Measurements Of Canopy Structure Research Articles

Connecting spaceborne lidar with NFI networks: A method for improved estimation of forest structure and biomass

Spaceborne lidar provides a unique opportunity to supplement the field plot measurements of national forest inventories (NFIs) by providing dense measurements of vertical canopy structure. For full waveform instruments such as the Global Ecosystem Dynamics Investigation (GEDI), measurements take the form of reflected energy as a function of height within an observed footprint. Many forest attributes cannot be directly computed from the waveforms, and thus statistical models relating the measurements to target attributes must be trained using field plot data. Because the discrete footprint samples produced by spaceborne lidar instruments have little chance of collocation with pre-existing field plots, model calibration remains a primary challenge. We leveraged the density and spatial correlation of GEDI observations to make predictions of the cumulative waveform over 326,787 NFI plots distributed across the contiguous United States. The predictions yield probability distributions, giving not only a predicted waveform but quantification of prediction uncertainty. The product of this work is a data set comprised of the NFI plots paired with the predictive waveform distributions. The predicted waveforms and their uncertainties can be used to train models with “error-in-variables” techniques that account for and filter the uncertainty in the predicted waveforms. This data set and the corresponding techniques allow statistically rigorous model training without requirements of collocation, yielding downstream forest attribute predictions that are consistent with NFI measurements and estimates. We demonstrate the data set by training a model for forest above ground biomass density (AGBD) and then use the model to produce a spatially complete 1 km map of AGBD for the contiguous United States. We further compare our AGBD predictions to US NFI estimates at a 64,000 hectare scale, showing the increase in precision to be proportional to the collection of almost 500,000 additional NFI plots across forested regions of CONUS.

Comparable diameter resulted in larger leaf area and denser foliage in the park trees than in street trees: A study on Norway maples of Karlsruhe city, Germany

The leaf area of trees is the main surface of energy and matter exchange between the plant canopy and the atmosphere. A better understanding of canopy structure variations in cities is a prerequisite to evaluating leaf area and ecosystem services. We selected 58 single-standing and healthy Norway maple trees (Acer platanoides L.), equally distributed between the park and street at the same tree size for canopy structure and leaf area measurements. The canopy structures of street trees and park trees were different. Street trees had significantly higher dieback (p < 0.01), crown damage (p < 0.01), and branch-free bole length (p < 0.001) than park trees. Even though we sampled trees with similar diameters, park tree crowns tended to be healthier and denser than street trees. The crown volume, crown projection area (CPA), light availability (Crown Light Exposure or CLE), and foliage density were lower in street trees than in park trees. The average foliage density of street trees is 20 % lower than park trees. All the above differences in crown volume and foliage density lead to a significantly lower leaf area in street trees. The total leaf area of a single street tree was only 83 m2 on average, compared to 186 m2 among park trees. We demonstrated that crown volume and growing habitats (i.e., park or street) are important explanatory variables for leaf area. We conclude that a precise and site-specific evaluation of leaf areas is a prerequisite for accuracy in quantifying ecosystem services from urban trees.

Effects of the fundamental axes of variation in structural diversity on the forest canopy temperature in an urban area

The spatial distribution and heterogeneity of forest canopy elements reveal the fundamental dimensions of plant structure variations. Forests characterized by greater structural complexity and diversity intercept solar radiation more effectively, directly influencing the thermal environment and energy balance of the canopy. However, the axes of variation in the distribution and heterogeneity of the canopy remain largely unknown, which limits our understanding of how structural diversity responds to canopy temperature variability. Here, we derived a set of structural diversity metrics from a dataset of canopy structure measurements obtained using unmanned aerial vehicle-light detection and ranging across major forest communities in an urban area in 2021 and 2022. We also explored the key axes of structural diversity variability and tested their predictive power for canopy temperature. The results showed that: (1) most of the variability within structural diversity (83.6 % and 81.8 %) was captured by the three key axes in 2021 and 2022. The first axis was primarily driven by structural heterogeneity, representing the heterogeneity of vegetation distribution within the canopy. The second axis was primarily influenced by the interaction between height and cover/openness, indicating the vertical structure and horizontal distribution pattern of the canopy. The third axis represented the horizontal coverage and density of the canopy. (2) In both 2021 and 2022, the second axis was identified as the most influential predictor of canopy temperature, as evidenced by R2 values of 0.46 and 0.28, respectively. The model incorporating all three axes of structural diversity achieved the highest accuracy in predicting the canopy temperature for 2021 (R2 = 0.68, AIC = 81.35, ΔAIC = 0, and RMSE = 0.89). Prior research on canopy temperature prediction has overlooked the true potential of principal component axes derived from structural diversity. The findings present a novel approach for selecting structural diversity indicators for future investigation.

