ABSTRACT In Hawaiʻi, groundwater draining from upland areas of high recharge provides virtually all drinking water and delivers freshwater nutrients (as well as contaminants) to ecosystems in the nearshore environment. Despite the importance of groundwater flow, its hidden nature makes it difficult to observe and quantify, which results in a poor understanding of its spatiotemporal dynamics from the uplands to the coast. We combine a storage‐discharge approach with a long‐term (7+ years) water balance to quantify the relationship between catchment storage and stream discharge in 11 (USGS‐gaged) watersheds across the Hawaiian Islands. We then use each watershed's storage‐discharge relationship to resolve daily estimates of catchment storage and the ‘groundwater leakage’ flux emanating from the watersheds. The resolved mean specific daily leakage is consistent with compiled measurements of submarine groundwater discharge (SGD) at the coast downstream of the 11 watersheds and upland leakage appears to be hydraulically connected to head responses in a basal aquifer. Example groundwater leakage hydrographs from Hālawa (Oʻahu) and Waiākea (Hawaiʻi) watersheds resolve temporal dynamics of groundwater flowing beneath the urban centres of Honolulu and Hilo, where contaminants enter aquifers, threaten drinking water supply, and degrade coastal environments. Although additional data are needed to validate results across space and time, resolving the groundwater leakage flux reveals a previously hidden portion of the hydrologic cycle that can inform modelling of water resources and water quality in leaky watersheds in Hawaiʻi and beyond.

Seagrass ecosystems underpin coastal biodiversity1 and provide vital ecosystem services, including shoreline protection2, food security3 and climate mitigation4. Despite growing recognition as a nature-based climate solution, seagrasses are among the least mapped and most poorly understood vegetated coastal ecosystems5. Here we present, to our knowledge, the first global 10-m spatial resolution maps and change analysis of seagrass extent in clear, shallow coastal waters, derived from 4.75 million Sentinel-2 MSI satellite images for two periods (2019-2020 and 2023-2024). Using a deep-learning classifier trained on curated reference data, we identified 148,506 km2 of seagrass globally, including 5,961 km2 of intertidal and 142,545 km2 of subtidal areas. Sixty-nine per cent of global seagrass extent is concentrated in The Bahamas, Cuba, the USA, Australia and Indonesia, yet only 21% of seagrass areas are located within marine-protected areas. Over the 4 years of the study, 5,969 km2 (4%) of seagrass was lost, and an additional 6,221 km2 (4.2%) was degraded from dense to sparse cover in tropical regions. Our findings identify seagrass meadow hotspots and vulnerable regions to inform conservation and climate policy.

Global trait axes reveal overarching dimensions of plant functional variation. However, how these dimensions are spatially organized within and across forest types remains unclear. We combined drone-based full-range imaging spectroscopy with crown-level measurements of 16 physiological, morphological and biochemical traits across temperate, subtropical and tropical forests in China to enable spatially-explicit trait mapping. Through site-training scenario, leaf-to-canopy scaling and spectral-domain modelling tests, we find that reliable canopy trait retrieval depends not only on trait and spectral coverage, but also on preserving trait-spectral relationships across sites and scales. Spectral predictions recovered observed multivariate covariation, summarizing crown variation into a leaf-economics dimension and two additional biochemical dimensions related to hydro-thermal regulation and defence/metabolism. Mapping these dimensions revealed distinct community-level trait organization alongside substantial species- and crown-level variation within forests. These findings link remotely sensed trait retrieval to environmental filtering and plant functional differentiation, providing a scalable framework for monitoring forest functional diversity.

Photosystem II (PSII) is among the most thermally sensitive components of photosynthesis, and emerging evidence suggests that plants in diverse biomes face an increasing risk of PSII damage under future climate change. However, uncertainties in the distribution and drivers of PSII thermal tolerance (T-crit) limit our ability to predict thermal risk in plant communities across spatial scales. Here, we evaluate whether intraspecific variation in T-crit corresponds to leaf reflectance spectra (400-2,500 nm) to identify mechanisms associated with T-crit in field conditions and assess the potential of its estimation using remote sensing platforms. We measured T-crit using temperature response curves of minimal fluorescence (F-o) along with corresponding leaf reflectance spectra in two foundation tree species: Populus fremontii (US Southwest) and Metrosideros polymorpha (Hawai'i). P. fremontii was sampled under both moderate (<40 degrees C) and extreme (>45 degrees C) heat. Consistent spectral signatures of T-crit emerged across species and sampling conditions, with the strongest signatures in P. fremontii under extreme heat. In P. fremontii, spectra captured up to roughly half of T-crit variation and allowed T-crit estimation (R-2 = 0.24-0.30; RMSE < 1.0 degrees C) and classification of high-versus low-T-crit (71%-77% accuracy). Across both species, T-crit tended to increase with spectral indices reflecting higher chlorophyll content and lower carotenoids, nonphotochemical quenching, and leaf water content. These findings suggest that variation in PSII thermal tolerance is linked to fundamental biochemical properties of leaves, which are reflected in their optical traits. As climate extremes intensify, spectral screening and scaling of T-crit via remote sensing may support improved conservation, management, and risk assessment in vulnerable ecosystems.

