Sylvain G. Leblanc

Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements

Leaf area index (LAI) is one of the surface parameters that has importance in climate, weather, and ecological studies, and has been routinely estimated from remote sensing measurements. Canada-wide LAI maps are now being produced using cloud-free Advanced Very High-Resolution Radiometer (AVHRR) imagery every 10 days at 1-km resolution. The archive of these products began in 1993. LAI maps at the same resolution are also being produced with images from the SPOT VEGETATION sensor. To improve the LAI algorithms and validate these products, a group of Canadian scientists acquired LAI measurements during the summer of 1998 in deciduous, conifer, and mixed forests, and in cropland. Common measurement standards using the commercial Tracing Radiation and Architecture of Canopies (TRAC) and LAI-2000 instruments were followed. Eight Landsat Thematic Mapper (TM) scenes at 30-m resolution were used to locate ground sites and to facilitate spatial scaling to 1-km pixels. In this paper, examples of Canada-wide LAI maps are presented after an assessment of their accuracy using ground measurements and the eight Landsat scenes. Methodologies for scaling from high- to coarse-resolution images that consider surface heterogeneity in terms of mixed cover types are evaluated and discussed. Using Landsat LAI images as the standard, it is shown that the accuracy of LAI values of individual AVHRR and VEGETATION pixels was in the range of 50–75%. Random and bias errors were both considerable. Bias was mostly caused by uncertainties in atmospheric correction of the Landsat images, but surface heterogeneity in terms of mixed cover types were also found to cause bias in AVHRR and SPOT VEGETATION LAI calculations. Random errors come from many sources, but pixels with mixed cover types are the main cause of random errors. As radiative signals from different vegetation types were quite different at the same LAI, accurate information about subpixel mixture of the various cover types is identified as the key to improving the accuracy of LAI estimates. D 2002 Elsevier Science Inc. All rights reserved.

A shortwave infrared modification to the simple ratio for LAI retrieval in boreal forests: an image and model analysis.

Abstract In preparation for new satellite sensors, such as VEGETATION on SPOT-4 and the MODerate Resolution Imaging Spectrometer (MODIS), we investigate the potential of the shortwave infrared (SWIR) signal to improve Leaf Area Index (LAI) retrieval in the boreal forests of Canada. Our study demonstrates that an empirical SWIR modification to the simple ratio (SR) vegetation index, termed the reduced simple ratio (RSR), has the potential to unify deciduous and conifer species in LAI retrieval, shows increased sensitivity to LAI, and demonstrates an improved correlation with LAI in individual jack pine and black spruce canopies. The unification of deciduous and conifer species suggests the possibility of not requiring a cover type stratification prior to retrieving LAI information from remotely sensed data, and has impact where no cover type information will be made or where the mix of cover types within a pixel is unknown. We use a geometric–optical canopy reflectance model to quantify the potential variation in jack pine and black spruce canopy reflectance caused by differences in background reflectance. The modeling study supports the results from the image analysis of the RSR showing increased sensitivity to LAI and reducing background effects in these conifer canopies.

Multi-angular optical remote sensing for assessing vegetation structure and carbon absorption

The utility of multi-angle optical remote sensing for terrestrial carbon cycle estimation is demonstrated through theoretical development, POLDER data analysis, and a case study of carbon cycle in a boreal forest. Progress in canopy-level photosynthesis modeling suggests that simpler big-leaf photosynthesis models are giving ways to more complex sunlit/shaded leaf separation models. This advancement in ecological modeling has increased the demand for advanced description of canopy architecture. Such demand may be mostly met through the use of multi-angle remote sensing techniques. In addition to leaf area index (LAI), another canopy parameter, the foliage clumping index, can be derived from multi-angle remote sensing. These two parameters are the basis for separating sunlit and shaded leaves. As leaf photosynthesis is nonlinearly related to incident radiation, such separation avoids the problems of big-leaf models that only make use of the total radiation absorption by the canopy without considering the distribution of radiation among leaves. A practical conclusion is that the traditional way of mapping the net primary productivity (NPP) through its correlation with the remotely sensed fraction of photosynthetically active radiation (FPAR) absorbed by plant canopies is only a very crude approximation and could be replaced with mapping LAI and clumping index and modeling NPP with advanced photosynthesis models. This is a step forward in remote sensing applications because single-angle remote sensing can only acquire information on the effective LAI related to the canopy gap fraction in the viewing direction and the amount of shaded leaf area is unknown.

