Physical interpretation of the correlation between multi-angle spectral data and canopy height

Recent empirical studies have shown that multi-angle spectral data can be useful for predicting canopy height, but the physical reason for this correlation was not understood. We follow the concept of canopy spectral invariants, specifically escape probability, to gain insight into the observed correlation. Airborne Multi-Angle Imaging Spectrometer (AirMISR) and airborne Laser Vegetation Imaging Sensor (LVIS) data acquired during a NASA Terrestrial Ecology Program aircraft campaign underlie our analysis. Two multivariate linear regression models were developed to estimate LVIS height measures from 28 AirMISR multi-angle spectral reflectances and from the spectrally invariant escape probability at 7 AirMISR view angles. Both models achieved nearly the same accuracy, suggesting that canopy spectral invariant theory can explain the observed correlation. We hypothesize that the escape probability is sensitive to the aspect ratio (crown diameter to crown height). The multi-angle spectral data alone therefore may not provide enough information to retrieve canopy height globally

Vegetation angular signatures of equatorial forests from DSCOVR EPIC and Terra MISR observations

In vegetation canopies cross-shading between finite dimensional leaves leads to a peak in reflectance in the retro-illumination direction. This effect is called the hot spot in optical remote sensing. The hotspot region in reflectance of vegetated surfaces represents the most information-rich directions in the angular distribution of canopy reflected radiation. This paper presents a new approach for generating hot spot signatures of equatorial forests from synergistic analyses of multiangle observations from the Multiangle Imaging SpectroRadiometer (MISR) on Terra platform and near backscattering reflectance data from the Earth Polychromatic Imaging Camera (EPIC) onboard NOAA’s Deep Space Climate Observatory (DSCOVR). A canopy radiation model parameterized in terms of canopy spectral invariants underlies the theoretical basis for joining Terra MISR and DSCOVR EPIC data. The proposed model can accurately reproduce both MISR angular signatures acquired at 10:30 local solar time and diurnal courses of EPIC reflectance (NRMSE 0.8). Analyses of time series of the hot spot signature suggest its ability to unambiguously detect seasonal changes of equatorial forests.Published versio

Simulation-Based Evaluation of the Estimation Methods of Far-Red Solar-Induced Chlorophyll Fluorescence Escape Probability in Discontinuous Forest Canopies

Peer reviewe

Implications of whole-disc DSCOVR EPIC spectral observations for estimating Earth's spectral reflectivity based on low-earth-orbiting and geostationary observations

Earth’s reflectivity is among the key parameters of climate research. National Aeronautics and Space Administration (NASA)’s Earth Polychromatic Imaging Camera (EPIC) onboard National Oceanic and Atmospheric Administration (NOAA)’s Deep Space Climate Observatory (DSCOVR) spacecraft provides spectral reflectance of the entire sunlit Earth in the near backscattering direction every 65 to 110 min. Unlike EPIC, sensors onboard the Earth Orbiting Satellites (EOS) sample reflectance over swaths at a specific local solar time (LST) or over a fixed area. Such intrinsic sampling limits result in an apparent Earth’s reflectivity. We generated spectral reflectance over sampling areas using EPIC data. The difference between the EPIC and EOS estimates is an uncertainty in Earth’s reflectivity. We developed an Earth Reflector Type Index (ERTI) to discriminate between major Earth atmosphere components: clouds, cloud-free ocean, bare and vegetated land. Temporal variations in Earth’s reflectivity are mostly determined by clouds. The sampling area of EOS sensors may not be sufficient to represent cloud variability, resulting in biased estimates. Taking EPIC reflectivity as a reference, low-earth-orbiting-measurements at the sensor-specific LST tend to overestimate EPIC values by 0.8% to 8%. Biases in geostationary orbiting approximations due to a limited sampling area are between −0.7% and 12%. Analyses of ERTI-based Earth component reflectivity indicate that the disagreement between EPIC and EOS estimates depends on the sampling area, observation time and vary between −10% and 23%.The NASA/GSFC DSCOVR project is funded by NASA Earth Science Division. W. Song, G. Yan, and X. Mu were also supported by the key program of National Natural Science Foundation of China (NSFC; Grant No. 41331171). This research was conducted and completed during a 13-month research stay of the lead author in the Department of Earth and Environment, Boston University as a joint Ph.D. student, which was supported by the Chinese Scholarship Council (201606040098). DSCOVR EPIC L1B data were obtained from the NASA Langley Research Center Atmospheric Science Data Center. The authors would like to thank the editor who handled this paper and the two anonymous reviewers for providing helpful and constructive comments and suggestions that significantly helped us improve the quality of this paper. (NASA Earth Science Division; 41331171 - key program of National Natural Science Foundation of China (NSFC); 201606040098 - Chinese Scholarship Council)Accepted manuscrip