Illegal wildlife trade is a major driver of biodiversity loss but the lack of understanding of mechanisms sustaining criminal networks hampers law enforcement. We integrated three decades of confiscation data, criminological modeling, and spatial network analysis to dissect the illegal parrot trade in Mexico, a global hotspot of parrot diversity and endemism. Our interdisciplinary approach revealed that the wildlife trade is the leading threat, elevating their extinction risk. Trade is shifting from poverty-driven to population density-linked patterns. Circuit theory-based landscape modeling identified critical trade routes from biodiverse habitats to urban centers, providing actionable targets for law enforcement. Our study demonstrates the effectiveness of combining criminological methods with landscape science to identify specific trafficking routes, a transferable framework for generating actionable intelligence. Crucially, because illicit wildlife networks frequently converge with broader organized crime, this spatial framework provides a powerful proxy for exposing the shared logistical corridors of global illicit markets.

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.

Habitat complexity plays a critical role in coral reef ecosystems by enhancing habitat availability, increasing ecological resilience, and offering coastal protection. Structure-from-motion (SfM) photogrammetry has become a standard approach for quantifying habitat complexity in reef monitoring programs. However, a major bottleneck remains in the two-dimensional (2D) classification of benthic cover in three-dimensional (3D) models, where experts are required to manually annotate individual colonies and identify coral species or taxonomic groups. With recent advances in deep learning and computer vision, automated classification of benthic habitats is possible. While some semi-automated tools exist, they are often limited in scope or do not provide semantic segmentation. In this investigation, we trained a convolutional neural network with the ResNet101 architecture on three years (2015, 2017, and 2019) of human-annotated 2D orthomosaics from Kiritimati, Kiribati. Our model accuracy ranged from 71% to 95%, with an overall accuracy of 84% and a mean intersection of union of 0.82, despite highly imbalanced training data, and it demonstrated successful generalizability when applied to new, untrained 2023 plots. Successful automation depends on training data that captures local ecological variation. As coral monitoring efforts move toward standardized workflows, locally developed models will be key to achieving fully automated, high-resolution classification of benthic communities across diverse reef environments.

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.

Globally, vegetation biodiversity is expected to decline as the rate of plant adaptation struggles to keep pace with rising temperatures. To support conservation efforts through remote sensing, we disentangled the nested effects of genetic and environmental influences on reflectance spectra, leveraging spectroscopy to assess plant adaptations to temperature. Specifically, we quantified the relative effect of plasticity and heritability on Populus fremontii (Fremont cottonwood) leaf reflectance using clonal replicates propagated from 16 populations and grown across three common gardens spanning a mean annual temperature gradient representing the thermal range of P. fremontii. We used variance partitioning to decompose phenotypic variation expressed in the leaf spectra into genotypic and environmental components to estimate broad-sense heritability. Heritability was strongly expressed in the spectral red edge ( 680–750 nm) and shortwave infrared ( 1400–3000 nm), though the heritability peak in the red edge was sensitive to extreme temperatures. By comparing distances of group centroids in principal component space, we determined that P. fremontii intraspecific spectral variation was shaped by the interaction between common garden site conditions and source population. Support vector machine models indicated pronounced environmental influence on spectral variation, as P. fremontii source population and garden location were classified at 71.8

Coral reefs provide important economic benefits to coastal businesses, supporting recreation and tourism and protecting property from storms. Yet, these benefits are at risk worldwide as corals decline rapidly, and investment in restoration is lacking. With their direct dependence on coral health, coastal businesses may represent an important sector for funding coral restoration; however, it is unclear whether businesses perceive coral reef services as valuable or themselves as reef stewards. We measured business perceptions of coral health and value in Hawaiʻi and identified traits correlated with business decisions to participate in coral restoration at three payment thresholds. We found that businesses see limited economic value in coral reefs. In areas where corals provide substantial ecosystem services (flood protection, tourism revenue), businesses did not consistently rate coral value as high. Nonetheless, businesses showed strong willingness to pay for coral restoration, which was linked to pro-nature motives, reputation, and Native Hawaiian ownership. Results highlight key strategies for engaging private entities in coral restoration.