A practical scheme for correcting multiple scattering effects on optical LAI measurements

Abstract Accurate and fast non-destructive measurements of leaf area index (LAI) of plant canopies are essential to environmental applications such as water and carbon cycle modelling. A commonly used technique to acquire LAI in situ is based on measurements of radiation transmittance through the canopy with optical instruments. The LAI-2000, that obtains measurements of effective LAI ( L e : LAI retrieved assuming random foliage distribution) based on gap fraction at five view angles, is designed to work under diffuse light conditions. The LAI-2000 makes use of blue light to minimise the effect of light scattering in the canopy on LAI measurements. However, actual field LAI measurements are still routinely done under a range of illumination conditions, including direct sunlight. The LAI values measured under conditions of either partial or full direct light are generally smaller than the ones obtained under diffuse conditions. Although this error source is prevailing in many field LAI measurements, hitherto the problem has not been tackled rigorously. To better understand and improve the LAI-2000 measurements taken under non-ideal conditions, measurements were taken in two deciduous and two coniferous forest sites at different times of cloudless days to study how the scattering of the blue light by plant canopies affects LAI measurements. The sites are located in Larose forest near Ottawa, Canada. It is shown through these measurements and modelling with the canopy radiative transfer model Five-scale [Remote Sens. Rev. 19 (2000) 293–305] that the blue light scattering causes underestimation of effective LAI by up to 20% when measured under direct sunlight. A correction for the scattering effect, as a function of solar zenith angle and the effective LAI measured under the sunlit condition, is found through an empirical fit to the measured data in a limited range as well as model-simulated data in the full possible range possible. It is also found that the LAI-2000 fourth ring (47–58° from zenith) gives a more consistent correction than the other rings and that this ring used alone is also suitable for effective LAI retrieval under diffuse conditions. The correction scheme can reduce the error in effective LAI measurements to within 2%. It is therefore suggested that in field programs with logistic constraints, the LAI-2000 time of operation during sunny days can be extended beyond the diffuse illumination conditions near sunrise and sunset since the influence of the direct sunlight on the LAI measurements can be mostly removed using the correction scheme provided in this study.

Retrieval of vegetation clumping index using hot spot signatures measured by POLDER instrument

Abstract The potential use of the information from a sampling of the bidirectional reflectance distribution function (BRDF) has suffered from the lack of solid applications in ecology, where it is expected to play the role of an advanced descriptor of vegetation as a complement to hyperspectral measurements. Such a shortcoming stems from the lack of consistent angular data sets with an adequate resolution at global scale. In this context, the POLDER instrument is particularly relevant because it acquires directional radiance signatures at a high angular resolution and thereby provides the first global BRDF product. In this paper, we investigate how to discriminate vegetation types in using only a portion of the BRDF, in particular, the two paramount directional signatures, which are the maximum (hot spot) and the minimum (dark spot) of reflectance observed in the backscattering and forward scattering regions, respectively. A directional index hot–dark spot (HDS) is formulated using these two signatures. It is defined as the normalized difference between the reflectances at the hot spot and dark spot. It is shown that the HDS can be linearly related with the foliage clumping index for three different vegetation types observed by the spaceborne POLDER sensor. The significance of the clumping index mapping for ecological studies is evaluated using the Boreal Ecosystem Productivity Simulator (BEPS). Considering foliage clumping in BEPS, the estimation of daily canopy photosynthesis can differ about 20% for a black spruce site. In this context, it is expected that the findings of this study will have a strong impact on the use of directional optical remote sensing to improve the assessment of terrestrial productivity and carbon cycle.

Investigation of directional reflectance in boreal forests with an improved four-scale model and airborne POLDER data

Airborne Polarization and Directional Earth Radiation (POLDER) data acquired during the boreal ecosystem-atmosphere study (BOREAS) and the four-scale model of Chen and Leblanc (1997) are used to investigate radiative transfer in boreal forest. The four-scale model is based on forest canopy architecture at different scales. New aspects are incorporated into the model to improve the physical representation of each canopy, as follows: 1) Elaborate branch architecture is added. 2) Different crown shapes are used for conifer and deciduous forests. 3) Bilayer version of the model is introduced for forest canopies with an important understory. 4) Natural repulsion effect is considered in the tree distribution statistics. Ground measurements from BOREAS sites are used as input parameters by the model to simulate measurements of bidirectional reflectance distribution function (BRDF) from four forest canopies (old black spruce, old aspen, and old and young jack pine) acquired by the POLDER instrument from May-July 1994. The model is able to reproduce with great accuracy the BRDF of the four forests. The importance of the branch architecture and the self-shadowing of the foliage is emphasized.

Multiple-scattering scheme useful for geometric optical modeling

Geometrical optical (GO) models have been widely used in remote sensing applications because of their simplicity and ability to simulate angular variation of remote sensing signals from the Earths surface. GO models are generally accurate in the visible part of the solar spectrum, but less accurate in near-infrared (NIR) part in which multiple scattering in plant canopies is the strongest. Although turbid-media radiative transfer (RT) methods have been introduced to GO models to cope with the second-order and higher order scattering, the problem of canopy geometrical effects on multiple scattering still remains and becomes the main obstacle in GO model applications. In this paper, we propose and test a multiple scattering scheme to simulate angular variation in multiply scattered radiation in plant canopies. This scheme is based on various view factors between sunlit and shaded components (both foliage and background) in the canopy and allows the geometrical effects to propagate to the second-order and higher order scattering simulations. As the view factors depend on the canopy geometry, the scheme is particularly useful in GO models. This new scheme is implemented in the 4-Scale Model, which previously used band-specific multiple scattering factors. After the use of the scheme, these factors are removed and the multiple scattering at a given wavelength and angle of observation can be automatically computed. Improvements made with this scheme are shown in comparison with the top-of-canopy (i.e., PARABOLA) and airborne (i.e., POLDER) measurements with modeled results with and without the scheme. Examples of canopy-level hyperspectral signatures simulated using the scheme are also shown.

