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Despite the limited coverage of coral reefs in the world’s oceans, they play a crucial role in global marine biodiversity and providing essential ecosystem services. This study explores the influence of chlorophyll-a (Chl-a) concentration in the water column on the estimation of bottom reflectance (rb) in coral reefs monitored by the Allen Coral Atlas coral reef monitoring system, using satellite imagery from a Sentinel-2 MSI sensor. We conducted a comprehensive analysis, considering Chl-a global distribution and variability, and its combined effect with water column depth over rb calculation. Our results demonstrated that the impact of Chl-a on rb estimation becomes significant when the water column depth exceeds 3 m. While suggesting the optionality of using regional Chl-a values, our study highlights potential overestimations of Chl-a in optically complex environments, such as along the Brazilian coast. This research contributes to refining coral reef monitoring systems and underscores the importance of accurate Chl-a assessments for robust environmental evaluations.

Ambitious biodiversity goals to protect 30% or more of the Earth’s surface by 2030 (30x30) require strategic near-term targets. To define areas that must be protected to prevent the most likely and imminent extinctions, we propose Conservation Imperatives—16,825 unprotected sites spanning ~164 Mha of the terrestrial realm that harbor rare and threatened species. We estimate that protecting the Conservation Imperatives would cost approximately US$169 billion (90% probability: US$146—US$228 billion). Globally, 38% of the 16,825 sites are either adjacent to or within 2.5 km of an existing protected area, potentially reducing land acquisition and management costs. These sites should be prioritized for conservation action over the next 5 years as part of a broader strategy to expand the global protected area network. The expansion of global protected areas between 2018 and 2023 incorporated only 7% of sites harboring range-limited and threatened species, highlighting a renewed urgency to conserve these habitats. Permanently protecting only 0.74% of land found in the tropics, where Conservation Imperatives are concentrated, could prevent the majority of predicted near-term extinctions once adequately resourced. We estimate this cost to be from US$29 billion to US$46 billion per year over the next 5 years. Multiple approaches will be required to meet long-term protection goals: providing rights and titles to Indigenous Peoples and Local Communities (IPLCs) conserving traditional lands, government designation of new protected areas on federal and state lands, and land purchase or long-term leasing of privately held lands.

Summary: Coral reefs underpin the environmental, social, and economic fabrics of much of the world's tropical coast. Yet, the fine-scale distribution and composition of coral reefs have never been reported consistently across the planet. Here, we present new area estimates enabled by global geomorphic zone and benthic substrate maps at 5 m pixel resolution. We revise global coral reef estimates to 348,361 km2 of shallow coral reefs and 80,213 km2 (46,237–106,319 km2, 95% confidence interval) of coral habitat. The mapping used more than 1.5 million training samples supported by 480+ data contributions to deploy a coral reef classification of over 100 trillion pixels from the Sentinel-2 satellites and the Planet Dove CubeSat constellation. The publicly available maps are accessible via the Allen Coral Atlas and Google Earth Engine and are already being used by thousands of people to improve the conservation, management, and research of coral reef ecosystems. Science for society: Coral reefs possess a quarter of all marine life and contribute to the well-being and livelihoods of a billion people worldwide. Maps of ecosystems underpin many science and conservation activities, but until recently, there were no consistent high-resolution maps of the world’s coral reefs. In this paper, we describe new global coral reef maps from the Allen Coral Atlas, detailing the underlying methodology and our new understanding of the global distribution of coral reefs. The transparent and repeatable nature of our mapping framework allows the maps to be updated based on user feedback, and the ease of access has led to downstream applications such as coral bleaching monitoring and usage in scientific, management, and conservation activities. Hundreds of thousands of people have already accessed the maps, and they are already being used directly around the world for marine spatial planning, marine protected areas, environmental accounting and assessments, restoration, and education.

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.

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.

With the increasing availability and volume of remote sensing data, imaging spectroscopy is an expanding tool for agricultural studies. One of the fundamental applications in agricultural research is crop mapping and classification. Previous studies have mostly focused at local to regional scales, and classifications were usually performed for a limited number of crop types. Leveraging fine spatial resolution (60 cm) imaging spectroscopy data collected by the Global Airborne Observatory (GAO), we investigated canopy-level spectral variations in 16 crop species from different agricultural regions in the U.S. Inter-specific differences were quantified through principal component analysis (PCA) of crop spectra and their Euclidean distances in the PC space. We also classified the crop species using support vector machines (SVM), demonstrating high classification accuracy with a test kappa of 0.97. A separate test with an independent dataset also returned high accuracy (kappa = 0.95). Classification using full reflectance spectral data (320 bands) and selected optimal wavebands from the literature resulted in similar classification accuracies. We demonstrated that classification involving diverse crop species is achievable, and we encourage further testing based on moderate spatial resolution imaging spectrometer data.

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.

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.

Functional variations in tropical forests can be determined from remotely sensed forest trait and structural attributes at spatial resolutions relevant to satellite-based observations, according to a coarse resolution analysis of airborne remotely sensed data in Malaysian Borneo.

Abstract Turbidity monitoring in shallow coastal waters is fundamental to marine ecosystem research, management and protection. Satellite‐based water turbidity monitoring can be conducted at a greater spatial extent and higher temporal frequency than field measurements. The new Planet Dove satellite constellation has a daily revisit frequency and higher spatial resolution than Sentinel or Landsat satellites, allowing Planet Dove to track water turbidity dynamics in greater detail when suitable atmospheric correction is provided. We developed a new shallow coastal water turbidity estimation algorithm for Planet Dove and similar multi‐spectral satellites. Our algorithm accounts for bottom reflectance in total water‐leaving radiance to derive turbidity values in shallow coastal waters. We tested the algorithm with data from 235 Dove satellite images at five sites with different water conditions (Pelekane Bay, Big Island, Hawai‘i; Hilo Bay, Big Island, Hawai‘i; Kilo Nalu and Ala Wai, O‘ahu, Hawai‘i; Fagatele Bay, American Samoa; Vieques Island, Puerto Rico). We then validated satellite‐derived turbidity results (RMSE = 0.79–1.12 FNU [Formazin Nephelometric Unit]) using 75 days of field‐measured data, ranging in turbidity from 0.1 to 11.6 FNU in the five sites. Results show that our algorithm accurately detects turbidity in critical nearshore environments. In Hawai‘i, we used ~6700 Dove images to support a weekly turbidity monitoring study at a large geographic scale. We found this new, shallow‐water algorithm can be effectively applied to Dove satellite data to monitor water turbidity at high temporal resolution.

