Canopy Reflectance Model Research Articles

Improving snow cover mapping in forests through the use of a canopy reflectance model

Improving snow cover mapping in forests through the use of a canopy reflectance model

Improving snow cover mapping in forests through the use of a canopy reflectance model

MODIS, the moderate resolution imaging spectroradiometer, will be launched in 1998 as part of the first earth observing system (EOS) platform. Global maps of land surface properties, including snow cover, will be created from MODIS imagery. The MODIS snow-cover mapping algorithm that will be used to produce daily maps of global snow cover extent at 500 m resolution is currently under development. With the exception of cloud cover, the largest limitation to producing a global daily snow cover product using MODIS is the presence of a forest canopy. A Landsat Thematic Mapper (TM) time-series of the southern Boreal Ecosystem-Atmosphere Study (BOREAS) study area in Prince Albert National Park, Saskatchewan, was used to evaluate the performance of the current MODIS snow-cover mapping algorithm in varying forest types. A snow reflectance model was used in conjunction with a canopy reflectance model (GeoSAIL) to model the reflectance of a snow-covered forest stand. Using these coupled models, the effects of varying forest type, canopy density, snow grain size and solar illumination geometry on the performance of the MODIS snow-cover mapping algorithm were investigated. Using both the TM images and the reflectance models, two changes to the current MODIS snow-cover mapping algorithm are proposed that will improve the algorithm's classification accuracy in forested areas. The improvements include using the normalized difference snow index and normalized difference vegetation index in combination to discriminate better between snow-covered and snow-free forests. A minimum albedo threshold of 10% in the visible wavelengths is also proposed. This will prevent dense forests with very low visible albedos from being classified incorrectly as snow. These two changes increase the amount of snow mapped in forests on snow-covered TM scenes, and decrease the area incorrectly identified as snow on non-snow-covered TM scenes.

LIBERTY—Modeling the Effects of Leaf Biochemical Concentration on Reflectance Spectra

The conifer leaf model LIBERTY (Leaf Incorporating Biochemistry Exhibiting Reflectance and Transmittance Yields) is an adaptation of radiative transfer theory for determining the optical properties of powders. LIBERTY provides a simulation, at a fine spectral resolution, of quasiinfinite leaf reflectance (as represented by stacked leaves) and single leaf reflectance. Single leaf reflectance and transmittance are important input variables to vegetation canopy reflectance models. A prototype parameterization of LIBERTY was based upon measurements of pine needles and known absorption coefficients of pure component leaf biochemicals. The estimated infinite-reflectance output was compared with the spectra of both dried and fresh pine needles with root mean square errors (RMSE) of 2.87% and 1.73%, respectively. The comparisons between measured and estimated reflectance and transmittance values for single needles were also very accurate with RSME of 1.84% and 1.12%, respectively. Initial inversion studies have demonstrated that significant improvements can be made to LIBERTY by utilizing in vivo absorption coefficients which have been determined by the inversion process. These results demonstrate the capability of LIBERTY to model accurately the spectral response of pine needles.

Monitoring of vegetation parameters on large areas by the inversion of a canopy reflectance model

Analytical canopy reflectance (CR) models have reached the level of adequacy that makes it possible to estimate vegetation parameters by inversion of such models. The growing efficiency of algorithms and the increasing power of computers urge the development of procedures for the estimation of vegetation phytometrical parameters on large areas using satellite data and inversion of theoretical CR models. In this article, clusterization of a Landsat Thematic Mapper (TM) quarter scene is performed in the space of spectral signatures, and the CR model is inverted for these clusters. Optical parameters of the atmosphere which are needed for the atmospheric correction are estimated on the same image. The estimated Leaf Area Index (LAI) pattern is in good accordance to the land use map. Estimated LAI and chlorophyll content of forests are systematically biased.

