Three-dimensional Vegetation Structure Research Articles

Improving coniferous forests leaf area index estimation by filling the occluded point cloud from airborne laser scanning

Accurate estimation of Leaf Area Index (LAI) is pivotal for understanding vegetation dynamics and ecosystem productivity. Light Detection and Ranging (LiDAR), with its capability to directly capture the three-dimensional (3D) structure of vegetation, offers a promising method for LAI estimation. However, dense leaves, especially coniferous forest, can significantly occlude the penetration of laser beams during Airborne Laser Scanning (ALS) as they traverse the canopy from above. This occlusion results in incomplete point cloud, leading to underestimation of LAI due to the omission of some leaves. Considering the characteristic of leaf clumping growth in coniferous vegetation, a method based on voxelizing point cloud and modeling correlations among neighborhood voxels was developed in this study to fill the occluded point cloud at plot level. The proposed method was validated by applying it to Loblolly pine (Pinus taeda L.) plots. Validation against field-measured LAI demonstrates that the method proposed for filling the occluded point cloud markedly enhances LAI retrieval accuracy, achieving an R2 of 0.7069 and an RMSE of 0.4760. In contrast, LAI retrieval using the original canopy point cloud yielded an R2 of 0.2835 and an RMSE of 1.5883. This advancement highlights a shift from focusing on enhancing retrieval algorithms to addressing data completeness and reliability, offering new perspectives for LAI retrieval research.

Multi-resolution gridded maps of vegetation structure from GEDI

Large-extent maps of three-dimensional vegetation structure are important for understanding the hydrologic cycle, climate, carbon fluxes, and habitat. We aggregated over 7 billion lidar shots from the Global Ecosystem Dynamics Investigation (GEDI) to produce analysis-ready, gridded rasters of 36 vegetation structure metrics at three spatial resolutions (1, 6, and 12 km). We used 8 statistics to grid shots in every pixel, specifically the mean, bootstrapped standard error of the mean, median, standard deviation, interquartile range, Shannon’s Diversity Index, and shot count. We quantified uncertainty of the mean by randomly selecting 100 subsets of shots (i.e. bootstrapping) within each pixel. We also assessed the accuracy of several gridded metrics using fine spatial resolution airborne laser scanning data. The gridded metrics are generally more accurate at mid latitudes due to higher shot density and lower density of vegetation. Statistics associated with the central or maximum tendency of a metric are more accurate than statistics related to variability of metric values within the pixel.

Riparian vegetation plastic monitoring: A harmonized protocol for sampling macrolitter in vegetated riverine habitats

Many studies highlighted that rivers transported land-based plastics to the sea. However, most of the litter remains stuck in the fluvial ecosystem, also blocked by vegetation. To date, research on riverine macrolitter focused on floating and riverbank monitoring, thus methods to sample riverbank and floating litter have been developed. Concerning rivers, few recent studies highlighted the role of riparian vegetation in entrapping plastics. Given that vegetation represents a large part of riverine ecosystems and that the dynamics of plastics entrapped by vegetation are neglected, it appears pivotal to study in more detail how vegetation contributes to plastic retention. However, as current protocols and guidelines considered only floating and riverbank plastics without providing standardized and updated strategies to monitor litter in vegetation, here we aimed to develop a new standardized protocol and tools to assess plastics in vegetation. Specifically, we focused on unveiling the three-tridimensional structure of vegetation in relation to plastic occurrence, while considering seasonal and hydromorphological aspects. To investigate the trapping effect of vegetation, we developed a three-dimensional vegetation structure index (3DVI) related to plastics. The 3DVI index considers plant structure (i.e., number of branches) and diversity (i.e., species). To test the 3DVI, we conducted an in-situ case study in central Italy. We found that both primary and secondary riparian vegetation blocked plastic litter. In detail, 3DVI correlated with the number of plastics, highlighting that the densest and most diverse communities trap more plastics. Furthermore, we provided for the first time the assessment of seasonality for the macroplastic entrapment by riparian vegetation and a preliminary quantification of wind-blown plastics. Our results should be of interest to promote the development of standardized and harmonized monitoring strategies for riparian habitat management and conservation.