Static and Dynamic Performance Evaluation of a Solid-State LiDAR for 3D Object Detection in Greenhouse Spray Applications

Highlights Comprehensive evaluation of the measurement accuracy of an inexpensive solid-state LiDAR for object detection. Development of an algorithm to acquire point clouds of objects with various shapes under both static and dynamic conditions. Utilization of pseudo-color images to assess the surfaces of regular-shaped cartons and irregular artificial plants. Proposal for integrating the solid-state LiDAR into variable-rate spray applications for greenhouses. Abstract. An effective variable-rate spraying system for greenhouses requires accurate canopy structure parameters of plants to ensure proper pesticide dosage adjustment. While conventional laser systems integrated into spray systems can provide precise point cloud data of plants, they still present a high expense. This study examines the performance of a recently introduced, cost-effective, and high-resolution solid-state LiDAR (Intel RealSense L515) in relation to its potential for greenhouse spray applications. Additionally, a specialized point cloud acquisition algorithm was developed for this solid-state LiDAR to obtain the geometrical parameters of objects. To assess the LiDAR sensor's suitability for greenhouse spray applications, the performance of the LiDAR sensor and the algorithm was evaluated using five different sized regular-shaped cartons and three artificial plants with complex geometry. Various factors were analyzed, such as the horizontal distances between objects and the LiDAR sensor (0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 m), the tilt angle of the LiDAR sensor relative to the ground (45°, 60°, and 75°), the height of the LiDAR sensor from the ground (ranging from 0.3 to 0.8 m with 0.5 m distance intervals), and the forward speed of the LiDAR sensor (0.1, 0.3, 0.6, and 0.9 m s-1). The findings revealed that the optimal detection distance for this LiDAR sensor is 1.0 m. Increasing or decreasing the detection distance of the object relative to the LiDAR sensor diminished the measurement accuracy. The accuracy of the derived geometrical variables was affected by the height and tilt angle of the LiDAR sensor. Nevertheless, the geometrical parameters obtained from the solid-state LiDAR showed a favorable correspondence with the results of manual measurements. The highest root mean square error (RMSE) and coefficient of variation (CV) for the overall test were 14.3 mm and 14.3% in the X (length) direction, 14.3 mm and 14.3% in the Y (width) direction, and 10.8 mm and 10.8% in the Z (height) direction, respectively. The contour Edge Similarity Score for objects measured using the solid-state LiDAR and images obtained with an RGB camera exceeded 0.90. These findings suggest that the proposed solid-state LiDAR and the specifically designed algorithm could be effectively adapted to acquire the geometrical parameters of objects and to develop precise variable-rate spraying systems for greenhouse applications. Keywords: Canopy structure measurements, Point cloud, Precision agriculture, Precision spray technology, Variable-rate spraying systems.

A field test of forest canopy structure measurements with the CanopyCapture smartphone application.