Physically based classification and satellite mapping of biophysical characteristics in the southern boreal forest

Fundamental problems inherent to the existing land cover and biophysical characteristic algorithms are fourfold: (1) their failure to deal physically with global and interannual variations in surface reflectance arising from Sun and view angle variations, (2) decoupling of the land cover classification algorithm from the biophysical characteristic inference algorithm with no ability to control biophysical parameter estimation error arising from misclassification, (3) invalid statistical assumptions within classification algorithms used to model reflectance distribution functions, and (4) sole reliance on vegetation indices that can limit performance for several major land cover classes. To address these problems, we develop an integrated, physically based classification and biophysical characteristics estimation algorithm that utilizes canopy reflectance models to account directly for signature variations from Sun angle, topographic, and other variations. Our approach fuses into a single algorithm both land cover classification and biophysical characteristics estimation, permitting one set of physically based canopy reflectance models to be used for both. The use of canopy reflectance models eliminates the need for unrealistic assumptions, such as multivariate‐normal distributions, underlying many classification algorithms. Using the algorithm, we have classified a 10,000 km2 area of the BOREAS southern study area. Our classification shows that low‐productivity wetland conifer is the dominant land cover and that nearly 7% of the area is occupied by boreal fens, a major source of methane. In addition, nearly 23% of the area has been disturbed by either fire or logging in the last 20 years, suggesting an important role for disturbance to the regional carbon budget. A thorough evaluation of the physically based classifier within the southern study area shows accuracies superior to those obtained with conventional statistically based algorithms, implying even better performance when extended over multiple Landsat frames since the physically based approach can account directly for regional variations in reflectance resulting from varying illumination and viewing conditions (topography, Sun angle). The conifer biomass density estimation algorithm is based on our discovery of a convenient natural relationship between crown height and volumetric density that renders the biomass density for black spruce stands independent of tree height, and a function only of sunlit canopy fraction. This permits us to calculate directly the relationship between reflectance and biomass density. An evaluation of the algorithm using ground sites shows our algorithm can estimate black spruce biomass density with a root‐mean‐square error of 2.73 kg/ym2 for correctly classified sites. Our evaluation also demonstrates the importance of correct classification. Rootmean‐square errors for misclassified sites were 3.96 kg/m2. Using this approach we have estimated the biomass density in the BOREAS southern study area for the dominant land cover type in the circumpolar boreal ecosystem, wetland black spruce. These results show a bimodality to the biomass density regional distribution, controlled perhaps by underlying topographic and edaphic factors.

Retrieving land surface reflectances using the ATSR‐2: A theoretical study

The measurement of absolute land reflectance from Earth‐orbiting satellites requires the separation of the signal due to the reflection of solar radiation by the surface from that due to scattering by the atmosphere. Thus for accurate but generally applicable quasi‐operational retrieval techniques it is necessary to derive atmospheric parameters, such as the aerosol type and loading, simultaneously with the surface reflectances. The assumption of large‐scale horizontal homogeneity across an image, or the presence of very low reflectance pixels within an image, is avoided, since these are not necessarily applicable in the general case. In this paper, results of simulated retrievals using the visible and near‐infrared channels of the ATSR‐2 satellite radiometer are presented on the basis of techniques which exploit the dual look and other features of the instrument. The pixel‐based retrieval scheme comprises a fast representation of atmospheric scattering and a simple surface reflectance approximation. The effect of the atmospheric approximation on the accuracy of retrieval of the surface reflectance and atmospheric optical depth is tested by using a full multiple‐scattering radiative transfer model with simpler surface model. The effect on retrieval accuracy of the surface model is tested using a full surface vegetation canopy reflectance model with inclusion of the hot‐spot effect, combined with a successive orders of scattering atmospheric radiative transfer model, taken to second order. The results indicate that the scheme could provide accurate retrieval of surface reflectances even where no extraneous information on the surface bidirectional reflectance or on the atmospheric optical thickness is available. The scheme is also applicable to the AATSR instrument to be launched on the ENVISAT (Environmental Satellite) and, more widely, to other satellite radiometers with multiple views of the Earth's surface.

Validation of a Markov chain canopy reflectance model

The Markov chain canopy reflectance model (MCRM) by Kuusk (1995 b) has been tested versus the ray tracing model on two different computer maquettes of field crops (Barley and Beet), and on the field data collected in the frame of the Franco-English Collaborative Reflectance Experiment in 1989 and 1990 on sugar-beet plots. Separate comparisons of single and multiple scattering components of the MCRM and the ray tracing procedure demonstrated good agreement of the models. Inversion of the MCRM on field data returned good estimates of LAI in the range LAI 0.1-4 using nadir reflectance data in three SPOT and two Landsat TM channels. The estimated chlorophyll content was well correlated to the measured one, although underestimated to some extent. The use of directional data at 45 zenith angle and four azimuth angles improved the estimates of both the LAI and the chlorophyll content. It also permitted the estimation of additional parameters of the canopy structure (leaf size, LAD, the Markov parameter).

