The Laegeren Site: An Augmented Forest Laboratory
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Given the increased pressure on forests and their diversity in the context of global change, new ways of monitoring diversity are needed. Remote sensing has the potential to inform essential biodiversity variables on the global scale, but validation of data and products, particularly in remote areas, is difficult. We show how radiative transfer (RT) models, parameterized with a detailed 3-D forest reconstruction based on laser scanning, can be used to upscale leaf-level information to canopy scale. The simulation approach is compared with actual remote sensing data, showing very good agreement in both the spectral and spatial domains. In addition, we compute a set of physiological and morphological traits from airborne imaging spectroscopy and laser scanning data and show how these traits can be used to estimate the functional richness of a forest at regional scale. The presented RT modeling framework has the potential to prototype and validate future spaceborne observation concepts aimed at informing variables of biodiversity, while the trait-based mapping of diversity could augment in situ networks of diversity, providing effective spatiotemporal gap filling for a comprehensive assessment of changes to diversity.
KeywordsImaging spectroscopy Laser scanning 3-D reconstruction Radiative transfer model Diversity Upscaling Functional traits
Global change is altering biodiversity in an unprecedented manner (Parmesan and Yohe 2003), and its impact on humankind may be large (Chapin III et al. 2000; Isbell et al. 2017). Forests are of special relevance because they hold most of the terrestrial biomass (Bar-On et al. 2018), are a hot spot of biodiversity (Wilson et al. 2012), and are subject to climate- and human-induced changes (Gardner 2010; Hansen et al. 2013). To monitor and potentially mitigate changes in biodiversity, Pereira et al. (2013) defined a set of essential biodiversity variables (EBVs), which should be comprehensive, concise, and standardized. Originally, most of these EBVs were to be measured in situ within ecosystems, but because forest plots are particularly scarce in the regions where change is happening the fastest (Chave et al. 2014), remote sensing (RS) has been acknowledged as a vital component to contribute to the aims of EBVs in the form of RS-enabled EBVs (RS-EBVs; Pettorelli et al. 2016; O’Connor et al. 2015). More specifically, RS technologies such as imaging spectroscopy and laser scanning have been attributed with the potential to play an important role in providing the necessary information for RS-EBVs, be it at regional, national, or global scale (Skidmore et al. 2015; Jetz et al. 2016).
Still in its early stage is the design and use of the EBV framework to include and combine RS-EBVs with in-situ measurements. In-situ measurements are often based on point measurements of individual species, whereas RS-EBVs are area-based, with spatial characteristics depending on sensor resolution and coverage, similar to the concept of grain and extent in ecology (Turner 1989).
For large-scale assessments (i.e., regional, continental, global), cost and effort of fieldwork is a limiting factor with respect to in-situ observations. Data from the newest generation of optical satellites (e.g., Landsat 8 and Sentinel-2) have high potential for a global biodiversity assessment due to their high spatial resolution (10–30 m), multispectral information, and temporal coverage, with repeat passes within 5–6 days, depending on the area of interest. Nevertheless, due to their recent launch, these sensors do not provide a long time series, and the complementarity of lower resolution satellite data or airborne or terrestrial RS data in combination with in-situ observation is beneficial to map changes at decadal or longer timescales.
All optical RS approaches use reflected light of the vegetation canopy to infer information about its state (Schaepman et al. 2009; Homolov́a et al. 2013). Leaf-level biochemistry (e.g., traits such as chlorophyll and water content) has strong links with leaf reflectance and transmittance (Jacquemoud and Baret 1990). However, when light interacts with the canopy, a multitude of scattering and absorption processes have to be considered (North 1996), taking place at different levels (e.g., leaf, tree, canopy; Niinemets et al. 1998) of the canopy. Thus, passive optical observational approaches of forested ecosystems are susceptible to the effects of forest structure because directional effects associated with illumination and observation geometry may interact with signals related to leaf-level biochemistry (Hilker et al. 2008; Knyazikhin et al. 2013). Consequently, the reflectance signal at the canopy level is influenced by both vegetation structure and leaf-level physiology, and disentangling those based on passive optical data alone remains a difficult problem (Kotz et al. 2004). The effect of vegetation structure on RS indices and products (e.g., RS-EBVs) is difficult to assess, and its impact on current observations and predictions may be large. The validation of advanced wall-to-wall RS products becomes increasingly difficult because of spatiotemporal mismatches of in-situ observations with RS data. Hence, we need a framework to be able to upscale and validate leaf-level physiological traits to the level of RS data to test potential observables for RS-informed EBVs.
