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In this study, we show for the first time a global Clumping Index (CI) map at 500 m resolution derived using the Bidirectional Reflectance Distribution Function (BRDF) product from Moderate Resolution Imaging Spectroradiometer (MODIS). We found that the hotspot calculated from the MODIS BRDF product is underestimated in comparison with POLarization and Directionality of the Earth's Reflectances (POLDER) measurements very near the hotspot, so the MODIS-derived CI could be overestimated without correcting the bias. We developed an approach to correct the MODIS hotspot magnitude against synchronous co-registered POLDER-3 data. Field measured CIs by the Tracing Radiation and Architecture of Canopies instrument from 38 sites were used to evaluate the global CI map. Validation indicates that a strong correlation between MODIS-derived NDHD and CI indeed exists, and red band is better than near infrared band in effectively capturing the structural differences among cover types.

Image inpainting is an important research field in remote sensing image processing. Pixel missing patches on remote sensing image due to clouds and stripes were often processed through traditional synthesis and image inpainting techniques or multi-spectral and multi-temporal based methods. These methods have some limitations in practical applications. In this study, a new method was developed to restore pixel-missing patches on remote sensing images. It uses a corresponding recent image (reference image) as a Look-up table and fills up pixel missing patches using the non-missing pixels in the pixel missing image. The restoration effect of this new method was tested using two experiments. The restored image was almost identical to the original one visually. This new method is applicable when the reference image and pixel missing image are acquired from same or different sensors. It is also insensitive to the precision of atmospheric corrections for the reference and pixel missing images.

Since the introduction of Terrestrial Laser Scanning (TLS) instruments, there now exists a means of rapidly digitizing intricate structural details of vegetation canopies using Light Detection and Ranging (LiDAR) technology. In this investigation, Intelligent Laser Ranging and Imaging System (ILRIS-3D) data was acquired of individual tree crowns at olive (Olea europaea L.) plantations in Córdoba, Spain. In addition to conventional tripod-mounted ILRIS-3D scans, the unit was mounted on a platform (12m above ground) to provide nadir (top-down) observations of the olive crowns. 24 structurally variable olive trees were selected for in-depth analysis. From the observed 3D laser pulse returns, quantitative retrievals of tree crown structure and foliage assemblage were obtained. Robust methodologies were developed to characterize diagnostic architectural parameters, such as tree height (r2=0.97, rmse=0.21m), crown width (r2=0.97, rmse=0.13m), crown height (r2=0.86, rmse=0.14m), crown volume (r2=0.99, rmse=2.6m3), and Plant Area Index (PAI) (r2=0.76, rmse=0.26m2/m2). With the development of such LiDAR-based methodologies to describe vegetation architecture, the forestry, agriculture, and remote sensing communities are now faced with the possibility of replacing current labour-intensive inventory practices with, modern TLS systems. This research demonstrates that TLS systems can potentially be the new observational tool and benchmark for precise characterization of vegetation architecture for improved agricultural monitoring and management. © 2010 Elsevier B.V., In addition, this work was made possible with the financial support of the Natural Sciences and Engineering Research Council (NSERC) and the Canadian Foundation of Innovation (CFI).

Thermal adaptation of gross primary production and ecosystem respiration has been well documented over broad thermal gradients. However, no study has examined their interaction as a function of temperature, i.e. the thermal responses of net ecosystem exchange of carbon (NEE). In this study, we constructed temperature response curves of NEE against temperature using 380 site-years of eddy covariance data at 72 forest, grassland and shrubland ecosystems located at latitudes ranging from ~29° N to 64° N. The response curves were used to define two critical temperatures: transition temperature (Tb) at which ecosystem transferring from carbon source to sink and optimal temperature (To) at which carbon uptake is maximized. Tb was strongly correlated with annual mean air temperature. To was strongly correlated with mean temperature during the net carbon uptake period across the study ecosystems. Our results suggested that ecosystem CO2 flux switched from source to sink when air temperature reached annual mean temperature in spring and reached maximum uptake at mean temperature of the net carbon uptake period. Our results imply that the net ecosystem exchange of carbon adapt to the temperature across the geographical range due to intrinsic connections between vegetation primary production and ecosystem respiration. Thermal adaptation of gross primary production and ecosystem respiration has been well documented over broad thermal gradients. However, no study has examined their interaction as a function of temperature, i.e. the thermal responses of net ecosystem exchange of carbon (NEE). In this study, we constructed temperature response curves of NEE against temperature using 380 site-years of eddy covariance data at 72 forest, grassland and shrubland ecosystems located at latitudes ranging from ~29° N to 64° N. The response curves were used to define two critical temperatures: transition temperature (Tb) at which ecosystem transferring from carbon source to sink and optimal temperature (To) at which carbon uptake is maximized. Tb was strongly correlated with annual mean air temperature. To was strongly correlated with mean temperature during the net carbon uptake period across the study ecosystems. Our results suggested that ecosystem CO2 flux switched from source to sink when air temperature reached annual mean temperature in spring and reached maximum uptake at mean temperature of the net carbon uptake period. Our results imply that the net ecosystem exchange of carbon adapt to the temperature across the geographical range due to intrinsic connections between vegetation primary production and ecosystem respiration.

Spatially and temporally distributed information on the sizes of biomass carbon (C) pools (BCPs) and soil C pools (SCPs) is vital for improving our understanding of biosphere–atmosphere C fluxes. Because the sizes of C pools result from the integrated effects of primary production, age-effects, changes in climate, atmospheric CO2 concentration, N deposition, and disturbances, a modeling scheme that interactively considers these processes is important. We used the InTEC model, driven by various spatio-temporal datasets to simulate the long-term C-balance in a boreal landscape in eastern Canada. Our results suggested that in this boreal landscape, mature coniferous stands had stabilized their productivity and fluctuated as a weak C-sink or C-source depending on the interannual variations in hydrometeorological factors. Disturbed deciduous stands were larger C-sinks (NEP2004 = 150 gC m−2 yr−1) than undisturbed coniferous stands (e.g. NEP2004 = 8 gC m−2 yr−1). Wetlands had lower NPP but showed temporally consistent C accumulation patterns. The simulated spatio-temporal patterns of BCPs and SCPs were unique and reflected the integrated effects of climate, plant growth and atmospheric chemistry besides the inherent properties of the C pool themselves. The simulated BCPs and SCPs generally compared well with the biometric estimates (BCPs: r = 0.86, SCPs: r = 0.84). The largest BCP biases were found in recently disturbed stands and the largest SCP biases were seen in locations where moss necro-masses were abundant. Reconstructing C pools and C fluxes in the ecosystem in such a spatio-temporal manner could help reduce the uncertainties in our understanding of terrestrial C-cycle.

