Remote sensing of woody shrub cover in desert grasslands using MISR with a geometric-optical canopy reflectance model

A new method is described for the retrieval of fractional cover of large woody plants (shrubs) at the landscape scale using moderate resolution multi-angle remote sensing data from the Multiangle Imaging SpectroRadiometer (MISR) and a hybrid geometric-optical (GO) canopy reflectance model. Remote sensing from space is the only feasible method for regularly mapping woody shrub cover over large areas, an important application because extensive woody shrub encroachment into former grasslands has been seen in arid and semi-arid grasslands around the world during the last 150 years. The major difficulty in applying GO models in desert grasslands is the spatially dynamic nature of the combined soil and understory background reflectance: the background is important and cannot be modeled as either a Lambertian scatterer or by using a fixed bidirectional reflectance distribution function (BRDF). Candidate predictors of the background BRDF at the Sun-target-MISR angular sampling configurations included the volume scattering kernel weight from a Li-Ross BRDF model; diffuse brightness (ρ0) from the Modified Rahman-Pinty-Verstraete (MRPV) BRDF model; other Li-Ross kernel weights (isotropic, geometric); and MISR near-nadir bidirectional reflectance factors (BRFs) in the blue, green, and near infra-red bands. The best method was multiple regression on the weights of a kernel-driven model and MISR nadir camera blue, green, and near infra-red bidirectional reflectance factors. The results of forward modeling BRFs for a 5.25 km² area in the USDA, ARS Jornada Experimental Range using the Simple Geometric Model (SGM) with this background showed good agreement with the MISR data in both shape and magnitude, with only minor spatial discrepancies. The simulations were shown to be accurate in terms of both absolute value and reflectance anisotropy over all 9 MISR views and for a wide range of canopy configurations (r² = 0.78, RMSE = 0.013, N = 3969). Inversion of the SGM allowed estimation of fractional shrub cover with a root mean square error (RMSE) of 0.03 but a relatively weak correlation (r² = 0.19) with the reference data (shrub cover estimated from high resolution IKONOS panchromatic imagery). The map of retrieved fractional shrub cover was an approximate spatial match to the reference map. Deviations reflect the first-order approximation of the understory BRDF in the MISR viewing plane; errors in the shrub statistics; and the 12 month lag between the two data sets.     

Mapping shrub abundance in desert grasslands using geometric-optical modeling and multi-angle remote sensing with CHRIS/Proba

This work examines the application of a geometric-optical canopy reflectance model to provide measures of woody shrub abundance in desert grasslands at the landscape scale. The approach is through inversion of the non-linear simple geometric model (SGM) against 631 nm multi-angle reflectance data from the Compact High Resolution Imaging Spectrometer (CHRIS) flown on the European Space Agency's Project for On-Board Autonomy (Proba) satellite. Separation of background and upper canopy contributions was effected by a linear scaling of the parameters of the Walthall bidirectional reflectance distribution function model with the weights of a kernel-driven model. The relationship was calibrated against a small number of sample locations with highly contrasting background/upper canopy configurations, before application over an area of about 25 km². The results show that with some assumptions, the multi-angle remote sensing signal from CHRIS/Proba can be explained in terms of a combined soil-understory background response and woody shrub cover and exploited to map this important structural attribute of desert grasslands.     

Modelling the reflectance anisotropy of Chihuahuan Desert grass–shrub transition canopy–soil complexes

The goal of the research presented here is to assess the factors controlling the remotely sensed signal returned in the solar wavelengths from Chihuahuan Desert grass–shrub transition canopy–soil complexes. The specific objectives were twofold: to evaluate the importance of the different elements (overstorey, understorey, soil) in the bidirectional reflectance distribution function (BRDF) of a Chihuahuan Desert grass–shrub transition zone; and to explore the behaviour of simple parametric and explicit scattering models with respect to observations. The first objective was approached by simulations using the Radiosity Graphics Method (RGM), with surface parameters provided by measurements of plant locations and dimensions obtained over two contrasting 25?m² plots. The second was approached through simulations of bidirectional reflectance factors (BRFs) by both the RGM and a Simplified Geometric Model (SGM) developed for inversion purposes. The modelled BRFs were assessed against multi-angle observations (MAO) – samples of the BRDF at a wavelength of 650?nm acquired from the air at up to six view zenith angles and three solar zenith angles. The results show that the understorey of small forbs and sub-shrubs plays an important role in determining the brightness and reflectance anisotropy of grass–shrub transition landscapes in relation to that of larger shrubs such as mesquite and ephedra. This is owing to the potentially high density of these plants and to the fact that there is also a varying proportion of black grama grass and prone grass litter associated with snakeweed abundance. Both of these components darken the scene. The SGM performed well measured against both the RGM and the MAO at the MAO acquisition angles (R² of 0.98 and 0.92, respectively) and good correlations were obtained between RGM and SGM when modelling was performed at a wider range of angular configurations (R²≈0.90). The SGM was shown to be highly sensitive to its adjustable parameters. Both models underestimated BRF magnitude with respect to the MAO by a small amount (<6%), showing increasing divergence from the backscattering into the forward-scattering direction. A remaining problem for operational model inversions using MAO is the a priori estimation of understorey and grass abundance.     