Radiative transfer (RT) modeling has been used for several decades to simulate and understand the signals in passive optical data (Myneni et al. 1995, 1997; Meroni et al. 2004; Lewis and Disney 2007; Gastellu-Etchegorry et al. 1996). In addition, RT models (RTMs) have been used with existing medium- to low-resolution spaceborne missions for the retrieval of products such as leaf area index (LAI) or fraction of absorbed photosynthetic radiation (fAPAR) through inversion (Myneni et al. 1997; Running et al. 2004). One particular issue with RTMs of vegetation is their parameterization. While modeling approaches simulating low-resolution data [such as Moderate Resolution Imaging Spectroradiometer (MODIS) or MEdium Resolution Imaging Spectrometer (MERIS)] mainly used one-dimensional parameterizations of the vegetation (Jacquemoud 1993; Huemmrich 2001; Verhoef and Bach 2007), higher-resolution sensors will need 3-D parameterization to account for effects like shadowing and multiple scattering (Asner and Warner 2003; Disney et al. 2006; Widlowski et al. 2015). The first RTMs incorporating 3-D forest structure were called geometric-optical radiative transfer (GORT)-type models (Ni et al. 1999). While they were better at modeling directional effects than 1-D models, they still lacked multiple scattering and did not have full energy balance closure of incoming and outgoing radiation across all spectral domains. More advanced models use Monte Carlo ray tracing (MCRT) to add multiple scattering and provide a sound physical representation of the photon’s interaction with vegetation canopies (Disney et al. 2006). While the inclusion of more physical processes (e.g., multiple scattering) certainly improves MCRT-type models over simpler approaches, their parameterization and benchmarking remains an issue. A large effort in testing RT models was undertaken in the course of the radiation transfer modeling intercomparison (RAMI) exercise, where different models were tested using a set of artificial scenes of different complexity, including 3-D scenes, to see if the models produced comparable results (Widlowski et al. 2008, 2015). However, this benchmarking remained relative (i.e., representing actual forest patches that could be validated with real-world Earth observation (EO) data acquired over the same area was not an aim of the RAMI exercise). One reason, among others, for this was the lack of suitable technologies and methods to capture and represent the 3-D vegetation structure at small scales (e.g., branches, leaves, and/or shoots).
Today, laser scanning is an established tool for retrieving quantitative measures of canopy structure (Nelson 1997; Lefsky et al. 1999; Næsset 2002; Morsdorf et al. 2004; Popescu et al. 2002; Morsdorf et al. 2006, 2010; Nelson 2013; Wulder et al. 2012). Airborne (ALS)-, terrestrial (TLS)-, and unmanned aerial vehicle (UAV)-based laser scanning (Morsdorf et al. 2017) provide a direct means to assess vegetation structure by combining the known position and orientation of the sensor with the time of flight of a laser pulse to produce a point cloud of exact 3-D coordinates. Measurements can be made across scales (e.g., stand, tree, branch, and leaf level) with finer scales often captured by close-range laser scanning (Morsdorf et al. 2018). The amount of structural detail contained in the point cloud can be overwhelming, and the extraction of meaningful information remains a challenge (Wulder et al. 2013; Morsdorf et al. 2018). Due to large data sets, automated methods for the extraction of either semantic information, such as single-tree detection based on ALS (Hyyppa et al. 2001; Morsdorf et al. 2004; Kaartinen et al. 2012; Wang et al. 2016) or tree geometry reconstruction from TLS (Cote et al. 2009; Raumonen et al. 2013) or the derivation of biophysical variables such as LAI (Morsdorf et al. 2006), are preferable over manual and/or empirical approaches.
Here we describe how we (i) designed and implemented an observational scheme to gather in-situ and structural data across several scales to simulate the 3-D radiative regime of the forest, (ii) tested the simulation by comparing simulated and actual RS data in their spectral and spatial information dimension, and (iii) use the approach to demonstrate how remotely sensed functional traits can be used to compute regional-scale functional richness, showcasing the information content of RS-EBVs.