Evaluation of national and global LAI products derived from optical remote sensing instruments over Canada

Leaf area index (LAI) is an important surface variable for monitoring the status of vegetation and as input in a number of ecosystem process models. There are currently several coarse-resolution LAI maps over Canada, including a Canada Centre for Remote Sensing ten-day, 1-km resolution, Canada-wide product based on SPOT-4 VEGETATION (VGT), a MODIS eight-day, 1-km resolution, global product and a monthly, 7-km resolution, global map produced using POLDER-1. These products are difficult to validate because of their large spatial extent and coarse resolution. In this study we use in situ LAI measurements collected over a wide range of forest types and ecological zones in Canada to derive 30-m resolution reference LAI maps based on robust error-in-measurement regressions to Landsat Enhanced Thematic Mapper Plus vegetation indices. The reference maps and LAI products were aggregated to a coarser resolution (3 km for MODIS and VGT and 7 km for POLDER) before comparison to account for registration errors, and variability in sensor projected point spread functions. Spatially corresponding aggregated pixels with both high-quality reference and coarse scale LAI retrievals were compared. The comparison shows reasonable agreement (biases less than 25% or one LAI) between the VGT and reference LAI. The MODIS LAI product showed weak correlations (R2<0.25) over all sites at the scale of comparison and typically overestimated reference LAI in mixed forests by approximately 200%. The POLDER LAI product, only available in June 1997, showed almost no correlation to the summer reference LAI datasets. It underestimated reference LAI for an early growing season with an extent, in some cases, greater than the seasonal differences in LAI. This independent validation of three large area LAI products suggests that there may be substantial biases due to the lack of regional tuning of retrieval algorithms. These biases are far larger than the uncertainties in the reference-based LAI scenes in the case of the MODIS product. This suggests that reliable LAI maps may require regional calibration to meet the Global Terrestrial Observing System mapping requirements of plusmn15% uncertainties

Correction to the plant canopy gap-size analysis theory used by the Tracing Radiation and Architecture of Canopies instrument

A plant canopy gap-size analyzer, the Tracing Radiation and Architecture of Canopies (TRAC), developed by Chen and Cihlar [Appl Opt. 34, 6211(1995)] and commercialized and by 3rd Wave Engineering (Nepean, Canada), has been used around the world to quantify the fraction of photosynthetically activeradiation absorbed by plant canopies, the leaf area index (LAI), and canopy architectural parameters. The TRAC is walked under a canopy along transects to measure sunflecks that are converted into a gap-size distribution. A numerical gap-removal technique is performed to remove gaps that are not theoretically possible in a random canopy. The resulting reduced gap-size distribution is used to quantify the heterogeneity of the canopy and to improve LAI measurements. It is explicitly shown here that the original derivation of the clumping index was missing a normalization factor. For a very clumped canopy with a large gap faction, the resulting LAI can be more than 100% smaller than previously estimated. A test case is used to demonstrate that the new clumping index derivation allows a more accurate change of LAI to be measured.

Canada-wide foliage clumping index mapping from multiangular POLDER measurements

In this paper, vegetation canopy structural information is retrieved over Canada from multiangular Advanced Earth Observing Satellite (ADEOS-1) polarization and directionality of the earths reflectance (POLDER) data based on canopy radiative transfer simulations using the Five-Scale model. The retrieval methodology makes use of the angular signature of the reflectance at the hot spot, where the sun and view angles coincide, and at the dark spot, where the reflectance is at its minimum. The POLDER data show that the normalized difference hot spot dark spot (NDHD) constructed from the hot spot and dark spot reflectances has no correlation with the nadir-normalized normalized difference vegetation index (NDVI), from which vegetation properties are often inferred, indicating that this angular index has additional information. Five-Scale simulations are used to assess the effects of foliage distribution on this angular index for different crown sizes, spatial distribution of crowns and foliage inside crowns, and foliage density variations. The simulations show that the NDHD is related to canopy structure quantified using a clumping index. This latter relationship is further exploited to derive a Canada-wide clumping index map at 7 km by 7 km resolution using spaceborne POLDER data. This map provides a critical new source of information for advanced modelling of radiation interaction with vegetation and energy and mass (water and carbon) exchanges between the surface and the atmosphere.