International audience; Ecosystem models are important tools for diagnosing the carbon cycle and projecting its behavior across space and time. Despite the fact that ecosystems respond to drivers at multiple time scales, most assessments of model performance do not discriminate different time scales. Spectral methods, such as wavelet analyses, present an alternative approach that enables the identification of the dominant time scales contributing to model performance in the frequency domain. In this study we used wavelet analyses to synthesize the performance of 21 ecosystem models at 9 eddy covariance towers as part of the North American Carbon Program's site‐level intercomparison. This study expands upon previous single‐site and single‐model analyses to determine what patterns of model error are consistent across a diverse range of models and sites. To assess the significance of model error at different time scales, a novel Monte Carlo approach was developed to incorporate flux observation error. Failing to account for observation error leads to a misidentification of the time scales that dominate model error. These analyses show that model error (1) is largest at the annual and 20–120 day scales, (2) has a clear peak at the diurnal scale, and (3) shows large variability among models in the 2–20 day scales. Errors at the annual scale were consistent across time, diurnal errors were predominantly during the growing season, and intermediate‐scale errors were largely event driven. Breaking spectra into discrete temporal bands revealed a significant model‐by‐band effect but also a nonsignificant model‐by‐site effect, which together suggest that individual models show consistency in their error patterns. Differences among models were related to model time step, soil hydrology, and the representation of photosynthesis and phenology but not the soil carbon or nitrogen cycles. These factors had the greatest impact on diurnal errors, were less important at annual scales, and had the least impact at intermediate time scales.

A model has been developed to calculate the spatial distribution of the photosynthetic photon flux density (PPFD) in elliptical forest openings of given slopes and orientations. The PPFD is separated into direct and diffuse components. The direct component is calculated according to the opening and radiation geometries, and pathlength of the solar beam through the forest canopy. The diffuse component is obtained from the sky, tree, and landscape view factors. In this model, the distribution of foliage area with height and the effect of foliage clumping on both direct and diffuse radiation transmission are considered. The model has been verified using measurements for six quantum sensors (LI-COR Inc.) located at different positions in a small clear-cut (0.37 ha) in a 90-year-old western hemlock-Douglas fir forest.

International audience; [1] Our current understanding of terrestrial carbon processes is represented in various models used to integrate and scale measurements of CO 2 exchange from remote sensing and other spatiotemporal data. Yet assessments are rarely conducted to determine how well models simulate carbon processes across vegetation types and environmental conditions. Using standardized data from the North American Carbon Program we compare observed and simulated monthly CO 2 exchange from 44 eddy covariance flux towers in North America and 22 terrestrial biosphere models. The analysis period spans ∼220 site-years, 10 biomes, and includes two large-scale drought events, providing a natural experiment to evaluate model skill as a function of drought and seasonality. We evaluate models' ability to simulate the seasonal cycle of CO 2 exchange using multiple model skill metrics and analyze links between model characteristics, site history, and model skill. Overall model performance was poor; the difference between observations and simulations was ∼10 times observational uncertainty, with forested ecosystems better predicted than nonforested. Model-data agreement was highest in summer and in temperate evergreen forests. In contrast, model performance declined in spring and fall, especially in ecosystems with large deciduous components, and in dry periods during the growing season. Models used across multiple biomes and sites, the mean model ensemble, and a model using assimilated parameter values showed high consistency with observations. Models with the highest skill across all biomes all used prescribed canopy phenology, calculated NEE as the difference between GPP and ecosystem respiration, and did not use a daily time step.

The Pleiades Promoter Project integrates genomewide bioinformatics with large-scale knockin mouse production and histological examination of expression patterns to develop MiniPromoters and related tools designed to study and treat the brain by directed gene expression. Genes with brain expression patterns of interest are subjected to bioinformatic analysis to delineate candidate regulatory regions, which are then incorporated into a panel of compact human MiniPromoters to drive expression to brain regions and cell types of interest. Using single-copy, homologous-recombination “knockins” in embryonic stem cells, each MiniPromoter reporter is integrated immediately 5′ of the Hprt locus in the mouse genome. MiniPromoter expression profiles are characterized in differentiation assays of the transgenic cells or in mouse brains following transgenic mouse production. Histological examination of adult brains, eyes, and spinal cords for reporter gene activity is coupled to costaining with cell-type–specific markers to define expression. The publicly available Pleiades MiniPromoter Project is a key resource to facilitate research on brain development and therapies.

[1] A new leaf area index (LAI) data set in 10 day intervals with consideration of the understory reflectance and foliage clumping effects over North America for 1 year is developed. The data set brings effectively together measurements from multiple sensors with complementary capabilities (SPOT-VEGETATION, Multiangle Imaging Spectroradiometer, POLDER). First, the temporal consistency analysis indicated the new product is on par with other available LAI data sets currently used by the community. Second, with the removal of the background (understory in forests, moss, litter, and soil) effect on the forest overstory LAI retrieval, slightly different LAI reductions were found between needleleaf and broadleaf forests. This is caused by the more clumped nature of needleleaf forests, especially at higher LAI values, which allows more light to penetrate through the overstory canopy, making the understory more visible for equal LAI as compared to broadleaf forests. This is found over a representative set of 105 CEOS Benchmark Land Multisite Analysis and Intercomparison of Products sites in North America used for indirect validation. Third, the data set was directly validated and compared with Moderate Resolution Imaging Spectroradiometer Collection 5 LAI product using results from the BigFoot project for available forest test sites. This study demonstrates that the fusion of data inputs between multiple sensors can indeed lead to improved products and that multiangle remote sensing can help us to address effectively the issues (separating the signal from the understory and overstory, foliage clumping) that could not be solved via the means of the conventional mono-angle remote sensing.

Leaf Area Index (LAI) is an essential parameter for process-based ecological and climate models. Spectral vegetation indices calculated from remote sensing data are widely used for LAI retrieval at large scales. The applicability of two vegetation indices, namely Simple Ratio (SR) and Reduced Simple Ration (RSR), for retrieving LAI at Maoershan mountain in Heilongjiang province of China was investigated through analyzing the correlations of SR and RSR calculated from Landsat-5 TM data acquired on 24 June, 2009 with LAI measured at 23 typical plots with Li-Cor LAI-2000 during 12 to 20 July, 2009. The fitted model with SR as the predator captured 54.7% of variations of LAI among these 23 plots while the variations of LAI explained by the fitted model with RSR as the predictor increased to 75.4%, indicating the better performance of RSR over SR in retrieving forest LAI in the study area owing to the ability of RSR to reduce the influence of soil background reflectance through the incorporation of the reflectance of shortwave infrared wavelength into SR. LAI derived from models based on SR and RSR was well correlated (R2=0.7219, N=180932). The mean of LAI estimated using the SR-based model was 0.37 larger than that estimated using the RSR-based model for the entire study area. LAI estimated using the former model was smaller than that estimated by the latter model when LAI estimated by the latter was larger than 5.6, indicating that RSR saturates slower than SR under the condition of high LAI. However, RSR is more sensitive to the influence of topography and shadows of clouds than SR.