Large area mapping of southwestern forest crown cover, canopy height, and biomass using the NASA Multiangle Imaging Spectro-Radiometer

A rapid canopy reflectance model inversion experiment was performed using multi-angle reflectance data from the NASA Multi-angle Imaging Spectro-Radiometer (MISR) on the Earth Observing System Terra satellite, with the goal of obtaining measures of forest fractional crown cover, mean canopy height, and aboveground woody biomass for large parts of south-eastern Arizona and southern New Mexico (> 200,000 km²). MISR red band bidirectional reflectance estimates in nine views mapped to a 250 m grid were used to adjust the Simple Geometric-optical Model (SGM). The soil-understory background signal was partly decoupled a priori by developing regression relationships with the nadir camera blue, green, and near-infrared reflectance data and the isotropic, geometric, and volume scattering kernel weights of the LiSparse–RossThin kernel-driven bidirectional reflectance distribution function (BRDF) model adjusted against MISR red band data. The SGM's mean crown radius and crown shape parameters were adjusted using the Praxis optimization algorithm, allowing retrieval of fractional crown cover and mean canopy height, and estimation of aboveground woody biomass by linear rescaling of the dot product of cover and height. Retrieved distributions of crown cover, mean canopy height, and aboveground woody biomass for forested areas showed good matches with maps from the United States Department of Agriculture (USDA) Forest Service, with R² values of 0.78, 0.69, and 0.81, and absolute mean errors of 0.10, 2.2 m, and 4.5 tons acre^- 1 (10.1 Mg ha^- 1), respectively, after filtering for high root mean square error (RMSE) on model fitting, the effects of topographic shading, and the removal of a small number of outliers. This is the first use of data from the MISR instrument to produce maps of crown cover, canopy height, and woody biomass over a large area by seeking to exploit the structural effects of canopies reflected in the observed anisotropy patterns in these explicitly multiangle data.     

A method to convert AVHRR Normalized Difference Vegetation Index time series to a standard viewing and illumination geometry

The bi-directional reflectance distribution function (BRDF) alters the seasonal and inter-annual variations exhibited in Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) data and this hampers the detection and, consequently, the interpretation of temporal variations in land-surface vegetation. The magnitude and sign of bi-directional effects in commonly used AVHRR data sets depend on land-surface properties, atmospheric composition and the type of atmospheric correction that is applied to the data. We develop an approach to estimate BRDF effects in AVHRR NDVI time series using the Moderate Resolution Imaging Spectrometer (MODIS) BRDF kernels and subsequently adjust NDVI time series to a standard illumination and viewing geometry. The approach is tested on NDVI time series that are simulated for representative AVHRR viewing and illumination geometry. These time series are simulated with a canopy radiative transfer model coupled to an atmospheric radiative transfer model for four different land cover types—tropical forest, boreal forest, temperate forest and grassland - and five different atmospheric conditions - turbid and clear top-of-atmosphere, turbid and clear top-of-atmosphere with a correction for ozone absorption and Rayleigh scattering applied (Pathfinder AVHRR Land data) and ground-observations (fully corrected for atmospheric effects). The simulations indicate that the timing of key phenological stages, such as start and end of growing season and time of maximum greenness, is affected by BRDF effects. Moreover, BRDF effects vary with latitude and season and increase over the time of operation of subsequent NOAA satellites because of orbital drift. Application of the MODIS kernels on simulated NVDI data results in a 50% to 85% reduction of BRDF effects. When applied to the global 18-year global Normalized Difference Vegetation Index (NDVI) Pathfinder data we find BRDF effects similar in magnitude to those in the simulations. Our analysis of the global data shows that BRDF effects are especially large in high latitudes; here we find that in at least 20% of the data BRDF errors are too large for accurate detection of seasonal and interannual variability. These large BRDF errors tend to compensate, however, when averaged over latitude.     

