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Urban tree canopies are fundamental to mitigating the impacts of climate change within cities as well as providing a range of other important ecosystem, health, and amenity benefits. However, urban tree planting initiatives do not typically utilize data about both the horizontal and vertical dimensions of the tree canopy, despite height being a critical determinant of the quality and value of urban canopy cover. We present a novel pipeline that uses airborne LiDAR data to train a multi-task machine learning model to generate estimates of both canopy cover and height in urban areas. We apply this to multi-source multi-spectral imagery for the case study of Chicago, USA. Our results indicate that a multi-task UNet convolutional neural network can be used to generate reliable estimates of canopy cover and height from aerial and satellite imagery. We then use these canopy estimates to allocate 75,000 trees from Chicago's recent green initiative under four scenarios, minimizing the urban heat island effect and then optimizing for an equitable canopy distribution, comparing results when only canopy cover is used, and when both canopy cover and height are considered. Through the introduction of this novel pipeline, we show that including canopy height within decision-making processes allows the distribution of new trees to be optimised to further reduce the urban heat island effect in localities where trees have the highest cooling potential and allows trees to be more equitably distributed to communities with lower quality canopies. [ABSTRACT FROM AUTHOR]

This study presents an improved theoretical formulation of the gap probability ( Pgap ) model, typically applied to indirectly estimate LAI in woody ecosystems. Specifically, we present the woody element projection function ( G W ), which characterises the angular contribution of non-leaf facets in woody ecosystems, and explain how it may be used to improve the accuracy of indirect LAI retrieval via the application of the Pgap model. G W enables separate treatment of the leaf and wood projection functions in the theoretical model, important in the typical case when Pgap includes the influence of both leaf and wood canopy elements. This study then validates the improved theoretical model using experimental data. Here, Pgap was calculated from a 3D scattering model, parameterised with highly-detailed 3D explicit tree models reconstructed from empirical data of a sampled forest stand. The experimental data was then used to quantify additional effects of view zenith angle ( VZA ), leaf angle distribution (LAD), and the influence of woody components on the indirect estimation of LAI and within-crown clumping via application of the Pgap model. Additionally, we quantify within-crown clumping of reconstructed tree models for leaf and woody elements both together and separately for the first time. LAI errors up to 25% at zenith were found when ignoring G W and were shown to be a function of VZA . Conversely, at the approximate 57.3° (1 radian) VZA , results show that there was no effect of G W due to the wood projection function converging with leaf projection functions. Within-crown clumping factors for the modelled dataset were as low as 0.35. Consequently, making a common assumption of a random distribution of canopy elements at the crown scale would lead to an LAI error of up to 65% for the 3D forest stand. We also conclude that when estimating LAI via the Pgap model, separate treatment of canopy material projection ‘ G ’ functions are required at VZA’s other than 1 radian. The findings of this study and the extended physical formulation presented here impact upon indirect Pgap LAI retrieval methods from sensors of all platforms in clumped canopy environments or canopies with woody (non-leaf) elements contributing to the extinction of light. [ABSTRACT FROM AUTHOR]

Abstract: Significant progress on quantifying state and trends in vegetation structure in savanna and woodland ecosystems has been made by integrating in situ measurements with lidar datasets. However, large area ground-based monitoring campaigns required for calibration are both costly to maintain, and reduce the generality of results. Estimation of directional gap probability (Pgap ) from waveform lidar which is both direct (i.e. physically-based) and minimises or removes requirements for field calibration would be a significant advance for large area sampling. We present a new model for estimating Pgap from small footprint airborne waveform lidar data that accounts for differences in canopy (ρv ) and ground (ρg ) reflectivity and compare this new method with published discrete return lidar methods. We use lidar surveys acquired at multiple altitudes using RIEGL LMS-Q680i and RIEGL LMS-Q560 waveform systems over a savanna woodland in the Einasleigh Uplands bioregion of northern Queensland, Australia. The waveform model for Pgap was found to fit observed waveform data in cases where the assumption of constant ρv and ρg was satisfied. Pgap estimates from the waveform model were shown to be relatively insensitive to variation in sensor altitude. This was in contrast to other methods of estimating Pgap where differences up to ~0.15 Pgap have been observed. Comparison of lidar-derived Pgap with ground measurements showed the new waveform model produced estimates corresponding to within 5% Pgap . We suggest the waveform model to retrieve ρv /ρg and Pgap is a significant advance in retrieval of canopy structure parameters from small footprint lidar, reducing the need for local calibration, and providing direct estimates of Pgap . If the assumptions of relatively stable ρv /ρg are shown to hold across a greater range of sensor, survey, and canopy structure configurations we suggest this method may have wide practical application for retrieval of Pgap . [Copyright &y& Elsevier]