The role of intraspecific trait variation in functional ecology has gained traction in recent years as many papers have observed its importance in driving community diversity and ecology. Yet much of the work in this field relies on field-based trait surveys. Here, we used continuous canopy trait information derived from remote sensing data of a highly polymorphic tree species, Metrosideros polymorpha, to quantify environmental controls on intraspecific trait variation. M. polymorpha, an endemic, keystone tree species in Hawai’i, varies morphologically, chemically, and genetically across broad elevation and soil substrate age gradients, making it an ideal model organism to explore large-scale environmental drivers of intraspecific trait variation. M. polymorpha canopy reflectance (visible to shortwave infrared; 380–2510 nm) and light detection and ranging (LiDAR) data collected by the Global Airborne Observatory were modeled to canopy trait estimates of leaf mass per area, chlorophyll a and b, carotenoids, total carbon, nitrogen, phosphorus, phenols, cellulose, and top of canopy height using previously developed leaf chemometric equations. We explored how these derived traits varied across environmental gradients by extracting elevation, slope, aspect, precipitation, and soil substrate age data at canopy locations. We then obtained the feature importance values of the environmental factors in predicting each leaf trait by training random forest models to predict leaf traits individually. Of these environmental factors, elevation was the most important predictor for all canopy traits. Elevation not only affected canopy traits directly but also indirectly by influencing the relationships between soil substrate age and canopy traits as well as between nitrogen and other traits, as indicated by the change in slope between the variables at different elevation ranges. In conclusion, intraspecific variation in M. polymorpha traits derived from remote sensing adheres to known leaf economic spectrum (LES) patterns as well as interspecific LES traits previously mapped using imaging spectroscopy.

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.

We describe the production of maps of buildings on Hawai’i Island, based on complementary information contained in two different types of remote sensing data. The maps cover 3200 km2 over a highly varied set of landscape types and building densities. A convolutional neural network was first trained to identify building candidates in LiDAR data. To better differentiate between true buildings and false positives, the CNN-based building probability map was then used, together with 400–2400 nm imaging spectroscopy, as input to a gradient boosting model. Simple vector operations were then employed to further refine the final maps. This stepwise approach resulted in detection of 84%, 100%, and 97% of manually labeled buildings, at the 0.25, 0.5, and 0.75 percentiles of true building size, respectively, with very few false positives. The median absolute error in modeled building areas was 15%. This novel integration of deep learning, machine learning, and multi-modal remote sensing data was thus effective in detecting buildings over large scales and diverse landscapes, with potential applications in urban planning, resource management, and disaster response. The adaptable method presented here expands the range of techniques available for object detection in multi-modal remote sensing data and can be tailored to various kinds of input data, landscape types, and mapping goals.

Making oil palm agriculture as efficient as possible is essential to ensuring that this economically important crop can be grown sustainably. To determine how oil palm growth rates vary across tropical landscapes, we used repeat airborne LiDAR data to map the height growth of >500,000 oil palms in Malaysian Borneo over a two-year period coinciding with the 2015–16 El Niño drought. Despite uncharacteristically dry and hot weather conditions, oils palms grew an average of 1.6 m yr−1 in height over this period. However, oil palm growth rates varied across the landscape and in relation to plant age, tending to be fastest for younger individuals growing closer to forest edges, further from rivers and at higher elevations. Our results highlight the ability of oil palms to grow even during periods of drought and showcase how cutting-edge remote sensing technologies can help improve the efficiency and sustainability of oil palm agriculture.

The recently described crustose calcifying red algal species Ramicrusta hawaiiensis, known only from mesophotic depths off Lehua Island, west of Kaua’i Island, was found in shallow benthic reef habitats (3-18 m deep) along the western coast of Hawai’i Island. Molecular and microscopy techniques were used for genetic confirmation and for detailed morphological and anatomical examination. Two independent benthic cover survey datasets collected from west Hawai’i Island were used to investigate temporal and geographic distribution of Ramicrusta. In both datasets, we report Ramicrusta at approximately 60% of the sites surveyed. Benthic cover for this alga varies among sites and among years and its presence in west Hawai’i is evident since at least 2003. These findings help to document Hawaiian coral reef ecosystem change and benthic community composition reshuffling. This study also emphasizes the critical importance of taxonomy and proper identification of macroalgal species to understand the potential for phase-shifts of dominant taxa in coral reef ecosystems after environmental disturbances and fluctuations in abiotic factors. In the last decade, members of the red algal order Peyssonneliales have increased in abundance and overgrown other benthic species in reef ecosystems in the Caribbean and tropical Pacific. The novel aspect of finding abundant Ramicrusta in much shallower water than originally described, the decadal presence of Ramicrusta, and its potential for competition with other benthic organisms make this research valuable to coral reef ecology and justify further investigation of Ramicrusta ecology and biology in the Hawaiian Islands and globally.

Turbidity from land-based runoff has been identified as a possible driver of coral bleaching refugia, as particulate matter in turbid habitats may block excessive irradiance and alter the food supply to corals during ocean heatwaves. However, negative effects of turbidity have also been documented worldwide, and high-resolution data across reef regions are required to identify habitats where turbidity is harmful or helpful during heatwaves. We monitored turbidity across five Main Hawaiian Islands during a 2019 ocean heatwave using Planet Dove satellites (3.7 m spatial resolution, extracted at 100 m resolution) to examine relationships between turbidity and coral bleaching occurrence and severity. We also attributed turbidity to urban and non-urban land uses to determine how turbidity associated with different human activities affects coral bleaching. We found that, while turbidity had a negative relationship with coral bleaching severity, this effect weakened at higher temperatures, especially in rural areas. However, turbidity maintained a slightly negative effect on bleaching severity in urban settings. In addition, while thermal stress was the predominant driver of bleaching occurrence, interactions between temperature and local variables like turbidity and wave energy were the most significant drivers of bleaching severity. This study represents one of the first high-resolution analyses of turbid refugia at large spatial scales. We show how new data sources from remote sensing can provide scalable yet context-specific information to coastal managers addressing land-sea runoff.

It is unclear whether tropical forest fragments within plantation landscapes are resilient to drought. Here the authors analyse LiDAR and ground-based data from the 2015-16 El Niño event across a logging intensity gradient in Borneo. Although regenerating forests continued to grow, canopy height near oil palm plantations decreased, and a strong edge effect extended up to at least 300 m away.