Most forests of the world are recovering from a past disturbance. It is well known that forest disturbances profoundly affect carbon stock and fluxes in forest ecosystems, yet it has been a great challenge to assess disturbance impacts in estimates of forest carbon budgets. Net sequestration or loss of CO2 by forests after disturbance follows a predictable pattern with forest recovery. Forest age, which is related to time since disturbance, is the most available surrogate variable for various forest carbon analyses that concern the impact of disturbance. In this study, we compiled the first continental forest age map of North America by combining forest inventory data, historical fire data, optical satellite data and the dataset from NASA's LEDAPS project. Mexico and interior Alaska are excluded from this initial map due to unavailability of all required data sets, but work is underway to develop some different methodology for these areas. We discuss the significance of disturbance legacy from the past, as represented by current forest age structure in different regions of the US and Canada, tracking back disturbances caused by human and nature over centuries and at various scales. We also show how such information can be used with inventory data for analyzing carbon management opportunities, and other modeling applications. By combining geographic information about forest age with estimated C dynamics by forest type, it is possible to conduct a simple but powerful analysis of the net CO2 uptake by forests, and the potential for increasing (or decreasing) this rate as a result of direct human intervention in the disturbance/age status. The forest age map may also help address the recent concern that the terrestrial C sink from forest regrowth in North America may saturate in the next few decades. Finally, we describe how the forest age data can be used in large-scale carbon modeling, both for land-based biogeochemistry models and atmosphere-based inversion models, in order to improve the spatial accuracy of carbon cycle simulations.

A Poisson model is developed to describe sunfleck or gap size distributions beneath clumped plant canopies. This model is based on the assumption that foliage clumps are randomly distributed in space and foliage elements are randomly distributed within each clump. Using this model, the foliage clumping index, leaf area index ( L ), clump area index, element area index in each clump, and element and clump widths were successfully derived for two artificial canopies and a thinned and pruned Douglas-fir forest stand. It is shown that existing theories for deriving L from measurements of canopy gap fraction have limitations, and the use of canopy architectural information derived from canopy gap size distribution can substantially improve the technique for indirectly measuring L of plant canopies.

To eliminate the confusion in the definition of leaf area index (L) for non-flat leaves, the projection coefficients of several objects including spheres, cylinders, hemicircular cylinders, and triangular and square bars are investigated through mathematical derivation and numerical calculation for a range of ellipsoidal angular distributions. It is shown that the projection coefficient calculated based on half the total intercepting area is close to a constant of 0.5 when the inclination angle of the objects is randomly (spherically) distributed, whereas the calculated results based on the object's largest projected area are strongly dependent on the shape of the objects. Therefore, it is suggested that the leaf area index of non-flat leaves be defined as half the total intercepting area per unit ground surface area and that the definition of L based on the projected leaf area be abandoned.

[1] Ecosystem models that simulate biogeochemical processes usually ignore hydrological controls that govern them. It is quite possible that topographically driven water fluxes significantly influence the spatial distribution of C sources and sinks because of their large contribution to the local water balance. To investigate this, we simulated biogeochemical processes along with the associated feedback mechanisms in a boreal ecosystem using a spatially explicit hydroecological model, boreal ecosystem productivity simulator (BEPS)–TerrainLab V2.0, that has a tight coupling of ecophysiological, hydrological, and biogeochemical processes. First, the simulated dynamics of snowpack, soil temperature, net ecosystem productivity (NEP), and total ecosystem respiration (TER) were validated with high-frequency measurements for 2 years. The model was able to explain 80% of the variability in NEP and 84% of the variability in TER. Further, we investigated the influence of topographically driven subsurface base flow on soil C and N cycling and on the spatiotemporal patterns of C sources and sinks using three hydrological modeling scenarios that differed in hydrological conceptualizations. In general, the scenarios that had nonexplicit hydrological representation overestimated NEP, as opposed to the scenario that had an explicit (realistic) representation. The key processes controlling the NEP differences were attributed to the combined effects of variations in photosynthesis (due to changes in stomatal conductance and nitrogen (N) availability), heterotrophic respiration, and autotrophic respiration, all of which occur simultaneously affecting NEP. Feedback relationships were also found to exacerbate the differences. We identified six types of NEP differences (biases), of which the most commonly found was due to an underestimation of the existing C sources, highlighting the vulnerability of regional-scale ecosystem models that ignore hydrological processes.

SummaryInflammatory bowel disease (IBD) is characterized by heavy production of proinflammatory cytokines such as tumour necrosis factor (TNF)-α and interleukin (IL)-1β. Interactions of the autonomic nervous system with local immune cells play an important role in the development of IBD, and the balance of autonomic nerve function is broken in IBD patients with sympathetic overactivity. However, the function of catecholamines in the progress of colitis is unclear. In this study, we examined the role of catecholamines via α2-adrenoreceptor in acute murine colitis. The expression of tyrosine hydroxylase (TH) and dopamine b-hydroxylase (DBH), two rate-limiting enzymes in catecholamine synthesis, was detected by immunohistochemistry in murine colitis. Murine colitis was induced by dextran sodium sulphate or trinitrobenzene sulphonic acid (TNBS), and the mice were administered RX821002 or UK14304, α2-adrenoceptor antagonists or agonists. Colitis was evaluated by clinical symptoms, myeloperoxidase assay, TNF-α and IL-1β production and histology. Lamina propria mononuclear cells (LPMCs) from mice with TNBS colitis were cultured in the absence or presence of RX821002 or UK14304, and stimulated further by lipopolysaccharide. TH and DBH are induced in LPMCs of inflamed colon, the evidence of catecholamine synthesis during the process of colitis. RX821002 down-regulates the production of proinflammatory cytokines from LPMCs, while UK14304 leads to exacerbation of colitis. Together, our data show a critical role of catecholamines via α2-adrenoreceptors in the progress of acute colitis, and suggest that use of the α2-adrenoceptor antagonist represents a novel therapeutic approach for the management of colitis.

In daily, monthly, and annual respiration models for regional and global applications, the diurnal variation of temperature is generally ignored. As the effect of temperature on respiration is nonlinear, this ignorance may cause considerable errors in respiration estimation, but these errors have not yet been systematically investigated. This is in fact a central issue in temporal scaling of ecosystem models which are often applied in time steps equal to or larger than a day. In this study, we develop an integrated daily heterotrophic respiration model, and demonstrate first theoretically the importance of considering the diurnal amplitude of soil temperature and the vertical soil carbon distribution pattern in daily respiration estimation using the daily mean temperature. Measurements of soil respiration with roots exclusion made in a mature black spruce site in Saskatchewan, Canada, in July–September 2004 are used to validate the model. Daily heterotrophic respiration rates were underestimated by up to 15%, with a mean value of 4.5%, when only the mean daily temperature was used. This underestimation occurred under the conditions that the diurnal temperature amplitude in the forest was less than 12°C and the vertical distribution of organic carbon in the top 15–30 cm was uniform. Based on the integrated daily model, this underestimation at the same site would be 38% if the amplitude increases to 20°C, and in soils with steep vertical carbon distributions with a 20°C diurnal amplitude, it can increase to 44%. The magnitude of this underestimation is theoretically proportional to [ln(Q 10)]2. During the experimental period, the value of Q 10 for heterotrophic respiration was found to be 4.0–4.5. If Q 10 = 2.0, this underestimation is reduced to about 10% at a diurnal temperature amplitude of 20°C. An edited version of this paper was published by AGU. Copyright 2009 American Geophysical Union.

