On the angular dependency of canopy gap fractions in pine, spruce and birch stands

The angular profiles of canopy gap fraction curves are influenced by canopy structure, and are commonly expected to vary with stand- and crown-level variables such as tree pattern, crown shape and leaf orientation. In this study, measurements of canopy structure, gap fractions and effective LA! in 986 plots of Scots pine, Norway spruce and Silver birch stands in Finland were used to assess how similar the angular canopy gap fraction profiles are for common boreal tree species. The profiles were characterized with help of the shape function psi(theta), defined as the normalized value of the canopy light extinction coefficient at zenith angle (theta). Variation in psi(theta) would be induced not only by a non-spherical leaf orientation, but also by differences in the directional clumping indices, such as could result from species-specific differences in crown shape. Our results showed that there is wide variation in the shape of psi in the individual plots of the three different species. The species-specific mean curves psi(theta), however, showed relatively small variation with theta, except for a sudden drop at large zenith angles, and the shape of the curves was similar for the different tree species. Results indicate that differences in crown shape of the study species do not significantly affect the angular profiles of canopy gap fraction. (C) 2015 The Authors. Published by Elsevier B.V.Peer reviewe

Photon recollision probability in heterogeneous forest canopies: : compatibility with a hybrid GO model

Peer reviewe

Vegetation earth system data record from DSCOVR EPIC observations

Poster presented at 2017 AGU Fall Meeting, New Orleans, Louisiana. POSTER ID: A33D-238

Quantitative characterization of clumping in Scots pine crowns

Peer reviewe

Influence of forest floor vegetation on the total forest reflectance and its implications for LAI estimation using vegetation indices

Recently a simple analytic canopy bidirectional reflectance factor (BRF) model based on the spectral invariants theory was presented. The model takes into account that the recollision probability in the forest canopy is different for the first scattering than the later ones. Here this model is extended to include the forest floor contribution to the total forest BRF. The effect of the understory vegetation on the total forest BRF as well as on the simple ratio (SR) and the normalized difference (NDVI) vegetation indices is demonstrated for typical cases of boreal forest. The relative contribution of the forest floor to the total BRF was up to 69 % in the red wavelength range and up to 54 % in the NIR wavelength range. Values of SR and NDVI for the forest and the canopy differed within 10 % and 30 % in red and within 1 % and 10 % in the NIR wavelength range. The relative variation of the BRF with the azimuth and view zenith angles was not very sensitive to the forest floor vegetation. Hence, linear correlation of the modelled total BRF and the Ross-thick kernel was strong for dense forests (R2 > 0.9). The agreement between modelled BRF and satellite-based reflectance values was good when measured LAI, clumping index and leaf single scattering albedo values for a boreal forest were used as input to the model.Hiljattain on esitetty yksinkertainen analyyttinen puuston kaksisuuntaisen heijastuskertoimen (BRF) malli, joka perustuu spketristä riippumattomien parametrien teoriaan. Mallissa otetaan huomioon, että fotonin uudelleen siroamisen todennäköisyys metsässä poikkeaa ensimmäisellä kerralla sen myöhemmistä arvoista. Tässä tutkimuksessa mallia on edelleen kehitetty siten, että siinä huomioidaan metsän pohjan osuus metsän BRF:stä. Aluskasvillisuuden vaikutusta BRF:ään ja kasvillisuusindekseihin SR (yksinkertainen suhde) ja NDVI (normalisoitu kasvillisuuden erotusindeksi) havainnollistetaan esimerkeillä tyypillisestä boreaalisesta metsästä. Metsän pohjan suhteellinen osuus BRF:stä ulottui 69 prosenttiin punaisen alueen aallonpituuksilla ja 54 prosenttiin lähiinfrapunan aallonpituusalueella. Metsälle laskettujen SR:n ja NDVI:n arvot poikkesivat pelkälle puustolle lasketuista vastaavista arvoista 10 % ja 30 % punaisella aallonpituusalueella ja 1 % ja 10 % lähi-infrapunan aallonpituusalueella. BRF:n suhteellinen muutos katselukulmien vaihdellessa ei ollut kovin herkkä metsän pohjakasvillisuudelle. Siten mallinnettu metsän BRF oli lineaarisesti verrannollinen Ross:n tiheän metsän kerneliin (selitysaste > 0.9). Mallinnettu BRF ja satellidatasta peräisin olevat reflektanssiarvot vastasivat hyvin toisiaan, kun mallin syöttötietoina käytettiin boreaalisen metsän mitattuja lehtialaindeksin, ryhmittyneisyysindeksin ja lehden albedon arvoja

