Overstory Leaf Area Index Research Articles

Assessment of Active LiDAR Data and Passive Optical Imagery for Double-Layered Mangrove Leaf Area Index Estimation: A Case Study in Mai Po, Hong Kong

Remote sensing technology is a timely and cost-efficient method for leaf area index (LAI) estimation, especially for less accessible areas such as mangrove forests. Confounded by the poor penetrability of optical images, most previous studies focused on estimating the LAI of the main canopy, ignoring the understory. This study investigated the capability of multispectral Sentinel-2 (S2) imagery, airborne hyperspectral imagery (HSI), and airborne LiDAR data for overstory (OLe) and understory (ULe) LAI estimation of a multi-layered mangrove stand in Mai Po, Hong Kong, China. LiDAR data were employed to stratify the overstory and understory. Vegetation indices (VIs) and LiDAR metrics were generated as predictors to build regression models against the OLe and ULe with multiple parametric and non-parametric methods. The OLe model fitting results were typically better than ULe because of the dominant contribution of the overstory to the remotely sensed signal. A single red-edge VI derived from HSI data delivered the lowest RMSE of 0.12 and the highest R2adj of 0.79 for OLe model fitting. The synergetic use of LiDAR metrics and S2 VIs performed best for ULe model fitting with RMSE = 0.33, R2adj = 0.84. OLe estimation benefited from the high spatial and spectral resolution HSI that was found less confounded by the understory. In addition to their penetration attributes, LiDAR data could separately describe the upper and lower canopy, which reduced the noise from other components, thereby improving the ULe estimation.

Spatially Distributed Overstory and Understory Leaf Area Index Estimated from Forest Inventory Data

Forest change affects the relative magnitudes of hydrologic fluxes such as evapotranspiration (ET) and streamflow. However, much is unknown about the sensitivity of streamflow response to forest disturbance and recovery. Several physically based models recognize the different influences that overstory versus understory canopies exert on hydrologic processes, yet most input datasets consist of total leaf area index (LAI) rather than individual canopy strata. Here, we developed stratum-specific LAI datasets with the intent of improving the representation of vegetation for ecohydrologic modeling. We applied three pre-existing methods for estimating overstory LAI, and one new method for estimating both overstory and understory LAI, to measurements collected from a probability-based plot network established by the US Forest Service’s Forest Inventory and Analysis (FIA) program, for a modeling domain in Montana, MT, USA. We then combined plot-level LAI estimates with spatial datasets (i.e., biophysical and remote sensing predictors) in a machine learning algorithm (random forests) to produce annual gridded LAI datasets. Methods that estimate only overstory LAI tended to underestimate LAI relative to Landsat-based LAI (mean bias error ≥ 0.83), while the method that estimated both overstory and understory layers was most strongly correlated with Landsat-based LAI (r2 = 0.80 for total LAI, with mean bias error of -0.99). During 1984-2019, interannual variability of understory LAI exceeded that for overstory LAI; this variability may affect partitioning of precipitation to ET vs. runoff at annual timescales. We anticipate that distinguishing overstory and understory components of LAI will improve the ability of LAI-based models to simulate how forest change influences hydrologic processes.

Regional-scale data assimilation with the Spatially Explicit Individual-based Dynamic Global Vegetation Model (SEIB-DGVM) over Siberia

This study examined the regional performance of a data assimilation (DA) system that couples the particle filter and the Spatially Explicit Individual-based Dynamic Global Vegetation Model (SEIB-DGVM). This DA system optimizes model parameters of defoliation and photosynthetic rate, which are sensitive to phenology in the SEIB-DGVM, by assimilating satellite-observed leaf area index (LAI). The experiments without DA overestimated LAIs over Siberia relative to the satellite-observed LAI, whereas the DA system successfully reduced the error. DA provided improved analyses for the LAI and other model variables consistently, with better match with satellite observed LAI and with previous studies for spatial distributions of the estimated overstory LAI, gross primary production (GPP), and aboveground biomass. However, three main issues still exist: (1) the estimated start date of defoliation for overstory was about 40 days earlier than the in situ observation, (2) the estimated LAI for understory was about half of the in situ observation, and (3) the estimated overstory LAI and the total GPP were overestimated compared to the previous studies. Further DA and modeling studies are needed to address these issues.