Remote sensing of biophysical variables in boreal forest stands of Picea mariana

Using two hybrid radiative transfer models to represent conifer canopies and stands, algorithms to infer several important structural parameters of stands of black spruce (Picea mariana), the most common boreal forest dominant, were developed and evaluated. Spectral mixture analysis and multi-spectral reflectance data for 31 black spruce stands of varying density and structure were used to infer the values for the areal proportions of sunlit canopy, sunlit background and shadow fraction, which we call radiometric elements, and the areal proportions of these radiometric elements were strongly related to leaf area index, biomass density, and annual above ground net primary productivity. The best overall correspondence between the radiometric elements and biophysical variables was found from the shadow fraction obtained with the cone-based canopy reflectance model corrected for variations in solar zenith angle.

A computer-efficient plant canopy reflectance model

Two analytical models of vegetation canopy reflectance are combined. The new model considers the diffuse and specular reflection of optical radiation on leaves, the canopy hot spot and nonlambertian soil. Diffuse fluxes are treated in a four-stream approximation. An elliptical distribution has been used for leaf inclination. Comparisons of the models demonstrate a good agreement. The FORTRAN-77 code of the model can be used in MS-DOS and UNIX environment. A complete set of algorithms of the new model is appended.

ＳＡＩＬモデルにおける葉の表と裏の反射率の違いの影響

Canopy reflectance model is a useful tool to correct the effect of measuring conditions like as the changes in observation angle and solar irradiance angle. So that, if we use the corrected reflectance data by the model for estimating vegetation index such as NDVI, we can derive more accurate information of vegetative distribution from satellite data.Nowadays, the famous model for canopy reflectance is the SAIL model. (Verhoef, 1984) This study notices that the leaf reflectance parameter of the SAIL model is considered only front surface of leaves. And so, we survey the influence on canopy reflectance. As a result of it, we find that it is not adequate to predict canopy reflectance by using only front surface reflectance of leaves.

Inversion of a soil bidirectional reflectance model for use with vegetation reflectance models

The need for anisotropic soil reflectance in canopy reflectance modeling is assessed for different sampling and canopy conditions. Based on the results for grasslands, a soil model is inverted with ground‐based radiometer data from the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE). A general solution applicable over different spectral bands, solar angles, and soil moisture levels is determined using a diverse data set. With this solution, the model can be used as a lower boundary condition in FIFE canopy modeling. Despite the previously reported independence of retrieved model parameters to data sampling conditions, solutions determined with more limited data sets vary significantly. Moreover, the semiphysically based model may not accurately predict reflectance in angular regions where data are absent in the inversion process. These findings are important for the Earth Observing System multiangle imaging spectroradiometer (MISR), which will gather data in essentially one azimuthal plane per pass like the instrument used in this study did.

A Markov chain model of canopy reflectance

Markov properties of stand geometry are incorporated into an analytical multispectral canopy reflectance model. The correlation of leaf positions in adjacent layers significantly influences the gap probability in a stand and, consequently, the canopy reflectance (CR) and its angular distribution. The sensitivity analysis demonstrated that the effect is greatest on the angular distribution of multiply scattered radiation. Validation of the new CR model that considers the Markov stand geometry demonstrated an improved agreement between model calculations and measured directional reflectance distribution of near infrared (NIR) reflectance for barley and soybean stands. As a consequence, inversion of the new model in the NIR spectral region and in two spectral bands simultaneously allowed significantly better estimation of the leaf area index of a stand than the Nilson-Kuusk model which assumes a Poisson stand geometry.

