km2 of Hawai'i Island to aid in conservation efforts, demonstrating the potential of imaging spectroscopy to classify vegetation on large geographic scales. The final chapter builds on the prior chapters to present a M. polymorpha genetic diversity map for Hawai'i Island. In conclusion, this dissertation examines the spectral ecology of a model system to advance the understanding of ecological dynamics and address a pressing conservation challenge.

In Hawaiʻi, native macroalgae or “limu” are of ecological, cultural, and economic value. Invasive algae threaten native algae and coral that serve a key role in the reef ecosystem. Spectroscopy can be a valuable tool for species discrimination, while simultaneously providing insight into chemical processes occurring within photosynthetic organisms. The spectral identity and separability of Hawaiian macroalgal taxonomic groups and invasive and native macroalgae are poorly known and thus were the focus of this study. A macroalgal spectroscopic library of 30 species and species complexes found in Hawaiʻi was created. Spectral reflectance signatures were aligned with known absorption bands of division-specific photosynthetic pigments. Discriminant analysis was used to explore if taxonomic groups of algae and native versus invasive algae were separable. Discriminant analyses resulted in high overall classification accuracies. Algae were correctly classified based on taxonomic divisions 96.5% of the time and by species 83.2% of the time. Invasive versus native algae was correctly classified at a rate of 93% and higher. Analyses suggest there is spectral separability of algal taxonomic divisions and native-invasive status, which could have significant implications for coastal management. This study lays the groundwork for testing spectral mapping of native and invasive algal species using current airborne and forthcoming spaceborne imaging spectroscopy.