Monitoring LAI, Chlorophylls, and Carotenoids Content of a Woodland Savanna Using Hyperspectral Imagery and 3D Radiative Transfer Modeling

Leaf pigment contents, such as chlorophylls a and b content (C a b ) or carotenoid content (Car), and the leaf area index (LAI) are recognized indicators of plants’ and forests’ health status that can be estimated through hyperspectral imagery. Their measurement on a seasonal and yearly basis is critical to monitor plant response and adaptation to stress, such as droughts. While extensively done over dense canopies, estimation of these variables over tree-grass ecosystems with very low overstory LAI (mean site LAI < 1 m 2 /m 2 ), such as woodland savannas, is lacking. We investigated the use of look-up table (LUT)-based inversion of a radiative transfer model to retrieve LAI and leaf C a b and Car from AVIRIS images at an 18 m spatial resolution at multiple dates over a broadleaved woodland savanna during the California drought. We compared the performances of different cost functions in the inversion step. We demonstrated the spatial consistency of our LAI, C a b , and Car estimations using validation data from low and high canopy cover parts of the site, and their temporal consistency by qualitatively confronting their variations over two years with those that would be expected. We concluded that LUT-based inversions of medium-resolution hyperspectral images, achieved with a simple geometric representation of the canopy within a 3D radiative transfer model (RTM), are a valid means of monitoring woodland savannas and more generally sparse forests, although for maximum applicability, the inversion cost functions should be selected using validation data from multiple dates. Validation revealed that for monitoring use: The normalized difference vegetation index (NDVI) outperformed other indices for LAI estimations (root mean square error (RMSE) = 0.22 m 2 /m 2 , R 2 = 0.81); the band ratio ρ 0.750 μ m ρ 0.550 μ m retrieved C a b more accurately than other chlorophylls indices (RMSE = 5.21 μ g/cm 2 , R 2 = 0.73); RMSE over the 0.5–0.55 μ m interval showed encouraging results for Car estimations.

Simulating solar-induced chlorophyll fluorescence in a boreal forest stand reconstructed from terrestrial laser scanning measurements

Solar-induced chlorophyll fluorescence (SIF) has been shown to be a suitable remote sensing proxy of photosynthesis at multiple scales. However, the relationship between fluorescence and photosynthesis observed at the leaf level cannot be directly applied to the interpretation of retrieved SIF due to the impact of canopy structure. We carried out a SIF modelling study for a heterogeneous forest canopy considering the effect of canopy structure in the Discrete Anisotropic Radiative Transfer (DART) model. A 3D forest simulation scene consisting of realistic trees and understory, including multi-scale clumping at branch and canopy level, was constructed from terrestrial laser scanning data using the combined model TreeQSM and FaNNI for woody structure and leaf insertion, respectively. Next, using empirical data and a realistic range of leaf-level biochemical and physiological parameters, we conducted a local sensitivity analysis to demonstrate the potential of the approach for assessing the impact of structural, biochemical and physiological factors on top of canopy (TOC) SIF. The analysis gave insight into the factors that drive the intensity and spectral properties of TOC SIF in heterogeneous boreal forest canopies. DART simulated red TOC fluorescence was found to be less affected by biochemical factors such as chlorophyll and dry matter contents or the senescent factor than far-red fluorescence. In contrast, canopy structural factors such as overstory leaf area index (LAI), leaf angle distribution and fractional cover had a substantial and comparable impact across all SIF wavelengths, with the exception of understory LAI that affected predominantly far-red fluorescence. Finally, variations in the fluorescence quantum efficiency (Fqe) of photosystem II affected all TOC SIF wavelengths. Our results also revealed that not only canopy structural factors but also understory fluorescence should be considered in the interpretation of tower, airborne and satellite SIF datasets, especially when acquired in the (near-) nadir viewing direction and for forests with open canopies. We suggest that the modelling strategy introduced in this study, coupled with the increasing availability of TLS and other 3D data sources, can be applied to resolve the interplay between physiological, biochemical and structural factors affecting SIF across ecosystems and independently of canopy complexity, paving the way for future SIF-based 3D photosynthesis models.