Radiation transmission through plant canopies is shown to be determined not only by the geometry of the foliage elements (leaves), but also by the geometry of foliage clumps. When the geometry of tree branches (thin slabs of foliage in which leaves are confined) was considered with a Poisson model, it was found that beam transmission measurements at a 62° rather than a 57.5° incident angle can provide a good estimate of the effective leaf area index (Le). The values of Le calculated from the model were approximately 55–65% of the actual leaf area index when the branches were assumed to consist of one layer of randomly dispersed leaves. The calculated results are consistent with experimental data obtained from a Douglas-fir forest stand, which was largely composed of near-horizontal branches.

Gridding the land surface into coarse homogeneous pixels may cause important biases on ecosystem model estimations of carbon budget components at local, regional and global scales. These biases result from overlooking subpixel variability of land surface characteristics. Vegetation heterogeneity is an important factor introducing biases in regional ecological modeling, especially when the modeling is made on large grids. This study suggests a simple algorithm that uses subpixel information on the spatial variability of land cover type to correct net primary productivity (NPP) estimates, made at coarse spatial resolutions where the land surface is considered as homogeneous within each pixel. The algorithm operates in such a way that NPP obtained from calculations made at coarse spatial resolutions are multiplied by simple functions that attempt to reproduce the effects of subpixel variability of land cover type on NPP. Its application to a carbon-hydrology coupled model(BEPS-TerrainLab model) estimates made at a 1-km resolution over a watershed (named Baohe River Basin) located in the southwestern part of Qinling Mountains, Shaanxi Province, China, improved estimates of average NPP as well as its spatial variability.

[1] In atmospheric transport models, the covariation of the atmospheric transport and annually neutral biospheric CO2 flux is usually evident as the annual zonal mean surface CO2 concentration gradient. Using the NIES transport model and CO2 flux from the Biome-BGC model, the covariations of different transport mechanisms and CO2 flux were examined and quantified. Including the covariation of the total transport (processes included in the NIES model) and CO2 flux, the annual average pole to pole CO2 concentration gradient is 3.5 ppm and interhemispheric difference of the average extratropical surface concentration is 2.5 ppm. The conventional covariation mechanism of the seasonal variation of planetary boundary layer mixing height and CO2 flux accounts for approximately 45% of the CO2 concentration gradient. Another important contribution to the CO2 concentration gradient in this model is the covariation of the extratropical anomaly transport (mainly by cyclones and anticyclones) and the biospheric flux, which accounts for about 55%. This alternate physical mechanism is the association of stronger meridional (north–south) anomaly transport (under strong baroclinic instability condition) with higher CO2 concentration from soil respiration in the winter and weaker anomaly transport (weak baroclinic instability condition) with lower CO2 concentration from photosynthetic uptake in the summer. The net result of the meridional transport and flux covariation is a north–south annual zonal mean CO2 concentration gradient.

[1] Using the Integrated Terrestrial Ecosystem Carbon Cycle model (InTEC), six simulations with different input scenarios of climate, CO2, and nitrogen (N) deposition are conducted to study the changes of soil carbon (C) content in Canada's forests and wetlands during 1901–2000. Simulated total C stored in Canada's forest and wetland soils is 164.5 Pg C and accounts for about 7% of the global total of 2400 Pg C to the depth of 2 m, implying the significance of Canada's forest and wetland soils in the global terrestrial C cycle. Soils of Canadian forests and wetlands sequestrated 3.9 Pg C (2.6 Pg C in forests and 1.3 Pg C in wetlands) during 1901–2000 because of the integrated effects of climate, CO2 fertilization, N deposition, and forest age factors. The changes of soil C content during 1901–2000 ranged spatially from −2 kg C m−2 to 4 kg C m−2, depending on fire disturbance history, climate change pattern, and N deposition rates. Soil C increased by 2 to 4 kg C m−2 in Eastern Hudson Plains, Eastern Middle Boreal Shield, Southern Boreal Shield, and Atlantic Maritime and decreased by more than 1 kg C m−2 in Southern Boreal Plains. Simulations shows that climate influences growing conditions, growing season length, net N mineralization, and N fixation and therefore was the biggest driver of the increase in total soil C content during 1901–2000, followed by CO2 fertilization and N deposition. The climate-induced increase of soil C occurred mainly in the cool and wet period from the middle 1940s to the middle 1970s. Overall, an increase of 1°C in mean annual temperature induced heterotrophic respiration to increase by 62 Tg C a−1. In contrast to the century-scale trend from 1901 to 2000, during the last two decades (1981–2000), CO2 fertilization was the biggest driver of the increase in soil C, while climate change alone caused soil C to decrease.

regional GPP from mixing ratio measurements, we also compared the estimates of regional GPP with estimates made using eddy covariance (EC) flux measurements, although their respective source areas are different. They had similar seasonal patterns, but the regional estimates were consistently smaller than the local EC flux derived GPP throughout the growing season in 2003. These estimates of annual regional GPP were 649–664 g C m � 2 for 2003 while the EC-derived annual GPP was 819–847 g C m � 2 . The annual difference was about 20–25%. The EC flux footprint of the tower was relatively homogeneous old black spruce while the concentration footprint, which was a few orders of magnitude larger than the flux footprint, covered boreal evergreen and deciduous broadleaf forests, grassland, cropland, and lakes. Nonforested land occupied about 10–50% of the concentration footprint depending on wind direction and speed and was less productive than the black spruce forest. The discrepancies between regional and local GPP estimates reflected the differences in underlying land surfaces represented by the different footprint areas.