Feedback loops between 3D vegetation structure and ecological functions of animals.

Ecosystems function in a series of feedback loops that can change or maintain vegetation structure. Vegetation structure influences the ecological niche space available to animals, shaping many aspects of behaviour and reproduction. In turn, animals perform ecological functions that shape vegetation structure. However, most studies concerning three-dimensional vegetation structure and animal ecology consider only a single direction of this relationship. Here, we review these separate lines of research and integrate them into a unified concept that describes a feedback mechanism. We also show how remote sensing and animal tracking technologies are now available at the global scale to describe feedback loops and their consequences for ecosystem functioning. An improved understanding of how animals interact with vegetation structure in feedback loops is needed to conserve ecosystems that face major disruptions in response to climate and land-use change.

Combining acoustic tracking and LiDAR to study bat flight behaviour in three-dimensional space

BackgroundHabitat structure strongly influences niche differentiation, facilitates predator avoidance, and drives species-specific foraging strategies of bats. Vegetation structure is also a strong driver of echolocation call characteristics. The fine-scale assessment of how bats utilise such structures in their natural habitat is instrumental in understanding how habitat composition shapes flight- and acoustic behaviour. However, it is notoriously difficult to study their species-habitat relationship in situ.MethodsHere, we describe a methodology combining Light Detection and Ranging (LiDAR) to characterise three-dimensional vegetation structure and acoustic tracking to map bat behaviour. This makes it possible to study fine-scale use of habitat by bats, which is essential to understand spatial niche segregation in bats. Bats were acoustically tracked with microphone arrays and bat calls were classified to bat guild using automated identification. We did this in multiple LiDAR scanned vegetation plots in forest edge habitat. The datasets were spatially aligned to calculate the distance between bats’ positions and vegetation structures.ResultsOur results are a proof of concept of combining LiDAR with acoustic tracking. Although it entails challenges with combining mass-volumes of fine-scale bat movements and vegetation information, we show the feasibility and potential of combining those two methods through two case studies. The first one shows stereotyped flight patterns of pipistrelles around tree trunks, while the second one presents the distance that bats keep to the vegetation in the presence of artificial light.ConclusionBy combining bat guild specific spatial behaviour with precise information on vegetation structure, the bat guild specific response to habitat characteristics can be studied in great detail. This opens up the possibility to address yet unanswered questions on bat behaviour, such as niche segregation or response to abiotic factors in interaction with natural vegetation. This combination of techniques can also pave the way for other applications linking movement patterns of other vocalizing animals and 3D space reconstruction.

StrucNet: a global network for automated vegetation structure monitoring.

Climate change and increasing human activities are impacting ecosystems and their biodiversity. Quantitative measurements of essential biodiversity variables (EBV) and essential climate variables are used to monitor biodiversity and carbon dynamics and evaluate policy and management interventions. Ecosystem structure is at the core of EBVs and carbon stock estimation and can help to inform assessments of species and species diversity. Ecosystem structure is also used as an indirect indicator of habitat quality and expected species richness or species community composition. Spaceborne measurements can provide large-scale insight into monitoring the structural dynamics of ecosystems, but they generally lack consistent, robust, timely and detailed information regarding their full three-dimensional vegetation structure at local scales. Here we demonstrate the potential of high-frequency ground-based laser scanning to systematically monitor structural changes in vegetation. We present a proof-of-concept high-temporal ecosystem structure time series of 5 years in a temperate forest using terrestrial laser scanning (TLS). We also present data from automated high-temporal laser scanning that can allow upscaling of vegetation structure scanning, overcoming the limitations of a typically opportunistic TLS measurement approach. Automated monitoring will be a critical component to build a network of field monitoring sites that can provide the required calibration data for satellite missions to effectively monitor the structural dynamics of vegetation over large areas. Within this perspective, we reflect on how this network could be designed and discuss implementation pathways.

