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Submarine groundwater discharge (SGD) is a recognized contributor to the hydrological and biogeochemical functioning of coral reef ecosystems located along coastlines. However, the distribution, size, and thermal properties of SGD remain poorly understood at most land–reef margins. We developed, deployed, and demonstrated an operational method for airborne detection and mapping of SGD using the 200 km coastline of western Hawai'i Island as a testing and analysis environment. Airborne high spatial resolution (1 m) thermal imaging produced relative sea surface temperature (SST) maps that aligned geospatially with boat-based transects of SGD presence–absence. Boat-based SST anomaly measurements were highly correlated with airborne SST anomaly measurements (R2 = 0.85; RMSE = 0.04 °C). Resulting maps of the relative difference in SST inside and outside of SGD plumes, called delta-SST, revealed 749 SGD plumes in 200 km of coastline, with nearly half of the SGD plumes smaller than 0.1 ha in size. Only 9% of SGD plumes were ≥1 ha in size, and just 1% were larger than 10 ha. Our findings indicate that small SGD is omnipresent in the nearshore environment. Furthermore, we found that the infrequent, large SGD plumes (>10 ha) displayed the weakest delta-SST values, suggesting that large discharge plumes are not likely to provide cooling refugia to warming coral reefs. Our operational approach can be applied frequently over time to generate SGD information relative to terrestrial substrate, topography, and pollutants. This operational approach will yield new insights into the role that land-to-reef interactions have on the composition and condition of coral reefs along coastlines. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 ( R 2 ) 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The structure of coral-reef fish assemblages is affected by natural and anthropogenic factors such as the architectural complexity, benthic composition and physical characteristics of the habitat, fishing pressure and land-based input. The coral-reef ecosystem of South Kona, Hawai'i hosts diverse reef habitats with a relatively high live coral cover, but a limited number of studies have focused on the ecosystem or the fish assemblages. Here, we surveyed fish assemblages at 119 sites in South Kona in 2020 and 2021 and investigated the associations between the fish assemblages and environmental variables obtained from published Geographic Information System (GIS) layers, including depth, latitude, reef rugosity, housing density and benthic cover. The fish assemblages in South Kona were dominated by a relatively small number of widely occurring species. Multivariate analyses indicated that fish assemblage structure strongly correlated with depth, reefscape-level rugosity and sand cover individually, while the final parsimonious model included latitude, depth, housing density within 3-km of shore, chlorophyll-a concentration and sand cover. Univariate analysis revealed negative associations between housing density and fish species richness and abundance. Effects of environmental factors specific to fish trophic groups were also found. Reefscape-level rugosity had strong positive influences on the distributions of all herbivores (browsers, grazers and scrapers), while housing density had strong negative influences only on the abundance of browsers. Positive associations were also found between live coral cover and the presence of scrapers, as well as the abundance of corallivorous fish. This study intensively surveyed shallow coral reefs along the coastline of South Kona and was the most complete spatial survey on the reef fish assemblages to date. As it utilized GIS layers to assess large-scale patterns in the fish assemblages, future studies including in-situ environmental data may further reveal local-scale patterns and insights into factors affecting the structure of fish assemblages in Hawai'i. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Corals are a major habitat-building life-form on tropical reefs that support a quarter of all species in the ocean and provide ecosystem services to millions of people. Marine heat waves continue to threaten and shape reef ecosystems by killing individual coral colonies and reducing their diversity. However, marine heat waves are spatially and temporally heterogeneous, and so too are the environmental and biological factors mediating coral resilience during and following thermal events. This combination results in highly variable outcomes at both the coral bleaching and mortality stages of every event. This, in turn, impedes the assessment of changing reef-scale patterns of thermal tolerance or places of resistance known as reef refugia. We developed a largescale, high-resolution coral mortality monitoring capability based on airborne imaging spectroscopy and applied it to a major marine heat wave in the Hawaiian Islands. While water depth and thermal stress strongly mediated coral mortality, relative coral loss was also inversely correlated with preheat-wave coral cover, suggesting the existence of coral refugia. Subsequent mapping analyses indicated that potential reef refugia underwent up to 40% lower coral mortality compared with neighboring reefs, despite similar thermal stress. A combination of human and environmental factors, particularly coastal development and sedimentation levels, differentiated resilient reefs from other more vulnerable reefs. Our findings highlight the role that coral mortality mapping, rather than bleaching monitoring, can play for targeted conservation that protects more surviving corals in our changing climate. [ABSTRACT FROM AUTHOR]