[1] Carbon dynamics in peatlands are controlled, in large part, by their wetness as defined by water table depth and volumetric liquid soil moisture content. A common type of peatland is raised bogs that typically have a multiple-layer canopy of vascular plants over a Sphagnum moss ground cover. Their convex form restricts water supply to precipitation and water is shed toward the margins, usually by lateral subsurface flow. The hydraulic gradient for lateral subsurface flow is governed by the peat surface topography at the mesoscale (∼200 m to 5 km). To investigate the influence of mesoscale topography on wetness, evapotranspiration (ET), and gross primary productivity (GPP) in a bog during the snow-free period, we compare the outputs of a further developed version of the daily Boreal Ecosystem Productivity Simulator (BEPS) with observations made at the Mer Bleue peatland, located near Ottawa, Canada. Explicitly considering mesoscale topography, simulated total ET and GPP correlate well with measured ET (r = 0.91) and derived gross ecosystem productivity (GEP; r = 0.92). Both measured ET and derived GEP are simulated similarly well when mesoscale topography is neglected, but daily simulated values are systematically underestimated by about 10% and 12% on average, respectively, due to greater wetness resulting from the lack of lateral subsurface flow. Owing to the differences in moss surface conductances of water vapor and carbon dioxide with increasing moss water content, the differences in the spatial patterns of simulated total ET and GPP are controlled by the mesotopographic position of the moss ground cover.

The concept of combining multi-angle and hyperspectral remote sensing has been developed and utilized in an existing satellite observation system CHRIS (Compact High-Resolution Imaging Spectrometer) onboard the Project for On-Board Autonomy (PROBA) platform developed by the European Space Agency (ESA). We attempt to refine this measurement concept by testing a new one: a system that acquires hyperspectral signals only in the nadir direction but measures in two additional directions in two spectral bands, red and NIR. Through model experiments, we demonstrate that the combination of the hotspot and darkspot reflectances has the strongest signals about the vegetation structure quantified using the foliage clumping index. The results suggest that the multi-angle hyperspectral data exhibit much redundancy, and that the multi-spectral measurements at off-nadir angles in addition to the nadir hyperspectral data would be sufficient to reconstruct hyperspectral signatures at off-nadir angles.

It is with high spectral resolution, medium spatial resolution, and multi-directional imagery from CHRIS that forest structural parameters can be retrieved over our test site, the Greater Victoria Watershed District. This analysis requires an understanding of the anisotropic nature of forest canopies as measured by spaceborne hyperspectral sensors and modeled by radiative transfer models. The Evaluation and Validation of CHRIS for National Forests Project (EVC) was selected by ESA's science team for their hyperspectral sensor, CHRIS as part of the PROBA mission. On September 2nd to 4th, 2006, a triplet acquisition over the Greater Victoria Watershed District (GVWD), taken in Mode-1, provided us with 15 look angles. The Minimum Zenith Angles (MZA) for each date were +20deg, -2deg and -23deg respectively, each of which has five nominal Fly-by Zenith Angles (FZA) of plusmn55deg, plusmn36deg and 0deg. This triplet has been processed and analyzed in order to assess the utility of CHRIS data for mappin.g forest parameters. CHRIS algorithms for producing accurate estimates of forest parameters such as conifer forest species and biomass were compared with 5-Scale Model simulations. The spectral information content provides information on the content of the forest canopy while the multi-angle imagery offers information on the structural components of the forest canopy. This paper provides an update on the status of this work in progress.

Gridding the land surface into coarse homogeneous pixels may cause important biases on ecosystem model estimations of carbon budget components at local, regional and global scales. One of the main causes resulted in these biases is overlooking of sub-pixel variability of topography, especially in a mountainous area. This study analyzes the significance of topography to correct net primary productivity (NPP) estimates, made at coarse spatial resolutions where the land surface is considered as homogeneous within each pixel. Its application to a remote sensing process-based model estimates made at a 1-km resolution over a mountainous forested watershed located in Baohe River Basin in China. Results of this study show that NPP spatial scaling in complex terrain depends on the amount of the distortion of the soil moisture field at the coarse resolution, and the spatial redistribution and movement of soil water in complex terrain tightly affect NPP distribution, suggest that it is indeed necessary to consider topography in NPP spatial scaling.

[1] The temporal variation of the CO 2 mixing ratio in the atmosphere at a given height results from several processes, including photosynthesis and respiration of the underlying ecosystems, the vertical mixing of the atmosphere near the surface and in the planetary boundary layer (PBL), and entrainment of the air above the PBL. Theoretically, if all atmospheric processes are modeled accurately, we can estimate the magnitude of ecosystem photosynthesis and respiration from the variations in the measured CO 2 mixing ratio. Through analyzing the CO 2 concentration measured at several heights (30 m, 122 m, and 396 m) on the Wisconsin tall tower, we demonstrate that it is possible to derive the daily carbon flux resulting from CO 2 uptake from hourly CO 2 mixing ratio data. At 30 m, the concentration-derived daily gross primary productivity (GPP) is well correlated with measured daily GPP derived from flux measurements (r 2 = 0.70), but the former was 20% larger than the latter. The correlation increased considerably for 10-day averages (r2 = 0.87). As the variations at lower heights have larger diurnal CO 2 amplitudes, the concentration-derived GPP is more accurate at lower heights. The footprint distance of CO 2 concentration during the daytime under the influence of the mixed layer is estimated to be of the order of 10 km, or a footprint area of 10 3 -10 4 km 2 , which is much larger than that of CO 2 fluxes measured using eddy covariance methods (typically 1 km 2 ). The difference in these footprint areas may partly explain the differences between these two flux estimates at the Wisconsin tower. These differences also signify the importance of retrieving flux information from the mixing ratio as it provides a means to upscale from local sites to a region.

Both surface topography and vegetation heterogeneity are important factors introducing biases in regional ecological modeling, especially when the modeling is made at large grids. Several studies have demonstrated that gridding the land surface into coarse homogeneous pixels may cause important biases on ecosystem model estimations of carbon budget components at local, regional and global scales. These biases result from overlooking sub-pixel variability of land surface characteristics. This study suggests a simple algorithm that uses sub-pixel information on the spatial variability of vegetation and surface topography to correct net primary productivity (NPP) estimates, made at coarse spatial resolutions where the land surface is considered as homogeneous within each pixel. A spatial scaling algorithm is developed to correct biases in coarse-resolution NPP estimation. This algorithm considers the effect of sub-pixel heterogeneities of land cover, leaf area index (LAI), slope and elevation. Its application to a carbon-hydrology coupled model estimates made at a 1-km resolution over a watershed (named Baohe River Basin) located in the southwestern part of Qinling Mountains, in China, Shaanxi Province, China, improved estimates of average NPP as well as its temporal and spatial variability.

Understanding the spatial and temporal variation in carbon fluxes is essential to constrain models that predict climate change. However, our current knowledge of spatial and temporal patterns is uncertain, particularly over land. The ESA GLOBCARBON project aims to generate estimates of at-land products quasi-independent of the original Earth Observation source for use in Dynamic Global Vegetation Models, a central component of the ESSP Global Carbon Project. The service features global estimates of: burned area, fAPARS, LAI and vegetation growth cycle. The demonstrator focused on six complete years, from 1998 to 2003 when overlap exists between ESA Earth Observation sensors (ATSR-2, AATSR and MERIS) and VEGETATION but has recently been extended to 2007. This paper presents early results of the first re-processing in the GLOBCARBON project, which was undertaken after comments from users involved in beta testing.