Abstract Most marine animals have a pelagic larval phase that develops in the coastal or open ocean. The fate of larvae has profound effects on replenishment of marine populations that are critical for human and ecosystem health. Larval ecology is expected to be tightly coupled to oceanic features, but for most taxa we know little about the interactions between larvae and the pelagic environment. Here, we provide evidence that surface slicks, a common coastal convergence feature, provide nursery habitat for diverse marine larvae, including > 100 species of commercially and ecologically important fishes. The vast majority of invertebrate and larval fish taxa sampled had mean densities 2–110 times higher in slicks than in ambient water. Combining in-situ surveys with remote sensing, we estimate that slicks contain 39% of neustonic larval fishes, 26% of surface-dwelling zooplankton (prey), and 75% of floating organic debris (shelter) in our 1000 km2 study area in Hawai‘i. Results indicate late-larval fishes actively select slick habitats to capitalize on concentrations of diverse prey and shelter. By providing these survival advantages, surface slicks enhance larval supply and replenishment of adult populations from coral reef, epipelagic, and deep-water ecosystems. Our findings suggest that slicks play a critically important role in enhancing productivity in tropical marine ecosystems.

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.

Increases in sea surface temperature impact animal metabolism, which in turn could influence benthic structure and resulting algal-coral balance. We utilized a long-term coral reef dataset from the west coast of Hawai‘i Island to investigate impacts of annual positive and negative sea surface temperature anomalies (SSTA) on benthic cover [algal turf, macroalgae, crustose coralline algae (CCA), and coral], herbivore density (sea urchins, grazers, browsers, and scrapers) and the relationship between benthic cover and herbivore density. Results showed significantly lower coral cover, but higher CCA cover with positive SSTA. Additionally, the density of sea urchins, grazers and browsers increased with increasing SSTA. Warming disrupted the normal relationship between herbivores and benthic cover on reefs, particularly for grazers where higher densities were coupled with lower algal turf cover only during negative SSTA. The direction of the relationship between benthic cover and herbivore type changed with positive SSTA, where increased algal turf cover was associated with increased herbivore density. Here, herbivores are likely responding accordingly to increases in food availability due to increased metabolism under warming. Despite herbivore populations increasing in density over the past two decades, algal turf cover remains on an upward trajectory. These results indicate that warming can alter herbivore-algal dynamics, where greater herbivore densities may be required to cause a reduction in algal turf cover. Protection of herbivores in addition to reducing nutrient input onto reefs will be essential in driving a reduction in algal turf cover on Hawaiian reefs.

Mangroves, seagrasses, and coral reefs interact in tropical regions throughout the world. These ecosystems exhibit strong synergies, as the health of each ecosystem supports the functioning of adjacent habitats. We present a global spatial analysis of mangrove, seagrass, and reef communities, identifying regions where these habitats co-occur. While only an estimated 18% of interaction zones are covered by protected areas, boundaries between mangroves, seagrasses, and reefs represent areas of high conservation efficiency, where benefits of conservation amplify synergistically as land-sea ecosystems are jointly managed. We discuss four types of conservation efficiencies in these coastal ecosystems: (1) increased resistance to disturbance through inter-ecosystem feedbacks, (2) high biodiversity within small geographic areas, (3) habitat portfolio effects giving rise to climate refugia, and (4) synergistic ecosystem services, where building one ecosystem service inherently increases others. Given these synergistic benefits, global campaigns to expand marine and terrestrial protection should focus on the tightly connective interface between mangroves, seagrasses, and reefs, in order to more efficiently build resilience within and between these habitats.

Coral reefs are undergoing changes caused by coastal development, resource use, and climate change. The extent and rate of reef change demand robust and spatially explicit monitoring to support management and conservation decision-making. We developed and demonstrated an airborne-assisted approach to design and upscale field surveys of reef fish over an ecologically complex reef ecosystem along Hawai‘i Island. We also determined the minimal set of mapped variables, mapped reef strata, and field survey sites needed to meet three goals: (i) increase field survey efficiency, (ii) reduce field sampling costs, and (iii) ensure field sampling is geostatistically robust for upscaling to regional estimates of reef fish composition. Variability in reef habitat was best described by a combination of water depth, live coral and macroalgal cover, fine-scale reef rugosity, reef curvature, and latitude as a proxy for a regional climate-ecosystem gradient. In combination, these factors yielded 18 distinct reef habitats, or strata, throughout the study region, which subsequently required 117 field survey sites to quantify fish diversity and biomass with minimal uncertainty. The distribution of field sites was proportional to stratum size and the variation in benthic habitat properties within each stratum. Upscaled maps of reef survey data indicated that fish diversity is spatially more uniform than fish biomass, which was lowest in embayments and near land-based access points. Decreasing the number of field sites from 117 to 45 and 75 sites for diversity and biomass, respectively, resulted in a manageable increase of statistical uncertainty, but would still yield actionable trend data over time for the 60 km reef study region on Hawai‘i Island. Our findings suggest that high-resolution benthic mapping can be combined with stratified-random field sampling to generate spatially explicit estimates of fish diversity and biomass. Future expansions of the methodology can also incorporate temporal shifts in benthic composition to drive continuously evolving fish monitoring for sampling and upscaling. Doing so reduces field-based labor and costs while increasing the geostatistical power and ecological representativeness of field work.

Over the past decade, coral restoration efforts have increased as reefs continue to decline at unprecedented rates. Identifying suitable coral outplanting locations to maximize coral survival continues to be one of the biggest challenges for restoration practitioners. Here, we demonstrate methods of using derivatives from imaging spectroscopy from the Global Airborne Observatory (GAO) to identify suitable coral outplant sites and report on the survival rates of restored coral at those sites. Outplant sites for a community-based, citizen science outplant event in Bávaro, Dominican Republic, were identified using expert-defined criteria applied to a suitability model from data layers derived from airborne imagery. Photo quadrat analysis of the benthic community confirmed the accuracy of airborne remote sensing maps with live coral cover averaging 3.5–4% and mean algal cover (macro algae and turf) ranging from 28 to 32%. Coral outplant sites were selected at 3–7 m depth with maximized levels of habitat complexity (i.e., rugosity) and live coral cover and minimized levels of macroalgal cover, as predicted by the imaging spectrometer data. In November 2019, 1,722 Acropora cervicornis fragments (80–180 mm in length) were outplanted to these sites. Surveys conducted in January 2020 in four of these sites confirmed that 92% of outplants survived after 3 months. By October 2020 (11 months after outplanting), survivorship remained above 76%. These results demonstrate higher than average success rates for coral outplant survival for this species. An online tool was developed to enable replication and facilitate future selection of coral restoration sites. Our objective is to present a case study that uses GAO-derived map products within a suitability model framework to provide a quantitative and replicable method for selecting coral restoration sites with the goal of increasing outplant survival over time.