Coral bleaching poses a severe threat to the health and survival of global coral reef ecosystems, with recent events surpassing historical heat stress records. To address this crisis, improved long-term monitoring, communication, and coordination are urgently required to enhance conservation, management, and policy responses. This study reviews global coral bleaching survey methodologies and datasets spanning 1963 to 2022, identifying key challenges in methodological standardization, including database duplication and inconsistencies in naming and reporting bleaching metrics. These issues hinder comparative analyses and contribute to discrepancies in global bleaching impact assessments. We developed a typology of twenty-nine coral bleaching methods used across various scales, encompassing remote sensing tools, underwater surveys, and specimen collection. Analysis of 77,370 observations from three major datasets revealed that 9.36% of entries lacked methodological descriptions. Among recorded methods, belt transects (42%), line and point intercept transects (33%), and random surveys (17%) were the most widely applied. Practitioner surveys underscored the dominance of in situ transect and visual methods, highlighting the growing adoption of photo quadrats—an emerging yet underrepresented technique in existing datasets. To enhance global coral bleaching assessments, we propose a standardized framework that ensures open access and accessible data that aligns with decision-makers’ needs for efficient data aggregation and interoperability to better understand temporal and spatial bleaching events. A globally coordinated coalition should unify protocols, improve data-sharing capabilities, and empower regional networks through targeted training, incentives, and open communication channels. Strengthening field capacity in coral taxonomy and standardized survey methodologies, alongside integrating advanced tools, will improve data quality and comparability. Additionally, creating precise geolocated datasets will bridge on-the-ground observations with advanced remote sensing systems, refining the accuracy of satellite-based early warning tools. Establishing interoperable online platforms will further streamline data integration and accessibility, providing a robust foundation to support global responses to coral bleaching and foster impactful conservation initiatives.

Introduction Enrichment planting is a widely used method of forest restoration that involves planting seedlings from native species in degraded forests. Variability of the micro-environmental conditions within the target area largely affects the growth of the seedlings; therefore, area-wide and high-resolution data that reflect habitat quality are vital to the success of such projects. Light Detection and Ranging (LiDAR) data provide opportunities to characterize the three-dimensional spatial heterogeneity of forests over broad spatial extents. Objectives We assessed the use of airborne LiDAR data in the evaluation of site suitability for the enrichment planting of dipterocarp trees in Malaysian Borneo. Methods Predictor variables derived from the LiDAR data were used to represent the topographical and forest-structure information and to predict the growth of 10 planted dipterocarp species. We fitted field-measured diameter growth data collected from the trees 15-20 years after planting and LiDAR predictors from Random Forest regression models. Results Prediction models were developed for eight species with r2 values of 0.06-0.43, while models for the other two species were unable to provide reasonable fits. Selected significant predictors in the models agreed well with the known traits and habitat preferences. For the four species with effective prediction models, we generated growth prediction maps to illustrate their growth potential. Conclusions This study highlights the utility of micro-habitat information derived from airborne LiDAR data in forest restoration. The prediction maps generated here could contribute to enrichment planting guidelines in broad-scale restoration schemes.

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.

Spatial heterogeneity in tropical forest productivity and resulting rates of carbon uptake and storage emerge from variation in ecosystem structure and functional traits reflecting differences in climate, edaphic conditions, evolutionary history, and natural and anthropogenic disturbance histories. Yet, models poorly represent this heterogeneity. Remote sensing data provide landscape-scale measures of tropical forest heterogeneity in structure and functional traits that can be used to advance terrestrial biosphere models. To examine whether forest functional traits related to photosynthetic capacity can be used to improve predictions of tropical biomass dynamics and carbon fluxes, we parameterized the Ecosystem Demography model version 2.2 (ED2.2) using canopy traits derived from visible to shortwave infrared (VSWIR) airborne imaging spectroscopy data across an edaphic gradient in Borneo. We find significant site-level differences in relationships between SLA and foliar nutrient concentrations, suggesting that remotely sensed foliar traits can be used to capture variation in photosynthetic capacity at large, edaphically varying spatial scales. We further show that plant functional types parameterized with site-constrained trait values yield more accurate predictions of canopy demography, forest productivity and above-ground biomass dynamics than simulations that depend solely on parameterization of edaphic conditions. However, the most substantial improvements result from allowing for site-level variation in background disturbance rates in the model. Our study reveals the importance of capturing tropical forest heterogeneity in terrestrial biosphere models, particularly as it relates to nutrient availability and disturbance processes.