In this paper, we report on a process-based approach to estimate leaf chlorophyll content from hyperspectral remote sensing imagery. Extensive field and laboratory measurements were conducted for ten sites in black spruce (Picea mariana) forests near Sudbury, Ontario, Canada in 2003 and 2004. Leaf optical spectra and chlorophyll content, leaf and canopy biophysical parameters, and forest background optical properties were collected. Hyperspectral remote sensing images were acquired by the compact airborne spectrographic imager (CASI) over the study sites within one week of ground measurements. Using measured data as inputs, a geometrical- optical model 4-Scale was investigated to estimate forest canopy reflectance. The simulated canopy reflectance agrees well with the CASI measured reflectance. A look-up-table approach was developed to provide the probabilities of viewing sunlit foliage and background, and to determine a spectral multiple scattering factor as functions of leaf area index, view zenith angle, and solar zenith angle. With the look-up-tables, leaf reflectance spectra were inverted from hyperspectral remote sensing imagery. Leaf chlorophyll content was estimated from the retrieved leaf reflectance spectra using the modified leaf-level PROSPECT inversion model.

Forests play a critical role in mitigating climate change because of their high carbon storage and productivity. China has experienced a pronounced increase in forest area resulting from afforestation and reforestation activities since the 1970s. However, few comprehensive analyses have been made to assess the recent dynamics of biomass carbon sinks in China's forests. This study refined biomass carbon sinks of China's forests based on eight forest inventories from 1973 to 2013. These sinks increased from 25.0 to 166.5 Tg C yr−1 between 1973 and 2008, and then decreased to 130.9 Tg C yr−1 for the period of 2009–2013 because the increases in forest area and biomass carbon density became slower. About 7% and 93% of this sink reduction occurred in planted and natural forests. The carbon sinks for young, middle-aged and premature forests decreased by 27.3, 27.0, and 7.6 Tg C yr−1, respectively. 42% of this decrease was offset by mature and overmature forests. During 2009–2013, forest biomass carbon sinks decreased in all regions but the north and northwest regions. The drivers for changes of forest biomass sinks differ spatially. More intensive harvest of young and middle-aged forests and snow damage were the major drivers for the decreases of biomass carbon sinks in the east (8.0 Tg C yr−1) and south (19.8 Tg C yr−1) regions. The carbon sink reduction in the southwest region (16.7 Tg C yr−1) was mainly caused by increased timber harvesting and natural disturbances, such as droughts in Yunnan province, snow damage in Guizhou province and forest fires in Sichuan province. In the northeast region, the sink reduction occurred mainly in Heilongjiang province (7.9 Tg C yr−1) and was caused dominantly by the combined effects of diseases, windthrow and droughts. The carbon sink increase was primarily attributed to forest growth and decreased deforestation in the north (10.0 Tg C yr−1) and northwest (2.3 Tg C yr−1) regions.

Forest ecosystem is an important component of terrestrial ecosystem and plays an important role in global changes. Aboveground biomass (AGB) of forest ecosystem is an important factor in global carbon cycle studies. The purpose of this study was to retrieve the yearly Net Primary Productivity (NPP) of forest from the 8-days-interval MODIS-LAI images of a year and produce a yearly NPP distribution map. The LAI, DBH (diameter at breast height), tree height, and tree age field were measured in different 80 plots for Chinese fir, Masson pine, bamboo, broadleaf, mix forest in Liping County. Based on the DEM image and Landsat TM images acquired on May 14th, 2000, the geometric correction and terrain correction were taken. In addition, the "6S"model was used to gain the surface reflectance image. Then the correlation between Leaf Area Index (LAI) and Reduced Simple Ratio (RSR) was built. Combined with the Landcover map, forest stand map, the LAI, aboveground biomass, tree age map were produced respectively. After that, the 8-days- interval LAI images of a year, meteorology data, soil data, forest stand image and Landcover image were inputted into the BEPS model to get the NPP spatial distribution. At last, the yearly NPP spatial distribution map with 30m spatial resolution was produced. The values in those forest ecological parameters distribution maps were quite consistent with those of field measurements. So it's possible, feasible and time-saving to estimate forest ecological parameters at a large scale by using remote sensing.

This article serves as an introduction to this special issue, "China's Forest Carbon Sequestration", representing major results of a project sponsored by the Canadian International Development Agency and the Chinese Academy of Sciences. China occupies a pivotal position globally as a principle emitter of carbon dioxide, as host to some of the world's largest reforestation efforts, and as a key player in international negotiations aimed at reducing global greenhouse gas emission. The goals of this project are to develop remote sensing approaches for quantifying forest carbon balance in China in a transparent manner, and information and tools to support land-use decisions for enhanced carbon sequestration (CS) that are science based and economically and socially viable. The project consists of three components: (i) remote sensing and carbon modeling, (ii) forest and soil assessment, and (iii) integrated assessment of the socioeconomic implications of CS via forest management. Articles included in this special issue are highlights of the results of each of these components. (C) 2006 Elsevier Ltd. All rights reserved.

This research attempts to model the complexity of planting trees to increase China's CO(2) sequestration potential by using a GIS-based integrated assessment (IA) approach. We use the IA model to assess the impact of China's Grain for Green reforestation and afforestation program on farmer and state incomes as well as CO(2) sequestration in Liping County, Guizhou Province. The IA model consists of five sub-models for carbon sequestration, crop income, timber income, Grain for Green, and carbon credits. It also includes a complementary qualitative module for assessing program impacts by gender and ethnicity. Using four scenarios with various assumptions about types of trees planted, crop incomes by township, CO(2) credit prices, state subsidies, methods for estimating carbon sequestered, and harvesting of trees, we find great variation in the impact of the Grain for Green program on incomes and on carbon sequestered over a 48 year period at both the county and township levels.

[1] This paper discusses the quality and the accuracy of the Joint Research Center (JRC) fraction of absorbed photosynthetically active radiation (FAPAR) products generated from an analysis of Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data. The FAPAR value acts as an indicator of the presence and state of the vegetation and it can be estimated from remote sensing measurements using a physically based approach. The quality of the SeaWiFS FAPAR products assessed in this paper capitalizes on the availability of a 6-year FAPAR time series over the full globe. This evaluation exercise is performed in two phases involving, first, an analysis of the verisimilitude of the FAPAR products under documented environmental conditions and, second, a direct comparison of the FAPAR values with ground-based estimations where and when the latter are available. This second phase is conducted following a careful analysis of problems arising for performing such a comparison. This results in the grouping of available field information into broad categories representing different radiative transfer regimes. This strategy greatly helps the interpretation of the results since it recognizes the various levels of difficulty and sources of uncertainty associated with the radiative sampling of different types of vegetation canopies.