Native forests of Hawaiʻi Island are experiencing an ecological crisis in the form of Rapid ʻŌhiʻa Death (ROD), a recently characterized disease caused by two fungal pathogens in the genus Ceratocystis. Since approximately 2010, this disease has caused extensive mortality of Hawaiʻi's keystone endemic tree, known as ʻōhiʻa (Metrosideros polymorpha). Visible symptoms of ROD include rapid browning of canopy leaves, followed by death of the tree within weeks. This quick progression leading to tree mortality makes early detection critical to understanding where the disease will move at a timescale feasible for controlling the disease. We used repeat laser‐guided imaging spectroscopy (LGIS) of forests on Hawaiʻi Island collected by the Global Airborne Observatory (GAO) in 2018 and 2019 to derive maps of foliar trait indices previously found to be important in distinguishing between ROD‐infected and healthy ʻōhiʻa canopies. Data from these maps were used to develop a prognostic indicator of tree stress prior to the visible onset of browning. We identified canopies that were green in 2018, but became brown in 2019 (defined as "to become brown"; TBB), and a corresponding set of canopies that remained green. The data mapped in 2018 showed separability of foliar trait indices between TBB and green ʻōhiʻa, indicating early detection of canopy stress prior to the onset of ROD. Overall, a combination of linear and non‐linear analyses revealed canopy water content (CWC), foliar tannins, leaf mass per area (LMA), phenols, cellulose, and non‐structural carbohydrates (NSC) are primary drivers of the prognostic spectral capability which collectively result in strong consistent changes in leaf spectral reflectance in the near‐infrared (700–1300 nm) and shortwave‐infrared regions (1300–2500 nm). Results provide insight into the underlying foliar traits that are indicative of physiological responses of M. polymorpha trees infected with Ceratocycstis and suggest that imaging spectroscopy is an effective tool for identifying trees likely to succumb to ROD prior to the onset of visible symptoms. [ABSTRACT FROM AUTHOR]

Highlights • Re-parametrization of nonlinear models to identify fast-growing species is proposed. • Expansion of parameters by defining graphical relationships to micro-site variables. • Effect of species composition and fertility increased over time but slope effect decreased. • The fastest growing species was more sensitive to slope and less to fertility. Abstract Reforestation in the tropics mitigates the negative effects of climate change by sequestering carbon in biomass. However, tree growth is limited by nutrient availability in many tropical regions. A clear understanding of nutrient constraints and topography on growth of native timber species is thus essential to improve both the economic return on reforestation and the ecosystem services in tropical degraded lands. To address this, we use 7-year growth data from a 75-ha reforestation experiment in central Panama to test a modeling approach to predict growth of these species. The experiment includes five valuable timber species in 21 treatments, including monocultures and mixtures. We first fit a non-linear growth model as a function of tree age, then expand the former model parameters as a function of variables related to species mixture and micro-site soil conditions. Finally, we built a final model for each species to predict growth along three axes: nutrient availability, slope and species mixture. The models successfully identified how variation in growth was related to micro-site conditions and the species mixture. Although all species were long-lived pioneers, most were overall more sensitive to nutrient availability and between-trees interactions than to slope. However, the fastest growing species on average was more sensitive to slope than the other species and less sensitive to nutrient availability, showing better performance than the other species even under adverse conditions. Our models aid identification of species with the best growth potential to use in reforestation on infertile soils, leading to a better species selection according to site conditions. [ABSTRACT FROM AUTHOR]

