Tracing Radiation And Architecture Of Canopies Research Articles

Intercomparison of leaf area index observed by destructive sampling, plant canopy analyzer and tracing radiation and architecture of canopies in paddy fields

ABSTRACT Leaf area index (LAI) is a canopy structure parameter used to characterize vegetation growth. It is increasingly important to obtain continuous and accurate ground LAI measurement data for the validation of remote-sensing products. In this study, direct destructive sampling method and indirect optical measurement including LAI-2200 plant canopy analyser (PCA) and Tracing Radiation and Architecture of Canopies (TRAC) were used for long-term continuous observation of paddy fields in southern China, to explore the variation of LAI during the growth period of paddy, and to quantitatively analyse the errors of indirect optical measurement. The results show that (1) LAI and Plant area index (PAI) increased first and then decreased in the growing period. The proportion of green leaf area gradually decreased at reproductive growth stage (less than 50%). There was a linear relationship between the proportion of green leaf area and PAI at this stage, with a good correlation (R2 = 0.73). This linear relationship can be used to convert PAI into LAI more conveniently. (2) Effective PAI (PAIe) calculated by the two methods (2000 method and Lang method) from PCA showed good consistency between each other in the growing season (the maximum value of R2 is 0.99). With the increase of zenith angle, the consistency between 2000 method and Lang method decreases (R2 = 0.83). Multiple scattering has an effect on PCA measurement, and this effect is more obvious at high zenith angle. Lang method is more sensitive to the change of zenith angle. (3) Compared to the destructive values, both 2000 method and Lang method are able to compute accurate LAI using four ring sensors (4 R), while TRAC underestimates the LAI and PAI from destructive sampling method. This study provided a new idea for reducing the impact of ground reference data errors on LAI product validation.

Estimation of Leaf Area Index in a Typical Northern Tropical Secondary Monsoon Rainforest by Different Indirect Methods

The leaf area index (LAI) is a crucial indicator for quantifying forest productivity and community ecological processes. Satellite remote sensing can achieve large-scale LAI monitoring, but it needs to be calibrated and validated according to the in situ measurements on the ground. In this study, we attempted to use different indirect methods to measure LAI in a tropical secondary forest. These methods included the LAI-2200 plant canopy analyzer (LAI-2200), Digital Hemispherical Photography (DHP), Tracing Radiation and Architecture of Canopies (TRAC), and Terrestrial Laser Scanning (TLS) (using single-station and multi-station measurements, respectively). Additionally, we tried to correct the measured LAI by obtaining indicators of woody components and clumping effects. The results showed that the LAI of this forest was large, with estimated values of 5.27 ± 1.16, 3.69 ± 0.74, 5.86 ± 1.09, 4.93 ± 1.33, and 3.87 ± 0.89 for LAI-2200, DHP, TRAC, TLS multi-station, and TLS single-station, respectively. There was a significant correlation between the different methods. LAI-2200 was significantly correlated with all other methods (p < 0.01), with the strongest correlation with DHP (r = 0.684). TRAC was significantly correlated with TLS single-station (p < 0.01, r = 0.283). TLS multi-station was significantly correlated with TLS single-station (p < 0.05, r = 0.266). With the multi-station measurement method, TLS could maximize the compensation for measurement bias due to the shadowing effects. In general, the clumping index of this forest was 0.94 ± 0.05, the woody-to-total area ratio was 3.23 ± 2.22%, and the total correction coefficient was 1.03 ± 0.07. After correction, the LAI estimates for all methods were slightly higher than before, but there was no significant difference among them. Based on the performance assessment of existing ground-based methods, we hope to enhance the inter-calibration between methods to improve their estimation accuracy under complex forest conditions and advance the validation of remote sensing inversion of the LAI. Moreover, this study also provided a practical reference to promote the application of LiDAR technology in tropical forests.

Correcting for the clumping effect in leaf area index calculations using one-dimensional fractal dimension

The clumping effect is the main issue causing the heterogeneity in vegetation canopies and the underestimation of leaf area index (LAI) obtained using indirect measurement methods. Significant efforts have been exerted to correct for the clumping effect and derive the true LAI. Recent research has shown that the fractal dimension (FD) is directly related to the clumping effect of foliage, yet practical methods are needed to calculate field estimates. Considering that widely used LAI applications such as digital hemispherical photography (DHP), tracing radiation and architecture of canopies (TRAC), and digital cover photography (DCP) estimate LAI with one-dimensional (1D) gap probability and gap size data, we propose a method to correct for the clumping effect using 1D FD. Resulting formulae describing the relationship between LAI, CI, and 1D FD were based on the box-counting method (BCM) and a binomial distribution model. Sixty-four simulated scenes including four RAdiation transfer Model Intercomparison (RAMI) actual canopies and field measurements from nine plots (four orchard plots and five coniferous forest plots) were used to validate the novel method. Results showed good agreement with reference LAI values for simulated scenes (R2 = 0.96 and RMSE = 0.35). The 1DFD method generated higher LAI estimates compared with the LAI measured using TRAC at the four orchard plots especially at high canopy closure, while its results were more consistent with LAI obtained by litter collection than those of comparable methods at coniferous forest plots (bias from −13.5% to 9.9% for DCP images, from −3.0% to 19.7% for DHP images, and from −3.8% to 17.0% for TRAC transects). Our validation efforts indicate that the method proposed herein corrects for the clumping effect of vegetated canopies more effectively with DCP images, DHP images, and TRAC measurement when compared with traditional indirect optical methods. The 1DFD method is expected to improve indirect measurement accuracy of LAI.