Separating overstory and understory leaf area indices for global needleleaf and deciduous broadleaf forests by fusion of MODIS and MISR data

Abstract. Forest overstory and understory layers differ in carbon and water cycle regimes and phenology, as well as ecosystem functions. Separate retrievals of leaf area index (LAI) for these two layers would help to improve modeling forest biogeochemical cycles, evaluating forest ecosystem functions and also remote sensing of forest canopies by inversion of canopy reflectance models. In this paper, overstory and understory LAI values were estimated separately for global needleleaf and deciduous broadleaf forests by fusing MISR and MODIS observations. Monthly forest understory LAI was retrieved from the forest understory reflectivity estimated using MISR data. After correcting for the background contribution using monthly mean forest understory reflectivities, the forest overstory LAI was estimated from MODIS observations. The results demonstrate that the largest extent of forest understory vegetation is present in the boreal forest zones at northern latitudes. Significant seasonal variations occur for understory vegetation in these zones with LAI values up to 2–3 from June to August. The mean proportion of understory LAI to total LAI is greater than 30 %. Higher understory LAI values are found in needleleaf forests (with a mean value of 1.06 for evergreen needleleaf forests and 1.04 for deciduous needleleaf forests) than in deciduous broadleaf forests (0.96) due to the more clumped foliage and easier penetration of light to the forest floor in needleleaf forests. Spatially and seasonally variable forest understory reflectivity helps to account for the effects of the forest background on LAI retrieval while compared with constant forest background. The retrieved forest overstory and understory LAI values were compared with an existing dataset for larch forests in eastern Siberia (40–75° N, 45–180° E). The retrieved overstory and understory LAI is close to that of the existing dataset, with an absolute error of 0.02 (0.06), relative error of 1.3 % (14.3 %) and RMSE of 0.93 (0.29) for overstory (understory). The comparisons between our results and field measurements in eight forest sites show that the R2 values are 0.52 and 0.62, and the RMSEs are 1.36 and 0.62 for overstory and understory LAI, respectively.

Relationships between overstory and understory structure and diversity in semi-natural mixed floodplain forests at Bosco Fontana (Italy)

Abstract: The “Bosco Fontana” natural reserve includes the last remaining mixed floodplain forest in northern Italy and one of the most endangered ecosystems in Europe. Its effective management is hindered by the complexity of interactions of mixed-tree species and the influence of environmental factors on understory plant diversity. In this study we analyzed the patterns of natural evolution in semi-natural floodplain forest stands at Bosco Fontana with the aim of better understanding its current natural processes and dynamics. Stand structure, taxonomic and functional diversity, species composition, and leaf area index (LAI) of overstory and understory layers were surveyed in permanent plots over two inventory years (1995, 2005). The influence of environmental factors on understory plant diversity was assessed using Ellenberg’s indices for light, soil moisture, soil nutrient and soil reaction. Results indicated that overstory species composition varies according to the soil moisture, with hornbeam prevailing in xeric sites and deciduous oak species in mesic sites. Xeric sites showed high functional dispersion in both drought and shade tolerant traits, while it was significantly lower in both overstory and understory in the moist site. Functional dispersion of drought tolerance in the overstory and understory layers was positively correlated, while species richness was negatively correlated between the two layers. Diversity in the understory was mainly correlated with soil conditions. Understory LAI was positively correlated with overstory LAI in xeric and mesic plots, while no correlations were found in the moist plot. Overall, our results suggest that site conditions (soil conditions and water availability) are the major drivers of understory and overstory dynamics in the study forest. Hence, local site conditions and the understory should be carefully considered in the management of mixed floodplain forests.

Response to CO2 enrichment of understory vegetation in the shade of forests.

Responses of forest ecosystems to increased atmospheric CO2 concentration have been studied in few free-air CO2 enrichment (FACE) experiments during last two decades. Most studies focused principally on the overstory trees with little attention given to understory vegetation. Despite its small contribution to total productivity of an ecosystem, understory vegetation plays an important role in predicting successional dynamics and future plant community composition. Thus, the response of understory vegetation in Pinus taeda plantation at the Duke Forest FACE site after 15-17 years of exposure to elevated CO2 , 6-13 of which with nitrogen (N) amendment, was examined. Aboveground biomass and density of the understory decreased across all treatments with increasing overstory leaf area index (LAI). However, the CO2 and N treatments had no effect on aboveground biomass, tree density, community composition, and the fraction of shade-tolerant species. The increases of overstory LAI (~28%) under elevated CO2 resulted in a reduction of light available to the understory (~18%) sufficient to nullify the expected growth-enhancing effect of elevated CO2 on understory vegetation.