DSCOVR EPIC vegetation Earth system data record (DSCOVR EPIC L2 VESDR)

PresentationFirst author draf

Estimation of leaf area index and its sunlit portion from DSCOVR EPIC data: theoretical basis

This paper presents the theoretical basis of the algorithm designed for the generation of leaf area index and diurnal course of its sunlit portion from NASA's Earth Polychromatic Imaging Camera (EPIC) onboard NOAA's Deep Space Climate Observatory (DSCOVR). The Look-up-Table (LUT) approach implemented in the MODIS operational LAI/FPAR algorithm is adopted. The LUT, which is the heart of the approach, has been significantly modified. First, its parameterization incorporates the canopy hot spot phenomenon and recent advances in the theory of canopy spectral invariants. This allows more accurate decoupling of the structural and radiometric components of the measured Bidirectional Reflectance Factor (BRF), improves scaling properties of the LUT and consequently simplifies adjustments of the algorithm for data spatial resolution and spectral band compositions. Second, the stochastic radiative transfer equations are used to generate the LUT for all biome types. The equations naturally account for radiative effects of the three-dimensional canopy structure on the BRF and allow for an accurate discrimination between sunlit and shaded leaf areas. Third, the LUT entries are measurable, i.e., they can be independently derived from both below canopy measurements of the transmitted and above canopy measurements of reflected radiation fields. This feature makes possible direct validation of the LUT, facilitates identification of its deficiencies and development of refinements. Analyses of field data on canopy structure and leaf optics collected at 18 sites in the Hyytiälä forest in southern boreal zone in Finland and hyperspectral images acquired by the EO-1 Hyperion sensor support the theoretical basis.Shared Services Center NAS

Vegetation earth system data record from DSCOVR EPIC observations,

The NASA's Earth Polychromatic Imaging Camera (EPIC) onboard NOAA's Deep Space Climate Observatory (DSCOVR) mission was launched on February 11, 2015 to the Sun-Earth Lagrangian L1 point where it began to collect radiance data of the entire sunlit Earth every 65 to 110 min in June 2015. It provides imageries in near backscattering directions at ten ultraviolet to near infrared narrow spectral bands. The DSCOVR EPIC science product suite includes vegetation Earth system data record (VESDR) that provides leaf area index (LAI) and diurnal courses of normalized difference vegetation index (NDVI), sunlit LAI (SLAI), fraction of incident photosynthetically active radiation (FPAR) absorbed by the vegetation and Directional Area Scattering Function (DASF). The parameters at 10-km sinusoidal grid and 65-110 min temporal frequency are generated from the upstream EPIC MAIAC surface reflectance product. The DSCOVR EPIC science team also provides two ancillary science data products derived from 500m MODIS land cover type 3 product: 10 km Land Cover Type and Distribution of Land Cover Types within 10 km EPIC pixel. All products were released on June-7-2018 and publicly available from the NASA Langley Atmospheric Science Data Center (https://eosweb.larc.nasa.gov/project/dscovr/dscovr_epic_l2_vesdr_01). This presentation provides an overview of the EPIC VESDR research, which includes descriptions of the algorithm and product, initial assessment of its quality and obtaining new information on vegetation properties from the VESDR product.Accepted manuscrip