Coral reefs are essential to the cultural, ecological, and economic well-being of Hawai‘i’s communities, yet they face increasing threats from environmental changes and localized stressors, including coral disease. Detecting coral disease often relies on the visible appearance of lesions; however, in the case of black-band disease (BBD), this visual cue appears too late, as disease progression can cause an average rate of tissue loss of up to 5.7 cm2 per day over two months, followed by partial or full colony mortality. Reflectance spectroscopy offers a promising tool for detecting subtle spectral changes associated with coral health before visible symptoms emerge, yet few studies have applied this method to coral disease. In situ spectroscopy was used to measure the spectral reflectance of health conditions in Montiporid corals at ‘Anini Reef, Kaua‘i, USA. Discriminant analysis revealed that visually identical tissue types—live tissue on colonies with BBD (liveD) and live tissue on colonies without BBD (liveL)—were spectrally distinct. In contrast, BBD lesions (disease) and adjacent tissue that appeared healthy (transition) exhibited similar spectral signatures. Analyses identified three spectrally distinct tissue health conditions with a misclassification rate of 12.8%. These findings highlight the potential of reflectance spectroscopy for early coral disease detection, which could improve response times and support more effective coral reef conservation efforts.

Tropical forest canopies are the biosphere's most concentrated atmospheric interface for carbon, water and energy1,2. However, in most Earth System Models, the diverse and heterogeneous tropical forest biome is represented as a largely uniform ecosystem with either a singular or a small number of fixed canopy ecophysiological properties3. This situation arises, in part, from a lack of understanding about how and why the functional properties of tropical forest canopies vary geographically4. Here, by combining field-collected data from more than 1,800 vegetation plots and tree traits with satellite remote-sensing, terrain, climate and soil data, we predict variation across 13 morphological, structural and chemical functional traits of trees, and use this to compute and map the functional diversity of tropical forests. Our findings reveal that the tropical Americas, Africa and Asia tend to occupy different portions of the total functional trait space available across tropical forests. Tropical American forests are predicted to have 40% greater functional richness than tropical African and Asian forests. Meanwhile, African forests have the highest functional divergence-32% and 7% higher than that of tropical American and Asian forests, respectively. An uncertainty analysis highlights priority regions for further data collection, which would refine and improve these maps. Our predictions represent a ground-based and remotely enabled global analysis of how and why the functional traits of tropical forest canopies vary across space.

The Santa Monica Mountains National Recreation Area (SMMNRA) is a region of ecological importance, vulnerable to ecosystem degradation due to climate change and the spread of invasive plant species (IPS). Despite wide recognition of the presence and rapid expansion of invasive species in the SMMNRA, few studies have quantified the proportion and distribution of IPS in the region. A combination of high-resolution airborne imaging spectroscopy data and line-point transect data was used to model the distributions of five target IPS. A support vector machine model was applied to the imaging spectroscopy data and achieved an overall accuracy of 93.1%+/- 2.2% over 10 iterations. Additionally, spatial autocorrelation was used to delineate clustering patterns of the five species in both datasets. The species distribution maps and cluster maps derived from both the transects and remote sensing data were compared and contrasted to determine the effectiveness of both approaches for identifying regions of concentrated cover and small cover fractions of the five target IPS. Both datasets revealed species from the Poaceae family were most abundantly distributed in the SMMNRA, followed by Brassicaceae and Asteraceae, though species-level abundance rankings differed. Areas of expansive IPS cover were consistently identified in both the transect and imaging spectroscopy datasets, however, results diverged in heterogeneous landscapes. Transect-based clusters indicated low IPS dominance, while imaging spectroscopy more effectively captured spatial heterogeneity. These results emphasize the value of fine-spatial and spectral resolution data for detecting IPS patterns in complex environments and demonstrate how combining airborne imaging spectroscopy with field-based methods can inform IPS management.