The integrated terrestrial ecosystem C-budget model (InTEC) developed by Chen and co-workers has been used successfully to predict carbon dynamics of forests in Canada. It was tested here for forest soil organic carbon (SOC) density of China's northern temperate zone and southern subtropical zone. The results show that the simulated SOC density is highly correlated and in broad agreement with observations in Liping and in Changbaishan, representing the southern subtropical zone and the northern temperate zone in China, respectively. SOC density ranged from 2.2 to 11.2 kg/m 2 in Liping and from 3.4 to 14.8 kg/m 2 in Changbaishan. The correlation coefficients ( r 2 ) are 0.63 ( N =16) and 0.76 ( N =14) between the simulated and measured data in Liping and Changbaishan, respectively. The SOC densities under different vegetation types in Liping decrease in the order of mixed forest, broadleaf forest, Chinese fir, couch grass, and Chinese redpine, and in Changbaishan in the order of mixed forest, silver fir, larch forest, and birch forest.

Open boreal forests present a challenge in understanding remote sensing signals acquired under various solar and view geometry. Much research is needed to improve the ability to model the bidirectional reflectance distribution for retrieving the surface information using measurements at a few angles. The geometric-optical bidirectional reflectance model outlined in this paper differs from Li-Strahler's model in the following respects: (i) the assumption of random spatial distribution of trees is replaced by the Neyman distribution which is able to generate the clumpiness of a forest stand; (ii) the multiple mutual shadowing effect among tree crowns is considered using the combination of the negative binomial and the Neyman distribution theory; (iii) the probability of observing the sunlit background is modelled using a canopy gap size distribution function which is shown to affect the size and width of the hotspot; (iv) the branch architecture of conifer trees affecting the directional reflectance is simulated using a simple angular radiation penetration function; (v) the tree crown surface is treated as a complex surface with small structures which themselves generate mutual shadows and a hotspot. All these levels of canopy architecture are shown to have important effects on the directional distribution of the reflected radiance from boreal forests. The model results compare well with a data set from a spruce forest. The model after validation is used as a tool to retrieve the leaf area index (LAI) of plant canopies according to a vegetation index calculated from the modelled red and near infrared bidirectional reflectance factors at various solar and view angles. The importance of canopy architecture (tree density and distribution, and branch and shoot structure) in the retrieval of LAI will be demonstrated.

[1] Ecosystem CO2 exchange with the atmosphere and the planetary boundary layer (PBL) dynamics are correlated diurnally and seasonally. The strength of this kind of covariation is quantified as the rectifier effect, and it affects the vertical gradient of CO2 and thus the global CO2 distribution pattern. An 11-year (1990–1996, 1999–2002), continuous CO2 record from Fraserdale, Ontario (49°52′29.9″N, 81°34′12.3″W), along with a coupled vertical diffusion scheme (VDS) and ecosystem model named Boreal Ecosystem Productivity Simulator (BEPS), are used to investigate the interannual variability of the rectifier effect over a boreal forest region. The coupled model performed well (r2 = 0.70 and 0.87, at 40 m at hourly and daily time steps, respectively) in simulating CO2 vertical diffusion processes. The simulated annual atmospheric rectifier effect varies from 3.99 to 5.52 ppm, while the diurnal rectifying effect accounted for about a quarter of the annual total (22.8∼28.9%).The atmospheric rectification of CO2 is not simply influenced by terrestrial source and sink strengths, but by seasonal and diurnal variations in the land CO2 flux and their interaction with PBL dynamics. Air temperature and moisture are found to be the dominant climatic factors controlling the rectifier effect. The annual rectifier effect is highly correlated with annual mean temperature (r2 = 0.84), while annual mean air relative humidity can explain 51% of the interannual variation in rectification. Seasonal rectifier effect is also found to be more sensitive to climate variability than diurnal rectifier effect.

We developed algorithms for spatial scaling of leaf area index (LAI) using sub-pixel information. The study area is located near Liping County, Guizhou Province, in China. Methods for LAI spatial scaling were investigated on LAI images with 960 m resolution derived in two ways. LAI from distributed calculation (LAID) was derived using Landsat ETM+ data (30 m), and LAI from lumped calculation (LAIL) was obtained from the coarse (960 m) resolution data derived through resampling the ETM+ data. We found that lumped calculations can be considerably biased compared to the distributed (ETM+) case, suggesting that global and regional LAI maps can be biased if surface heterogeneity within the mapping resolution is ignored. Based on these results, we developed algorithms for removing the biases in lumped LAI maps using sub-pixel land cover-type information, and applied these to correct one coarse resolution LAI product which greatly improved its accuracy.

Leaf area index (LAI) is an important ecological and environmental parameter. A new LAI algorithm is developed using the principles of ground LAI measurements based on canopy gap fraction. First, the relationship between LAI and gap fraction at various zenith angles is derived from the definition of LAI. Then, the directional gap fraction is acquired from a remote sensing bidirectional reflectance distribution function (BRDF) product. This acquisition is obtained by using a kernel driven model and a large-scale directional gap fraction algorithm. The algorithm has been applied to estimate a LAI distribution in China in mid-July 2002. The ground data acquired from two field experiments in Changbai Mountain and Qilian Mountain were used to validate the algorithm. To resolve the scale discrepancy between high resolution ground observations and low resolution remote sensing data, two TM images with a resolution approaching the size of ground plots were used to relate the coarse resolution LAI map to ground measurements. First, an empirical relationship between the measured LAI and a vegetation index was established. Next, a high resolution LAI map was generated using the relationship. The LAI value of a low resolution pixel was calculated from the area-weighted sum of high resolution LAIs composing the low resolution pixel. The results of this comparison showed that the inversion algorithm has an accuracy of 82%. Factors that may influence the accuracy are also discussed in this paper.

[1] The magnitude and spatial distribution of the carbon sink in the extratropical Northern Hemisphere remain uncertain in spite of much progress made in recent decades. Vertical CO2 diffusion in the planetary boundary layer (PBL) is an integral part of atmospheric CO2 transport and is important in understanding the global CO2 distribution pattern, in particular, the rectifier effect on the distribution [Keeling et al., 1989; Denning et al., 1995]. Attempts to constrain carbon fluxes using surface measurements and inversion models are limited by large uncertainties in this effect governed by different processes. In this study, we developed a Vertical Diffusion Scheme (VDS) to investigate the vertical CO2 transport in the PBL and to evaluate CO2 vertical rectification. The VDS was driven by the net ecosystem carbon flux and the surface sensible heat flux, simulated using the Boreal Ecosystem Productivity Simulator (BEPS) and a land surface scheme. The VDS model was validated against half-hourly CO2 concentration measurements at 20 m and 40 m heights above a boreal forest, at Fraserdale (49°52′29.9″N, 81°34′12.3″W), Ontario, Canada. The amplitude and phase of the diurnal/seasonal cycles of simulated CO2 concentration during the growing season agreed closely with the measurements (linear correlation coefficient (R) equals 0.81). Simulated vertical and temporal distribution patterns of CO2 concentration were comparable to those measured at the North Carolina tower. The rectifier effect, in terms of an annual-mean vertical gradient of CO2 concentration in the atmosphere that decreases from the surface to the top of PBL, was found at Fraserdale to be about 3.56 ppmv. Positive covariance between the seasonal cycles of plant growth and PBL vertical diffusion was responsible for about 75% of the effect, and the rest was caused by covariance between their diurnal cycles. The rectifier effect exhibited strong seasonal variations, and the contribution from the diurnal cycle was mostly confined to the surface layer (less than 300 m).