A regional Sahelian grassland model to be coupled with multispectral satellite data. II: Toward the control of its simulations by remotely sensed indices

An approach for combining remote sensing spectral measurements with an ecosystem model was presented in an accompanying article (Mougin et al., 1995). The sahelian grassland ecosystem STEP model developed for that purpose was also described and validated. In order to fulfill a prerequisite for using coarse resolution optical satellite data with the STEP model, the present paper presents i) a modeling of the reflectance which is adapted to the sahelian landscape and ii) a study based on the coupled ecosystem-reflectance modeling to assess the potential of vegetation indices for inferring vegetation parameters. The modeling of the landscape reflectance is based on existing soil and canopy reflectance models, and considers area-weighted contributions from green and dry vegetation, and bare soil components. The ecosystem model provides the landscape reflectance models with inputs like vegetation cover fraction ( f v ) and leaf area index (LAI) to characterize the vegetation present. Atmospheric effects are also accounted for using an existing simplified radiative transfer model. Simulated top of the atmosphere reflectances confronted to real satellite data during a growing season indicate that the modeling is adequate to reproduce temporal profiles of vegetation indices when atmospheric conditions are not prohibitive. Simulated vegetation indices (NDVI, SAVI, GEMI, SR) compared to vegetation characteristics show that a good tracking of the evolution of LAI and v during the growing season is possible before maturation. A sensitivity study of the four VIs to green biomass, soil brightness, and atmospheric water vapor is carried out for the specific case of the Sahel. The SAW and NDVI are both found to be adequate if atmospheric effects are minimized. NDVI integrated over the growing season is compared to net primary productivity (NPP) for different sites, regions, and growing seasons. A near-linear relationship is found, but the same relationship may not be applicable to different regions or growing seasons. On the whole, the results suggest that vegetation indices contain information which are useful for the ecosystem model, despite the fact that perturbating factors make the retrieval of these informations difficult. The possibility of using satellite data to drive the STEP model, or control its simulations, will be assessed in a forthcoming article.

Extraction of vegetation biophysical parameters by inversion of the PROSPECT + SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS sensors

The PROSPECT leaf optical properties and SAIL canopy reflectance models were coupled and inverted using a set of 96 AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) equivalent spectra gathered in afield experiment on sugar beet plots expressing a large range in leaf area index, chlorophyll concentration, and soil color. In a first attempt, the model accurately reproduced the spectral reflectance of vegetation, using six variables: chlorophyll a + b concentration (C ab), water depth (C w), leaf mesophyll structure parameter (N), leaf area index (LAI), mean leaf inclination angle (θ l), and hot-spot size parameter (s). The four structural parameters (N, LAI, τ l, and s) were poorly estimated, indicating instability in the inversion process; however, the two biochemical parameters (C ab and C w) were evaluated reasonably well, except over very bright soils. In a second attempt, three of the four structure variables were assigned a fixed value corresponding to the average observed in the experiment. Inversions performed to retrieve the remaining structure variable, leaf area index, and the two biochemical variables showed large improvements in the accuracy of LAI, but slightly poorer performance for C ab and C w. Here again, poor results were obtained with very bright soils. The compensations observed between the LAI and C ab or C w led us to evaluate the performance of two more-synthetic variables, canopy chlorophyll content or canopy water content, for these the inversions produced reasonable estimates. The application of this approach to Landsat TM (Thematic Mapper) data provided similar results, both for the spectrum reconstruction capability and for the retrieval of canopy biophysical characteristics.

A fast, invertible canopy reflectance model

Taking advantage of positive features of the Nilson-Kuusk and the SAIL canopy reflectance (CA) models, a new fast CR model has been developed. The new model considers the diffuse and specular reflection of shortwave radiation on leaves, the canopy hot spot, and nonlambertian soil. Diffuse fluxes are treated in a four-stream approximation. An elliptical distribution has been used for leaf inclination. Comparisons of the models demonstrate a good agreement. The complete set of algorithms of the new model is appended.

Mapping forest vegetation using Landsat TM imagery and a canopy reflectance model

Estimates of mean tree size and cover for each forest stand from an invertible forest canopy reflectance model are part of a new forest vegetation mapping system. Image segmentation defines stands which are sorted into general growth forms using per-pixel image classifications. Ecological models based on terrain relations predict species associations for the conifer, hardwood, and brush growth forms. The combination of the model-based estimates of tree size and cover with species associations yields general-purpose vegetation maps useful for a variety of land management needs. Results of timber inventories in the Tahoe and Stanislaus National Forests indicate the vegetation maps form a useful basis for stratification. Patterns in timber volumes for the strata reveal that the cover estimates are more reliable than the tree size estimates. A map accuracy assessment of the Stanislaus National Forest shows high overall map accuracy and also illustrates the problems in estimating tree size.