Understanding how vegetation responds to drought is fundamental for understanding the broader implications of climate change on foundation tree species that support high biodiversity. Leveraging remote sensing technology provides a unique vantage point to explore these responses across and within species. We investigated interspecific drought responses of two Populus species ( P . fremontii , P . angustifolia ) and their naturally occurring hybrids using leaf‐level visible through shortwave infrared (VSWIR; 400–2500 nm) reflectance. As F 1 hybrids backcross with either species, resulting in a range of backcross genotypes, we heretofore refer to the two species and their hybrids collectively as ‘cross types’. We additionally explored intraspecific variation in P. fremontii drought response at the leaf and canopy levels using reflectance data and thermal unmanned aerial vehicle (UAV) imagery. We employed several analyses to assess genotype‐by‐environment (G × E) interactions concerning drought, including principal component analysis, support vector machine and spectral similarity index. Five key findings emerged: (1) Spectra of all three cross types shifted significantly in response to drought. The magnitude of these reaction norms can be ranked from hybrids> P. fremontii > P. angustifolia , suggesting differential variation in response to drought; (2) Spectral space among cross types constricted under drought, indicating spectral—and phenotypic—convergence; (3) Experimentally, populations of P. fremontii from cool regions had different responses to drought than populations from warm regions, with source population mean annual temperature driving the magnitude and direction of change in VSWIR reflectance. (4) UAV thermal imagery revealed that watered, warm‐adapted populations maintained lower leaf temperatures and retained more leaves than cool‐adapted populations, but differences in leaf retention decreased when droughted. (5) These findings are consistent with patterns of local adaptation to drought and temperature stress, demonstrating the ability of leaf spectra to detect ecological and evolutionary responses to drought as a function of adaptation to different environments. Synthesis. Leaf‐level spectroscopy and canopy‐level UAV thermal data captured inter‐ and intraspecific responses to water stress in cottonwoods, which are widely distributed in arid environments. This study demonstrates the potential of remote sensing to monitor and predict the impacts of drought on scales varying from leaves to landscapes.

Sewage pollution is a global threat to coastal ecosystems and amplifies the negative effects of climate change on coral reefs. Submarine groundwater discharge (SGD) is a major transport pathway for land-based pollution, but underlying drivers of SGD water quality are poorly understood, especially in nearshore coral reef ecosystems. We combined airborne mapping, field sampling, and statistical modeling to identify locations along the West Hawai‘i Island coastline where SGD is contaminated with sewage. Water samples collected from 47 distributed shoreline SGD locations were assayed for fecal indicator bacteria. A geostatistical model was used scale from field to regional levels at more than 1000 mapped SGD point locations to derive a geographic understanding of areas highly susceptible to contamination. We estimate that SGD delivers sewage-contaminated groundwater to at least 42% of reefs in West Hawaiʻi. Subsequent analyses indicate that contaminated points are associated with infrastructural build-up near the shoreline and an abundance of inland on-site sewage disposal systems. Mitigation of sewage pollution will require the prevention of numerous point sources from cesspools, septic leach fields, and similar sources.

In Hawaiʻi, native macroalgae or “limu” are of ecological, cultural, and economic value. Invasive algae threaten native macroalgae and coral, which 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 taxonomic division-specific photosynthetic pigments. Quadratic discriminant analysis was used to explore if taxonomic groups of algae and native versus invasive algae could be classified spectrally. 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 were correctly classified at a rate of 93% and higher, although the number of invasive algal species tested was limited. Analyses suggest that there is promise for the spectral separability of algae investigated in this study by algal taxonomic divisions and native-invasive status. This study created a spectral library that lays the groundwork for testing the spectral mapping of algae using current airborne and forthcoming spaceborne imaging spectroscopy, which could have significant implications for coastal management.

Remote sensing is a powerful tool for characterizing ecosystems at large scales. However, the relative importance of leaf traits and canopy structure in characterizing the spatial distribution of functionally distinct tropical forests – the most diverse, structurally complex, and heterogeneous ecosystems on Earth – remains under-explored. Using satellite-resolution LiDAR and imaging spectroscopy metrics, we map spatial turnover in tropical forest function, examine the relative importance of leaf traits and canopy structure, and analyze differences in aboveground carbon and demography. We find that leaf phosphorus, LMA, and canopy height are key distinguishing properties of forest types, achieving accuracies of 85-96% and correspond to differences in community growth and mortality rates. Our remotely sensed forest types align with ground-based forest definitions but enable mapping of their entire extent. At 30 m resolution, our method can be used at large scales with spaceborne data to reveal important differences in structure and function across tropical forests.