Vegetation ecological benefits index (VEBI): a 3D spatial model for evaluating the ecological benefits of vegetation

ABSTRACT Urban population explosion may increase ecological environment discomfort, thereby affecting negatively humans’ mental and physical performance. Therefore, it is important to detect and monitor vegetation and predict its ecological benefits. The complex composition of urban environment ground objects, such as steel roofs, plastic courts, and building shadows, significantly interferes with vegetation detection and monitoring. The optimized hyperspectral image-based vegetation index (OHSVI) constructed in this study effectively solves this problem. However, it is difficult to accurately predict the ecological benefits of vegetation based on the two-dimensional vegetation information extracted based on remote sensing images; this is related to the three-dimensional (3D) structure of vegetation and the 3D pattern of buildings. Therefore, we first proposed the vegetation ecological benefits index (VEBI) based on the 3D structure of vegetation to reveal how vegetation acts on its 3D surroundings. The method was tested in a playground, an academic building, and a parking space. The results showed that the vegetation extraction accuracy of the OHSVI exceeded 93%, which is better than that of the existing indices. Our findings suggest that VEBI may be efficient in predicting 3D vegetation ecological benefits combined with remote sensing and lidar datasets.

From remotely sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part I-Harnessing theory.

Solar-induced chlorophyll fluorescence (SIF) is a remotely sensed optical signal emitted during the light reactions of photosynthesis. The past two decades have witnessed an explosion in availability of SIF data at increasingly higher spatial and temporal resolutions, sparking applications in diverse research sectors (e.g., ecology, agriculture, hydrology, climate, and socioeconomics). These applications must deal with complexities caused by tremendous variations in scale and the impacts of interacting and superimposing plant physiology and three-dimensional vegetation structure on the emission and scattering of SIF. At present, these complexities have not been overcome. To advance future research, the two companion reviews aim to (1) develop an analytical framework for inferring terrestrial vegetation structures and function that are tied to SIF emission, (2) synthesize progress and identify challenges in SIF research via the lens of multi-sector applications, and (3) map out actionable solutions to tackle these challenges and offer our vision for research priorities over the next 5-10 years based on the proposed analytical framework. This paper is the first of the two companion reviews, and theory oriented. It introduces a theoretically rigorous yet practically applicable analytical framework. Guided by this framework, we offer theoretical perspectives on three overarching questions: (1) The forward (mechanism) question-How are the dynamics of SIF affected by terrestrial ecosystem structure and function? (2) The inference question: What aspects of terrestrial ecosystem structure, function, and service can be reliably inferred from remotely sensed SIF and how? (3) The innovation question: What innovations are needed to realize the full potential of SIF remote sensing for real-world applications under climate change? The analytical framework elucidates that process complexity must be appreciated in inferring ecosystem structure and function from the observed SIF; this framework can serve as a diagnosis and inference tool for versatile applications across diverse spatial and temporal scales.

Parameterization of Interaction between the Atmosphere and the Urban Surface: Current State and Prospects