Recent drought (2012–2016) caused unprecedented foliage dieback in giant sequoias ( Sequoiadendron giganteum ), a species endemic to the western slope of the southern Sierra Nevada in central California. As part of an effort to understand and map sequoia response to droughts, we studied the patterns of remotely sensed canopy water content (CWC), both within and among sequoia groves in two successive years during the drought period (2015 and 2016). Our aims were: (1) to quantify giant sequoia responses to severe drought stress at a landscape scale using CWC as an indicator of crown foliage status, and (2) to estimate the effect of environmental correlates that mediate CWC change within and among giant sequoia groves. We utilized airborne high fidelity imaging spectroscopy (HiFIS) and light detection and ranging (LiDAR) data from the Carnegie Airborne Observatory to assess giant sequoia foliage status during 2015 and 2016 of the 2012–2016 droughts. A series of statistical models were generated to classify giant sequoias and to map their location in Sequoia and Kings Canyon National Parks (SEKI) and vicinity. We explored the environmental correlates and the spatial patterns of CWC change at the landscape scale. The mapped CWC was highly variable throughout the landscape during the two observation years, and proved to be most closely related to geological substrates, topography, and site-specific water balance. While there was an overall net gain in sequoia CWC between 2015 and 2016, certain locations (lower elevations, steeper slopes, areas more distant from surface water sources, and areas with greater climate water deficit) showed CWC losses. In addition, we found greater CWC loss in shorter sequoias and those growing in areas with lower sequoia stem densities. Our results suggest that CWC change indicates sequoia response to droughts across landscapes. Long-term monitoring of giant sequoia CWC will likely be useful for modeling and predicting their population-level response to future climate change. [ABSTRACT FROM AUTHOR]

Borneo contains some of the world's most biodiverse and carbon-dense tropical forest, but this 750 000 km2 island has lost 62% of its old-growth forests within the last 40 years. Efforts to protect and restore the remaining forests of Borneo hinge on recognizing the ecosystem services they provide, including their ability to store and sequester carbon. Airborne laser scanning (ALS) is a remote sensing technology that allows forest structural properties to be captured in great detail across vast geographic areas. In recent years ALS has been integrated into statewide assessments of forest carbon in Neotropical and African regions, but not yet in Asia. For this to happen new regional models need to be developed for estimating carbon stocks from ALS in tropical Asia, as the forests of this region are structurally and compositionally distinct from those found elsewhere in the tropics. By combining ALS imagery with data from 173 permanent forest plots spanning the lowland rainforests of Sabah on the island of Borneo, we develop a simple yet general model for estimating forest carbon stocks using ALS-derived canopy height and canopy cover as input metrics. An advanced feature of this new model is the propagation of uncertainty in both ALS- and ground-based data, allowing uncertainty in hectare-scale estimates of carbon stocks to be quantified robustly. We show that the model effectively captures variation in aboveground carbon stocks across extreme disturbance gradients spanning tall dipterocarp forests and heavily logged regions and clearly outperforms existing ALS-based models calibrated for the tropics, as well as currently available satellite-derived products. Our model provides a simple, generalized and effective approach for mapping forest carbon stocks in Borneo and underpins ongoing efforts to safeguard and facilitate the restoration of its unique tropical forests. [ABSTRACT FROM AUTHOR]