A Simple Method for Retrieving Understory NDVI in Sparse Needleleaf Forests in Alaska Using MODIS BRDF Data

Global products of leaf area index (LAI) usually show large uncertainties in sparsely vegetated areas because the understory contribution is not negligible in reflectance modeling for the case of low to intermediate canopy cover. Therefore, many efforts have been made to include understory properties in LAI estimation algorithms. Compared with the conventional data bank method, estimation of forest understory properties from satellite data is superior in studies at a global or continental scale over long periods. However, implementation of the current remote sensing method based on multi-angular observations is complicated. As an alternative, a simple method to retrieve understory NDVI (NDVIu) for sparse boreal forests was proposed in this study. The method is based on the fact that the bidirectional variation in NDVIu is smaller than that in canopy-level NDVI. To retrieve NDVIu for a certain pixel, linear extrapolation was applied using pixels within a 5 × 5 target-pixel-centered window. The NDVI values were reconstructed from the MODIS BRDF data corresponding to eight different solar-view angles. NDVIu was estimated as the average of the NDVI values corresponding to the position in which the stand NDVI had the smallest angular variation. Validation by a noise-free simulation data set yielded high agreement between estimated and true NDVIu, with R2 and RMSE of 0.99 and 0.03, respectively. Using the MODIS BRDF data, we achieved an estimate of NDVIu close to the in situ measured value (0.61 vs. 0.66 for estimate and measurement, respectively) and reasonable seasonal patterns of NDVIu in 2010 to 2013. The results imply a potential application of the retrieved NDVIu to improve the estimation of overstory LAI for sparse boreal forests and ultimately to benefit studies on carbon cycle modeling over high-latitude areas.

Ground and remote sensing-based measurements of leaf area index in a transitional forest and seasonal flooded forest in Brazil

Leaf area index (LAI) is a key driver of forest productivity and evapotranspiration; however, it is a difficult and labor-intensive variable to measure, making its measurement impractical for large-scale and long-term studies of tropical forest structure and function. In contrast, satellite estimates of LAI have shown promise for large-scale and long-term studies, but their performance has been equivocal and the biases are not well known. We measured total, overstory, and understory LAI of an Amazon-savanna transitional forest (ASTF) over 3 years and a seasonal flooded forest (SFF) during 4 years using a light extinction method and two remote sensing methods (LAI MODIS product and the Landsat-METRIC method), with the objectives of (1) evaluating the performance of the remote sensing methods, and (2) understanding how total, overstory and understory LAI interact with micrometeorological variables. Total, overstory and understory LAI differed between both sites, with ASTF having higher LAI values than SFF, but neither site exhibited year-to-year variation in LAI despite large differences in meteorological variables. LAI values at the two sites have different patterns of correlation with micrometeorological variables. ASTF exhibited smaller seasonal variations in LAI than SFF. In contrast, SFF exhibited small changes in total LAI; however, dry season declines in overstory LAI were counteracted by understory increases in LAI. MODIS LAI correlated weakly to total LAI for SFF but not for ASTF, while METRIC LAI had no correlation to total LAI. However, MODIS LAI correlated strongly with overstory LAI for both sites, but had no correlation with understory LAI. Furthermore, LAI estimates based on canopy light extinction were correlated positively with seasonal variations in rainfall and soil water content and negatively with vapor pressure deficit and solar radiation; however, in some cases satellite-derived estimates of LAI exhibited no correlation with climate variables (METRIC LAI or MODIS LAI for ASTF). These data indicate that the satellite-derived estimates of LAI are insensitive to the understory variations in LAI that occur in many seasonal tropical forests and the micrometeorological variables that control seasonal variations in leaf phenology. While more ground-based measurements are needed to adequately quantify the performance of these satellite-based LAI products, our data indicate that their output must be interpreted with caution in seasonal tropical forests.