Abstract: Lidars have the unique ability to make direct, physical measurements of forest height and vertical structure in much denser canopies than is possible with passive optical or short wavelength radars. However the literature reports a consistent underestimate of tree height when using physically based methods, necessitating empirical corrections. This bias is a result of overestimating the range to the canopy top due to background noise and failing to correctly identify the ground. This paper introduces a method, referred to as “noise tracking”, to avoid biases when determining the range to the canopy top. Simulated waveforms, created with Monte-Carlo ray tracing over geometrically explicit forest models, are used to test noise tracking against simple thresholding over a range of forest and system characteristics. It was found that noise tracking almost completely removed the bias in all situations except for very high noise levels and very low (<10%) canopy covers. In all cases noise tracking gave lower errors than simple thresholding and had a lower sensitivity to the initial noise threshold. Finite laser pulses spread out the measured signal, potentially overriding the benefit of noise tracking. In the past laser pulse length has been corrected by adding half that length to the signal start range. This investigation suggests that this is not always appropriate for simple thresholding and that the results for noise tracking were more directly related to pulse length than for simple thresholding. That this effect has not been commented on before may be due to the possible confounding impacts of instrument and survey characteristics inherent in field data. This method should help improve the accuracy of waveform lidar measurements of forests, whether using airborne or spaceborne instruments. [Copyright &y& Elsevier]

Future cities are set to face ever increasing population and climate pressures, ecosystem services offered by urban forests have been recognised as providing significant mitigation for these pressures. Therefore, the ability to accurately quantify the extent and structure of urban forests, across large and highly dynamic cities, is vital for determining the value of services provided and to assess the effectiveness of policy to promote these important assets. Current inventory methods used in urban forestry are mostly reliant on plot networks measuring a range of structural and demographic metrics; however, limited sampling (spatially and temporally) cannot fully capture the dynamics and spatial heterogeneity of the urban matrix. The rapid increase in the availability of open-access remote sensing data and processing tools offers an opportunity for monitoring and assessment of urban forest structure that is synoptic and at high spatial and temporal resolutions. Here we present a framework to estimate urban forest structure that uses open-access data and software, is robust to differences in data sources, is reproducible and is transferable between cities. The workflow is demonstrated by estimating three metrics of 3D forest structure (canopy cover, canopy height and tree density) across the Greater London area (1577 km2). Random Forest was trained with open-access airborne LiDAR or iTree Eco inventory data, with predictor variables derived from Sentinel 2, climatic and topography data sets. Output were maps of forest structure at 100 m and 20 m resolution. Results indicate that forest structure can be accurately estimated across large urban areas; Greater London has a mean canopy cover of ∼16.5% (RMSE 11-17%), mean canopy height of 8.1–15.0 m (RMSE 4.9–6.2 m) m and is home to ∼4.6 M large trees (projected crown area >10 m2). Transferability to other cities is demonstrated using the UK city of Southampton, where estimates were generated from local and Greater London training data sets indicating application beyond geographic domains is feasible. The methods presented here can augment existing inventory practices and give city planners, urban forest managers and greenspace advocates across the globe tools to generate consistent and timely information to help assess and value urban forests. [ABSTRACT FROM AUTHOR]

The poor constraint of forest Above Ground Biomass (AGB) is responsible, in part, for large uncertainties in modelling future climate scenarios. Terrestrial Laser Scanning (TLS) can be used to derive unbiased and non-destructive estimates of tree structure and volume and can, therefore, be used to address key uncertainties in forest AGB estimates. Here we review our experience of TLS sampling strategies from 27 campaigns conducted over the past 5 years, across tropical and temperate forest plots, where data was captured with a RIEGL VZ-400 laser scanner. The focus is on strategies to derive Geometrical Modelling metrics (e.g. tree volume) over forest plots (≥1 ha) which require the accurate co-registration of 10s to 100s of individual point clouds. We recommend a 10 m × 10 m sampling grid as an approach to produce a point cloud with a uniform point distribution, that can resolve higher order branches (down to a few cm in diameter) towards the top of 30+ m canopies and can be captured in a timely fashion i.e. ∼ 3–6 days per ha. A data acquisition protocol, such as presented here, would facilitate data interoperability and inter-comparison of metrics between instruments/groups, from plot to plot and over time. [ABSTRACT FROM AUTHOR]