Pathogenic invasions are a major source of change in both agricultural and natural ecosystems. In forests, fungal pathogens can kill habitat-generating plant species such as canopy trees, but methods for remote detection, mapping and monitoring of such outbreaks are poorly developed. Two novel species of the fungal genus Ceratocystis have spread rapidly across humid and mesic forests of Hawaiʻi Island, causing widespread mortality of the keystone endemic canopy tree species, Metrosideros polymorpha (common name: ʻōhiʻa). The process, known as Rapid Ohia Death (ROD), causes browning of canopy leaves in weeks to months following infection by the pathogen. An operational mapping approach is needed to track the spread of the disease. We combined field studies of leaf spectroscopy with laboratory chemical studies and airborne remote sensing to develop a spectral signature for ROD. We found that close to 80% of ROD-infected plants undergo marked decreases in foliar concentrations of chlorophyll, water and non-structural carbohydrates, which collectively result in strong consistent changes in leaf spectral reflectance in the visible (400-700 nm) and shortwave-infrared (1300-2500 nm) wavelength regions. Leaf-level results were replicated at the canopy level using airborne laser-guided imaging spectroscopy, with quantitative spectral separability of normal green-leaf canopies from suspected ROD-infected brown-leaf canopies in the visible and shortwave-infrared spectrum. Our results provide the spectral-chemical basis for detection, mapping and monitoring of the spread of ROD in native Hawaiian forests. [ABSTRACT FROM AUTHOR]

Spatial information on forest functional composition is needed to inform management and conservation efforts, yet this information is lacking, particularly in tropical regions. Canopy foliar traits underpin the functional biodiversity of forests, and have been shown to be remotely measurable using airborne 350-2510 nm imaging spectrometers. We used newly acquired imaging spectroscopy data constrained with concurrent light detection and ranging (LiDAR) measurements from the Carnegie Airborne Observatory (CAO), and field measurements, to test the performance of the Spectranomics approach for foliar trait retrieval. The method was previously developed in Neotropical forests, and was tested here in the humid tropical forests of Malaysian Borneo. Multiple foliar chemical traits, as well as leaf mass per area (LMA), were estimated with demonstrable precision and accuracy. The results were similar to those observed for Neotropical forests, suggesting a more general use of the Spectranomics approach for mapping canopy traits in tropical forests. Future mapping studies using this approach can advance scientific investigations and applications based on imaging spectroscopy. [ABSTRACT FROM AUTHOR]

• Tree crowns with ROD symptoms were mapped over 4 years to track disease spread. • Affected forest area nearly doubled from 66,972 to 121,465 ha during study. • Spread mostly local, with crown exposure and wind linked with long distance spread. • Rate of local ROD spread was linked with several environmental factors. Fungal pathogens of the genus Ceratocystis recently introduced to the Island of Hawaiʻi have killed hundreds of thousands of native 'Ōhiʻa trees, an ecologically and culturally important keystone species. Symptoms of the associated disease, Rapid 'Ōhiʻa Death (ROD), have been found to be easily detectable with high resolution imaging spectroscopy. We used wall-to-wall maps of affected 'Ōhiʻa canopy built in four consecutive years (2016–2019) to analyze how changes in the distribution and density of browning canopy detections over this time period corresponded to environmental drivers at two spatial scales. Island-wide we found 256,387 brown crowns across the 4-year period. The total amount of affected forest area nearly doubled from 66,972.2 ha in 2016 to 121,464.7 ha in 2019, cumulatively representing 42.5% of the 264,372 ha of forest area observed at least once in the study. However, most browning activity was concentrated in 18 hotspots totaling approximately 29,836 ha with brown crown densities as high as 26 ha−1. Using Random Forest (RF) models, we assessed the correspondence between 32 mapped environmental variables and measures of ROD spread calulated at 1-year time steps on a 300 m × 300 m grid across the island. We found that modelled probability of regional infection, or "susceptibility", was dominated by distance to existing infection at the start of a time step; increasing distance from 0 to 600 m decreased susceptibility from 0.8 to 0.3. Other important factors – tree cover, average windspeeds, and LiDAR-derived 3-d crown exposure – were 4–10 times less important to the model. In a second RF model we studied regional infection "severity", measured as detection density in first year of infection. In contrast to susceptibility, the severity model was significantly affected by numerous factors including windspeed metrics, canopy height and exposure as well as most weather and climate metrics related to water regime. Severity model predictions were less dominated by proximity to existing infection. In a separate regional analysis at 2 m resolution we found that taller trees were more prone to infection, and that trees on younger "historic" lava flows (i.e., substrates < 230 years old) showed slight but significantly lower infection rates than immediately adjacent older substrates. Our results document the extent and severity of 'Ōhiʻa mortality across Hawaiʻi Island and provides insight into factors that appear to affect the spread of ROD; this information will inform management efforts to lessen the spread and impact of the disease on all the Hawaiian Islands. [ABSTRACT FROM AUTHOR]