Seasonal variation in MODIS LAI for a boreal forest area in Finland

Medium resolution leaf area index (LAI) products have usually been validated only at the peak of the growing season for boreal forest areas. In this study, we evaluated seasonal variations in MODIS Collection 5 (C5) LAI products for an evergreen coniferous forest area in southern Finland. A time series of field measurements of LAI and nine fine resolution satellite images (SPOT4 HRVIR, EO-1 Hyperion) for the snow free period in 2010 were used for developing empirical regression models for LAI estimation. We used two different reference LAI: the first one corresponded to an effective overstory LAI, and the second one was a clumping-corrected LAI that included both over- and understory vegetation. According to the results, MODIS LAI retrievals were closer to the effective LAI in the spring and autumn, when saturation of MODIS LAI main algorithm did not occur. On the other hand, in the summer, when saturation occurred, MODIS LAI was closer to the total LAI. Therefore, as a result of high LAI retrieved under the condition of saturation, the seasonal course of LAI was exaggerated in comparison to the reference LAI time series. Furthermore, MODIS LAI retrievals were unrealistically small before the leaf burst if considering the large proportion of evergreen coniferous forest in the study area. The results emphasize the need for multi-seasonal validation data sets in order to better understand the performance of LAI retrieval algorithms for evergreen coniferous forests throughout the snow free period.

Influences of canopy structure and physiological traits on flux partitioning between understory and overstory in an eastern Siberian boreal larch forest

Boreal forests play an important role in the global balance of energy and CO 2. Our previous study of elaborate eddy covariance observations in a Siberian boreal larch forest, conducted both above the forest canopy and at the forest floor, revealed a significant contribution of latent heat flux ( LE) from the cowberry understory to the whole ecosystem LE. Thus, in the present study, we examined what factors control the partitioning of whole ecosystem LE and CO 2 flux into the understory and overstory vegetation, using detailed leaf-level physiology (for both understory and overstory vegetation) and soil respiration property measurements as well as a multilayer soil-vegetation-atmosphere transfer (SVAT) model. The modeling results showed that the larch overstory's leaf area index (LAI) and vertical profile of leaf photosynthetic capacity were major factors determining the flux partitioning in this boreal forest ecosystem. This is unlike other forest ecosystems that tend to have dense LAI. We concluded that control of the larch overstory's LAI had a relationship with both the coexistence of the larch with the cowberry understory and with the water resources available to the total forest ecosystem.

Impacts of including forest understory brightness and foliage clumping information from multiangular measurements on leaf area index mapping over North America

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.

Dynamics of coast redwood sprout clump development in variable light environments

Post-thinning stump sprout response was assessed in a coast redwood [Sequoia sempervirens (D. Don) Endl.] stand from 5 to 9 years after five treatments were initiated. Nine years after treatment, leaf area index (LAI) ranged from 5.9 to 14.1 and the percent above canopy light (PACL) ranged from 0.8 to 3.9. Sprout responses included rapid self-thinning in all treatments with complete sprout mortality at low light regimes. Self-thinning of clumps increased with greater overstory LAI and poorer light regimes. Leaf area of individual sprout clumps also declined in low light regimes. The probability of sprout survival declined rapidly below 10 PACL and coincided with declining leaf area in sprout clumps. A conceptual model is presented that shows the sensitivity of redwood sprouts to light regime. Results have implications for management of multiaged stands and indicate these redwood coppice systems require relatively severe overstory treatments to provide sufficient light for sprout vigor and growth.

A satellite‐based method for monitoring seasonality in the overstory leaf area index of Siberian larch forest

Reliable monitoring of the leaf area index (LAI) is required to further understand the carbon, water, and energy cycles of forests. In this study, we proposed a new satellite‐based method to estimate the overstory LAI (LAIo) separately from the understory LAI (LAIu) for larch forests covering eastern Siberia. We modeled forest scenes representative of larch forest structure, with particular consideration of the typical clumped shoot structure of larch. Three‐dimensional radiative transfer simulations were then conducted under various forest conditions to establish the relationships between LAIo and seasonal increases in the normalized difference water index after leaf appearance. Model‐based sensitivity analyses indicated a maximum error of up to 26% under known noise levels. Averaged at the continental scale, total LAI from our estimates, the CYCLOPES version 3.1, and the Moderate Resolution Imaging Spectroradiometer MOD15 Collection 5 (main algorithm) showed similar ranges in summer. However, spatial pattern of LAI was slightly different, with smoother variability for CYCLOPES LAI. Our LAI and CYLOPES effective LAI reproduced a realistic seasonal variation with exact timing of spring increase in LAIo. The main drawbacks of MOD15 Collection 5 were unrealistically strong temporal variability, and the fact that LAI began to increase earlier than the overstory leaf appearance date. Overall, the results show that our new method is a good alternative to MOD15 Collection 5 and CYCLOPES, as it provides separate estimates of LAIo and LAIu and true LAI instead of effective LAI.