Vegetation structure controls habitat availability, ecosystem services, weather, climate and microclimate, but current landscape scale vegetation maps have lacked details of understorey vegetation and within-canopy structure at resolutions finer than a few tens of metres. In this paper, a novel signal processing method is used to correctly measure 3D voxelised vegetation cover from full-waveform ALS data at 1.5 m horizontal and 50 cm vertical resolution, including understorey vegetation and within-canopy structure. A new method for calibrating and validating the instrument specific ALS processing using high resolution TLS data is also presented and used to calibrate and validate the ALS derived data products over a wide range of land cover types within a heterogeneous urban area, including woodland, gardens and streets. This showed the method to accurately retrieve voxelised canopy cover maps with less than 0.4% of voxels containing false negatives, 10% of voxels containing false positives and a canopy cover accuracy within voxels of 24%. The method was applied across 100 km 2 and the resulting structure maps were compared to the more widely used discrete return ALS and Gaussian decomposed waveform ALS data products. These products were found to give little information on the within-canopy structure and so are only capable of deriving coarse resolution, plot-scale structure metrics. The detailed 3D canopy maps derived from the new method allow landscape scale ecosystem processes to be examined in more detail than has previously been possible, and the new method reveals details about the canopy understorey, creating opportunities for ecological investigations. The calibration method can be applied to any waveform ALS instrument and processing method. All code used in this paper is freely available online through bitbucket ( https://bitbucket.org/StevenHancock/voxel_lidar ). [ABSTRACT FROM AUTHOR]

The RAdiative transfer Model Intercomparison (RAMI) activity focuses on the benchmarking of canopy radiative transfer (RT) models. For the current fourth phase of RAMI, six highly realistic virtual plant environments were constructed on the basis of intensive field data collected from (both deciduous and coniferous) forest stands as well as test sites in Europe and South Africa. Twelve RT modelling groups provided simulations of canopy scale (directional and hemispherically integrated) radiative quantities, as well as a series of binary hemispherical photographs acquired from different locations within the virtual canopies. The simulation results showed much greater variance than those recently analysed for the abstract canopy scenarios of RAMI-IV. Canopy complexity is among the most likely drivers behind operator induced errors that gave rise to the discrepancies. Conformity testing was introduced to separate the simulation results into acceptable and non-acceptable contributions. More specifically, a shared risk approach is used to evaluate the compliance of RT model simulations on the basis of reference data generated with the weighted ensemble averaging technique from ISO-13528. However, using concepts from legal metrology, the uncertainty of this reference solution will be shown to prevent a confident assessment of model performance with respect to the selected tolerance intervals. As an alternative, guarded risk decision rules will be presented to account explicitly for the uncertainty associated with the reference and candidate methods. Both guarded acceptance and guarded rejection approaches are used to make confident statements about the acceptance and/or rejection of RT model simulations with respect to the predefined tolerance intervals. [ABSTRACT FROM AUTHOR]

Abstract: LiDAR has the potential to derive canopy structural information such as tree height and leaf area index (LAI), via models of the LiDAR signal. Such models often make assumptions regarding crown shape to simplify parameter retrieval and crown archetypes are typically assumed to contain a turbid medium to account for within-crown scattering. However, these assumptions may make it difficult to relate derived structural parameters to measurable canopy properties. Here, we test the impact of crown archetype assumptions by developing a new set of analytical expressions for modelling LiDAR signals. The expressions for three crown archetypes (cuboids, cones and spheroids) are derived from the radiative transfer solution for single order scattering in the optical case and are a function of crown macro-structure (height and crown extent) and LAI. We test these expressions against waveforms simulated using a highly-detailed 3D radiative transfer model, for LAI ranging from one to six. This allows us to control all aspects of the crown structure and LiDAR characteristics. The analytical expressions are fitted to both the original and the cumulative simulated LiDAR waveforms and the CV(RMSE) of model fit over archetype trees ranges from 0.3% to 21.2%. The absolute prediction error (APE) for LAI is 7.1% for cuboid archetypes, 18.6% for conical archetypes and 4.5% for spheroid archetypes. We then test the analytical expressions against more realistic 3D representations of broadleaved deciduous (birch) and evergreen needle-leaved (Sitka spruce) tree crowns. The analytical expressions perform more poorly (APE values up to 260.9%, typically ranging from 39.4% to 78.6%) than for the archetype shapes and ignoring clumping and lower branches has a significant influence on the performance of waveform inversion of realistic trees. The poor performance is important as it suggests that the assumption of crown archetypes can result in significant errors in retrieved crown parameters due to these assumptions not being met in real trees. Seemingly reasonable inferred values may arise due to coupling between parameters. Our results suggest care is needed in inferring biophysical properties based on crown archetypes. Relationships between the derived parameters and their physical counterparts need further elucidation. [Copyright &y& Elsevier]