Frequent fires are often proposed as a way of preventing woody encroachment in savannas, yet few studies have examined whether high-intensity fires can effectively reverse woody encroachment., We applied successive fire treatments to examine the effect of fire intensity on woody vegetation structure. The treatments included early dry season, low-intensity fires; late dry season, higher-intensity fires; and an unburnt control. We used pre- and post-fire airborne Li DAR to compare vegetation structural changes brought about by fires of different intensity., Early dry season fires were of lower intensity (1400-2100 kW m−1) than late dry season fires (2500-4300 kW m−1). The two treatments also differed in terms of fuel consumed, scorch heights and char heights, indicating that clear differences in fire intensity and severity were achieved., After 4 years and two fire applications, relative woody cover increased by between 20 and 110% in different height categories following low-intensity and control treatments and declined by between 3 and 70% following high-intensity fire treatments. Declines were markedly higher following two repeated high-intensity fires than following a high and then a moderate-intensity fire. Because woody shrubs in lower height classes can recover rapidly, repeated high-intensity fires would be needed to maintain lower cover., Tall trees are often assumed to be unaffected by fires. However, we found that the rate of tree loss was directly related to fire intensity, where 36% of trees were lost following repeated high-intensity fires, compared to 22% after a high- and then a moderate-intensity fire and 6% after two low-intensity fires (3% without fire)., Synthesis and applications. Using Li DAR data we show that high-intensity fires can, at least in the short term, significantly reduce woody cover in South African savannas. The use of repeated high-intensity fires simultaneously causes both a positive (reduction in cover of short shrubs) and a negative (loss of tall trees) outcome, and managers need to make trade-offs when contemplating the use of fire intensity to achieve specific goals. One potential solution may be to repeatedly apply high-intensity treatments to some areas, and not to others. This could generate a heterogeneous landscape where grasses become dominant and tall trees become scarce in some places, but in others, tall trees persist (or at least decline at slower rates), and shorter woody shrubs increase in dominance. Whether this would be acceptable, or practical, remains to be tested. [ABSTRACT FROM AUTHOR]

Factors controlling savanna woody vegetation structure vary at multiple spatial and temporal scales, and as a consequence, unraveling their combined effects has proven to be a classic challenge in savanna ecology. We used airborne LiDAR (light detection and ranging) to map three-dimensional woody vegetation structure throughout four savanna watersheds, each contrasting in geologic substrate and climate, in Kruger National Park, South Africa. By comparison of the four watersheds, we found that geologic substrate had a stronger effect than climate in determining watershed-scale differences in vegetation structural properties, including cover, height and crown density. Generalized Linear Models were used to assess the spatial distribution of woody vegetation structural properties, including cover, height and crown density, in relation to mapped hydrologic, topographic and fire history traits. For each substrate and climate combination, models incorporating topography, hydrology and fire history explained up to 30% of the remaining variation in woody canopy structure, but inclusion of a spatial autocovariate term further improved model performance. Both crown density and the cover of shorter woody canopies were determined more by unknown factors likely to be changing on smaller spatial scales, such as soil texture, herbivore abundance or fire behavior, than by our mapped regional-scale changes in topography and hydrology. We also detected patterns in spatial covariance at distances up to 50–450 m, depending on watershed and structural metric. Our results suggest that large-scale environmental factors play a smaller role than is often attributed to them in determining woody vegetation structure in southern African savannas. This highlights the need for more spatially-explicit, wide-area analyses using high resolution remote sensing techniques. [ABSTRACT FROM AUTHOR]