4.2 The Laegeren Site: Description and History
4.3.1 In-Situ Data
Ground data with varying spatial, spectral, and temporal resolution allow for the 3-D reconstruction of the Laegeren, its attribution with leaf optical properties (LOPs), and generation of a reference database for parameterization and validation purposes. We used multitemporal TLS on a 60 m × 60 m plot (Sect. 188.8.131.52) and an extensive forest inventory for an area of 300 m × 300 m, which is extended to 300 m × 900 m for the simulation of EO data. In the inventory data, the type and accurate position of the trees, as well as their crown dimension and offset due to leaning stems, social position, and vertical stratification of the crown, were recorded (Sect. 184.108.40.206). In addition, the occurrence and characterization of the understory was mapped in the field and interpolated to a 2 m × 2 m grid using an ALS-based classification (Leiterer et al. 2013).
220.127.116.11 Measurements of Leaf Optical Properties
To obtain the optical properties of tree foliage, we used an integrating sphere coupled with an Analytical Spectral Devices (ASD) FieldSpec-3. Measurements of hemispherical and directional reflectance and transmittance, both from the abaxial and adaxial side of the leaves, were taken. To take into account the vertical variability of LOPs, we sampled in three different crown parts (top, middle, bottom), representing different lighting conditions in the canopy (e.g., sunlit, transitional, shaded). Deciduous leaves were collected from ten individual trees of five species (Acer pseudoplatanus spp., Fagus excelsior, F. sylvatica, Ulmus glabra, and Tilia platyphyllos). Measurements of aerosol optical depth (AOD) and precipitable amount of water (PAW) were provided by the aerosol robotic network (AERONET) as level 2.0 quality-assured data. For details of the sampling and measurement scheme, see Schneider et al. (2014).
18.104.22.168 Forest Inventory
4.3.2 RS Data
22.214.171.124 Airborne Laser Scanning
To provide 3-D structure information across the whole study area, we relied on two airborne laser scanning campaigns, using a RIEGL LMS-Q680i scanner under leaf-on conditions and a RIEGL LMS-Q560 scanner under leaf-off conditions. Flight strips have an overlap of approximately 50%. Full-waveform features, namely, echo width and intensity, were extracted from the data using the software RiANALYZE and were assigned to the individual returns in the multiple-echo point cloud. The point cloud was filtered to classify ground and vegetation points, and the ground points were subsequently interpolated to a raster of 1 m resolution. For a detailed description of the digital terrain model (DTM) generation, see Leiterer et al. (2013). DTM accuracy was assessed using more than 500 TLS-measured road surface and bare soil points (see Sect. 126.96.36.199), which were related to the national land survey and resulted in a mean height uncertainty of about ±0.25 m. For each point of the full point cloud, the height above ground was calculated by subtracting the interpolated DTM value from the corresponding echo height above sea level, providing the vertical distance of the vegetation echoes to the terrain underneath.
188.8.131.52 Terrestrial Laser Scanning
4.3.3 Multispectral and Imaging Spectroscopy Data
4.4.1 In-Situ Data Processing
184.108.40.206 Optical Properties
LOPs were calculated separately for deciduous and coniferous trees. A linear spectral forward mixing was applied to calculate the reflectance and transmittance spectra of sunlit, transitional, and shaded leaves and needles. Because the spectra were found to match well with those in literature, the data were used directly instead of a forward simulation of a LOP model (Feret et al. 2008). This was done to reduce the number of parameters and associated uncertainties. The broadleaf species composition used for spectral mixing was derived from the forest inventory information and is dominated by beech (about 50%), with lesser contributions from maple, elm, linden, and ash.
One particular issue of the Laegeren site is its large variation in the spectral background. Because we had multitemporal full-waveform lidar data available for the Laegeren site, we used this information to classify the ground into distinct classes (gravel, litter, soil) and assigned matching spectra from our field measurements to these classes (Leiterer et al. 2013). As Schneider et al. (2014) showed, using several understory classes instead of a homogenous (black) background makes simulated top-of-canopy (TOC) and top-of-atmosphere (TOA) reflectance values more realistic.
220.127.116.11 3-D Reconstruction
Two different approaches for 3-D reconstruction of the vegetation structure were implemented and tested. The first approach relied on a single-tree identification and the second one on a direct computation of plant area index (PAI) values inside a voxel cell. Voxels are basically 3-D pixels, dividing the 3-D space into equal-sized cubes. The single-tree detection (individual tree crown, ITC) method used was based on Morsdorf et al. (2004), which derives tree location, height, and crown diameter to reconstruct the forest in 3-D based on simple geometric primitives like rotational paraboloids. However, as with most local maxima detection-based ITC methods, its performance within the mixed forest stands of the Laegeren site was suboptimal, with tree detection rates of only 50–70%. This is much lower than what can be expected for conifer forests, where rates of up to 90% can be achieved (Kaartinen et al. 2012; Wang et al. 2016). Conifers generally have conical crowns with one distinct peak (treetop), greatly facilitating their detection as local maxima in a digital surface model (DSM). The main difference between the voxel-grid and ITC approaches is the added level of semantics (Morsdorf et al. 2018) in the single-tree case, which might be relevant for some species- and individual-focused experiments (i.e., when trying to link EO-based traits with genetic information of the individual tree). If the aim of the 3-D reconstruction is an accurate simulation of the radiative regime, single-tree identification adds a layer of unnecessary complexity, so the voxel-grid approach led to better results (Schneider et al. 2014) and was subsequently used for upscaling of the trait information (Schneider et al. 2017).