Abstract: Understanding of mechanisms underlying carbon flux dynamics in the Eastern Arc Mountains and their catchment areas is lacking, due to data shortage (e.g. biome specific canopy structure) and spatial heterogeneity of tropical ecosystems. This study focuses on documenting leaf area index (LAI) for the main biomes in the Eastern Arc Mountains and their surroundings. In situ optical instruments, i.e. hemispherical photography and a SunScan device, were used to acquire ground LAI measurements. Spectral vegetation indices (VIs) extracted from Landsat Enhanced Thematic Mapper (ETM+) and Système Probatoire d''Observation de la Terre (SPOT) reflectance data were used, along with mean annual precipitation (MAP), as explanatory variables of LAI variation. The results indicate that LAI significantly increases with increasing MAP for woody biomes. Implementing long-term MAP as a second predictor variable into the VI–LAI models significantly improved LAI predictions by up to 10% using the normalised difference vegetation index (NDVI), modified soil adjusted vegetation index (MSAVI 2) and 2-band enhanced vegetation index (EVI 2). Varying forest disturbances and agricultural management practises may have contributed to observed discrepancies of LAI with MAP across biomes. The importance of altitudinal gradients is yet to be explained fully with more study required. However, LAI appears to be higher in low-altitude forests compared to forests at higher altitudes. Our results indicate that SPOT and Landsat-derived VIs, in combination with long-term MAP, may be a suitable tool to develop landscape maps of LAI in Eastern Africa. This study also presents the in situ LAI measurements for further validation of global products for areas that are currently under-represented in Earth Observation (EO) global validation networks. [Copyright &y& Elsevier]

Forests and coral reefs are structurally complex ecosystems threatened by climate change. In situ 3D imaging measurements provide unprecedented, quantitative, and detailed structural information that allows testing of hypotheses relating form to function. This affords new insights into both individual organisms and their relationship to their surroundings and neighbours. [ABSTRACT FROM AUTHOR]

Radiative transfer models that use spatially explicit 3D models to represent forest structure can simulate highly realistic Earth Observation (EO) data. Such simulations at the forest stand scale (≥ 1-ha) allow for more direct calibration and validation of EO products. Explicitly reconstructing 3D forest structures at scales that can be compared directly with satellite EO data (i.e., dozens to hundreds of meters) is challenging. Reconstructing large forest areas (≥ 1-ha) using a representative subset (i.e., forest subsampling) is a potentially more practical and feasible method. However, the impacts of forest subsampling on radiative transfer (RT) modeling were never formally tested in the spatially explicit forest scene. This study quantified the trade-offs involved in two main subsampling approaches when reconstructing the spatially explicit scene of a real forest for RT modeling. The two subsampling approaches were: (1) subplot subsampling - area-based, using the subplot (i.e. a fixed area) as the basic sampling and reconstruction unit; and (2) tree library subsampling - tree-based, using the individual tree as the basic sampling and reconstruction unit. We used the Discrete Anisotropic Radiative Transfer Model (DART) to simulate the Bidirectional Reflectance Factor (BRF) of the completely reconstructed 1-ha 3D-explicit forest scene, as well as the simplified forest scenes built from various subsets of the same forest. The simulated reflectance deviation of the simplified forest scenes was evaluated by comparing it with the fully reconstructed forest scene. The results showed that for subplot subsampling, as the sampling fraction increased from 10% to 90%, the normalized mean BRF deviation of radiative transfer simulations decreased from −2.7% to −0.0034% and its standard deviation decreased from 7.7% to 0.54%. Additionally, as the sampling fraction increased from 10% to 90%, the normalized mean BRF deviation of tree library subsampling decreased from −7.4% to −1.3% and its standard deviation decreased from 2.8% to 0.51%. Overall, sampling 20% of the forest area using the subplot subsampling method was an effective reconstruction strategy for the temperate deciduous forest. This strategy ensured a normalized mean BRF deviation of −2.9% and a standard deviation of 3.7% in the forest stand studied, with a relatively low reconstruction effort. This study shows that the forest subsampling approach allows for reconstructing 3D-explicit forest scenes for RT modeling at the forest stand scale (≥ 1-ha). Accordingly, highly realistic EO data can be modeled at the forest stand scale, which allows for more direct calibration and validation of EO products. • We quantified the impact of subsampling on 3D-explicit RTM of forest. • Subplot method with 20% sampling fraction is an effective reconstruction strategy. • The quality index for reproducing realistic RT modeling with subsample was proposed. [ABSTRACT FROM AUTHOR]