Corals are habitat-forming organisms on tropical and sub-tropical reefs, often displaying diverse phenotypic behaviors that challenge field-based monitoring and assessment efforts. Symbiont chlorophyll (Chl) is a long-recognized indicator of intra- and inter-specific variation in coral’s response to environmental variability and stress, but the quantitative Chl assessment of corals at the reef scale continues to prove challenging. We integrated field, airborne, and laboratory techniques to test and apply the use of reflectance spectroscopy for in situ and reef-scale estimation of Chl a and Chl c2 concentrations in a shallow reef environment of Kāne‘ohe Bay, O‘ahu. High-fidelity spectral signatures (420–660 nm) derived from field and airborne spectroscopy quantified Chl a and Chl c2 concentrations with demonstrable precision and accuracy. Airborne imaging spectroscopy revealed a 10-fold range of Chl concentrations across the reef ecosystem. We discovered a differential pattern of Chl a and Chl c2 use in symbiont algae in coexisting corals indicative of a physiological response to decreasing light levels with increasing water depth. The depth-dependent ratio of Chl c2:a indicated the presence of two distinct light-driven habitats spanning just 5 m of water depth range. Our findings provide a pathway for further study of coral pigment responses to environmental conditions using field and high-resolution airborne imaging spectroscopy.

Coral reefs are threatened globally by compounding stressors of accelerating climate change and deteriorating water quality. Water quality plays a central role in coral reef health. Yet, accurately quantifying water quality at large scales meaningful for monitoring impacts on coral health remains a challenge due to the complex optical conditions typical of shallow water coastal systems. Here, we report the performance of 32 remote sensing water quality models for suspended particulate matter and chlorophyll concentrations as well as colored dissolved organic matter absorption, over concentration ranges relevant for reef ecology using airborne imaging spectroscopy and field measurements across 62 stations in nearshore Hawaiian waters. Models were applied to reflectance spectra processed with a suite of approaches to compensate for glint and other above-water impacts on reflectance spectra. Results showed reliable estimation of particulate matter concentrations (RMSE = 2.74 mg L−1) and accurate but imprecise estimation of chlorophyll (RMSE = 0.46 μg L−1) and colored dissolved organic matter (RMSE = 0.03 m−1). Accurately correcting reflectance spectra to minimize sun and sky glint effects significantly improved model performance. Results here suggest a role for both hyperspectral and multispectral platforms and rapid application of simple algorithms can be useful for nearshore water quality monitoring over coral reefs.

Coral reefs are in decline worldwide, making it increasingly important to promote coral recruitment in new or degraded habitat. Coral reef morphology—the structural form of reef substrate—affects many aspects of reef function, yet the effect of reef morphology on coral recruitment is not well understood. We used structure-from-motion photogrammetry and airborne remote sensing to measure reef morphology (rugosity, curvature, slope, and fractal dimension) across a broad continuum of spatial scales and evaluated the effect of morphology on coral recruitment in three broadcast-spawning genera. We also measured the effect of other environmental and biotic factors such as fish density, adult coral cover, hydrodynamic larval import, and depth on coral recruitment. All variables combined explained 72% of coral recruitment in the study region. Coarse reef rugosity and curvature mapped at ≥2 m spatial resolution—such as large colonies, knolls, and boulders—were positively correlated with coral recruitment, explaining 22% of variation in recruitment. Morphology mapped at finer scales (≤32 cm resolution) was not significant. Hydrodynamic larval import was also positively related to coral recruitment in Porites and Montipora spp., and grazer fish density was linked to significantly lower recruitment in all genera. In addition, grazer density, reef morphology, and hydrodynamic import had differential effects on coral genera, reflecting genus-specific life history traits, and model performance was lower in gonochoric species. Overall, coral reef morphology is a key indicator of recruitment potential that can be detected by remote sensing, allowing potential larval sinks to be identified and factored into restoration actions.