Abstract Anthropogenic carbon dioxide (CO2) emissions dominate uncertainties in the global carbon budget. Global inventories, such as the National Greenhouse Gas Inventories, have latencies of 12–24 months and may not keep pace with rapidly changing infrastructure, particularly in the developing world. Our work reveals that airborne and satellite imaging spectrometers provide 3–30 m spatial resolution and accurate quantification of CO2 emissions at the facility scale. Examples from 17 coal and gas fired power plants across the United States demonstrate robust correlation and 21% agreement on average between our remotely sensed estimates and simultaneous in situ measured emissions. We highlight four examples of coal‐fired power plants in India, Poland, and South Korea, where we quantify significant carbon dioxide emissions from power plants where limited public emissions data exist. Leveraging previous work on methane (CH4) plume detection, we present a strategy to exploit joint CO2 and CH4 plume imaging to quantify carbon emissions across widely distributed industrial infrastructure, including facilities that co‐emit CO2 and CH4. We show an example of a coal operation, where we attribute 25% of greenhouse gas emissions to coal extraction (CH4) and the remaining 75% to energy generation (CO2). Satellite spectrometers could track high emitting coal‐fired power plants that collectively contribute to 60% or more of global coal CO2 emissions. Multiple revisits and coordinated targeting of these high emitting facilities by multiple spaceborne instruments will be key to reducing uncertainties in global anthropogenic CO2 emissions and supporting emissions mitigation strategies.

Our ability to completely and repeatedly map natural environments at a global scale have increased significantly over the past decade. These advances are from delivery of a range of on-line global satellite image archives and global-scale processing capabilities, along with improved spatial and temporal resolution satellite imagery. The ability to accurately train and validate these global scale-mapping programs from what we will call “reference data sets” is challenging due to a lack of coordinated financial and personnel resourcing, and standardized methods to collate reference datasets at global spatial extents. Here, we present an expert-driven approach for generating training and validation data on a global scale, with the view to mapping the world’s coral reefs. Global reefs were first stratified into approximate biogeographic regions, then per region reference data sets were compiled that include existing point data or maps at various levels of accuracy. These reference data sets were compiled from new field surveys, literature review of published surveys, and from individually sourced contributions from the coral reef monitoring and management agencies. Reference data were overlaid on high spatial resolution satellite image mosaics (3.7 m × 3.7 m pixels; Planet Dove) for each region. Additionally, thirty to forty satellite image tiles; 20 km × 20 km) were selected for which reference data and/or expert knowledge was available and which covered a representative range of habitats. The satellite image tiles were segmented into interpretable groups of pixels which were manually labeled with a mapping category via expert interpretation. The labeled segments were used to generate points to train the mapping models, and to validate or assess accuracy. The workflow for desktop reference data creation that we present expands and up-scales traditional approaches of expert-driven interpretation for both manual habitat mapping and map training/validation. We apply the reference data creation methods in the context of global coral reef mapping, though our approach is broadly applicable to any environment. Transparent processes for training and validation are critical for usability as big data provide more opportunities for managers and scientists to use global mapping products for science and conservation of vulnerable and rapidly changing ecosystems.

Abstract Plant defense chemistry is often hypothesized to drive ecological and evolutionary success in diverse tropical forests, yet detailed characterizations of plant secondary metabolites in tropical plants are logistically challenging. Here, we explore a new integrative approach that combines visible‐to‐shortwave infrared (VSWIR) spectral reflectance data with detailed plant metabolomics data from 19 Protium (Burseraceae) tree species. Building on the discovery that different Protium species have unique chemistries yet share many secondary metabolites, we devised a method to test for associations between metabolites and VSWIR spectral data. Given species‐level variation in metabolite abundance, we correlated the concentration of particular chemicals with the reflectance of the spectral bands in a wavelength band per secondary metabolite matrix. We included 45 metabolites that were shared by at least 5 Protium species and correlated their per‐species foliar abundances against each one of 210 wavelength bands of field‐measured VSWIR spectra. Finally, we tested whether classes of similar metabolites showed similar relationships with spectral patterns. We found that many secondary metabolites yielded strong correlations with VSWIR spectra of Protium. Furthermore, important Protium metabolite classes such as procyanidins (condensed tannins) and phytosterols were grouped together in a hierarchical clustering analysis (Ward’s algorithm), confirming similarity in their associations with plant spectral patterns. We also found a significant correlation in the phenolics content between juvenile and canopy trees of the same species, suggesting that species‐level variation in defense chemistry is consistent across life stages and geographic distribution. We conclude that the integration of spectral and metabolic approaches could represent a powerful and economical method to characterize important aspects of tropical plant defense chemistry.

Reef rugosity, a metric of three-dimensional habitat complexity, is a central determinant of reef condition and multi-trophic occupancy including corals, fishes and invertebrates. As a result, spatially explicit information on reef rugosity is needed for conservation and management activities ranging from fisheries to coral protection and restoration. Across archipelagos comprising islands of varying geologic stage and age, rugosity naturally varies at multiple spatial scales based on island emergence, subsidence, and erosion. Reef rugosity may also be changing due to human impacts on corals such as marine heatwaves and nearshore coastal development. Using a new high-resolution, large-area mapping technique based on airborne imaging spectroscopy, we mapped the rugosity of reefs to 22 m depth throughout the eight Main Hawaiian Islands. We quantified inter- and intra-island variation in reef rugosity at fine (2 m) and coarse (6 m) spatial resolutions, and tested potential abiotic and human drivers of rugosity patterns. We found that water depth and reef slope remain the dominant drivers of fine- and coarse-scale rugosity, but nearshore development is a secondary driver of rugosity. Our results and maps can be used by fisheries management and reef conservations to track geologic versus human impacts on reefs, design effective marine managed areas, and execute activities to improve reef resilience.