The TomoSense experiment was funded by the European Space Agency (ESA) to support research on remote sensing of forested areas by means of Synthetic Aperture Radar (SAR) data, with a special focus on the use of tomographic SAR (TomoSAR) to retrieve information about the vertical structure of the vegetation at different frequency bands. The illuminated scene is the temperate forest at the Eifel National Park, North-West Germany. Dominant species are beech and spruce trees. Forest height ranges roughly from 10 to 30 m, with peaks up to over 40 m. Forest Above Ground Biomass (AGB) ranges from 20 to 300 Mg/ha, with peaks up to over 400 Mg/ha. SAR data include P-, L-, and C-band surveys acquired by flying up to 30 trajectories in two headings to provide tomographic imaging capabilities. L- and C-band data were acquired by simultaneously flying two aircraft to gather bistatic data along different trajectories. The SAR dataset is complemented by 3D structural canopy measurements made via terrestrial laser scanning (TLS), Unoccupied Aerial Vehicle lidar (UAV-L) and airborne laser scanning (ALS), and in-situ forest census. This unique combination of SAR tomographic and multi-scale lidar data allows for direct comparison of canopy structural metrics across wavelength and scale, including vertical profiles of canopy wood and foliage density, and per-tree and plot-level above ground biomass (AGB). The resulting TomoSense data-set is free and openly available at ESA for any research purpose. The data-set includes ALS-derived maps of forest height and AGB, forest parameters at the level of single trees, TLS raw data, and plot-average TLS vertical profiles. The provided SAR data are coregistered, phase calibrated, and ground steered, to enable a direct implementation of any kind of interferometric or tomographic processing without having to deal with the subtleties of airborne SAR processing. Moreover, the data-base comprises SAR tomographic cubes representing forest scattering in 3D both in Radar and geographical coordinates, intended for use by non-Radar experts. For its unique features and completeness, the TomoSense data-set is intended to serve as an important basis for future research on microwave scattering from forested areas in the context of future Earth Observation missions. • Unique Tomographic SAR data-set comprising P-, L-, and C-Band data. • Bistatic L- and C-band acquisitions by simultaneously flying two aircraft. • 3D structural measurements by terrestrial and airborne Lidar and forest census. • SAR SLC data are coregistered, phase calibrated, and ground steered. • The data-base comprises SAR tomographic cubes representing forest scattering in 3D. [ABSTRACT FROM AUTHOR]

Digital hemispherical photography is a simple, non-destructive method for estimating canopy biophysical parameters for ecological applications and validation of remote sensing products. Determination of optimum and repeatable acquisition procedures is well documented in the literature but so far this has not focused on evaluating the levelling procedure used to align the camera. In this paper, the standard recommendation that tripod levelling is a necessity is tested by comparing it with a hand-levelled procedure. The results show that the average difference between the two procedures is < 2% for effective plant area index and < 1% for gap fraction at the VALERI plot scale, which generally falls within the variance. Users implementing the hand-levelled technique can expect large reductions in data acquisition time, allowing many more samples to be collected without compromising the overall quality of the data retrieved. [ABSTRACT FROM AUTHOR]