BackgroundSeveral smartphone applications have been developed for the purpose of low-cost and convenient assessments of vegetation canopy structure and understorey illumination. Like standard hemispherical photography, most of these applications require user decisions about image processing, posing challenges for repeatability of measurements. Here I report a test of CanopyCapture, an application that instantaneously estimates percentage canopy gap fraction without any input from the user, and has the added advantage of an intuitive levelling mechanism.MethodsGap fraction estimates by CanopyCapture were compared with gap fraction values computed by the LAI-2200C Canopy Analyzer, in two contrasting evergreen temperate forests in New Zealand: an even-aged southern beech (Nothofagus) stand and an old-growth podocarp/broadleaf forest. These comparisons were repeated using a wide-angle adapter to enhance the smartphone camera’s field of view from 45 to 65°. I also asked if CanopyCapture results depended on sky condition (sunny vs. overcast) and on the type of smartphone used.ResultsCanopyCapture output was significantly correlated with gap fraction computed by the LAI-2200C (R2 = 0.39), and use of the wide-angle adapter lifted this value to 0.56. However, CanopyCapture output was not significantly correlated with LAI-2200C output in the even-aged Nothofagus stand, where there was less spatial variation in canopy structure. Despite being much less sensitive to variation in gap fraction than the LAI-2200C, CanopyCapture was nevertheless able to detect differences in average gap fraction between the two forests studied. CanopyCapture results beneath intact canopies were not significantly affected by sky condition, but reflection of direct light off tree trunks in sunny weather caused slight overestimation of gap fraction beneath broken canopies and gaps. Uneven or patchy cloud cover can also cause erroneous readings beneath large canopy openings. Three different models of smartphone gave different results.ConclusionsCanopyCapture offers a rapid and repeatable proxy for comparisons of average canopy gap fraction in multiple stands/forests, provided large sample sizes are used. Measurement under even overcast skies is recommended, and studies involving multiple operators will need to standardize smartphones to ensure comparability of results. Although wide-angle adapters can improve performance, CanopyCapture’s low sensitivity prevents high-resolution comparisons of the light environments of individual understorey plants within a stand.

Estimation of grassland aboveground biomass using digital photograph and canopy structure measurements

Estimation of grassland aboveground biomass using digital photograph and canopy structure measurements

Twenty-five-centimeter pre-grazing canopy height in palisade grass and forage peanut

Short-term grazing behavior variables are sensitive to the canopy structure and have an impact on daily forage intake. This study evaluated the effect of pre- and post-grazing canopy heights on the forage harvesting process at a patch scale in a mixture of Brachiaria brizantha (Hochst. ex A. Rich.) Stapf. syn. Urochloa brizantha R.D. Webster cv. Marandu (palisade grass) and Arachis pintoi Krapov. & W.C. Greg. cv. Belomonte (forage peanut). Treatments were allocated to a split-plot arrangement in a completely randomized design. The plots, in their entirety, consisted of two pre-grazing canopy heights: 25 cm (CH25) and 35 cm (CH35); subplots consisted of three levels of defoliation severity: no defoliation (DS0); 20% depletion of pre-grazing canopy height (DS20); and 40% depletion of pre-grazing canopy height (DS40), with eight replications. Heifers were allowed to graze the patches (0.7 × 0.7 m) and their grazing behavior was recorded. Canopy structure measurements were taken both before and after grazing. Patches from CH35 presented greater stem mass for grass (p = 0.001) and legume (p = 0.002) than did patches from CH25. Bite rate, bite mass and instantaneous intake rate were greater for CH25 than for CH35 (p < 0.001, p = 0.068, and p = 0.074), and bite mass and instantaneous intake rate were lower for DS20 compared to DS0 (p = 0.032 and p = 0.016). Greater stem mass in the grazing strata negatively influenced the instantaneous intake rate.

Associations of bird and bat species richness with temperature and remote sensing‐based vegetation structure on a tropical mountain

AbstractGiven the current challenges of global change, e.g., through increasing temperatures and changes in habitat quality, it is essential to gain a better understanding of how species are linked to the conditions of their environment. Bats and birds fulfill crucial ecosystem functions as highly mobile long‐distance seed dispersers, pollinators, and insect predators and might be influenced not only by abiotic conditions such as temperature, but also by structural parameters of their habitats. We hypothesized that mean annual temperature and vegetation structures are key determinants of flying vertebrate species richness along the slopes of Mt. Kilimanjaro, Tanzania. We used Light Detection and Ranging (LiDAR) to characterize the three‐dimensional habitat structure across different forest strata and recorded mean annual temperature on 58 study sites along the elevational gradient of Mt. Kilimanjaro. Our results show that both bat and bird species richness were significantly associated with temperature and a LiDAR derived measure of canopy density. In combination, temperature and LiDAR‐derived vegetation structure described over 90% of the variability in bat and bird species richness along the elevational gradient. Our findings reveal that climate data and measures of canopy structure obtained by remote sensing can be used to approximate the richness of flying vertebrates on tropical mountains and are highly suitable monitoring tools in conservation programs for these functionally important taxa.