The sizes and shapes of tree crowns are of fundamental importance in ecology, yet understanding the forces that determine them remains elusive. A cardinal question facing ecologists is the degree to which general and non-specific vs. ecological and context-dependent processes are responsible for shaping tree crowns. Here, we test this question for the first time across diverse tropical ecosystems. Using trees from 20 plots varying in elevation, precipitation, and ecosystem type across the paleo- and neo-tropics, we test the relationship between crown dimensions and tree size. By analyzing these scaling relationships across environmental gradients, biogeographic regions, and phylogenetic distance, we extend Metabolic Scaling Theory (MST) predictions to include how local selective pressures shape variation in crown dimensions. Across all sites, we find strong agreement between mean trends and MST predictions for the scaling of crown size and shape, but large variation around the mean. While MST explained approximately half of the observed variation in tree crown dimensions, we find that local, ecosystem, and phylogenetic predictors account for the half of the residual variation. Crown scaling does not change significantly across regions, but does change across ecosystem types, where savanna tree crowns grow more quickly with tree girth than forest tree crowns. Crowns of legumes were wider and more voluminous than those of other taxa. Thus, while MST can accurately describe the central tendency of tree crown size, local ecological conditions and evolutionary history appear to modify the scaling of crown shape. Importantly, our extension of MST incorporating these differences accounts for the mechanisms driving variation in the scaling of crown dimensions across the tropics. We present allometric equations for the prediction of crown dimensions across tropical ecosystems. These results are critical when scaling the function of individual trees to larger spatial scales or incorporating the size and shape of tree crowns in global biogeochemical models.

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.

Endangered Bornean elephants are severely threatened by ongoing habitat transformation and increasing levels of human-elephant conflict. Understanding how elephants move across intact and transformed landscapes, as well as within them, is therefore of vital importance for the successful implementation of conservation management initiatives. We combined remote sensing and GPS telemetry data to identify broad habitat utilization and key movement areas to aid elephant management and conflict mitigation in three spatially-isolated populations in central and eastern Sabah, Malaysian Borneo. Home ranges were estimated using Brownian Bridge Movement Models and specific behavioral movement traits were identified by pathway analyses. These behavioral traits enabled a fine-scale evaluation of movements between and adjacent to forest patches and the role of large-scale agriculture in shaping elephant movements. Both natural (topological) and anthropogenic (agricultural) landscape features were found to have a broad influence on elephant movements. All elephant populations exhibited human-mediated behavioral responses, regardless of disturbance level. Throughout their range, elephants appeared to actively select relatively degraded forests, as measured by aboveground carbon density. However, elephants actively avoided urbanized areas, including roads and villages. Throughout the elephant range, high-speed, low-trajectory movements were found at low aboveground carbon locations, with 27% of all such movements located in large-scale agriculture. Our results suggest that agriculture impacts movement strategies of elephants, with evidence of repeat agricultural use pointing towards an active rationale for this behavior. Elephants were also found to use ridgelines as movement pathways, providing further context for the protection of such forested areas. The Lower Kinabatangan population, located in small remnant forests, travelled further to meet their ecological needs, suggesting the population is under added strain. Our work represents the broadest landscape assessment of Bornean elephant movements to-date and has important implications for both future work and habitat-level protected area management strategies.

Foliar trait adaptation to sun and shade has been extensively studied in the context of photosynthetic performance of plants, focusing on nitrogen allocation, light capture and use via chlorophyll pigments and leaf morphology; however, less is known about the potential sun-shade dichotomy of other functionally important foliar traits. In this study, we measured 19 traits in paired sun and shade leaves along a 3,500-m elevation gradient in southern Peru to test whether the traits differ with canopy position, and to assess if relative differences vary with species composition and/or environmental filters. We found significant sun-shade differences in leaf mass per area (LMA), photosynthetic pigments (Chl ab and Car), and δ13C. Sun-shade offsets among these traits remained constant with elevation, soil substrates, and species compositional changes. However, other foliar traits related to structure and chemical defense, and those defining general metabolic processes, did not differ with canopy position. Our results suggest that whole-canopy function is captured in many traits of sun leaves; however, photosynthesis-related traits must be scaled based on canopy light extinction. These findings show that top-of-canopy measurements of foliar chemistry from spectral remote sensing approaches map directly to whole-canopy foliar traits including shaded leaves that cannot be directly observed from above.

High-resolution benthic habitat data fill an important knowledge gap for many areas of the world and are essential for strategic marine conservation planning and implementing effective resource management. Many countries lack the resources and capacity to create these products, which has hindered the development of accurate ecological baselines for assessing protection needs for coastal and marine habitats and monitoring change to guide adaptive management actions. The PlanetScope (PS) Dove Classic SmallSat constellation delivers high-resolution imagery (4 m) and near-daily global coverage that facilitates the compilation of a cloud-free and optimal water column image composite of the Caribbean’s nearshore environment. These data were used to develop a first-of-its-kind regional thirteen-class benthic habitat map to 30 m water depth using an object-based image analysis (OBIA) approach. A total of 203,676 km2 of shallow benthic habitat across the Insular Caribbean was mapped, representing 5% coral reef, 43% seagrass, 15% hardbottom, and 37% other habitats. Results from a combined major class accuracy assessment yielded an overall accuracy of 80% with a standard error of less than 1% yielding a confidence interval of 78–82%. Of the total area mapped, 15% of these habitats (31,311.7 km2) are within a marine protected or managed area. This information provides a baseline of ecological data for developing and executing more strategic conservation actions, including implementing more effective marine spatial plans, prioritizing and improving marine protected area design, monitoring condition and change for post-storm damage assessments, and providing more accurate habitat data for ecosystem service models.

Global shallow water bathymetry maps offer critical information to inform activities such as scientific research, environment protection, and marine transportation. Methods that employ satellite-based bathymetric modeling provide an alternative to conventional shipborne measurements, offering high spatial resolution combined with extensive coverage. We developed an automated bathymetry mapping approach based on the Sentinel-2 surface reflectance dataset in Google Earth Engine. We created a new method for generating a clean-water mosaic and a tailored automatic bathymetric estimation algorithm. We then evaluated the performance of the models at six globally diverse sites (Heron Island, Australia; West Coast of Hawaiʻi Island, Hawaiʻi; Saona Island, Dominican Republic; Punta Cana, Dominican Republic; St. Croix, United States Virgin Islands; and The Grenadines) using 113,520 field bathymetry sampling points. Our approach derived accurate bathymetry maps in shallow waters, with Root Mean Square Error (RMSE) values ranging from 1.2 to 1.9 m. This automatic, efficient, and robust method was applied to map shallow water bathymetry at the global scale, especially in areas which have high biodiversity (i.e., coral reefs).