Full waveform lidar has a unique capability to characterise vegetation in more detail than any other practical method. The reflectance, calculated from the energy of lidar returns, is a key parameter for a wide range of applications and so it is vital to extract it accurately. Fifteen separate methods have been proposed to extract return energy (the amount of light backscattered from a target), ranging from simple to mathematically complex, but the relative accuracies have not yet been assessed. This paper uses a simulator to compare all methods over a wide range of targets and lidar system parameters. For hard targets the simplest methods (windowed sum, peak and quadratic) gave the most consistent estimates. They did not have high accuracies, but low standard deviations show that they could be calibrated to give accurate energy. This may be why some commercial lidar developers use them, where the primary interest is in surveying solid objects. However, simulations showed that these methods are not appropriate over vegetation. The widely used Gaussian fitting performed well over hard targets (0.24% root mean square error, RMSE), as did the sum and spline methods (0.30% RMSE). Over vegetation, for large footprint (15 m) systems, Gaussian fitting performed the best (12.2% RMSE) followed closely by the sum and spline (both 12.7% RMSE). For smaller footprints (33 cm and 1 cm) over vegetation, the relative accuracies were reversed (0.56% RMSE for the sum and spline and 1.37% for Gaussian fitting). Gaussian fitting required heavy smoothing (convolution with an 8 m Gaussian) whereas none was needed for the sum and spline. These simpler methods were also more robust to noise and far less computationally expensive than Gaussian fitting. Therefore it was concluded that the sum and spline were the most accurate for extracting return energy from waveform lidar over vegetation, except for large footprint (15 m), where Gaussian fitting was slightly more accurate. These results suggest that small footprint (≪ 15 m) lidar systems that use Gaussian fitting or proprietary algorithms may report inaccurate energies, and thus reflectances, over vegetation. In addition the effect of system pulse length, sampling interval and noise on accuracy for different targets was assessed, which has implications for sensor design. [ABSTRACT FROM AUTHOR]

Accurately quantifying tree and forest structure is important for monitoring and understanding terrestrial ecosystem functioning in a changing climate. The emergence of laser scanning, such as Terrestrial Laser Scanning (TLS) and Unoccupied Aerial Vehicle Laser Scanning (UAV-LS), has advanced accurate and detailed forest structural measurements. TLS generally provides very accurate measurements on the plot-scale (a few ha), whereas UAV-LS provides comparable measurements on the landscape-scale (>10 ha). Despite the pivotal role dense tropical forests play in our climate, the strengths and limitations of TLS and UAV-LS to accurately measure structural metrics in these forests remain largely unexplored. Here, we propose to combine TLS and UAV-LS data from dense tropical forest plots to analyse how this fusion can further advance 3D structural mapping of structurally complex forests. We compared stand (vertical point distribution profiles) and tree level metrics from TLS, UAV-LS as well as their fused point cloud. The tree level metrics included the diameter at breast height (DBH), tree height (H), crown projection area (CPA), and crown volume (CV). Furthermore, we evaluated the impact of point density and number of returns for UAV-LS data acquisition. DBH measurements from TLS and UAV-LS were compared to census data. The TLS and UAV-LS based H, CPA and CV measurements were compared to those obtained from the fused point cloud. Our results for two tropical rainforest plots in Australia demonstrate that TLS can measure H, CPA and CV with an accuracy (RMSE) of 0.30 m (H average =27.32 m), 3.06 m2 (CPA average =66.74 m2), and 29.63 m3 (CV average =318.81 m3) respectively. UAV-LS measures H, CPA and CV with an accuracy (RMSE) of <0.40 m, <5.50 m2, and <30.33 m3 respectively. However, in dense tropical forests single flight UAV-LS is unable to sample the tree stems sufficiently for DBH measurement due to a limited penetration of the canopy. TLS can determine DBH with an accuracy (RMSE) of 5.04 cm, (DBH average =45.08 cm), whereas UAV-LS can not. We show that in dense tropical forests stand-alone TLS is able to measure macroscopic structural tree metrics on plot-scale. We also show that UAV-LS can be used to quickly measure H, CPA, and CV of canopy trees on the landscape-scale with comparable accuracy to TLS. Hence, the fusion of TLS and UAV-LS, which can be time consuming and expensive, is not required for these purposes. However, TLS and UAV-LS fusion opens up new avenues to improve stand-alone UAV-LS structural measurements at the landscape-scale by applying TLS as a local calibration tool. • TLS can measures the DBH, tree height and crown metrics in tropical rainforests. • UAV-LS has great potential for monitoring of tree height and crown metrics. • TLS and UAV-LS fusion opens up avenues for landscape-scale structure calibration. [ABSTRACT FROM AUTHOR]