Tree canopy effect on grass and grass/legume mixtures in eastern Nebraska

A study to determine the feasibility of producing forage for grazing livestock under trees was conducted as a step toward evaluating the potential for silvopasture systems in the northern and central Great Plains. The effects of overstory leaf area index (LAI), percentage understory light transmittance (LT), and soil moisture (SM) on yield and crude protein (CP) of big bluestem [Andropogon gerardii Vitman; (BB)], smooth bromegrass [Bromus inermis Leyss.; (SB)], and mixtures with birdsfoot trefoil [Lotus corniculatus L.; (BFT)] were examined. The study was conducted in both Scotch pine (Pinus sylvestris L.) and green ash (Fraxinus pennsylvancia Marsh.) tree plantations, at the University of Nebraska Agriculture Research and Development Center near Mead, Nebraska. Thirty-six plots representing a wide range of canopy cover were selected at each location and seeded in April 2000 to BB, SB, or mixtures with BFT. Measurements of LAI, LT, and SM were taken throughout the 2001-growing season and plots were harvested in June and September 2001. Soil moisture generally did not explain much of the variability in yield or CP for BB, SB, or BFT. Cumulative LAI or LT averaged over the growing season was the best predictor of yield or CP, particularly under the pine. Yields of BB and SB increased as LAI decreased or LT increased. Conversely, the CP of BB and SB increased as LT decreased for both the June and September harvests. Both BB and SB maintain relatively high productivity under partial shading; however, BFT yields were low at LT levels below 75%.

The role of stand density on growth efficiency, leaf area index, and resin flow in southwestern ponderosa pine forests

We examined the response of growth efficiency (GE), leaf area index (LAI), and resin flow (RF) to stand density manipulations in ponderosa pine ( Pinus ponderosa Dougl. ex Laws.) forests of northern Arizona, USA. The study used a 40 year stand density experiment including seven replicated basal area (BA) treatments ranging from 7 to 45 m2·ha–1. Results were extended to the larger region using published and unpublished datasets on ponderosa pine RF. GE was quantified using basal area increment (BAI), stemwood production (NPPs), or volume increment (VI) per leaf area (Al) or sapwood area (As). GE per Al was positively correlated with BA, regardless of numerator (BAI/Al, NPPs/Al, and VI/Al; r2 = 0.84, 0.95, and 0.96, respectively). GE per As exhibited variable responses to BA. Understory LAI increased with decreasing BA; however, total (understory plus overstory) LAI was not correlated with BA, GE, or RF. Opposite of the original research on this subject, resin flow was negatively related to GE per Al because Al/As ratios decline with increasing BA. BAI, and to a lesser degree BA, predicted RF better than growth efficiency, suggesting that the simplest measurement with the fewest assumptions (BAI) is also the best approach for predicting RF.

View angle effects on relationships between MISR vegetation indices and leaf area index in a recently burned ponderosa pine forest