Over 60% of the world’s reefs experience damage from local activities such as overfishing, coastal development, and watershed pollution. Land-based sources of pollution are a critical threat to coral reefs, and understanding “ridge-to-reef” changes is urgently needed to improve management and coral survival in the Anthropocene. We review existing literature on spatial-ecological connections between land use and coral health, specifically examining vegetative, agricultural, urban, and other land-use types. In general, forested land use is positively related to metrics of coral condition, while anthropogenic land uses like urban development and agriculture drive a decline in coral cover, diversity, colony size, and structural complexity. However, land-use and land-cover impacts vary across time and space, and small portions of the landscape (e.g., discrete segments of unpaved roads, grazed and scalded hillsides) may have an outsized effect on reef pollution, presenting opportunities for targeted conservation. Some coral species show resilience under land-use and land-cover change, and the impact of land use on coral recovery from bleaching remains an active area of research. Finally, a spatial bibliography of existing literature reveals that most ridge-to-reef studies focus on a handful of regional hotspots, surface water, and watershed-scale dynamics; more research is needed to address groundwater connectivity and to compare land-use impacts across multiple regions and scales. Approaches from landscape ecology that assess spatial patterns of, and synergies between, interlocking land cover may assist conservation managers in designing more resilient reefscapes.

Climate, species composition, and soils are thought to control carbon cycling and forest structure in Amazonian forests. Here, we add a demographics scheme (tree recruitment, growth, and mortality) to a recently developed non-demographic model—the Trait-based Forest Simulator (TFS)—to explore the roles of climate and plant traits in controlling forest productivity and structure. We compared two sites with differing climates (seasonal vs. aseasonal precipitation) and plant traits. Through an initial validation simulation, we assessed whether the model converges on observed forest properties (productivity, demographic and structural variables) using datasets of functional traits, structure, and climate to model the carbon cycle at the two sites. In a second set of simulations, we tested the relative importance of climate and plant traits for forest properties within the TFS framework using the climate from the two sites with hypothetical trait distributions representing two axes of functional variation (“fast” vs. “slow” leaf traits, and high vs. low wood density). The adapted model with demographics reproduced observed variation in gross (GPP) and net (NPP) primary production, and respiration. However, NPP and respiration at the level of plant organs (leaf, stem, and root) were poorly simulated. Mortality and recruitment rates were underestimated. The equilibrium forest structure differed from observations of stem numbers suggesting either that the forests are not currently at equilibrium or that mechanisms are missing from the model. Findings from the second set of simulations demonstrated that differences in productivity were driven by climate, rather than plant traits. Contrary to expectation, varying leaf traits had no influence on GPP. Drivers of simulated forest structure were complex, with a key role for wood density mediated by its link to tree mortality. Modeled mortality and recruitment rates were linked to plant traits alone, drought-related mortality was not accounted for. In future, model development should focus on improving allocation, mortality, organ respiration, simulation of understory trees and adding hydraulic traits. This type of model that incorporates diverse tree strategies, detailed forest structure and realistic physiology is necessary if we are to be able to simulate tropical forest responses to global change scenarios.

Coral reefs face an uncertain future and may not recover naturally from anthropogenic climate change. Coral restoration is needed to rehabilitate degraded reefs and to sustain biodiversity. There is a need for baseline data on global reef distribution, composition, and condition to provide targets for conservation and restoration. Remote sensing can address this issue and is currently underutilized in reef research and restoration. This synthesis integrates current capabilities of remote sensing with key reef restoration criteria, to facilitate coral restoration success. Research into the development of a spectral database for corals, linking habitat type and extent with predator abundance, and identification of species-specific acoustic signatures are needed to advance the use of remote sensing in reef restoration design and monitoring. Reciprocally, reef restoration efforts should innovate at ecosystem, regional, and global levels using remote sensing, to preserve as much of the coral reef biome as possible with continued ocean-climate change.

As humans continue to alter Earth systems, conservationists look to remote sensing to monitor, inventory, and understand ecosystems and ecosystem processes at large spatial scales. Multispectral remote sensing data are commonly integrated into conservation decision-making frameworks, yet imaging spectroscopy, or hyperspectral remote sensing, is underutilized in conservation. The high spectral resolution of imaging spectrometers captures the chemistry of Earth surfaces, whereas multispectral satellites indirectly represent such surfaces through band ratios. Here, we present case studies wherein imaging spectroscopy was used to inform and improve conservation decision-making and discuss potential future applications. These case studies include a broad array of conservation areas, including forest, dryland, and marine ecosystems, as well as urban applications and methane monitoring. Imaging spectroscopy technology is rapidly developing, especially with regard to satellite-based spectrometers. Improving on and expanding existing applications of imaging spectroscopy to conservation, developing imaging spectroscopy data products for use by other researchers and decision-makers, and pioneering novel uses of imaging spectroscopy will greatly expand the toolset for conservation decision-makers.

We present a new method for the detection of coral bleaching using satellite time-series data. While the detection of coral bleaching from satellite imagery is difficult due to the low signal-to-noise ratio of benthic reflectance, we overcame this difficulty using three approaches: 1) specialized pre-processing developed for Planet Dove satellites, 2) a time-series approach for determining baseline reflectance statistics, and 3) a regional filter based on a preexisting map of live coral. The time-series was divided into a baseline period (April-July 2019), when no coral bleaching was known to have taken place, and a bleaching period (August 2019-present), when the bleaching was known to have occurred based on field data. The identification of the bleaching period allowed the computation of a Standardized Bottom Reflectance (SBR) for each region. SBR transforms the weekly bottom reflectance into a value relative to the baseline reflectance distribution statistics, increasing the sensitivity to bleaching detection. We tested three scales of the temporal smoothing of the SBR (weekly, cumulative average, and three-week moving average). Our field verification of coral bleaching throughout the main Hawaiian Islands showed that the cumulative average and three-week moving average smoothing detected the highest proportion of coral bleaching locations, correctly identifying 11 and 10 out of 18 locations, respectively. However, the three-week moving average provided a better sensitivity in coral bleaching detection, with a performance increase of at least one standard deviation, which helps define the confidence level of a detected bleaching event.