An Ensemble Kalman Filter (EnKF) is used to assimilate canopy reflectance data into an ecosystem model. We demonstrate the use of an augmented state vector approach to enable a canopy reflectance model to be used as a non-linear observation operator. A key feature of data assimilation (DA) schemes, such as the EnKF, is that they incorporate information on uncertainty in both the model and the observations to provide a best estimate of the true state of a system. In addition, estimates of uncertainty in the model outputs (given the observed data) are calculated, which is crucial in assessing the utility of model predictions. Results are compared against eddy-covariance observations of CO2 fluxes collected over three years at a pine forest site. The assimilation of 500 m spatial resolution MODIS reflectance data significantly improves estimates of Gross Primary Production (GPP) and Net Ecosystem Productivity (NEP) from the model, with clear reduction in the resulting uncertainty of estimated fluxes. However, foliar biomass tends to be over-estimated compared with measurements. Issues regarding this over-estimate, as well as the various assumptions underlying the assimilation of reflectance data are discussed. [Copyright &y& Elsevier]

The concept of canopy spectral invariants expresses the observation that simple algebraic combinations of leaf and canopy spectral transmittance and reflectance become wavelength independent and determine a small set of canopy structure specific variables. This set includes the canopy interceptance, the recollision and the escape probabilities. These variables specify an accurate relationship between the spectral response of a vegetation canopy to the incident solar radiation at the leaf and the canopy scale and allow for a simple and accurate parameterization for the partitioning of the incoming radiation into canopy transmission, reflection and absorption at any wavelength in the solar spectrum. This paper presents a solid theoretical basis for spectral invariant relationships reported in literature with an emphasis on their accuracies in describing the shortwave radiative properties of the three-dimensional vegetation canopies. The analysis of data on leaf and canopy spectral transmittance and reflectance collected during the international field campaign in Flakaliden, Sweden, June 25–July 4, 2002 supports the proposed theory. The results presented here are essential to both modeling and remote sensing communities because they allow the separation of the structural and radiometric components of the measured/modeled signal. The canopy spectral invariants offer a simple and accurate parameterization for the shortwave radiation block in many global models of climate, hydrology, biogeochemistry, and ecology. In remote sensing applications, the information content of hyperspectral data can be fully exploited if the wavelength-independent variables can be retrieved, for they can be more directly related to structural characteristics of the three-dimensional vegetation canopy. [Copyright &y& Elsevier]

With the launch of NASA''s Terra satellite and the MODerate Resolution Imaging Spectroradiometer (MODIS), operational Bidirectional Reflectance Distribution Function (BRDF) and albedo products are now being made available to the scientific community. The MODIS BRDF/Albedo algorithm makes use of a semiempirical kernel-driven bidirectional reflectance model and multidate, multispectral data to provide global 1-km gridded and tiled products of the land surface every 16 days. These products include directional hemispherical albedo (black-sky albedo), bihemispherical albedo (white-sky albedo), Nadir BRDF-Adjusted surface Reflectances (NBAR), model parameters describing the BRDF, and extensive quality assurance information. The algorithm has been consistently producing albedo and NBAR for the public since July 2000. Initial evaluations indicate a stable BRDF/Albedo Product, where, for example, the spatial and temporal progression of phenological characteristics is easily detected in the NBAR and albedo results. These early beta and provisional products auger well for the routine production of stable MODIS-derived BRDF parameters, nadir reflectances, and albedos for use by the global observation and modeling communities. [Copyright &y& Elsevier]

Terrestrial laser scanning (TLS) was introduced for basic forest measurements, such as tree height and diameter, in the early 2000s. Recent advances in sensor and algorithm development have allowed us to assess in situ 3D forest structure explicitly and revolutionised the way we monitor and quantify ecosystem structure and function. Here, we provide an interdisciplinary focus to explore current developments in TLS to measure and monitor forest structure. We argue that TLS data will play a critical role in understanding fundamental ecological questions about tree size and shape, allometric scaling, metabolic function and plasticity of form. Furthermore, these new developments enable new applications such as radiative transfer modelling with realistic virtual forests, monitoring of urban forests and larger scale ecosystem monitoring through long-range scanning. Finally, we discuss upscaling of TLS data through data fusion with unmanned aerial vehicles, airborne and spaceborne data, as well as the essential role of TLS in validation of spaceborne missions that monitor ecosystem structure. • Terrestrial laser scanning (TLS) provides explicit in situ 3D forest structure. • We provide a review on current developments in TLS to monitor forest structure. • TLS data opens a realm of untapped ecological questions. [ABSTRACT FROM AUTHOR]