Cities have a significant impact on the environment, forming such microclimatic features as an urban heat island, an increase in the intensity of convective weather events, etc. Numerical models of the atmosphere with an integrated block that describes the interaction between the urbanized surface and the atmosphere – urban parameterization – reproduce well the meteorological features of the urban environment. The review studies on urban parameterizations are mostly outdated, and the recent ones do not fully cover aspects of the methods used in the models to describe physical processes. The paper is devoted to updating information on urban parameterizations, comparing the approaches used in them to describe physical processes and forming proposals for their improvement. Based on the most common urban parameterizations of various levels of complexity, the main groups of physical processes describing “urban surface – atmosphere” interaction are identified. They are the surface energy balance, radiation heat transfer, surface moisture balance, turbulent heat and moisture exchange in the urban canopy, anthropogenic influence on heat and moisture fluxes, radiation and turbulent interaction with urban vegetation. The main approaches to parameterization of physical processes defined within each block are described. Modern trends in the development of urban parameterizations are highlighted: 1) over the past 10 years, parameterizations have become more complicated due to the addition of the building energy model, a three-dimensional structure of urban vegetation, and vertical resolution when calculating turbulent fluxes; 2) at the same time, little attention is paid to revising the original empirical formulas, often obtained on the basis of single field or laboratory e-xperiments. Ways to improve urban parameterizations are proposed by clarifying the basic dependencies used mainly in the calculation of turbulent fluxes, particularly, using the results of highly detailed Large-eddy simulation modeling, which, with growing computational power, is increasingly used to simulate explicit heat transfer between the atmosphere and individual elements of the urban environment.

Assessing the effects of large herbivores on the three-dimensional structure of temperate forests using terrestrial laser scanning

Three-dimensional (3D) vegetation structure is vital in maintaining the functions of forest ecosystems and species diversity. Temperate forests of Northeast Asia support a variety of fauna but is also affected by large herbivore disturbances or management practice (e.g., livestock grazing), resulting in varying degrees of habitat degradation and fragmentation in many areas. However, traditional methods lack the quantification of 3D vegetation structure at a fine scale, which makes it difficult to obtain complete and accurate detailed data on the multi-level structure below the canopy and limits our understanding of the impact of disturbance on habitat quality and species diversity. In this study, terrestrial laser scanning (TLS) data were used to quantify the 3D habitat structural characteristics of different stand types affected by forest management or large herbivore activities (deer-browsed forest, livestock-grazed forest, secondary forest, and mixed forest) in Northeast China. TLS-derived parameters in this study revealed the subtle changes of habitat structures below the canopy: 1) livestock-grazed forest had a much lower canopy height, tree number, and stem volume than that of the other stand types; 2) the vegetation area index (VAI) of low canopy height (0–5 m) in livestock-grazed forest and deer-browsed forest were significantly lower than those in secondary and mixed forest with less disturbance; 3) intensive large herbivores activities led to the larger understory gaps and visibility in livestock-grazed and deer-browsed forest. TLS-derived parameters in this study are implicated in wildlife habitat selection and fitness, which contribute to revealing fine scale habitat structure-species diversity relationships. In forest management, it is proposed to use TLS to quantify the structural and functional attributes of habitats from a new perspective, thus guiding conservation practices at local scale to balance the relationship between plants and animals.

Characterizing subcanopy structure of Mediterranean forests by terrestrial laser scanning data

Vegetation structure is one of the key factors in forest ecosystems. Especially understory structure has major implications for wildlife habitat selection, reproduction, and survival. Structural indices traditionally used to characterize understory vegetation are based on field vegetation surveys describing general features such as leaf area index (LAI), canopy cover or vegetation height, hiding much of the three-dimensional vegetation structure complexity. The application of terrestrial laser scanning (TLS) in forest ecological and management applications is becoming more effective. In this study, we use TLS data to quantify spatial attributes of forest subcanopy in four different forest strata ranging from 0.5 m to 10 m from the ground. We collected data in 12 plots of mature European beech (Fagus sylvatica L.) forests and 12 plots of mature black pine (Pinus nigra subsp. laricio Maire) forests, located in the Sila National Park, Italy. We propose a TLS-based approach to estimate a fine-scale vegetation density using the Plant Density Index (PDI) and to test the PDI at different height classes. We found a significant relationship between the PDI and the number of trees belonging to the dominant layer, using the Spearman correlation coefficient (r = 0.83, pval = 0.001). Basing on PDI values, a cluster analysis of the four subcanopy strata was carried out for deriving clusters of structurally homogeneous forest plots. Results identified three clusters in terms of the vegetation features in the horizontal height classes: the first cluster primarily includes Beech forests characterized by plots with the highest tree densities; the second one includes both Beech and Pine forests characterized by dense ground vegetation and shrubs and an intermediate tree density; the third group is represented by Pine forests with massive presence of vegetation lower strata and moderate tree density. Then, PCA allowed identifying the relationship between the considered subcanopy layers and forest plots.