Integration of multiple technologies greatly increases the spatial and temporal scales over which ecological patterns and processes can be studied, and threats to protected ecosystems can be identified and mitigated. A range of technology options relevant to ecologists and conservation practitioners are described, including ways they can be linked to increase the dimensionality of data collection efforts. Remote sensing, ground-based, and data fusion technologies are broadly discussed in the context of ecological research and conservation efforts. Examples of technology integration across all of these domains are provided for large-scale protected area management and investigation of ecological dynamics. Most technologies are low-cost or open-source, and when deployed can reach economies of scale that reduce per-area costs dramatically. The large-scale, long-term data collection efforts presented here can generate new spatio-temporal understanding of threats faced by natural ecosystems and endangered species, leading to more effective conservation strategies.

Abstract Background Tropical forests provide a crucial carbon sink for a sizable portion of annual global CO2 emissions. Policies that incentivize tropical forest conservation by monetizing forest carbon ultimately depend on accurate estimates of national carbon stocks, which are often based on field inventory sampling. As an exercise to understand the limitations of field inventory sampling, we tested whether two common field-plot sampling approaches could accurately estimate carbon stocks across approximately 76 million ha of Perúvian forests. A 1-ha resolution LiDAR-based map of carbon stocks was used as a model of the country’s carbon geography. Results Both field inventory sampling approaches worked well in estimating total national carbon stocks, almost always falling within 10 % of the model national total. However, the sampling approaches were unable to produce accurate spatially-explicit estimates of the carbon geography of Perú, with estimates falling within 10 % of the model carbon geography across no more than 44 % of the country. We did not find any associations between carbon stock errors from the field plot estimates and six different environmental variables. Conclusions Field inventory plot sampling does not provide accurate carbon geography for a tropical country with wide ranging environmental gradients such as Perú. The lack of association between estimated carbon errors and environmental variables suggests field inventory sampling results from other nations would not differ from those reported here. Tropical forest nations should understand the risks associated with primarily field-based sampling approaches, and consider alternatives leading to more effective forest conservation and climate change mitigation.

Abstract Background Spatially explicit forest carbon (C) monitoring aids conservation and climate change mitigation efforts, yet few approaches have been developed specifically for the highly heterogeneous landscapes of oceanic island chains that continue to undergo rapid and extensive forest C change. We developed an approach for rapid mapping of aboveground C density (ACD; units = Mg or metric tons C ha−1) on islands at a spatial resolution of 30 m (0.09 ha) using a combination of cost-effective airborne LiDAR data and full-coverage satellite data. We used the approach to map forest ACD across the main Hawaiian Islands, comparing C stocks within and among islands, in protected and unprotected areas, and among forests dominated by native and invasive species. Results Total forest aboveground C stock of the Hawaiian Islands was 36 Tg, and ACD distributions were extremely heterogeneous both within and across islands. Remotely sensed ACD was validated against U.S. Forest Service FIA plot inventory data (R2 = 0.67; RMSE = 30.4 Mg C ha−1). Geospatial analyses indicated the critical importance of forest type and canopy cover as predictors of mapped ACD patterns. Protection status was a strong determinant of forest C stock and density, but we found complex environmentally mediated responses of forest ACD to alien plant invasion. Conclusions A combination of one-time airborne LiDAR data acquisition and satellite monitoring provides effective forest C mapping in the highly heterogeneous landscapes of the Hawaiian Islands. Our statistical approach yielded key insights into the drivers of ACD variation, and also makes possible future assessments of C storage change, derived on a repeat basis from free satellite data, without the need for additional LiDAR data. Changes in C stocks and densities of oceanic islands can thus be continually assessed in the face of rapid environmental changes such as biological invasions, drought, fire and land use. Such forest monitoring information can be used to promote sustainable forest use and conservation on islands in the future.

Monitoring tropical forests using spaceborne and airborne remote sensing capabilities is important for informing environmental policies and conservation actions. Developing large-scale machine learning estimation models of forest structure is instrumental in bridging the gap between retrospective analysis and near-real-time monitoring. However, most approaches use moderate spatial resolution satellite data with limited capabilities of frequent updating. Here, we take advantage of the high spatial and temporal resolutions of Planet Dove images and aim to automatically estimate top-of-canopy height (TCH) for the biologically diverse country of Peru from satellite imagery at 1 ha spatial resolution by building a model that associates Planet Dove textural features with airborne light detection and ranging (LiDAR) measurements of TCH. We use and modify features derived from Fourier textural ordination (FOTO) of Planet Dove images using spectral projection and train a gradient boosted regression for TCH estimation. We discuss the technical and scientific challenges involved in the generation of reliable mechanisms for estimating TCH from Planet Dove satellite image spectral and textural features. Our developed software toolchain is a robust and generalizable regression model that provides a root mean square error (RMSE) of 4.36 m for Peru. This represents a helpful advancement towards better monitoring of tropical forests and improves efforts in reducing emissions from deforestation and forest degradation (REDD+), an important climate change mitigation approach.

Coral reef ecosystems are under stress due to human-driven climate change and coastal activities. Satellite-based monitoring approaches offer an alternative to traditional field sampling measurements for detecting coral reef composition changes, especially given the advantages in their broad spatial coverage and high temporal frequency. However, the effect of benthic composition changes on water-leaving reflectance remains underexplored. In this study, we examined benthic change detection abilities of four representative satellite sensors: Landsat-8, Sentinel-2, Planet Dove and SkySat. We measured the bottom reflectance of different benthic compositions (live coral, bleached coral, dead coral with algal cover, and sand) in the field and developed an analytical bottom-up radiative transfer model to simulate remote sensing reflectance at the water surface for different compositions at a variety of depths and in varying water clarity conditions. We found that green spectral wavelengths are best for monitoring benthic changes such as coral bleaching. Moreover, we quantified the advantages of high spatial resolution imaging for benthic change detection. Together, our results provide guidance as to the potential use of the latest generation of multi-spectral satellites for monitoring coral reef and other submerged coastal ecosystems.