Investigating the spectral and ecological characteristics of grassland communities across an ecological gradient of the Inner Mongolian grasslands with in situ hyperspectral data

Grassland degradation is serious in the Mongolian plateau, especially in Inner Mongolia, China. Accurate monitoring of grassland types and qualities is increasingly important for the purposes of grassland conservation and restoration. Using in situ hyperspectral reflectance data and ground-based ecological measurements, we explored the potential for large-scale monitoring grassland communities using imaging spectroradiometers. We compared the spectral reflectance of the major types of grasslands and field plots with/without livestock grazing. We also did statistical analysis about the relationship between hyperspectral indices and aboveground biomass (AGB) of the surveyed grassland communities. The results showed that: (1) the dominant plant species varied across meadow, typical, and desert steppe, and they also varied between fenced and grazed plots; (2) in situ hyperspectral data are useful for differentiating grassland communities of meadow, typical, and desert steppe and grassland communities with and without livestock grazing; and (3) the prediction accuracies of vegetation indices for AGB decreased from desert to typical and meadow steppe, and the results were contrary for the prediction accuracies of red edge inflection point (REIP). REIP may not be suitable for estimating AGB of the low-density grassland communities. The above results implied that care must be taken while using statistical models to link spectral and ecological measurements in large geographical scales since there is lack of portability over different types of grassland communities. This study provides foundations for future large-scale efforts of monitoring grassland communities in Inner Mongolia using imaging spectroradiometers.     

Forward and inverse modelling of canopy directional reflectance using a neural network

This article explores the use of artificial neural networks for both forward and inverse canopy modelling. The forward neural modelling paradigm involved training a network for predicting the bidirectional reflectance distribution function (BRDF) of a canopy given the density of the trees, their height, crown shape, viewing, and illumination geometry. The neural network model was able to predict the BRDF of unseen canopy sites with 90% accuracy. Analysis of the signal captured by the model indicates that the canopy structural parameters, and illumination and viewing geometry, are essential for predicting the BRDF of vegetated surfaces. The inverse neural network model involved learning the underlying relationship between canopy structural parameters and their corresponding bidirectional reflectance. The inversion results show that the R2 between the network predicted canopy parameters and the actual canopy parameters was 0.85 for density and 0.75 for both the crown shape and the height parameters. The results of both forward and inverse modelling suggest that neural networks can model accurately the BRDF of vegetated canopies.     

Retrieval of crop canopy properties: a comparison between model inversion from hyperspectral data and image classification

The aim of this work was to investigate different approaches for the estimation of canopy structure properties from multiangular measurements at the field scale. Hyperspectral multiangular data were acquired on potato canopies using a spectroradiometer (GER-1500) and corresponding multiangular images using the VIFIS (Variable Interference Filter Imaging Spectrometer). The data obtained using the spectroradiometer were employed in the inversion of the PROSAIL model. The images obtained from the VIFIS were classified into the component image fractions: shaded and sunlit leaves and soil. These classification results were then used directly in the inversion of a simple ray-tracing canopy model. The inversion technique was based on a look-up table approach using a simple ray-tracing model of a plant canopy. Field sampling was carried out for the direct measurement of leaf area index (LAI) and other canopy properties. The experimental error in the data of both sensors was large since the canopy appeared non-homogeneous at the measurement height used, mainly because of the crop row structure. However LAI values retrieved from both approaches were realistic and allowed the discrimination of potato canopies that had received different nitrogen fertilization treatments. The relative merits and practicalities of the two approaches (multiangular hyperspectral reflectance versus image classification) are discussed.     

Retrieval of canopy height using moderate-resolution imaging spectroradiometer (MODIS) data

In this study we use the 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) product to develop multivariate linear regression models that estimate canopy heights over study sites at Howland Forest, Maine, Harvard Forest, Massachusetts and La Selva Forest, Costa Rica using (1) directional escape probabilities that are spectrally independent and (2) the directional spectral reflectances used to derive the directional escape probabilities. These measures of canopy architecture are compared with canopy height information retrieved from the airborne Laser Vegetation Imaging Sensor (LVIS). Both the escape probability and the directional reflectance approaches achieve good results, with correlation coefficients in the range 0.54-0.82, although escape probability results are usually slightly better. This suggests that MODIS 500 m BRDF data can be used to extrapolate canopy heights observed by widely-spaced satellite LIDAR swaths to larger areas, thus providing wide-area coverage of canopy height.     