The structure of a forest canopy is the key determinant of light transmission, use and understory availability. Airborne light detection and ranging (LiDAR) has been used successfully to measure multiple canopy structural properties, thereby greatly reducing the fieldwork required to map spatial variation in structure. However, lidar metrics to date do not reflect the full extent of the three-dimensional information available from the data. To this end, we developed a new metric, the polar grid fraction (GRID), based on gridding lidar returns in polar coordinates, in order to more closely match measurements provided by field instruments on leaf area index (LAI), gap fraction (GF) and percentage photosynthetically active radiation transmittance (tPAR). The metric summarizes the arrangement of lidar point returns for a single ground location rather than to an area surrounding the location. Compared with more traditional proportion-based and height percentile-based estimators, the GRID estimator increased validationR2by 14.5% for GF and 6.0% for tPAR over the next best estimator. LAI was still best estimated with the more traditional statistic based on the proportion of ground returns in 14 m × 14 m moving kernels. By applying the models to a 2 × 2 m grid across the lidar coverage area, extreme values occurred in the estimations of all three response variables when using proportion-based and height percentile-based estimators. However, no extreme values were estimated by models using the GRID estimator, indicating that models based on GRID may be less influenced by spurious data. These results suggest that the GRID estimator is a strong candidate for any project requiring estimates of canopy metrics for large areas. [ABSTRACT FROM PUBLISHER]

Species information is a key component of any forest inventory. However, when performing forest inventory from aerial scanning Lidar data, species classification can be very difficult. We investigated changes in classification accuracy while identifying five individual tree species (Douglas-fir, western redcedar, bigleaf maple, red alder, and black cottonwood) in the Pacific Northwest United States using two data sets: discrete point Lidar data alone and discrete point data in combination with waveform Lidar data. Waveform information included variables which summarize the frequency domain representation of all waveforms crossing individual trees. Discrete point data alone provided 79.2 percent overall accuracy (kappa = 0.74) for all 5 species and up to 97.8 percent (kappa = 0.96) when comparing individual pairs of these 5 species. Incorporating waveform information improved the overall accuracy to 85.4 percent (kappa = 0.817) for five species, and in several two-species comparisons. Improvements were most notable in comparing the two conifer species and in comparing two of the three hardwood species. [ABSTRACT FROM AUTHOR]

In precision forestry, tree species identification is one of the critical variables of forest inventory. Lidar, specifically full-waveform Lidar, holds high promise in the ability to identify dominant hardwood tree species in forests. Raw waveform Lidar data contain more information than can be represented by a limited series of fitted peaks. Here we attempt to use this information with a simple transformation of the raw waveform data into the frequency domain using a fast Fourier transform. Some relationships are found among the influences of component frequencies across a given species. These relationships are exploited using a classification tree approach to separate three hardwood tree species native to the Pacific Northwest of the United States. We are able to correctly classify 75% of the trees ([image omitted] 0.615) in our training data set. Each tree's species was predicted using a classification tree built from all the other training trees. Two of the species grow in proximity and grow to a similar form, making differentiation difficult. Across all the classification trees built during the analysis, a small group of frequencies is predominantly used as predictors to separate the species. [ABSTRACT FROM AUTHOR]