Coral reef ecosystems are rapidly changing, and a persistent problem with monitoring changes in reef habitat complexity rests in the spatial resolution and repeatability of measurement techniques. We developed a new approach for high spatial resolution (

Non-structural carbohydrates (NSC) are products of photosynthesis, and leaf NSC concentration may be a prognostic indicator of climate-change tolerance in woody plants. However, measurement of leaf NSC is prohibitively labor intensive, especially in tropical forests, where foliage is difficult to access and where NSC concentrations vary enormously by species and across environments. Imaging spectroscopy may allow quantitative mapping of leaf NSC, but this possibility remains unproven. We tested the accuracy of NSC remote sensing at leaf, canopy and stand levels using visible-to-shortwave infrared (VSWIR) spectroscopy with partial least squares regression (PLSR) techniques. Leaf-level analyses demonstrated the high precision (R2 = 0.69–0.73) and accuracy (%RMSE = 13%–14%) of NSC estimates in 6136 live samples taken from 4222 forest canopy species worldwide. The leaf spectral data were combined with a radiative transfer model to simulate the role of canopy structural variability, which led to a reduction in the precision and accuracy of leaf NSC estimation (R2 = 0.56; %RMSE = 16%). Application of the approach to 79 one-hectare plots in Amazonia using the Carnegie Airborne Observatory VSWIR spectrometer indicated the good precision and accuracy of leaf NSC estimates at the forest stand level (R2 = 0.49; %RMSE = 9.1%). Spectral analyses indicated strong contributions of the shortwave-IR (1300–2500 nm) region to leaf NSC determination at all scales. We conclude that leaf NSC can be remotely sensed, opening doors to monitoring forest canopy physiological responses to environmental stress and climate change.

The remaining native forests on the Hawaiian Islands have been recognized as threatened by changing climate, increasing insect outbreak, new deadly pathogens, and growing populations of canopy structure-altering invasive species. The objective of this study was to assess long-term, net changes to upper canopy structure in sub-montane forests on the eastern slope of Mauna Kea volcano, Hawai‘i, in the context of continuing climate events, insect outbreaks, and biological invasion. We used high-resolution multi-temporal Light Detection and Ranging (LiDAR) data to quantify near-decadal net changes in forest canopy height and gap distributions at a critical transition between alien invaded lowland and native sub-montane forest at the end of a recent drought and host-specific insect (Scotorythra paludicola) outbreak. We found that sub-montane forests have experienced a net loss in average canopy height, and therefore structure and aboveground carbon stock. Additionally, where invasive alien tree species co-dominate with native trees, the upper canopy structure became more homogeneous. Tracking the loss of forest canopy height and spatial variation with airborne LiDAR is a cost-effective way to monitor forest canopy health, and to track and quantify ecological impacts of invasive species through space and time.

Plant species identification and mapping based on remotely-sensed spectral signatures is a challenging task with the potential to contribute enormously to ecological studies. Success in this task rests upon the appropriate collection and use of costly field-based training data, and researchers are in need of ways to improve collection efficiency based on quantitative evidence. Using imaging spectrometer data collected by the Carnegie Airborne Observatory for hundreds of field-identified tree crowns in Kruger National Park, South Africa, we developed woody plant species classification models and evaluated how classification accuracy increases with increasing numbers of training crowns. First, we show that classification accuracy must be estimated while respecting the crown as the basic unit of data; otherwise, accuracy will be overestimated and the amount of training data needed to perform successful classification will be underestimated. We found that classification accuracy and the number of training crowns needed to perform successful classification varied depending on the number and spectral separability of species in the model. We also used a modified Michaelis-Menten function to describe the empirical relationship between training crowns and model accuracy, and show how this function may be useful for predicting accuracy. This framework can assist researchers in designing field campaigns to maximize the efficiency of field data collection, and thus the amount of biodiversity information gained from remote species identification models.

Forests are an important part natural ecosystems, by for example providing food, fiber, habitat, and biodiversity, all of which contribute to stable natural systems. Assessing and modeling the structure and characteristics of forests, e.g., Leaf Area Index (LAI), volume, biomass, etc., can lead to a better understanding and management of these resources. In recent years, Terrestrial Laser Scanning (TLS) has been recognized as a tool that addresses many of the limitations of manual and traditional forest data collection methods. In this study, we propose a density-based approach for estimating the LAI in a structurally-complex forest environment, which contains variable and diverse structural attributes, e.g., non-circular stem forms, dense canopy and below-canopy vegetation cover, and a diverse species composition. In addition, 242 TLS scans were collected using a portable low-cost scanner, the Compact Biomass Lidar (CBL), in the Hawaii Volcanoes National Park (HAVO), Hawaii Island, USA. LAI also was measured for 242 plots in the site, using an AccuPAR LP-80 ceptometer. The first step after cleaning the point cloud involved detecting the higher forest canopy in the light detection and ranging (lidar) point clouds, using normal change rate assessment. We then estimated Leaf Area Density (LAD), using a voxel-based approach, and divided the canopy point cloud into five layers in the Z (vertical) direction. These five layers subsequently were divided into voxels in the X direction, where the size of these voxels were obtained based on inter-quartile analysis and the number of points in each voxel. We hypothesized that the intensity returned to the lidar system from woody materials, like branches, would be higher than from leaves, due to the liquid water absorption feature of the leaves and higher reflectance for woody material at the 905 nm laser wavelength. We also differentiated between foliar and woody materials using edge detection in the images from projected point clouds and evaluated the density of these regions to support our hypothesis. Density of points, or the number of points divided by the volume of a grid, in a 3D grid size of 0.1 m, was calculated for each of the voxels. The grid size was determined by investigating the size of the branches in the lower portion of the canopy. Subsequently, we fitted a Kernel Density Estimator (KDE) to these values, with the threshold set based on half of the area under the curve in each of the density distributions. All the grids with a density below the threshold were labeled as leaves, while those grids above the threshold were identified as non-leaves. Finally, we modeled LAI using the point densities derived from the TLS point clouds and the listed analysis steps. This model resulted in an R 2 value of 0.88. We also estimated the LAI directly from lidar data using the point densities and calculating LAD, which is defined as the total one-sided leaf area per unit volume. LAI can be obtained as the sum of the LAD values in all the voxels. The accuracy of LAI estimation was 90%, with an RMSE value of 0.31, and an average overestimation of 9 % in TLS-derived LAI, when compared to field-measured LAI. Algorithm performance mainly was affected by the vegetation density and complexity of the canopy structures. It is worth noting that, since the LAI values cannot be considered spatially independent throughout all the plots in this site, we performed semivariogram analysis on the field-measured LAI data. This analysis showed that the LAI values can be assumed to be independent in plots that are at least 30 m apart. As a result, we divided the data into six subsets in which the plots were 30 m spaced. The R 2 values for these subsets, based on modeling of the field-measured LAI using leaf point density values, ranged between 0.84−0.96. The results bode well for using this method for efficient, automatic, and accurate/precise estimation of LAI values in complex forest environments, using a low-cost, rapid-scan TLS.