Earth is experiencing unprecedented climate change driven by anthropogenic activities. The Paris Climate Agreement is the most recent international agreement pushing nations to curtail greenhouse gas emissions and balance carbon sources and sinks. To help meet the standards set forth in the Paris Climate Agreement, countries can incorporate ecosystems known to sequester and store large amounts of carbon, referred to as natural climate solutions, into their nationally determined contributions (NDC). Freshwater, brackish, and saline aquatic and wetland ecosystems (i.e., wet carbon, WC) are known for having some of the highest rates of carbon sequestration, and have the capacity to store carbon for decades to centuries. However, integration of WC into NDC is challenging due to lack of comprehensive understanding of carbon stores and fluxes (i.e., carbon state). To study the carbon state of WC ecosystems at the level that is needed for policy, use of remote sensing platforms (e.g., UAV, airborne, and spaceborne) is necessary. Imaging spectrometers, specifically hyperspectral narrow-band imagers, have the capability to provide more accurate carbon budget assessments, and discern more environmental variables that influence carbon budgets, compared to multispectral instruments. However, due to their limited availability, compared to multispectral sensors, there is a significant shortfall in imaging spectroscopy studies of WC ecosystems. This review provides an overview of previous imaging spectroscopy-WC research, highlights current knowledge gaps, and identifies future directions for research that will allow for improved study of WC ecosystems. While the past decade has seen increases in imaging spectroscopy-WC studies, we found WC habitats are globally understudied. Furthermore, the majority of research that has been done does not directly contribute to NDC efforts; however, this majority does provide the foundational work for future NDC research to be built upon. Imaging spectroscopy is a promising tool that can further advance NDC of WC ecosystems through continued development and integration.

Accurate retrieval of canopy nutrient content has been made possible using visible-to-shortwave infrared (VSWIR) imaging spectroscopy. While this strategy has often been tested on closed green plant canopies, little is known about how nutrient content estimates perform when applied to pixels not dominated by photosynthetic vegetation (PV). In such cases, contributions of bare soil (BS) and non-photosynthetic vegetation (NPV), may significantly and nonlinearly reduce the spectral features relied upon for nutrient content retrieval. We attempted to define the loss of prediction accuracy under reduced PV fractional cover levels. To do so, we utilized VSWIR imaging spectroscopy data from the Global Airborne Observatory (GAO) and a large collection of lab-calibrated field samples of nitrogen (N) content collected across numerous crop species grown in several farming regions of the United States. Fractional cover values of PV, NPV, and BS were estimated from the GAO data using the Automated Monte Carlo Unmixing algorithm (AutoMCU). Errors in prediction from a partial least squares N model applied to the spectral data were examined in relation to the fractional cover of the unmixed components. We found that the most important factor in the accuracy of the partial least squares regression (PLSR) model is the fraction of photosynthetic vegetation (PV) cover, with pixels greater than 60% cover performing at the optimal level, where the coefficient of determination (R2) peaks to 0.66 for PV fractions of more than 60% and bare soil (BS) fractions of less than 20%. Our findings guide future spaceborne imaging spectroscopy missions as applied to agricultural cropland N monitoring.

Accurate monitoring of crop nitrogen (N) across spatial and temporal scales is a fundamental goal for meeting precision agriculture requirements and promoting sustainable agriculture. The planning and implementation of several spaceborne imaging spectroscopy missions in recent years holds great promise for such large scale and intricate monitoring. Several N retrieval models have been developed for specific crop species, but a generalized model across diverse species is lacking. By leveraging imaging spectroscopy data collected by the Global Airborne Observatory (GAO), and leaf samples collected from commercial and research farms, we used partial least squares regression to calibrate and validate the retrieval of mass-based crop canopy N concentrations across diverse species and several major agricultural regions in the contiguous United States. The performance statistics indicated high precision and accuracy of the model results, suggesting that the development of a generalized N retrieval model is possible (R2: 0.78; RMSE: 0.49% N). Maps derived from GAO data provided quantitative crop N information at fine spatial resolution (i.e., 0.6 m), capturing both inter- and intra-species variations across agricultural locations. The algorithm was also successfully tested on simulated moderate resolution (i.e., 30 m) imagery, corresponding to data to be collected by forthcoming spaceborne imaging spectroscopy missions. Imaging spectroscopy offers an effective approach to quantify crop N concentration that could be incorporated to promote sustainable agriculture and improve global food security.