An empirical study on the utility of BRDF model parameters and topographic parameters for mapping vegetation in a semi‐arid region with MISR imagery

In this study we show that multiangle remote sensing is useful for increasing the accuracy of vegetation community type mapping in desert regions. Using images from the National Aeronautics and Space Administration (NASA) Multiangle Imaging Spectroradiometer (MISR), we compared roles played by Bidirectional Reflectance Distribution Function (BRDF) model parameters with those played by topographic parameters in improving vegetation community type classifications for the Jornada Experimental Range and the Sevilleta National Wildlife Refuge in New Mexico, USA. The BRDF models used were the Rahman–Pinty–Verstraete (RPV) model and the RossThin‐LiSparseReciprocal (RTnLS) model. MISR nadir multispectral reflectance was considered as baseline because nadir observation is the most basic remote sensing observation. The BRDF model parameters and the topographic parameters were considered as additional data. The BRDF model parameters were obtained by inversion of the RPV model and the RTnLS model against the MISR multiangle reflectance data. The results of 32 classification experiments show that the BRDF model parameters are useful for vegetation mapping; they can be used to raise classification accuracies by providing information that is not available in the spectral‐nadir domain, or from ancillary topographic parameters. This study suggests that the Moderate Resolution Imaging Spectroradiometer (MODIS) and MISR BRDF model parameter data products have great potential to be used as additional information for vegetation mapping.     

An assessment of angular variations of red and NIR reflectances in multi-date IRS-1D Wide Field Sensor data

A study has been carried out to assess angular variations in red and near infrared (NIR) reflectance of different features of the Earth's surface in a common overlap area of accumulated four-date Indian Remote Sensing Satellite (IRS-1D) Wide Field Sensor (WiFS) data from the first fortnight of October 2003. An improved dark object subtraction (DOS) method has been used to perform image based atmospheric corrections. Red and NIR reflectance variations of four structurally different classes—dense vegetation (shrub), sparse crop (pearl millet/maize), wasteland and forest with Sun-target-sensor geometry were analysed. A linearly constrained least squares technique was used to estimate red and NIR model coefficients of the linear Ross Thick-Li Sparse (RTLS) semi- empirical Bidirectional Reflectance Distribution Function (BRDF) model and compared with Moderate Resolution Imaging Spectrometer (MODIS) BRDF product coefficients. The relative reflectance difference between two dates as well as anisotropic factors for red and NIR for all classes and dates were also computed. Red and NIR reflectance of the four land cover classes at different locations with different observation geometry were estimated using both WiFS derived and MODIS BRDF product RTLS model coefficients and compared with WiFS observed reflectance. It was found that the mean relative difference in red and NIR reflectances between consecutive dates varied between 4–11% and 6–8%, respectively, while the computed mean anisotropy factors varied between 3–10% in the red and 8–11% in the NIR. A small anisotropy in the Normalized Difference Vegetation Index (NDVI) as a function of the scattering angle was observed for the four land cover classes. This may imply that angular effects in WiFS are relatively small and do not exceed 10–11 % for the land cover classes considered here.     

Real‐Time Linear BRDF MIP‐Mapping

We present a new technique to jointly MIP‐map BRDF and normal maps. Starting with generating an instant BRDF map, our technique builds its MIP‐mapped versions based on a highly efficient algorithm that interpolates von Mises‐Fisher (vMF) distributions. In our BRDF MIP‐maps, each pixel stores a vMF mixture approximating the average of all BRDF lobes from the finest level. Our method is capable of jointly MIP‐mapping BRDF and normal maps, even with high‐frequency variations, at real‐time while preserving high‐quality reflectance details. Further, it is very fast, easy to implement, and requires no precomputation.     