We evaluated the stability of a complex regression model developed to predict the annual height growth of young Douglas-fir. This model is highly nonlinear and is fit in an iterative manner for annual growth coefficients from data with multiple periodic remeasurement intervals. The traditional methods for such a sensitivity analysis either involve laborious math or rely on prior knowledge of parameter behavior. To achieve our goals, we incorporate a bootstrap approach to obtain estimates of the distribution of predicted height growth for any set of input variables. This allows for a sensitivity analysis with knowledge of the probability of a given outcome. The bootstrap distributions should approximate the variation we might expect from running the model on numerous independent datasets. From the variation in the model parameters, we are able to produce ranges of height growth prediction error falling under a given probability of occurrence. By evaluating these ranges under several combinations of input variables that represent extreme situations, we are able to visualize the stability of the model under each situation. Each of the four components of the model can be investigated separately, which allows us to determine which components of the model might benefit from reformulation. In this case we find that the model is less stable in extremely high site index, especially under low vegetation competition. Other than the computing time involved with the bootstrap, most of the analysis is fairly quick and easy to perform. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 (<1 m) mapping of nearshore bathymetry and three-dimensional habitat complexity (rugosity) using airborne high-fidelity imaging spectroscopy. Using this new method, we mapped coral reef habitat throughout two bays to a maximum depth of 25 m and compared the results to the laser-based SHOALS bathymetry standard. We also compared the results derived from imaging spectroscopy to a more conventional 4-band multispectral dataset. The spectroscopic approach yielded consistent results on repeat flights, despite variability in viewing and solar geometries and sea state conditions. We found that the spectroscopy-based results were comparable to those derived from SHOALS, and they were a major improvement over the multispectral approach. Yet, spectroscopy provided much finer spatial information than that which is available with SHOALS, which is valuable for analyzing changes in benthic composition at the scale of individual coral colonies. Monitoring temporal changes in reef 3D complexity at high spatial resolution will provide an improved means to assess the impacts of climate change and coastal processes that affect reef complexity. [ABSTRACT FROM AUTHOR]

Rapid ‘Ōhi‘a Death (ROD) is a disease aggressively killing large numbers of Metrosideros polymorpha (‘ōhi‘a), a native keystone tree species on Hawaii Island. This loss threatens to deeply alter the biological make-up of this unique island ecosystem. Spatially explicit information about the present and past advancement of the disease is essential for its containment; yet, currently such data are severely lacking. To this end, we used the Carnegie Airborne Observatory to collect Laser-Guided Imaging Spectroscopy data and high-resolution digital imagery across >500,000 ha of Hawaii Island in June–July 2017. We then developed a method to map individual tree crowns matching the symptoms of both active (brown; desiccated ‘ōhi‘a crowns) and past (leafless tree crowns) ROD infection using an ensemble of two distinct machine learning approaches. Employing a very conservative classification scheme for minimizing false-positives, model sensitivity rates were 86.9 and 82.5, and precision rates were 97.4 and 95.3 for browning and leafless crowns, respectively. Across the island of Hawaii, we found 43,134 individual crowns suspected of exhibiting the active (browning) stage of ROD infection. Hotspots of potential ROD infection are apparent in the maps. The peninsula on the eastern side of Hawaii known as the Puna district, where the ROD outbreak likely originated, contained a particularly high density of brown crown detections. In comparison, leafless crown detections were much more numerous (547,666 detected leafless crowns in total) and more dispersed across the island. Mapped hotspots of likely ROD incidence across the island will enable scientists, administrators, and land managers to better understand both where and how ROD spreads and how to apply limited resources to limiting this spread. [ABSTRACT FROM AUTHOR]

Tryptophan was substituted for residues in all four transmembrane domains of connexin32. Function was assayed using dual cell two-electrode voltage clamp after expression in Xenopus oocytes. Tryptophan substitution was poorly tolerated in all domains, with the greatest impact in TM1 and TM4. For instance, in TM1, 15 substitutions were made, six abolished coupling and five others significantly reduced function. Only TM2 and TM3 included a distinct helical face that lacked sensitivity to tryptophan substitution. Results were visualized on a comparative model of Cx32 hemichannel. In this model, a region midway through the membrane appears highly sensitive to tryptophan substitution and includes residues Arg-32, Ile-33, Met-34, and Val-35. In the modeled channel, pore-facing regions of TM1 and TM2 were highly sensitive to tryptophan substitution, whereas the lipid-facing regions of TM3 and TM4 were variably tolerant. Residues facing a putative intracellular water pocket (the IC pocket) were also highly sensitive to tryptophan substitution. Although future studies will be required to separate trafficking-defective mutants from those that alter channel function, a subset of interactions important for voltage gating was identified. Interactions important for voltage gating occurred mainly in the mid-region of the channel and focused on TM1. To determine whether results could be extrapolated to other connexins, TM1 of Cx43 was scanned revealing similar but not identical sensitivity to TM1 of Cx32. [ABSTRACT FROM AUTHOR]