While nadir-viewing passive multispectral sensors have limited utility for characterizing the full dimensionality of forest canopies, multi-angle remote sensors such as the Multi-angle Imaging SpectroRadiometer (MISR) may improve detection of canopy architecture, canopy cover, and leaf area index (LAI) of forest canopy versus understory vegetation. Our objective was to determine whether data from the MISR sensor could improve estimates of LAI across a post-fire ponderosa pine forest located in the Black Hills of South Dakota. We measured LAI during the 2002 and 2003 growing seasons and created continuous LAI maps using Landsat TM and ETM+ data (mean R 2 = 0.81). We fit linear regression models of total and canopy LAI, using a series of MISR at-nadir and off-nadir or anisotropic vegetation indices as predictor variables, for each of five sampling periods. We found the best LAI model fits using either a new hotspot-adjusted normalized difference vegetation index, NDVI HS, NDVI calculated from the − 60° view angle, or NDVI at-nadir and either the Hotspot–DarkSpot Index (HDS) or the normalized difference anisotrophic index (NDAX) ( R 2 = 0.56–0.91). However, differences in fits of these best models and those including NDVI at-nadir ranged from only 1 to 8%. Reflectance anisotropy patterns related strongly to understory vegetation phenology. We found that the relationships among NDVI or the enhanced vegetation index (EVI) and canopy or total LAI showed little variation across view angles when the understory vegetation was senesced and significant anisotropy when understory green LAI was greatest. These findings demonstrate the value of multi-temporal measurements during periods of understory phenological change, even if the overstory LAI is relatively stable. We also evaluated the performance of the MISR LAI product and found moderate fits between the MISR LAI and our field- and Landsat-derived canopy LAI ( R 2 = 0.21–0.44).

Estimation of stand-level leaf area for boreal bryophytes

Bryophytes dominate the carbon and nitrogen cycling of many poorly drained terrestrial ecosystems and are important in the vegetation-atmosphere exchange of carbon and water, yet few studies have estimated their leaf area at the stand scale. This study quantified the bryophyte-specific leaf area (SLA) and leaf area index (LAI) in a group of different-aged boreal forest stands in well and poorly drained soils. Species-specific SLA (for three feather mosses, four Sphagnum spp. and Aulacomnium palustre mixed with Tomentypnum nitens) was assessed by determining the projected area using a flatbed scanner and cross-sectional geometry using a dissecting microscope. The hemisurface leaf area was computed as the product of SLA and live biomass and was scaled by coverage data collected at all stands. Pleurozium schreberi dominated the spatial coverage, biomass and leaf area in the well-drained stands, particularly the oldest, while S. fuscum and A. palustre were important in the poorly drained stands. Live moss biomass ranged from 47 to 230 g m(-2) in the well-drained stands dominated by feather mosses and from 102 to 228 g m(-2) in the poorly drained stands. Bryophyte SLA varied between 135 and 473 cm(2) g(-1), for A. palustre and S. capillifolium, respectively. SLA was strongly and significantly affected by bryophyte species, but did not vary between stands; in general, there was no significant difference between the SLA of non-Sphagnum mosses. Bryophyte LAI increased with stand age, peaking at 3.1 and 3.7 in the well and poorly drained stands, respectively; this represented approximately 40% of the overstory LAI in the well-drained stands and 100-1,000% in the poorly drained stands, underscoring the important role bryophytes play in the water and carbon budgets of these boreal forests.

Temporal and light-based changes in carbon uptake and storage in the spring ephemeral Podophyllum peltatum (Berberidaceae)

The single leaf of the spring ephemeral Podophyllum peltatum (Berberidaceae), emerges in the spring, when forest over-story leaf area index (LAI) is low (∼0.8) and senesces approximately 12 weeks later when LAI is high (∼4.0). This study characterized (i) seasonal changes in gas exchange characteristics (maximum light-saturated photosynthetic rate, foliar dark respiration rate, apparent quantum efficiency, light saturation point and light compensation point) and carbohydrate storage in field populations and (ii) examined the hypothesis that observed seasonal changes in photosynthetic characteristics represent photosynthetic acclimation to maximize photosynthetic carbon gain as light availability declined. In field populations, as LAI increased, total chlorophyll concentration increased while declines occurred in all gas exchange characteristics except apparent quantum efficiency. Over the same period, soluble sugar content of the rhizome declined and starch content increased rapidly during the first 4 weeks after which it stabilized. Foliar tissue exhibited little change in carbohydrate pool over the season. Laboratory populations grown at high and low light levels differed in all gas exchange parameters except light saturation point, but illustrated similar seasonal changes in gas exchange characteristics to both each other and the field population. The similarity of temporal changes in gas exchange characteristics of field (seasonal decline in light availability) and laboratory (no seasonal decline in light availability) suggests that P. peltatum possesses limited ability to photosynthetically acclimate to declining light availability. It appears that seasonal carbon gain in P. peltatum may be primarily controlled by leaf lifespan and the fraction of lifespan occurring under high illumination conditions early in the season.