18.104.22.168 Linking Field and RS Data
4.4.2 Radiative Transfer Modeling
The RTM used to upscale and validate leaf-level traits such as chlorophyll and leaf water content is Discrete Anisotropic Radiative Transfer (DART; Gastellu-Etchegorry et al. 2015). Generally, a DART scene is built out of voxels with a predefined size. To simulate vegetation such as grass or tree crowns, voxels can be filled by turbid media parameterized with PAI and leaf optical properties (LOPs). Further details of the DART model and examples of DART simulations can be found in Gastellu-Etchegorry et al. (2015). We use flux tracking in reflectance mode with the sun and the atmosphere as the only radiation sources and used DART version 5.6.0 (v739). Optical properties described in Sect. 22.214.171.124 and the forest reconstruction described in Sect. 126.96.36.199 are used to parameterize the forest canopy, background, and terrain in DART. For details of model parameterization, see Schneider et al. (2014); for details on the model-based upscaling of leaf-level traits, see Schneider et al. (2017). For the modeling results shown in Sect. 188.8.131.52, we used sun and observation angles as in the actual APEX and RapidEye acquisitions, respectively. We evaluate the performance of the combined 3-D reconstruction and RT simulation approach in two ways: spectrally, by comparing averaged simulated spectra on the core (i.e., covered by TLS measurements) site with those obtained by the APEX instrument, and, spatially, by comparing simulated bands of RapidEye over an area of 900 m × 300 m with DART-simulated reflectance at those particular wavelength regions.
4.4.3 Validation of Trait Predictions Using the RTM Approach
Three functional traits were derived from ALS data, canopy height (CH), PAI, and foliage height diversity (FHD), forming a set of morphological traits. These three were chosen because they are ecologically relevant and can be easily derived from airborne laser scanning data. Three additional functional traits—chlorophylls (CHL), carotenoids (CAR), and equivalent water thickness (EWT)—were chosen and computed using specific band ratios from the IS data (Schneider et al. 2017), forming a set of physiological traits. Both CH and CHL have been identified as primary observables for RS-EBVs, so their validation and scaling is particularly relevant. The traits were computed at a spatial aggregation unit of 6 m; for the ALS data, all echo values within a 6 m × 6 m grid cell were used for the computation, while for the IS data only sunlit pixels within the grid cell were retained for subsequent index computation. The shadow mask used for extracting sunlit pixels was derived from a DSM based on the ALS data and the solar illumination angle at the time of the IS overflight. For more details on the selected traits and their computation, please refer to Schneider et al. (2017).
4.4.4 Computation of Functional Richness
To showcase how the RTM-validated EO traits can be used for spatially explicit diversity assessments, we compute the functional richness within the 3-D trait space using a spatial subset of pixels (e.g., in a 60 m × 60 m box containing 100 pixels). In the case of the morphological traits, the 3-D trait space is spanned by the axes CH, PAI, and FHD, whereas for the physiological traits the trait space is spanned by the axes CHL, CAR, and EWT. The richness within the trait space is based on volume of a 3-D convex hull of all pixels’ trait values (i.e., the larger the variation of the respective traits, the larger the volume of the convex hull). As an example, if all trait values were the same, the richness would be zero because no volume would be spanned in the 3-D trait space. Computing the richness using a pixel-based approach has the advantage of resolving both inter- and intraspecific variation of the traits, with the latter being potentially as large as the former (e.g., as observed in our leaf spectra). For details on the definition and computation of richness and other diversity-related metrics in the scope of this work, please refer to Schneider et al. (2017).