Mountain ecology under climate change

Trophic Ecology Climate warming causes shifts in the distributions of organisms and different organisms may move at different rates, resulting in changes in the composition and functioning of ecological communities. These effects are rarely considered in forecasts about the effects of climate change on biodiversity. Using experimental translocations, Descombes et al. investigated how differential upslope migration in alpine plants and their insect herbivores affects community interactions. Lowland herbivores modify the three-dimensional vegetation structure at higher altitudes, and this modified vegetation structure favors the coexistence of plant species, especially by favoring small-stature species. Reorganized trophic interactions will play an important role in driving plant community changes under future climate change. Science , this issue p. [1469][1] [1]: /lookup/doi/10.1126/science.abd7015

Novel trophic interactions under climate change promote alpine plant coexistence.

Herbivory and plant defenses exhibit a coupled decline along elevation gradients. However, the current ecological equilibrium could be disrupted under climate change, with a faster upward range shift of animals than plants. Here, we experimentally simulated this upward herbivore range shift by translocating low-elevation herbivore insects to alpine grasslands. We report that the introduction of novel herbivores and increased herbivory disrupted the vertical functional organization of the plant canopy. By feeding preferentially on alpine plants with functional traits matching their low-elevation host plants, herbivores reduced the biomass of dominant alpine plant species and favored encroachment of herbivore-resistant small-stature plant species, inflating species richness. Supplementing a direct effect of temperature, novel biotic interactions represent a neglected but major driver of ecosystem modifications under climate change.

Trappings of Success: Predator Removal for Duck Nest Survival in Alberta Parklands

Nest survival is most limited by nest predation, which often is increased by anthropogenic causes including habitat fragmentation, mesopredator release and predator subsidies. In mallards and other upland-nesting duck species in the North American prairies, the rate of nest survival is the vital rate most influential to population dynamics, with 15%–20% survival required for maintenance of stable populations. Predator removal during the nesting season has increased duck nest survival on township-sized (9324 ha) areas of agricultural ecosystems in eastern locations of the prairie pothole region (PPR). However, predator removal has not been evaluated in western parkland habitats of the PPR where three-dimensional structure of vegetation is considerably greater. During 2015–2017, we evaluated nest survival on control and predator-removal plots at two study areas in the parklands of central Alberta, Canada. In the second year of the study, we transposed predator removal to control for habitat effects. Estimates of 34-day nest survival did not significantly differ between trapped ( x ¯ = 20.9%, 95% CI = 13.2%–33.7%) and control ( x ¯ = 17.8%, 95% CI = 10.5%–30.0%) plots in any year. We do not recommend predator removal be continued in Alberta parklands due to its ineffectiveness at improving duck nest survival at the local scale.

Detection of coarse woody debris using airborne light detection and ranging (LiDAR)

Coarse woody debris (CWD) is an essential component of forest ecosystems that provides habitat for diverse species, functions in water and nutrient cycling, and can be a potential surface fuel in wildfires. CWD detection and mapping would enhance forestry and wildlife research and management but passive remote sensing technologies cannot provide information on features beneath forest canopy, while field-based CWD inventories are not practical for mapping CWD over large areas. Airborne light detecting and ranging (LiDAR) is a remote sensing technology that provides detailed information on three-dimensional vegetation structure that could overcome limitations of passive remote sensing technologies and field-based inventories. Our objectives were to evaluate whether airborne LiDAR could be used to detect individual pieces of CWD. We measured 1679 pieces of CWD at 144 field plots from 2015 to 2016. We acquired high-density (∼24 first returns/m2) LiDAR data in 2014, filtered out canopy and sub-canopy returns using a height threshold based on field measurements of CWD, and used height-filtered data to determine which field-measured pieces of CWD were visible in the resulting point cloud. CWD pieces that were detected constituted 50% of plot CWD volume, and there was a strong, positive correlation between total plot CWD volume and volume of detected pieces (r = 0.96). Overall, we detected 23% of the individual pieces of CWD we measured. Large pieces of CWD were most likely to be detected, with the majority of pieces ≥30 cm diameter or ≥13.9 m long detected. Canopy density, shrub density, and forest type did not influence detection probability. CWD detection rates increased from 1 pulses/m2 to 16 pulses/m2, and CWD detection rate was constant from 16 pulses/m2 to 24 pulses/m2. Our results demonstrate that airborne LiDAR can be used to detect CWD. LiDAR-based detection and mapping of CWD will be most useful for applications that focus on larger and longer pieces of CWD or applications focused on total CWD volume.