Forest structure and aboveground biomass in the southwestern United States from MODIS and MISR

Red band bidirectional reflectance factor data from the NASA MODerate resolution Imaging Spectroradiometer (MODIS) acquired over the southwestern United States were interpreted through a simple geometric-optical (GO) canopy reflectance model to provide maps of fractional crown cover (dimensionless), mean canopy height (m), and aboveground woody biomass (Mg ha^− 1) on a 250 m grid. Model adjustment was performed after dynamic injection of a background contribution predicted via the kernel weights of a bidirectional reflectance distribution function (BRDF) model. Accuracy was assessed with respect to similar maps obtained with data from the NASA Multiangle Imaging Spectroradiometer (MISR) and to contemporaneous US Forest Service (USFS) maps based partly on Forest Inventory and Analysis (FIA) data. MODIS and MISR retrievals of forest fractional cover and mean height both showed compatibility with the USFS maps, with MODIS mean absolute errors (MAE) of 0.09 and 8.4 m respectively, compared with MISR MAE of 0.10 and 2.2 m, respectively. The respective MAE for aboveground woody biomass was ~ 10 Mg ha^− 1, the same as that from MISR, although the MODIS retrievals showed a much weaker correlation, noting that these statistics do not represent evaluation with respect to ground survey data. Good height retrieval accuracies with respect to averages from high resolution discrete return lidar data and matches between mean crown aspect ratio and mean crown radius maps and known vegetation type distributions both support the contention that the GO model results are not spurious when adjusted against MISR bidirectional reflectance factor data. These results highlight an alternative to empirical methods for the exploitation of moderate resolution remote sensing data in the mapping of woody plant canopies and assessment of woody biomass loss and recovery from disturbance in the southwestern United States and in parts of the world where similar environmental conditions prevail.     

Characterizing vegetation cover in global savannas with an annual foliage clumping index derived from the MODIS BRDF product

The global savanna biome is characterized by enormous diversity in the physiognomy and spatial structure of the vegetation. The foliage clumping index can be calculated from bidirectional reflectance distribution function (BRDF) data. It measures the response of the darkspot reflectance to increased shadow associated with clumped vegetation and is related to leaf area index. Clumping index theoretically declines with increasing woody cover until the tree canopy begins to become uniform. In this study, clumping index is calculated for Moderate Resolution Imaging Spectroradiometer BRDF data for the Australian tropical savanna, the tropical savannas of South America, and the tropical savannas of east, west and southern Africa and compared with site-based measurements of tree canopy cover, and with area-based classifications of land cover. There were differences in sensitivity of clumping index between red and near-infrared reflectance channels, and between savanna systems with markedly different woody vegetation physiognomy. Clumping index was broadly related to foliage cover from historical site data in Australia and in West Africa and Kenya, but not in Southern Africa nor with detailed site-based demographic data in the cerrado of Brazil. However, clumping index decreased with proportion of woody cover in land cover datasets for east Africa, Australia and the Colombian Llanos. There was overlap in the range of clumping index values for forest, cerrado and campo land covers in Brazil. Clumping index was generally negatively correlated with percentage tree cover from the MODIS Vegetation Continuous Fields product, but regional differences in the relationship were evident. There were large differences in the frequency distributions of clumping index from savanna, woody savanna and grassland land cover classes between global ecoregions. The clumping index shows differing sensitivity to savanna woody cover for red and NIR reflectance, and requires regional calibration for application as a universal indicator.     

Separating physiologically and directionally induced changes in PRI using BRDF models

Monitoring of photosynthetic efficiency (ε) over space and time is a critical component of climate change research as it is a major determinant of the amount of carbon accumulated by terrestrial ecosystems. While the past decade has seen progress in the remote estimation of ε at the leaf, canopy and stand level using the photochemical reflectance index PRI (based on the normalized difference of reflectance at 531 and 570 nm), little is known about the temporal and spatial requirements for up-scaling PRI to landscape and global levels using satellite observations. One potential way to investigate these requirements is using automated tower-based remote sensing platforms, which observe stand level reflectance with high spatial, temporal, and spectral resolution. Prediction of ε from PRI diurnally or over a full year requires observations of canopy reflectance over multiple view and sun-angles. As a result, these observations are subject to directional reflectance effects which can be interpreted in terms of the bidirectional reflectance distribution function (BRDF) using semi-empirical kernel driven models. These semi-empirical models use a combination of physically based BRDF shapes and empirical observations to standardize multi-angular observations to a common viewing and illumination geometry. Directional reflectance effects are thereby modeled as a linear superposition of mathematical kernels, representing the bi-direction variation in reflectance from isotropic, geometric, and volumetric scattering components of the vegetation canopy. However, because variations in plant physiological conditions can also introduce bidirectional reflectance variations, we introduce an approach to separate bidirectional effects arising purely from plant physiological status from other effects by stratifying PRI observations into categories based on environmental conditions for which the expected physiological variability is low. Within each of these PRI strata, the derived physically based BRDF shapes were used to standardize multi-angular PRI measurements to a common viewing and illumination geometry. The method significantly enhanced the relationship found between PRI and ε (from r² = 0.38 for the directionally uncorrected case to r² = 0.82 for the directionally corrected case) from data measured continuously over the course of 1 year over an evergreen conifer forest using an automated platform. Results show that isotropic PRI scattering is highly correlated to changes in ε, while geometric scattering can be related to canopy level shading. Instrumentation and approaches such as the one demonstrated in this study may be integrated into current efforts aiming at predicting ε at global scales using satellite observations.     