4.5 Results and Discussion
4.5.1 Forward Simulation of Passive Optical Imagery and Comparison With EO Data
184.108.40.206 Spectral Validation
220.127.116.11 Spatial Validation
4.5.2 Functional Diversity of Laegeren Site
4.6 Conclusion and Outlook
Modern RS technologies increasingly face a validation paradox—i.e., it is very difficult to provide ground-based validation data that match the spatial (resolution and extent), temporal, and thematic characteristics of modern EO data sets. As an example, ALS-derived tree height is assumed to be more accurate than field measurements, but it cannot be proved using field data alone. By using laser scanning-derived 3-D structure together with LOPs in an RT model approach, we have shown a way to overcome such mismatches and provide a framework that could be established across a range of sites around the globe to prototype and validate EO-based data and products in the future. Such a forward validation will as well pave the way for products that are not measurable in the field, but still might be relevant in the context of ecosystem function and diversity. The RTM approach provides a physical and mechanistic way to learn about the information content of EO data, and a combination of this approach with recent developments in the machine learning domain could provide interesting perspectives.
With the trait-based functional richness assessment, we demonstrated how a spatially extended monitoring using the complementary technologies imaging spectroscopy and lidar would work and what kind of insights into ecosystem functioning it could generate. In addition, the trait maps and the derived functional richness could be used for spatiotemporal gap filling of in-situ observational networks such as the global forest biodiversity initiative, complementing the diversity information that these provide.
In the future, these data streams in conjunction with the EBV concept (Fernández et al., Chap. 18) will give policy-makers around the world useful tools to assess and report on the biodiversity. To speed up this process, the European Space Agency funded the GlobDiversity project starting in 2017 in the tradition of similar projects for some of the essential climate variables. The project’s goal is to demonstrate the capability and utility of producing a set of selected RS-EBV data sets in different regions and biomes around the globe and with high spatial resolution (10–30 m) using the newest-generation satellite data, such as Sentinel-2 and Landsat 8. In addition, the project shall suggest in a reference document how to describe RS-EBVs and how they could be engineered and validated. We believe that the 3-D reconstruction and RT modeling approach highlighted in this chapter could be applied across a global range of sites to fulfill this task.
The contributions of F.M., F.D.S., and M.E.S. are supported by the University of Zurich Research Priority Program on Global Change and Biodiversity, and the contribution of D.K. was with support from the European Union’s 7th Framework Program (FP7/ 2014–2018) under EUFAR2 contract no. 312609.
- Asner GP, Warner AS (2003) Canopy shadow in ikonos satellite observations of tropical forests and savannas of. Environment 87(4):521–533Google Scholar
- Chave J, Réjou-Méchain M, Burquez A, Chidumayo E, Colgan MS, Delitti WB, Duque A, Eid T, Fearnside PM, Goodman RC, Henry M, Martínez‐Yrízar A, Mugasha WA, Muller-Landau HC, Mencuccini M, Nelson BW, Ngomanda A, Nogueira EM, Ortiz-Malavassi E, Pelissier R, Ploton P, Ryan CM, Saldarriaga JG, Vieilledent G (2014) Improved allometric models to estimate the aboveground biomass of tropical trees. Glob Chang Biol 20(10):3177–3190PubMedCrossRefGoogle Scholar
- Gastellu-Etchegorry JP, Yin T, Lauret N, Cajgfinger T, Gregoire T, Grau E, Feret JB, Lopes M, Guilleux J, Dedieu G, Malenovský Z, Cook BD, Morton D, Rubio J, Durrieu S, Cazanave G, Martin E, Ristorcelli T (2015) Discrete anisotropic radiative transfer (DART 5) for modeling airborne and satellite spectroradiometer and lidar acquisitions of natural and urban landscapes. Remote Sens 7(2):1667–1701CrossRefGoogle Scholar
- Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A, Thau D, Stehman SV, Goetz SJ, Loveland TR, Kommareddy A, Egorov A, Chini L, Justice CO, Townshend JRG (2013) High-resolution global maps of 21st-century forest cover change. Science 342(6160):850–853PubMedCrossRefPubMedCentralGoogle Scholar
- Hueni A, Biesemans J, Meuleman K, Dell’Endice F, Schlapfer D, Odermatt D, Kneubuehler M, Adriaensen S, Kempenaers S, Nieke J, Itten K (2009) Structure, components, and interfaces of the airborne prism experiment (apex) processing and archiving facility. IEEE Trans Geosci Remote Sens 47(1):29–43CrossRefGoogle Scholar
- Kaartinen H, Hyyppa J, Yu X, Vastaranta M, Hyyppa H, Kukko A, Holopainen M, Heipke C, Hirschmugl M, Morsdorf F, Næsset E, Pitkanen J, Popescu S, Solberg S, Wolf BM, Wu JC (2012) An international comparison of individual tree detection and extraction using airborne laser scanning. Remote Sens 4:950–974CrossRefGoogle Scholar
- Knyazikhin Y, Lewis P, Disney MI, Mottus M, Rautiainen M, Stenberg P, Kaufmann RK, Marshak A, Schull MA, Latorre Carmona P, Vanderbilt V, Davis AB, Baret F, Jacquemoud S, Lyapustin A, Yang Y, Myneni RB (2013) Reply to Ollinger et al.: remote sensing of leaf nitrogen and emergent ecosystem properties. Proc Natl Acad Sci 110(27):E2438PubMedCrossRefGoogle Scholar
- Morsdorf F, Marell A, Koetz B, Cassagne N, Pimont F, Rigolot E, Allgower B (2010) Discrimination of vegetation strata in a multi-layered mediterranean forest ecosystem using height and intensity information derived from airborne laser scanning. Remote Sens Environ 114(7):1403–1415CrossRefGoogle Scholar
- Pereira H, Ferrier S, Walters M, Geller G, Jongman R, Scholes R, Bruford M, Brummitt N, Butchart S, Cardoso A, Coops N, Dulloo E, Faith D, Freyhof J, Gregory R, Heip C, Hoft R, Hurtt G, Jetz W, Karp D, McGeoch M, Obura D, Onoda Y, Pettorelli N, Reyers B, Sayre R, Scharlemann J, Stuart S, Turak E, Walpole M, Wegmann M (2013) Essential biodiversity variables. Science 339(6117):277–278PubMedCrossRefPubMedCentralGoogle Scholar
- Pettorelli N, Wegmann M, Skidmore A, Mücher S, Dawson T, Fernandez M, Lucas R, Schaepman M, Wang T, O’Connor B, Jongman R, Kempeneers P, Sonnenschein R, Leidner A, Bohm M, He K, Nagendra H, Dubois G, Fatoyinbo T, Hansen M, Paganini M, de Klerk H, Asner G, Kerr J, Estes A, Schmeller D, Heiden U, Rocchini D, Pereira H, Turak E, Fernandez N, Lausch A, Cho M, Alcaraz-Segura D, McGeoch M, Turner W, Mueller A, St-Louis V, Penner J, Vihervaara P, Belward A, Reyers B, Geller G (2016) Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions. Remote Sensing in Ecology and Conservation 2(3):122–131CrossRefGoogle Scholar
- Schaepman ME, Jehle M, Hueni A, D’Odorico P, Damm A, Weyermann J, Schnei- d FD, Laurent V, Popp C, Seidel FC, Lenhard K, Gege P, Küchler C, Brazile J, Kohler P, Vos LD, Meuleman K, Meynart R, Schlapfer D, Kneubühler M, Itten KI (2015) Advanced radiometry measurements and earth science applications with the airborne prism experiment (apex). Remote Sens Environ 158:207–219CrossRefGoogle Scholar
- Wang Y, Hyyppa J, Liang X, Kaartinen H, Yu X, Lindberg E, Holmgren J, Qin Y, Mallet C, Ferraz A, Torabzadeh H, Morsdorf F, Zhu L, Liu J, Alho P (2016) International benchmarking of the individual tree detection methods for modeling 3-d canopy structure for silviculture and forest ecology using airborne laser scanning. IEEE Trans Geosci Remote Sens 54(9):5011–5027CrossRefGoogle Scholar
- Widlowski JL, Mio C, Disney M, Adams J, Andredakis I, Atzberger C, Brennan J, Busetto L, Chelle M, Ceccherini G, Colombo R, Cote JF, Eenmae A, Essery R, Gastellu-Etchegorry JP, Gobron N, Grau E, Haverd V, Homolova L, Huang H, Hunt L, Kobayashi H, Koetz B, Kuusk A, Kuusk J, Lang M, Lewis PE, Lovell JL, Malenovsky Z, Meroni M, Morsdorf F, Mottus M, Ni-Meister W, Pinty B, Rautiainen M, Schlerf M, Somers B, Stuckens J, Verstraete MM, Yang W, Zhao F, Zenone T (2015) The fourth phase of the radiative transfer model intercomparison (rami) exercise: actual canopy scenarios and conformity testing. Remote Sens Environ 169:418–437CrossRefGoogle Scholar
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