Close-range laser scanning in forests: towards physically based semantics across scales.

Laser scanning with its unique measurement concept holds the potential to revolutionize the way we assess and quantify three-dimensional vegetation structure. Modern laser systems used at close range, be it on terrestrial, mobile or unmanned aerial platforms, provide dense and accurate three-dimensional data whose information just waits to be harvested. However, the transformation of such data to information is not as straightforward as for airborne and space-borne approaches, where typically empirical models are built using ground truth of target variables. Simpler variables, such as diameter at breast height, can be readily derived and validated. More complex variables, e.g. leaf area index, need a thorough understanding and consideration of the physical particularities of the measurement process and semantic labelling of the point cloud. Quantified structural models provide a framework for such labelling by deriving stem and branch architecture, a basis for many of the more complex structural variables. The physical information of the laser scanning process is still underused and we show how it could play a vital role in conjunction with three-dimensional radiative transfer models to shape the information retrieval methods of the future. Using such a combined forward and physically based approach will make methods robust and transferable. In addition, it avoids replacing observer bias from field inventories with instrument bias from different laser instruments. Still, an intensive dialogue with the users of the derived information is mandatory to potentially re-design structural concepts and variables so that they profit most of the rich data that close-range laser scanning provides.

Terrestrial laser scanning reveals below-canopy bat trait relationships with forest structure

Three-dimensional structure of vegetation plays a key role in animal ecology and the arrival of LiDAR technologies has given ecologists the ability to understand species-habitat relationships in increasing detail. However, few studies have investigated the trait-environment relationships that underpin diverse animal relationships with vegetation structure. We used terrestrial laser scanning (TLS) and acoustic bat surveys to investigate relationships between forest structure and bat communities across a vegetation structural gradient at community, species and trait levels. We developed 20 measures of site scale vegetation structure and also quantified landscape scale vegetation cover and water availability. We predicted that overall bat activity would increase in open and decrease in cluttered vegetation, but this would vary with species, underpinned by ecomorphological traits. Overall bat activity was negatively associated with stem density, with total activity halving (from 380 to 190callsnight−1) as stem densities increased from 60 to 1350stemsha−1, while foraging activity declined from 8 to <1 feeding buzzes night−1 over the same range. Bat activity varied among species and structures and foraging strategy explained more of this variability than call, body size or wing traits. As predicted, above-canopy and edge-space foraging bats were negatively associated with local-scale clutter, while closed-space species were positively associated with cluttered stands. Stem density was the strongest predictor of bat-environment relationships, although there was evidence for differences in bat habitat use across different structural elements. Our study is the first to link detailed LiDAR-derived 3D forest structural metrics to multiple animal traits.