Mapping and monitoring conifer mortality using remote sensing in the Lake Tahoe Basin

A prolonged drought in the western United States has resulted in alarming levels of mortality in conifer forests. Satellite remote sensing holds the potential for mapping and monitoring the effects of such environmental changes over large geographic areas in a timely manner. Results from the application of a forest canopy reflectance model using multitemporal Landsat TM imagery and field measurements, indicate conifer mortality can be effectively mapped and inventoried. The test area for this project is the Lake Tahoe Basin Management Unit in the Sierra Nevada of California. The Landsat TM images are from the summers of 1988 and 1991. The Li-Strahler canopy model estimates several forest stand parameters, including tree size and canopy cover for each conifer stand, from reflectance values in satellite imagery. The difference in cover estimates between the dates forms the basis for stratifying stands into mortality classes, which are used as both themes in a map and the basis of the field sampling design. Field measurements from 61 stands collected in the summer of 1992 indicate 15 % of the original timber volume in the true fir zone died between 1988 and 1992. The resulting low standard error of 11 % for this estimate indicates the utility of these mortality classes for detecting areas of high mortality. Also, the patterns in the estimated mean timber volume loss for each class follow the expected trends. The results of this project are immediately useful for fire hazard management, by providing both estimates of the degree of overall mortality and maps showing its location. They also indicate current remote sensing technology may be useful for monitoring the changes in vegetation that are expected to result from climate change.

A multispectral canopy reflectance model

The leaf optical model PROSPECT by Jacquemoud and Baret (1990), the soil reflectance spectrum representation with basis functions by Price (1990), and the skylight ratio spectral representation by McCartney (1978) have been integrated into Kuusk's (1994) fast canopy reflectance model. The resulting new multispectral canopy reflectance model describes the directional reflectance of a homogeneous vegetation canopy over 400–2500 nm with a high spectral resolution. The number of the input parameters of the model does not depend on the number of spectral bands used. The model has high computational efficiency, and thus it can rather easily be inverted to determine the vegetation parameters from remote optical measurements. The model is tested with the corn and soybean reflectance data of Ranson et al. (1984; 1985).

Validation of bidirectional and hemispherical reflectances from a geometric-optical model using ASAS imagery and pyranometer measurements of a spruce forest

Aircraft imagery and ground measurements acquired for a spruce forest stand in Howland, Maine as part of the 1990 Forest Ecosystem Dynamics Multisensor Aircraft Campaign (FEDMAC) are used to validate the Li—Strahler geometric-optical forest canopy reflectance model and to demonstrate that both spectral bidirectional reflectance factors and hemispherical reflectance can be estimated with some success. With the geometric-optical model, a vegetated surface is treated as an assemblage of partially illuminated tree crowns of ellipsoidal shape, and through geometric-optics and Boolean set theory, the proportion of sunlit and shadowed canopy and background is modeled as a function of view angle. The model is driven by ground measurements of spectral reflectance and tree crown shape, size, and spacing. Atmospherically corrected multiangular radiance measurements of the FEDMAC spruce site from the Advanced Solid State Array Spectroradiometer (ASAS) were found to fit the shape of the modeled reflectance function quite well along the principal and cross-principal planes. Furthermore, integration of the modeled reflectance functions yielded spectral surface albedos (hemispherical reflectance), which, when extended to the full solar spectrum, were found to agree closely with pyranometer measurements obtained at the spruce site.

Remote Estimation of Crown Size, Stand Density, and Biomass on the Oregon Transect

In this paper, we invert a canopy reflectance model using multispectral satellite data in order to estimate remotely the parameters of crown size, tree count density, cover, foliage biomass, and total standing biomass for nine coniferous forest stands on a transect of western and central Oregon (USA). Issues involved in the inversion of the model are topographic correction, component signature estimation, spatial pattern analysis, and direct biomass estimation. Retrieved parameters correlate well with observed values from ground measurements of the test sites. The model inversion technique is a useful tool for mapping and timber inventory of forests at large scales.