The boreal ecosystem encircles the Earth above about 48° N, covering Alaska, Canada, and Eurasia. It is second in areal extent only to the world’s tropical forests and occupies about 21% of the Earth’s forested land surface (Whittaker and Likins 1975). Nutrient cyding rates are relatively low in the cold wet boreal soils. Whittaker and Likins (1975) estimate the annual net primary productivity of the boreal forest at 800 g C m-2y-1 and its tundra at 140 g C m-2y-1, in contrast to tropical forests averaging 2200 g C m-2y-1 and temperate forests at 1250 g C m-2y-1. However, the relatively low nutrient cyding rates at high latitudes result in relatively high longterm boreal carbon storage rates averaging roughly 30 to 50 g C m-2y-1 (Harden et al. 1992), a result of relatively high root turnover from trees, shrubs and mosses with relatively low decomposition rates. Over the past few thousand years, these below-ground storage processes have created a large and potentially mobile reservoir of carbon in the peats and permafrost of the boreal ecosystem. Currently, the boreal ecosystem is estimated to contain approximately 13% of the Earth’s carbon, stored in the form of above-ground biomass and 43% of the Earth’s carbon stored below-ground in its soils (Schlesinger 1991). Meridional gradients in atmospheric CO2 concentrations suggest that forests above 40° N Sequester as much as 1 to 2 gigatons of carbon annually (Denning et al. 1995; Randerson et al. 1997), or nearly 15 to 30% of that injected into the atmosphere each year through fossil fuel combustion and deforestation. Given the enormous areal extent of the ecosystem, roughly 20 Mkm2 (Sellers et al. 1996b; >Fig. A.45), shifts in carbon flux of as little as 50 g C m-2y-1 can contribute or remove one gigaton of carbon annually from the atmosphere. The size of the boreal forest, its sensitivity to relatively small climatic variations, its influence on global climate and the global carbon cyde, therefore, make it critically important to better understand and represent boreal ecosystem processes correctly in global models.

The Boreal Ecosystem Atmosphere Study (BOREAS) was a large, multiyear internationally supported study designed to improve our understanding of the boreal forest biome and its interactions with the atmosphere, biosphere, and the carbon cycle in the face of global climate change. In the initial phase of this study (early 1990s), remote sensing played a key role by providing products needed for planning and modeling. During and after the main BOREAS field campaigns (1994 and 1996), innovative remote sensing approaches and analyses expanded our understanding of the boreal forest in four key areas: (1) definition of vegetation structure, (2) land-cover classification, (3) assessment of the carbon balance, and (4) links between surface properties, weather, and climate. In addition to six BOREAS special issues and over 500 journal papers, a principal legacy of BOREAS is its well-documented and publicly available database, which provides a lasting scientific resource and opportunity to further advance our understanding of this critical northern biome., Funding sources for BOREAS included NASA, the US National Science Foundation, the US Geological Survey, the US Forest Service, the US Environmental Protection Agency, the Canada Centre for Remote Sensing, Natural Resources Canada, Environment Canada, Natural Sciences and Engineering Research Council of Canada, Agriculture and Agri-Food Canada, National Research Council of Canada, Parks Canada, Canadian Forest Service, Institute for Space and Terrestrial Science (now CRESTech), Canadian Space Agency, and the Royal Society of Canada.

Leaf area index (LAI) retrieval from remote sensing is a very active research field. In heterogeneous canopies, simulations with the canopy reflectance model five-scale show that nadir view vegetation indices, such as NDVI and simple ratio (SR), based on near infrared and red reflectance are more closely related to the gap fraction at the solar zenith angle than the LAI. The gap fraction is important in canopy light interception. At the solar zenith angle, it can be used to estimate the amount of sunlit LAI. But the knowledge or LAI and foliage heterogeneity are both needed to estimate shaded leaves that are also important in the carbon cycle. In our previous studies, we developed a methodology to retrieve the foliage heterogeneity, represented by a clumping index, from remote sensing. The retrieval is accomplished with an anisotropy index using broadband directional reflectance at the hotspot and at the darkspot. The combination of nadir, hotspot, and darkspot views allows the LAI retrieval for a given cover type. However, directional measurements are not usually acquired with the same sun-target-sensor geometry, so the anisotropy kernel-based four-scale linear model for anisotropy reflectance (FLAIR) is used to interpolate the directional reflectance from ADEOS-POLDER data to acquire hotspot and darkspot reflectance at a common geometry in the near infrared band. A landcover map at 1-km based on SPOT-VGT data acquired in 1998 and directional POLDER data acquired over Canada in June 1997, are used to map the clumping index. The results show consistent clumping index values compared to in-situ values, and that SR and the anisotropy index are not correlated to each other, indicating that both indices are related to different canopy properties.

In-situ estimations of leaf area index (LAI), leaf clumping, and leaf angular distribution are often performed from canopy gap fraction measurements with optical sensors. Two new procedures are used in this study to improve the estimation of gap fraction from digital camera photographs,: 1) the digital number (DN) of mixed sky-canopy pixels is used to estimate the within pixel gap fraction instead of the usual threshold used to separate a pixel in gap or a foliage pixel, and 2), the within pixel gap fraction is calculated at different view zenith and azimuth angles to take into account multiple scattering effects. To estimate foliage clumping, a gap size distribution is calculated from a narrow view zenith angle range (less than 1/spl deg/). The clumping index is then extracted using 3 methods: 1) a refined gap size distribution theory developed for the TRAC instruments; 2) the Lang and Xiang (1986) logarithm gap fraction averaging and 3) a combination of 1) and 2). Clumping index variations with view zenith angle in the range from 15/spl deg/ to 70/spl deg/ are derived using the individual and combined methods. Analysis of the digital hemispherical photographs shows that 1) the three methods give different clumping estimates, but the angular variation patterns are similar, and 2) canopies with significant angular variation in clumping can induce large errors in the inverted leaf angle distribution when the clumping angular variation is not included in the retrieval. The practical implication of these findings is that LAI, clumping index, and foliage orientation can all be reliably retrieved using digital hemispherical photographs, considerably reducing the number and cost of instruments needed in fieldwork.