Imaging spectroscopy is a burgeoning tool for understanding ecosystem functioning on large spatial scales, yet the application of this technology to assess intra-specific trait variation across environmental gradients has been poorly tested. Selection of specific genotypes via environmental filtering plays an important role in driving trait variation and thus functional diversity across space and time, but the relative contributions of intra-specific trait variation and species turnover are still unclear. To address this issue, we quantified the variation in reflectance spectra within and between six uniform stands of Metrosideros polymorpha across elevation and soil substrate age gradients on Hawai‘i Island. Airborne imaging spectroscopy and light detection and ranging (LiDAR) data were merged to capture and isolate sunlit portions of canopies at the six M. polymorpha-dominated sites. Both intra-site and inter-site spectral variations were quantified using several analyses. A support vector machine (SVM) model revealed that each site was spectrally distinct, while Euclidean distances between site centroids in principal components (PC) space indicated that elevation and soil substrate age drive the separation of canopy spectra between sites. Coefficients of variation among spectra, as well as the intrinsic spectral dimensionality of the data, demonstrated the hierarchical effect of soil substrate age, followed by elevation, in determining intra-site variation. Assessments based on leaf trait data estimated from canopy reflectance resulted in similar patterns of separation among sites in the PC space and distinction among sites in the SVM model. Using a highly polymorphic species, we demonstrated that canopy reflectance follows known ecological principles of community turnover and thus how spectral remote sensing addresses forest community assembly on large spatial scales.

Mediterranean-type ecosystems are under chronic ecological stress. By assessing changes in plant species and functional groups across the landscape, identifying ecosystem degradation is possible. The Santa Monica Mountains are in close proximity to a densely populated urban area where non-native species invasions are being driven by changing fire regimes, climate change, and anthropogenic disturbances. Non-native growth impacts biodiversity levels and vegetation distributions of native plant communities that are critical for ecosystem health. This study uses analyses of line-point data from the National Park Service Inventory and Monitoring Program to assess ecosystem health in the Santa Monica Mountains from 2014 to 2020 and, in particular, to assess the effects of the 2018 Woolsey Fire on the balance of native and non-native species in the region. Results of this analysis show an increase in non-native cover since 2014 and rapid regrowth of non-native annual grasses and herbaceous cover after the 2018 Woolsey Fire while native communities regrew more slowly. A hotspot of non-native cover was identified in the Northern Simi Hills region, and rapid regrowth was seen after the Woolsey Fire. The hotspot is dominated by non-native annual grasses and annual herbaceous species, some of which returned to prefire populations within a year after the Woolsey Fire. These results raise concerns for the future of native vegetation composition and function throughout the park and highlight the damage densely populated non-native plant communities accumulate in the wake of disturbance events.

Vegetation classifications on large geographic scales are necessary to inform conservation decisions and monitor keystone, invasive, and endangered species. These classifications are often effectively achieved by applying models to imaging spectroscopy, a type of remote sensing data, but such undertakings are often limited in spatial extent. Here we provide accurate, high-resolution spatial data on the keystone species Metrosideros polymorpha, a highly polymorphic tree species distributed across bioclimatic zones and environmental gradients on Hawai’i Island using airborne imaging spectroscopy and LiDAR. We compare two tree species classification techniques, the support vector machine (SVM) and spectral mixture analysis (SMA), to assess their ability to map M. polymorpha over 28,000 square kilometers where differences in topography, background vegetation, sun angle relative to the aircraft, and day of data collection, among others, challenge accurate classification. To capture spatial variability in model performance, we applied Gaussian process classification (GPC) to estimate the spatial probability density of M. polymorpha occurrence using only training sample locations. We found that while SVM and SMA models exhibit similar raw score accuracy over the test set (96.0% and 93.4%, respectively), SVM better reproduces the spatial distribution of M. polymorpha than SMA. We developed a final 2 m × 2 m M. polymorpha presence dataset and a 30 m × 30 m M. polymorpha density dataset using SVM classifications that have been made publicly available for use in conservation applications. Accurate, large-scale species classifications are achievable, but metrics for model performance assessments must account for spatial variation of model accuracy.