Mapping Amazon Forest Productivity by Fusing GEDI Lidar Waveforms with an Individual-Based Forest Model

The Amazon rainforest plays an important role in the global carbon cycle. However, due to its structural complexity, current estimates of its carbon dynamics are very imprecise. The aim of this study was to determine the forest productivity and carbon balance of the Amazon, particularly considering the role of canopy height complexity. Recent satellite missions have measured canopy height variability in great detail over large areas. Forest models are able to transform these measurements into carbon dynamics. For this purpose, about 110 million lidar waveforms from NASA’s GEDI mission (footprint diameters of ~25 m each) were analyzed over the entire Amazon ecoregion and then integrated into the forest model FORMIND. With this model–data fusion, we found that the total gross primary productivity (GPP) of the Amazon rainforest was 11.4 Pg C a−1 (average: 21.1 Mg C ha−1 a−1) with lowest values in the Arc of Deforestation region. For old-growth forests, the GPP varied between 15 and 45 Mg C ha−1 a−1. At the same time, we found a correlation between the canopy height complexity and GPP of old-growth forests. Forest productivity was found to be higher (between 25 and 45 Mg C ha−1 a−1) when canopy height complexity was low and lower (10–25 Mg C ha−1 a−1) when canopy height complexity was high. Furthermore, the net ecosystem exchange (NEE) of the Amazon rainforest was determined. The total carbon balance of the Amazon ecoregion was found to be −0.1 Pg C a−1, with the highest values in the Amazon Basin between both the Rio Negro and Solimões rivers. This model–data fusion reassessed the carbon uptake of the Amazon rainforest based on the latest canopy structure measurements provided by the GEDI mission in combination with a forest model and found a neutral carbon balance. This knowledge may be critical for the determination of global carbon emission limits to mitigate global warming.

Новый подход к оценке площади поверхности хвои пихты сибирской

Новый подход к оценке площади поверхности хвои пихты сибирской

Research Status and Prospects on Plant Canopy Structure Measurement Using Visual Sensors Based on Three-Dimensional Reconstruction

Three-dimensional (3D) plant canopy structure analysis is an important part of plant phenotype studies. To promote the development of plant canopy structure measurement based on 3D reconstruction, we reviewed the latest research progress achieved using visual sensors to measure the 3D plant canopy structure from four aspects, including the principles of 3D plant measurement technologies, the corresponding instruments and specifications of different visual sensors, the methods of plant canopy structure extraction based on 3D reconstruction, and the conclusion and promise of plant canopy measurement technology. In the current research phase on 3D structural plant canopy measurement techniques, the leading algorithms of every step for plant canopy structure measurement based on 3D reconstruction are introduced. Finally, future prospects for a standard phenotypical analytical method, rapid reconstruction, and precision optimization are described.

Incorporating canopy structure from simulated GEDI lidar into bird species distribution models

The Global Ecosystem Dynamics Investigation (GEDI) lidar began data acquisition from the International Space Station in March 2019 and is expected to make over 10 billion measurements of canopy structure and topography over two years. Previously, airborne lidar data with limited spatial coverage have been used to examine relationships between forest canopy structure and faunal diversity, most commonly bird species. GEDI’s latitudinal coverage will permit these types of analyses at larger spatial extents, over the majority of the Earth’s forests, and most importantly in areas where canopy structure is complex and/or poorly understood. In this regional study, we examined the impact that GEDI-derived Canopy Structure variables have on the performance of bird species distribution models (SDMs) in Sonoma County, California. We simulated GEDI waveforms for a two-year period and then interpolated derived Canopy Structure variables to three grid sizes of analysis. In addition to these variables, we also included Phenology, Climate, and other Auxiliary variables to predict the probability of occurrence of 25 common bird species. We used a weighted average ensemble of seven individual machine learning models to make predictions for each species and calculated variable importance. We found that Canopy Structure variables were, on average at our finest resolution of 250 m, the second most important group (32.5%) of predictor variables after Climate variables (35.3%). Canopy Structure variables were most important for predicting probability of occurrence of birds associated with Conifer forest habitat. Regarding spatial analysis scale, we found that finer-scale models more frequently performed better than coarser-scale models, and the importance of Canopy Structure variables was greater at finer spatial resolutions. Overall, GEDI Canopy Structure variables improved SDM performance for at least one spatial resolution for 19 of 25 species and thus show promise for improving models of bird species occurrence and mapping potential habitat.