Robust estimation of BRDF model parameters

The effect of the Bidirectional Reflectance Distribution Function (BRDF) is one of the most important factors in correcting the reflectance obtained from remotely sensed data. Estimation of BRDF model parameters can be deteriorated by various factors; contamination of the observations by undetected subresolution clouds or snow patches, inconsistent atmospheric correction in multiangular time series due to uncertainties in the atmospheric parameters, slight variations of the surface condition during a period of observation, for example due to soil moisture changes, diurnal effects on vegetation structure, and geolocation errors [Lucht and Roujean, 2000]. In the present paper, parameter estimation robustness is examined using Bidirectional Reflectance Factor (BRF) data measured for paddy fields in Japan. We compare both the M-estimator and the least median of squares (LMedS) methods for robust parameter estimation to the ordinary least squares method (LSM). In experiments, simulated data that were produced by adding noises to the data measured on the ground surface were used. Experimental results demonstrate that if a robust estimation is sought, the LMedS method can be adopted for the robust estimation of a BRDF model parameter.     

A comparison of multi-spectral, multi-angular, and multi-temporal remote sensing datasets for fractional shrub canopy mapping in Arctic Alaska

Shrub cover appears to be increasing across many areas of the Arctic tundra biome, and increasing shrub cover in the Arctic has the potential to significantly impact global carbon budgets and the global climate system. For most of the Arctic, however, there is no existing baseline inventory of shrub canopy cover, as existing maps of Arctic vegetation provide little information about the density of shrub cover at a moderate spatial resolution across the region. Remotely-sensed fractional shrub canopy maps can provide this necessary baseline inventory of shrub cover. In this study, we compare the accuracy of fractional shrub canopy (> 0.5 m tall) maps derived from multi-spectral, multi-angular, and multi-temporal datasets from Landsat imagery at 30 m spatial resolution, Moderate Resolution Imaging SpectroRadiometer (MODIS) imagery at 250 m and 500 m spatial resolution, and MultiAngle Imaging Spectroradiometer (MISR) imagery at 275 m spatial resolution for a 1067 km² study area in Arctic Alaska. The study area is centered at 69 °N, ranges in elevation from 130 to 770 m, is composed primarily of rolling topography with gentle slopes less than 10°, and is free of glaciers and perennial snow cover. Shrubs > 0.5 m in height cover 2.9% of the study area and are primarily confined to patches associated with specific landscape features. Reference fractional shrub canopy is determined from in situ shrub canopy measurements and a high spatial resolution IKONOS image swath. Regression tree models are constructed to estimate fractional canopy cover at 250 m using different combinations of input data from Landsat, MODIS, and MISR. Results indicate that multi-spectral data provide substantially more accurate estimates of fractional shrub canopy cover than multi-angular or multi-temporal data. Higher spatial resolution datasets also provide more accurate estimates of fractional shrub canopy cover (aggregated to moderate spatial resolutions) than lower spatial resolution datasets, an expected result for a study area where most shrub cover is concentrated in narrow patches associated with rivers, drainages, and slopes. Including the middle infrared bands available from Landsat and MODIS in the regression tree models (in addition to the four standard visible and near-infrared spectral bands) typically results in a slight boost in accuracy. Including the multi-angular red band data available from MISR in the regression tree models, however, typically boosts accuracy more substantially, resulting in moderate resolution fractional shrub canopy estimates approaching the accuracy of estimates derived from the much higher spatial resolution Landsat sensor. Given the poor availability of snow and cloud-free Landsat scenes in many areas of the Arctic and the promising results demonstrated here by the MISR sensor, MISR may be the best choice for large area fractional shrub canopy mapping in the Alaskan Arctic for the period 2000-2009.     