Plant pathogens are increasingly compromising forest health, with impacts to the ecological, economic, and cultural goods and services these global forests provide. One response to these threats is the identification of disease resistance in host trees, which with conventional methods can take years or even decades to achieve. Remote sensing methods have accelerated host resistance identification in agricultural crops and for a select few forest tree species, but applications are rare. Ceratocystis wilt of ʻōhiʻa, caused by the fungal pathogen Ceratocystis lukuohia has been killing large numbers of the native Hawaiian tree, Metrosideros polymorpha or ʻŌhiʻa, Hawaii’s most common native tree and a biocultural keystone species. Here, we assessed whether resistance to C . lukuohia is detectable in leaf-level reflectance spectra (400–2500 nm) and used chemometric conversion equations to understand changes in leaf chemical traits of the plants as indicators of wilt symptom progression. We collected leaf reflectance data prior to artificially inoculating 2–3-year-old M . polymorpha clones with C. lukuohia . Plants were rated 3x a week for foliar wilt symptom development and leaf spectra data collected at 2 to 4-day intervals for 120 days following inoculation. We applied principal component analysis (PCA) to the pre-inoculation spectra, with plants grouped according to site of origin and subtaxon, and two-way analysis of variance to assess whether each principal component separated individuals based on their disease severity ratings. We identified seven leaf traits that changed in susceptible plants following inoculation (tannins, chlorophyll a+b, NSC, total C, leaf water, phenols, and cellulose) and leaf chemistries that differed between resistant and early-stage susceptible plants, most notably chlorophyll a+b and cellulose. Further, disease resistance was found to be detectable in the reflectance data, indicating that remote sensing work could expedite Ceratocystis wilt of ʻōhiʻa resistance screenings.

Coral reefs are threatened by climate change, overfishing, and pollution. Artificial reefs may provide havens for corals, both to escape warming surface waters and to assist in the geographic migration of corals to more habitable natural reef conditions of the future. The largest artificial reefs have been generated by nearly 2000 shipwrecks around the world, but the coral diversity on these wrecks is virtually unknown. Ship size and hull material, location relative to natural reef, time since sinking, ocean currents, and water depth may affect coral diversity. As a test of the biodiversity capacity of very large sunken structures relative to surrounding natural reef, we carried out technical diver-based surveys to quantify genus-level coral diversity on 29 warships sunk in Bikini Atoll and Chuuk Lagoon. We also assessed whether ship length, as an index of substrate availability, and water depth, as an indicator of light and temperature, can serve as predictors of coral diversity. We surveyed a total of 9105 scleractinian corals. The total number of genera identified at Bikini was 34, and at Chuuk it was 51, representing 67% and 72% of genera found on natural reefs at Bikini and Chuuk, respectively. Ship length, but not water depth, was positively correlated with relative abundance and richness at the genus level. Our results suggest that very large wrecks can serve as havens for reef-building corals with a broad genetic diversity, expressed at the genus level, commensurate with corals found on neighboring natural reefs. The role of large artificial reefs could include protecting coral biodiversity from warming surface waters.

The global decline of coral reefs urgently requires scalable colony‐level data about phenotypic variation to improve coral conservation and management. To address this, we leveraged historical bleaching phenotypes, airborne imaging spectroscopy, and recurrent temperature stress to map coral species composition and thermal tolerance across four focal reefs with a cumulative area of ~15 ha. Spectral data accurately distinguished benthic composition and coral species, and showed substantial capacity for mapping thermal tolerance in two species of healthy coral. We used thermal stress from a 2019 marine heatwave to demonstrate high prediction accuracy during a natural bleaching event, and to strengthen the links between predictions, conserved tolerance phenotypes, and spectral signatures. Large differences in the proportion of tolerant corals at individual reefs suggest that ecosystem‐scale “winners” and “losers” in future bleaching can be predicted, which may greatly increase the efficacy of management. This framework provides foundational evidence for the applicability of organismic‐scale remote sensing to coral conservation.