Coral reefs provide essential social, economic, and ecological services for millions of people worldwide. Yet, climate change and local anthropogenic stressors are damaging reefs globally, compromising their framework-building capacity and associated functionality. A reef carbonate budget provides a quantitative measure of growth and functional status, but utilization of remote sensing to scale-up such a metric remains limited. This study used census-based field surveys across depths in Hōnaunau Bay, Hawaiʻi to examine rates of carbonate production, and scaled-up estimates across the bay with high-resolution benthic-cover data derived from airborne imaging spectroscopy. Average net carbonate production was ~0.5 kg CaCO 3 m -2 y -1 across the 2–17 m depth gradient, ranging from -2.1 to 2.4 kg CaCO 3 m -2 y -1 at 3 and 6 m, respectively. The scaling model with the lowest root mean square error was achieved using a 2-m resolution map of live coral cover. Sea-urchin densities averaged 51 individuals m -2 , which were among the highest recorded densities on coral reefs globally. The subsequent high bioerosion from sea urchins suppressed estimated reef-growth potential, particularly in the shallow reef <6 m. Field estimates of net carbonate production translate to vertical reef accretion of ~0.5 mm y -1 across depths, indicating the reef in its present form is not keeping pace with the current rate of sea-level rise (3.55 mm y -1 ) in west Hawaiʻi. These results suggest a need for improved fisheries management in Hōnaunau Bay to enhance carnivorous-fish abundances, thereby helping to reduce sea-urchin densities and improve reef-growth capacity. Critically, an estimated threshold of ~26% live coral cover is currently needed to maintain positive net production across depths. This study demonstrates the utility of monitoring carbonate production by integrating field measurements and airborne imaging spectroscopy, and highlights the need for management decisions in west Hawaiʻi that enhance resilient carbonate budgets of coral reefs.

Agricultural tree cover is declining globally, including the loss of large, scattered trees that function as keystone structures. Understanding the drivers of agricultural tree loss could help prevent further declines. However, the drivers of agricultural tree mortality vary across scales, from individual trees to landscapes, complicating efforts to quantify mortality risk. We applied high-resolution remote sensing and multi-method occupancy models to test hypotheses of drivers of tree mortality in a pastoral landscape of Southwestern Panama. Our approach enabled us to identify individual tree mortality across a >20,000 ha area, encompassing a wide range of land use intensity. Neighboring tree cover was the strongest predictor of mortality, with a higher probability of death for isolated trees relative to trees with many neighbors. Landscape-level covariates also predicted mortality risk, including higher mortality closer to roads and in parcels with larger area. These results implicate land use intensity as a primary driver of agricultural tree loss in our study area. At the individual tree level, we found that larger trees were more likely to die than smaller trees. Our study suggests that the trees with high ecosystem service value in a fragmented landscape-large, isolated trees-also face the highest mortality risk. Supporting agricultural practices that maintain trees in pastures is likely to decrease tree mortality in our study site, broadly representative of cattle ranching landscapes across Latin America. Our workflow could be implemented in other landscapes globally to prioritize agricultural tree conservation, paving the way for increased tree survival and improved ecosystem services.

Recently, the importance of seagrasses in the functioning of coastal ecosystems and their ability to mitigate climate change has gained increased recognition. However, there has been a rapid global deterioration of seagrass ecosystems due to climate change and human-mediated disturbances. Accurate broad-scale mapping of seagrass extent is necessary for seagrass conservation and management actions. Traditionally, these mapping methods have primarily relied on spectral information, along with additional data such as manually designed spatial/texture features (e.g., from the Gray Level Co-Occurrence Matrix) and satellite-derived bathymetry. Despite the widely reported success of prior methods in mapping seagrass across small geographic areas, two challenges remain in broad-scale seagrass extent mapping: 1) spectral overlap between seagrass and other benthic habitats that results in the misclassification of coral/macroalgae to seagrass; 2) seagrass ecosystems exhibit spatial and temporal variability, most current models trained on data from specific locations or time periods encounter difficulties in generalizing to diverse locations or time periods with varying seagrass characteristics, such as density and species. In this study, we developed a novel deep learning model (i.e., Seagrass DenseNet: SGDenseNet) based on the DenseNet architecture to overcome these difficulties. The model was trained and validated using surface reflectance from Sentinel-2 MSI and 9,369 field data samples from four diverse regional shallow coastal water areas. Our model achieves an overall accuracy of 90% for seagrass extent mapping. Furthermore, we evaluated our deep learning model using 1,067 seagrass field data samples worldwide, achieving a producer’s accuracy of 81%. Our new deep learning model could be applied to map seagrass extents at a very broad-scale with high accuracy.

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.