Evaluating the potential of full‐waveform lidar for mapping pan‐tropical tree species richness

AbstractAimMapping tree species richness across the tropics is of great interest for effective conservation and biodiversity management. In this study, we evaluated the potential of full‐waveform lidar data for mapping tree species richness across the tropics by relating measurements of vertical canopy structure, as a proxy for the occupation of vertical niche space, to tree species richness.LocationTropics.Time periodPresent.Major taxa studiedTrees.MethodsFirst, we evaluated the characteristics of vertical canopy structure across 15 study sites using (simulated) large‐footprint full‐waveform lidar data (22 m diameter) and related these findings to in‐situ tree species information. Then, we developed structure–richness models at the local (within 25–50 ha plots), regional (biogeographical regions) and pan‐tropical scale at three spatial resolutions (1.0, 0.25 and 0.0625 ha) using Poisson regression.ResultsThe results showed a weak structure–richness relationship at the local scale. At the regional scale (within a biogeographical region) a stronger relationship between canopy structure and tree species richness across different tropical forest types was found, for example across Central Africa and in South America [R2 ranging from .44–.56, root mean squared difference as a percentage of the mean (RMSD%) ranging between 23–61%]. Modelling the relationship pan‐tropically, across four continents, 39% of the variation in tree species richness could be explained with canopy structure alone (R2 = .39 and RMSD% = 43%, 0.25‐ha resolution).Main conclusionsOur results may serve as a basis for the future development of a set of structure–richness models to map high resolution tree species richness using vertical canopy structure information from the Global Ecosystem Dynamics Investigation (GEDI). The value of this effort would be enhanced by access to a larger set of field reference data for all tropical regions. Future research could also support the use of GEDI data in frameworks using environmental and spectral information for modelling tree species richness across the tropics.

Measures of Canopy Structure from Low-Cost UAS for Monitoring Crop Nutrient Status

Deriving crop information from remotely sensed data is an important strategy for precision agriculture. Small unmanned aerial systems (UAS) have emerged in recent years as a versatile remote sensing tool that can provide precisely-timed, fine-grained data for informing management responses to intra-field crop variability (e.g., nutrient status and pest damage). UAS sensors with high spectral resolution used to compute informative vegetation indices, however, are practically limited by high cost and data dimensionality. This research extends spectral analysis for remote crop monitoring to investigate the relationship between crop health and 3D canopy structure using low-cost UAS equipped with consumer-grade RGB cameras. We used flue-cured tobacco as a case study due to its known sensitivity to fertility variation and nutrient-specific symptomology. Fertilizer treatments were applied to induce plant health variability in a 0.5 ha field of flue-cured tobacco. Multi-view stereo images from three UAS surveys collected during crop development were processed into orthoimages used to compute a visible band spectral index and photogrammetric point clouds using Structure from Motion (SfM). Plant structural metrics were then computed from detailed high resolution canopy surface models (0.05 m resolution) interpolated from the photogrammetric point clouds. The UAS surveys were complimented by nutrient status measurements obtained from plant tissues. The relationships between foliar nitrogen (N), phosphorus (P), potassium (K), and boron (B) concentrations and the UAS-derived metrics were assessed using multiple linear regression. Symptoms of N and K deficiencies were well captured and differentiated by the structural metrics. The strongest relationship observed was between canopy shape and N foliar concentration (adj. r2 = 0.59, increasing to adj. r2 = 0.81 when combined with the spectral index). B foliar concentration was consistently better predicted by canopy structure with a maximum adj. r2 = 0.41 observed at the latest growth stage surveyed. Overall, combining information about canopy structure and spectral reflectance increased model fit for all measured nutrients compared to spectral alone. These results suggest that an important relationship exists between relative canopy shape and crop health that can be leveraged to improve the usefulness of low cost UAS for precision agriculture.