Regional mapping of vegetation structure for biodiversity monitoring using airborne lidar data

Vegetation structure is identified as an important biodiversity indicator providing the physical environment that generates, supports, and maintains forest biodiversity. Airborne lidar systems (light detection and ranging) have the capacity to accurately measure three-dimensional vegetation structure, and have been widely used in wildlife habitat mapping and species distribution modeling. Large–area structural inventories using lidar-derived variables that characterize generic habitat structure have rarely been done, yet would be helpful for guiding biodiversity monitoring and conservation assessments of species at regional levels. This study provides a novel approach for processing regional-scale lidar data into categorical classes representing natural groupings of habitat structure. We applied cluster analysis on six lidar-derived habitat-related variables to classify vegetation structure into eight classes for the forested areas of ten natural subregions in boreal and foothill forests in Alberta, Canada. Structure classes were compared across different natural subregions and under anthropogenic/non-anthropogenic disturbance regimes. We found that the Lower Foothills Natural Subregions had the most complex vegetation structure, and wildfire was the most prevalent disturbance agent for all classes except for the rarest class (i.e. stands with high standard deviations of height and low canopy cover) which was more heavily altered by timber harvesting. This data product provides continuous, regional mapping of vegetation structure directly measured from lidar, with a spatial resolution (30m) relatively finer than what was provided by polygon-based forest inventories. This vegetation structure classification and its associated spatial distribution address the fundamental issue of habitat structure in biodiversity monitoring. It can serve as a base layer used together with species and land cover data for forest resources planning, species distribution and animal movement modeling, as well as prioritization of conservation efforts on critical habitat structures.

Detecting 3D Vegetation Structure with the Galileo Space Probe: Can a Distant Probe Detect Vegetation Structure on Earth?

Sagan et al. (1993) used the Galileo space probe data and first principles to find evidence of life on Earth. Here we ask whether Sagan et al. (1993) could also have detected whether life on Earth had three-dimensional structure, based on the Galileo space probe data. We reanalyse the data from this probe to see if structured vegetation could have been detected in regions with abundant photosynthetic pigments through the anisotropy of reflected shortwave radiation. We compare changing brightness of the Amazon forest (a region where Sagan et al. (1993) noted a red edge in the reflectance spectrum, indicative of photosynthesis) as the planet rotates to a common model of reflectance anisotropy and found measured increase of surface reflectance of 0.019 ± 0.003 versus a 0.007 predicted from only anisotropic effects. We hypothesize the difference was due to minor cloud contamination. However, the Galileo dataset had only a small change in phase angle (sun-satellite position) which reduced the observed anisotropy signal and we demonstrate that theoretically if the probe had a variable phase angle between 0–20°, there would have been a much larger predicted change in surface reflectance of 0.1 and under such a scenario three-dimensional vegetation structure on Earth could possibly have been detected. These results suggest that anisotropic effects may be useful to help determine whether exoplanets have three-dimensional vegetation structure in the future, but that further comparisons between empirical and theoretical results are first necessary.

Forest Inventory Attribute Prediction Using Lightweight Aerial Scanner Data in a Selected Type of Multilayered Deciduous Forest

Airborne laser scanning is a promising technique for efficient and accurate, remote-based forest inventory, due to its capacity for direct measurement of the three-dimensional structure of vegetation. The main objective of this study was to test the usability and accuracy of an individual tree detection approach, using reFLex software, in the evaluation of forest variables. The accuracy assessment was conducted in a selected type of multilayered deciduous forest in southern Italy. Airborne laser scanning data were taken with a YellowScan Mapper scanner at an average height of 150 m. Point density reached 30 echoes per m2, but most points belonged to the first echo. The ground reference data contained the measured positions and dimensions of 445 trees. Individual tree-detection rates were 66% for dominant, 48% for codominant, 18% for intermediate, and 5% for suppressed trees. Relative root mean square error for tree height, diameter, and volume reached 8.2%, 21.8%, and 45.7%, respectively. All remote-based tree variables were strongly correlated with the ground data (R2 = 0.71–0.79). At the stand-level, the results show that differences ranged between 4% and 17% for stand height and 22% and 40% for stand diameter. The total growing stock differed by −43% from the ground reference data, and the ratios were 64% for dominant, 58% for codominant, 36% for intermediate, and 16% for suppressed trees.