Integration and Case Analysis of BRDF Model based on Linear Kernel\|driven Model#br#

Anisotropic reflectance is the intrinsic characteristic of an object surface.over the past few decades,various BRDF models have been developed for investigating the relationship between the vegetation canopy and reflectance anisotropy.This helps to retrieve biophysical parameters from the anisotropic reflectance patterns of vegetation canopy.In this study,for the purpose of assisting potential users to use these models,and to improve the understanding of the BRDF modeling,several BRDF models that are widely used in the remote sensing community have been integrated with the current version of the MaKeMAT (Multi\|angular Kernel\|driven Model Analysis Tool),based on the Interactive Data Language (IDL).This work retains all functions of the current version of the MaKeMAT model,meanwhile,adds some new functions by integrating these physical BRDF models.Undoubtedly,this work facilitates the potential users to process BRDF data and make further analysis in their work by operating a simpler visual interface.This helps to build a rapid communication between the kernel\|driven BRDF models and the physical BRDF models.Our initial results show that this model\|integration practice is a valuable reference for potential users to devise a similar technique.Our case study in coupling these physical BRDF models with the kernel\|driven models present a high correlation between them,with the determination of coefficients (R²) reaching 0.899~0.989 in the red and NIR bands.     

Mapping vegetation community types in a highly disturbed landscape: integrating hierarchical object-based image analysis with lidar-derived canopy height data

Focusing on the semi-arid and highly disturbed landscape of San Clemente Island (SCI), California, we test the effectiveness of incorporating a hierarchical object-based image analysis (OBIA) approach with high-spatial resolution imagery and canopy height surfaces derived from light detection and ranging (lidar) data for mapping vegetation communities. The hierarchical approach entailed segmentation and classification of fine-scale patches of vegetation growth forms and bare ground, with shrub species identified, and a coarser-scale segmentation and classification to generate vegetation community maps. Such maps were generated for two areas of interest on SCI, with and without vegetation canopy height data as input, primarily to determine the effectiveness of such structural data on mapping accuracy. Overall accuracy is highest for the vegetation community map derived by integrating airborne visible and near-infrared imagery having very high spatial resolution with the lidar-derived canopy height data. The results demonstrate the utility of the hierarchical OBIA approach for mapping vegetation with very high spatial resolution imagery, and emphasizes the advantage of both multi-scale analysis and digital surface data for accurately mapping vegetation communities within highly disturbed landscapes.     

Seasonal landscape and spectral vegetation index dynamics in the Brazilian Cerrado: An analysis within the Large-Scale Biosphere-Atmosphere Experiment in Amazônia (LBA)

The Brazilian Cerrado biome comprises a vertically structured mosaic of grassland, shrubland, and woodland physiognomies with distinct phenology patterns. In this study, we investigated the utility of spectral vegetation indices in differentiating these physiognomies and in monitoring their seasonal dynamics. We obtained high spectral resolution reflectances, during the 2000 wet and dry seasons, over the major Cerrado types at Brasilia National Park (BNP) using the light aircraft-based Modland Quick Airborne Looks (MQUALS) package, consisting of a spectroradiometer and digital camera. Site-intensive biophysical and canopy structural measurements were made simultaneously at each of the Cerrado types including Cerrado grassland, shrub Cerrado, wooded Cerrado, Cerrado woodland, and gallery forest. We analyzed the spectral reflectance signatures, their first derivative analogs, and convolved spectral vegetation indices (VI) over all the Cerrado physiognomies. The high spectral resolution data were convolved to the MODIS, AVHRR, and ETM⁺ bandpasses and converted to the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) to simulate their respective sensors. Dry and wet season comparisons of the measured biophysical attributes were made with the reflectance and VI data for the different Cerrado physiognomies. We found that three major domains of Cerrado could be distinguished with the dry and wet season spectral signatures and vegetation indices. The EVI showed a higher sensitivity to seasonality than the NDVI; however, both indices displayed seasonal variations that were approximately one-half that found with the measured landscape green cover dynamics. Inter-sensor comparisons of seasonal dynamics, based on spectral bandpass properties, revealed the ETM⁺-simulated VIs had the best seasonal discrimination capability, followed by MODIS and AVHRR. Differences between sensor bandpass-derived VI values, however, varied with Cerrado type and between dry and wet seasons, indicating the need for inter-sensor VI translation equations for effective multi-sensor applications.