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

Generating Global Leaf Area Index from Landsat: Algorithm Formulation and Demonstration

This paper summarizes the implementation of a physically based algorithm for the retrieval of vegetation green Leaf Area Index (LAI) from Landsat surface reflectance data. The algorithm is based on the canopy spectral invariants theory and provides a computationally efficient way of parameterizing the Bidirectional Reflectance Factor (BRF) as a function of spatial resolution and wavelength. LAI retrievals from the application of this algorithm to aggregated Landsat surface reflectances are consistent with those of MODIS for homogeneous sites represented by different herbaceous and forest cover types. Example results illustrating the physics and performance of the algorithm suggest three key factors that influence the LAI retrieval process: 1) the atmospheric correction procedures to estimate surface reflectances; 2) the proximity of Landsatobserved surface reflectance and corresponding reflectances as characterized by the model simulation; and 3) the quality of the input land cover type in accurately delineating pure vegetated components as opposed to mixed pixels. Accounting for these factors, a pilot implementation of the LAI retrieval algorithm was demonstrated for the state of California utilizing the Global Land Survey (GLS) 2005 Landsat data archive. In a separate exercise, the performance of the LAI algorithm over California was evaluated by using the short-wave infrared band in addition to the red and near-infrared bands. Results show that the algorithm, while ingesting the short-wave infrared band, has the ability to delineate open canopies with understory effects and may provide useful information compared to a more traditional two-band retrieval. Future research will involve implementation of this algorithm at continental scales and a validation exercise will be performed in evaluating the accuracy of the 30-m LAI products at several field sites

Säteilyn kulku, sidonta ja sironta havumetsissä

This work develops methods to account for shoot structure in models of coniferous canopy radiative transfer. Shoot structure, as it varies along the light gradient inside canopy, affects the efficiency of light interception per unit needle area, foliage biomass, or foliage nitrogen. The clumping of needles in the shoot volume also causes a notable amount of multiple scattering of light within coniferous shoots. The effect of shoot structure on light interception is treated in the context of canopy level photosynthesis and resource use models, and the phenomenon of within-shoot multiple scattering in the context of physical canopy reflectance models for remote sensing purposes. Light interception. A method for estimating the amount of PAR (Photosynthetically Active Radiation) intercepted by a conifer shoot is presented. The method combines modelling of the directional distribution of radiation above canopy, fish-eye photographs taken at shoot locations to measure canopy gap fraction, and geometrical measurements of shoot orientation and structure. Data on light availability, shoot and needle structure and nitrogen content has been collected from canopies of Pacific silver fir (Abies amabilis (Dougl.) Forbes) and Norway spruce (Picea abies (L.) Karst.). Shoot structure acclimated to light gradient inside canopy so that more shaded shoots have better light interception efficiency. Light interception efficiency of shoots varied about two-fold per needle area, about four-fold per needle dry mass, and about five-fold per nitrogen content. Comparison of fertilized and control stands of Norway spruce indicated that light interception efficiency is not greatly affected by fertilization. Light scattering. Structure of coniferous shoots gives rise to multiple scattering of light between the needles of the shoot. Using geometric models of shoots, multiple scattering was studied by photon tracing simulations. Based on simulation results, the dependence of the scattering coefficient of shoot from the scattering coefficient of needles is shown to follow a simple one-parameter model. The single parameter, termed the recollision probability, describes the level of clumping of the needles in the shoot, is wavelength independent, and can be connected to previously used clumping indices. By using the recollision probability to correct for the within-shoot multiple scattering, canopy radiative transfer models which have used leaves as basic elements can use shoots as basic elements, and thus be applied for coniferous forests. Preliminary testing of this approach seems to explain, at least partially, why coniferous forests appear darker than broadleaved forests in satellite data.Tässä työssä kehitetään menetelmiä versorakenteen huomioimiseksi havumetsien valonkulkumalleissa. Havupuiden versorakenne vaihtelee kasvuston sisällä saatavilla olevan valon määrästä riippuen. Versorakenne vaikuttaa siihen kuinka paljon verso sitoo valoa suhteessa verson neulaspinta-alaan, neulasbiomassaan ja verson sisältämän typen määrään. Neulasten ryhmittyminen versoiksi aiheuttaa myös huomattavan määrän valon moninkertaista sirontaa yhden verson eri neulasten välillä. Versorakenteen vaikutusta valonsidontaan käsitellään metsikkötason fotosynteesi- ja resurssienkäyttömallien näkökulmasta ja versonsisäistä valon moninkertaista sirontaa metsien kaukokartoituksessa käytettävien fysikaalisten heijastusmallien näkökulmasta. Valon sidonta. Esitetään menetelmä havupuun verson sitoman fotosynteettisesti aktiivisen säteilyn (PAR) määrän estimoimiseksi. Menetelmässä käytetään mallia kasvuston yläpuoliselta taivaalta tulevan säteilyn suuntajakaumasta, verson kohdalta otetuista kalansilmäkuvista analysoitua kasvuston aukkoisuutta eri suunnissa ja mittauksia verson asennosta ja muodosta. Aineistoa metsikön valaistusoloista, versojen ja neulasten rakenteista sekä typpipitoisuuksista on kerätty purppurapihdasta (Abies amabilis (Dougl.) Forbes) ja kuusesta (Picea abies (L.) Karst.). Versorakenne mukautui kasvustonsisäiseen valonvaihteluun siten että varjostetummilla versoilla oli suurempi valonsidonnan tehokkuus. Versojen valonsidonnan tehokkuus vaihteli noin kaksinkertaisesti suhteessa neulaspinta-alaan, noin nelinkertaisesti suhteessa neulasten kuivapainoon ja noin viisinkertaisesti suhteessa typen määrään. Vertailu lannoitetun ja lannoittamattoman kuusimetsikön välillä osoitti ettei lannoitus juurikaan vaikuta versojen valonsidonnan tehokkuuteen. Valon sironta. Havupuiden verson rakenteesta aiheutuu valon moninkertaista sirontaa verson neulasten välillä. Monisirontaa simuloitiin käyttämällä geometrista mallia verson rakenteesta ja säteenjäljitysmenetelmää. Simulaatiotulosten pohjalta osoitetaan että verson sirontakertoimen riippuvuus neulasten sirontakertoimesta voidaan kuvata yksinkertaisella yksiparametrisella mallilla. Tämä parametri, jota kutsutaan uudelleentörmäystodennäköisyydeksi, kuvaa kuinka tiukasti neulaset ovat versossa ryhmittyneet. Parametri ei riipu säteilyn aallonpituudesta, ja se voidaan liittää aikaisempiin ryhmittymisindekseihin. Kuvaamalla versonsisäinen monisironta uudelleentörmäystodennäköisyyden avulla voidaan lehtiä peruselementteinä käyttävät valonkulkumallit muuntaa käyttämään versoja peruselementteinä, jolloin malleja voidaan soveltaa havumetsille. Tämän lähestymistavan alustava testaus näyttää, ainakin osittain, selittävän miksi havumetsät näyttävät satelliittiaineistossa lehtimetsiä tummemmilta