Satellite-based Estimates of Canopy Photosynthetic Parameters for an Alpine Meadow in Northern Tibet

Plant photosynthesis is the fundamental driver of all the biospheric functions. Alpine meadow on the Tibetan Plateau is sensitive to rapid climate change, and thus can be considered an indicator for the response of terrestrial ecosystems to climate change. However, seasonal variations in photosynthetic parameters, including the fraction of photosynthetically active radiation by canopy (FPAR), the light extinction coefficient (k) through canopy, and the leaf area index (LAI) of plant communities, are not known for alpine meadows on the Tibetan Plateau. In this study, we used field measurements of radiation components and canopy structure from 2009 to 2011 at a typical alpine meadow on the northern Tibetan Plateau to calculate these three photosynthetic parameters. We developed a satellite-based (NDVI and EVI) method derived from the Beer-Lambert law to estimate the seasonal dynamics of FPAR, k ,and LAI, and we compared these estimates with the Moderate Resolution Imaging Spectroradiometer (MODIS) FPAR (FPAR_MOD) and LAI product (LAI_MOD). The results showed that the average daily FPAR was 0.33, 0.37 and 0.35, respectively, from 2009 to 2011, and that the temporal variations could be explained by all four satellite-based FPAR estimations, including FPAR_MOD, an FPAR estimation derived from the Beer-Lambert law with a constant k (FPAR_LAI), and two FPAR estimations from the nonlinear functions between the ground measurements of FPAR (FAPRg) and NDVI/EVI (FPAR_NDVI and FPAR_EVI). We found that FPAR_MOD seriously undervalued FPARg by over 40%. Tower-based FPAR_LAI also significantly underestimated FPARg by approximately 20% due to the constant k (0.5) throughout the whole growing seasons. This indicated that using FPAR_LAI to validate the FPAR_MOD was not an appropriate method in this alpine meadow because the seasonal variation of k ranged from 0.19 to 2.95 in this alpine meadow. Thus, if the seasonal variation of k was taken into consideration, both FPAR_NDVI and FPAR_EVI provided better descriptions, with negligible overestimates of less than 5% of FAPRg (RMSE=0.05), in FPARg estimations than FPAR_MOD and FPAR_LAI. Combining the satellite-based (NDVI and EVI) estimations of seasonal FPAR and k, LAI_NDVI and LAI_EVI derived from the Beer-Lambert law also provided better LAIg estimations than LAI_MOD (less than 30% of LAIg). Therefore, this study concluded that satellite-based models derived from the Beer-Lambert law were a simple and efficient method for estimating the seasonal dynamics of FPAR, k and LAI in this alpine meadow.

Application of multidimensional structural characterization to detect and describe moderate forest disturbance

AbstractThe study of vegetation community and structural change has been central to ecology for over a century, yet the ways in which disturbances reshape the physical structure of forest canopies remain relatively unknown. Moderate severity disturbances affect different canopy strata and plant species, resulting in variable structural outcomes and ecological consequences. Terrestrial lidar (light detection and ranging) offers an unprecedented view of the interior arrangement and distribution of canopy elements, permitting the derivation of multidimensional measures of canopy structure that describe several canopy structural traits (CSTs) with known links to ecosystem function. We used lidar‐derived CSTs within a machine learning framework to detect and describe the structural changes that result from various disturbance agents, including moderate severity fire, ice storm damage, age‐related senescence, hemlock woolly adelgid, beech bark disease, and chronic acidification. We found that fire and ice storms primarily affected the amount and position of vegetation within canopies, while acidification, senescence, pathogen, and insect infestation altered canopy arrangement and complexity. Only two of the six disturbance agents significantly reduced leaf area, counter to common assumptions regarding many moderate severity disturbances. While findings are limited in their generalizability due to lack of replication among disturbances, they do suggest that the current limitations of standard disturbance detection methods—such as optical‐based remote sensing platforms, which are often above‐canopy perspectives—limit our ability to understand the full ecological and structural impacts of disturbance, and to evaluate the consistency of structural patterns within and among disturbance agents. A more broadly inclusive definition of ecological disturbance that incorporates multiple aspects of canopy structural change may potentially improve the modeling, detection, and prediction of functional implications of moderate severity disturbance as well as broaden our understanding of the ecological impacts of disturbance.