Remote sensing of leaf area index : enhanced retrieval from close-range and remotely sensed optical observations

A wide range of models used in agriculture, ecology, carbon cycling, climate and other related studies require information on the amount of leaf material present in a given environment to correctly represent radiation, heat, momentum, water, and various gas exchanges with the overlying atmosphere or the underlying soil. Leaf area index (LAI) thus often features as a critical land surface variable in parameterisations of global and regional climate models, e.g., radiation uptake, precipitation interception, energy conversion, gas exchange and momentum, as all areas are substantially determined by the vegetation surface. Optical wavelengths of remote sensing are the common electromagnetic regions used for LAI estimations and generally for vegetation studies. The main purpose of this dissertation was to enhance the determination of LAI using close-range remote sensing (hemispherical photography), airborne remote sensing (high resolution colour and colour infrared imagery), and satellite remote sensing (high resolution SPOT 5 HRG imagery) optical observations. The commonly used light extinction models are applied at all levels of optical observations. For the sake of comparative analysis, LAI was further determined using statistical relationships between spectral vegetation index (SVI) and ground based LAI. The study areas of this dissertation focus on two regions, one located in Taita Hills, South-East Kenya characterised by tropical cloud forest and exotic plantations, and the other in Gatineau Park, Southern Quebec, Canada dominated by temperate hardwood forest. The sampling procedure of sky map of gap fraction and size from hemispherical photographs was proven to be one of the most crucial steps in the accurate determination of LAI. LAI and clumping index estimates were significantly affected by the variation of the size of sky segments for given zenith angle ranges. On sloping ground, gap fraction and size distributions present strong upslope/downslope asymmetry of foliage elements, and thus the correction and the sensitivity analysis for both LAI and clumping index computations were demonstrated. Several SVIs can be used for LAI mapping using empirical regression analysis provided that the sensitivities of SVIs at varying ranges of LAI are large enough. Large scale LAI inversion algorithms were demonstrated and were proven to be a considerably efficient alternative approach for LAI mapping. LAI can be estimated nonparametrically from the information contained solely in the remotely sensed dataset given that the upper-end (saturated SVI) value is accurately determined. However, further study is still required to devise a methodology as well as instrumentation to retrieve on-ground green leaf area index . Subsequently, the large scale LAI inversion algorithms presented in this work can be precisely validated. Finally, based on literature review and this dissertation, potential future research prospects and directions were recommended.Ei saatavill