LiDAR Helps Differentiate Stand Health and Productivity Levels within a Northern Hardwood Forest

Light detection and ranging (LiDAR) data can provide detailed information about three-dimensional forest structure. However, links between forest structure and tree function have not been fully evaluated using LiDAR. We assessed the relationship of LiDAR-derived structural categories to tree health and productivity on 36 hardwood plots at the Hubbard Brook Experimental Forest, New Hampshire, USA. We established nine plot replicates for each of four LiDAR-based vegetation categories: 1) high crown and high understory closure; 2) high crown and low understory closure; 3) low crown and high understory closure; and 4) low crown and low understory closure. Ground-based measures of canopy structure, site, stand and individual tree measures were collected on plots during summer 2012. Significant differences among LiDAR categories were found for several response variables. Lower basal area increment for sugar maple (Acer saccharum), decreased foliar nutrition for yellow birch (Betula alleghaniensis), and lower overall crown health were all associated with high understory closure provided that overstory closure was also high. These results suggest that LiDAR measures can be used to assess competitive interactions between overstory and understory vegetation, and that LiDAR shows promise for identifying stands with reduced health and productivity due to factors such as competition or overstocking.

High rates of primary production in structurally complex forests.

Structure-function relationships are central to many ecological paradigms. Chief among these is the linkage of net primary production (NPP) with species diversity and canopy structure. Using the National Ecological Observatory Network (NEON) as a subcontinental-scale research platform, we examined how temperate-forest NPP relates to several measures of site-level canopy structure and tree species diversity. Novel multidimensional canopy traits describing structural complexity, most notably canopy rugosity, were more strongly related to site NPP than were species diversity measures and other commonly characterized canopy structural features. The amount of variation in site-level NPP explained by canopy rugosity alone was 83%, which was substantially greater than that explained individually by vegetation area index (31%) or Shannon's index of species diversity (30%). Forests that were more structurally complex, had higher vegetation-area indices, or were more diverse absorbed more light and used light more efficiently to power biomass production, but these relationships were most strongly tied to structural complexity. Implications for ecosystem modeling and management are wide ranging, suggesting structural complexity traits are broad, mechanistically robust indicators of NPP that, in application, could improve the prediction and management of temperate forest carbon sequestration.

Terrestrial gross primary production: Using NIRV to scale from site to globe.

Terrestrial photosynthesis is the largest and one of the most uncertain fluxes in the global carbon cycle. We find that near-infrared reflectance of vegetation (NIRV ), a remotely sensed measure of canopy structure, accurately predicts photosynthesis at FLUXNET validation sites at monthly to annual timescales (R2 =0.68), without the need for difficult to acquire information about environmental factors that constrain photosynthesis at short timescales. Scaling the relationship between gross primary production (GPP) and NIRV from FLUXNET eddy covariance sites, we estimate global annual terrestrial photosynthesis to be 147PgC/year (95% credible interval 131-163PgC/year), which falls between bottom-up GPP estimates and the top-down global constraint on GPP from oxygen isotopes. NIRV -derived estimates of GPP are systematically higher than existing bottom-up estimates, especially throughout the midlatitudes. Progress in improving estimated GPP from NIRV can come from improved cloud screening in satellite data and increased resolution of vegetation characteristics, especially details about plantphotosynthetic pathway.

Combining behavioural and LiDAR data to reveal relationships between canopy structure and orangutan nest site selection in disturbed forests

Primary tropical forests are becoming increasingly disturbed and fragmented, making it critically important to understand the conservation value of degraded forests. Many populations of even the largest and most iconic species are now found outside of primary habitats, and the long-term survival of these and many other species depends on appropriate management of degraded areas, whether protected or not. However, for conservation in degraded habitats to be successful, an adequate understanding of the minimal ecological requirements necessary for species persistence within them is required. We combined ground and helicopter nest surveys of critically endangered Bornean orangutans with high-resolution measurements of forest canopy structure from airborne Light Detection and Ranging (LiDAR) to understand orangutan nest site selection across multiple spatial scales in degraded forests of the Lower Kinabatangan region, Malaysian Borneo. We found orangutans to be selective when choosing nest sites, with nests more likely to be observed in canopies of tall and uniform height and closer to full canopy gaps, which was consistent across spatial scales and orangutan age and sex classes. These sites likely offer orangutans an improved vantage point and/or shelter from wind and rain. In contrast, no discernible relationships between nest site selection and canopy complexity, or nest abundance and landscape forest structure or aboveground carbon density were recorded. Our findings suggest that although orangutans do nest across a range of forest conditions, their optimum requirement for nesting strongly depends on forest patches with sufficient tall canopy of uniform height. These results serve to inform degraded forest conservation strategies across Borneo, particularly where orangutans are a focal species.