Variation of leaf area index estimation in forests based on remote sensing images of different spatial scales.

There are several important issues in quantitative remote sensing and product authenticity testing, including how well do the ground measurement points represent the remote sensing pixels, how to obtain the relative truth value of pixels, and how much spatial resolution can truly reflect fore-st leaf area index (LAI). In this study, the measured space scope of two plant canopy analyzers [LAI-2200 and tracing radiation and architecture of canopies (TRAC)] were calculated, which were combined with remote sensing images with three different spatial resolutions: GF-2 with 4.1 m spatial resolution, the Sentinel-2 with 10 m spatial resolution, and Landsat-8 OLI with 30 m spatial resolution, to get the relative true value of pixel at each scale. Under the condition of keeping the real observed area consistent with that obtained by remote sensing, the effects of different spatial resolution images for estimating forest LAI were compared and analyzed based on the unary exponential and multiple regression statistical models. Moreover, the optimal statistical models of the three images were tested on 30 m and 100 m scales and the spatial representation of dataset were evaluated, to find the most suitable scale for the description of forest LAI in the study area. The results showed that high resolution did not necessarily fully reflect LAI of forests. The statistical model based on three kinds of resolution images could well estimate forest LAI. Among the three models, the model based on the Sentinel-2 image had the highest accuracy, and the one based on the GF-2 images had the lowest. The test results at 30 and 100 m scales indicated that the forest LAI was overestimated by the GF-2 inversion model, and underestimated by the Landsat-8 inversion model. The statistical model based on Sentinel-2 could well estimated forest LAI in the study area.

Comparison of non-destructive LAI determination methods and optimization of sampling schemes in an open Populus euphratica ecosystem

Populus euphratica (P. euphratica) grows in the water-limited Tarim River Basin in spatially heterogeneous open ecosystems; thus, efforts to quantify the leaf area index (LAI) with optical instruments developed for homogeneous closed canopies have a high probability of failure. In this study, we explored methods for designing an acceptable sampling scheme to quantify the tree LAI for open P. euphratica canopies in arid areas. Field data were collected from three 30m×30m plots and one 100m×100m plot. We compared three indirect methods, i.e. i) allometry, ii) LAI-2000 canopy analyser, iii) Tracing Radiation and Architecture of Canopies (TRAC), and a new semi-direct method combining leaf density and crown volume (SDDV) method for quantifying the isolated tree and canopy LAI of a P. euphratica forest. We also analysed the effects of random and grid sampling designs on the accuracy of the LAI estimates obtained with the LAI-2000. The results showed that the allometric method is applicable to isolated trees with regular shapes; however, because the LAI of P. euphratica was calculated from an allometric equation based on the basal area (at 1.3m), the allometric equation is prone to failure if the basal area is beyond a specific range. Because there are no significant differences in the plot size between the allometric and the SDDV method predictions, the proposed SDDV method can be used as an alternative for field measurements. The combination of LAI-2000 and TRAC is found to be more reliable than TRAC only, and the field view of the LAI-2000 sensor and the clumping index are important factors for sparse vegetation LAI retrieval. The results from sampling optimization showed that for the LAI-2000 instrument, the best sampling method is grid sampling, and the sampling interval should not be less than 20m. For random sampling scheme, the number of sampling points in a 100m×100m plot should be greater than 86 with a coefficients of variation of 15% and an allowable error (AE) of 0.15m2m−2, respectively.

Foliage clumping index of main vegetation types in Daxing'an Mountains, Northeast China

The foliage clumping index quantifies the cluster degree of the leaf spatial distribution under random canopy. It is of comparable importance for establishment of ecological models. MODIS BRDF model parameter products (MCD43A1 data) and land cover types (MCD12Q1 data) were used in this study to simulate the reflectivity of the hot spots and dark spots, and calculate the normalized difference between hotspot and darkspot (NDHD) based on the Ross-Li semi-empirical model. Least square method was then used to simulate the relationship between NDHD and the foliage clumping index and foliage clumping index products of 500-m resolution in August 2014 were retrieved. Measurements of the foliage clumping index in Daxing'an Mountains were conducted by using the TRAC (Tracing Radiation and Architecture of Canopies) sampling instrument for mo-del validation and analysis. Results showed that it was a feasible algorithm to retrieve clumping index from MCD43A1 product with the correlation of simulated data and the measured data of significance (R2=0.8879). The MODIS near infrared wave band was more sensitive than that on red band to foliage clumping index change. With the increase of the solar zenith angle, the clumping index retrieved by Ross-Li model had a linear increase (R2=0.9699), which indicated that the foliage clumping index related to the solar zenith angle.

林地叶面积指数遥感估算方法的适用分析

Leaf area index( LAI) is a crucial vegetation structural parameter that has influence on the energy and carbon dioxide exchanges within and over forest canopies. The applications of remote sensing data provide the possibility of the relationship between LAI and vegetation index. In order to improve the estimates of forest leaf area index with remote sensing method,the ground LAI measurements were made by using the TRAC( Tracing Radiation and Architecture of Canopies) instrument in the urban forests of Beijing,and several spectral vegetation indices such as NDVI,SR,RSR and SAVI were calculated from Landsat Thematic Mapper( TM) image. With the establishment of the statistical models dependent on single vegetation index alone and the improved BP( back-propagation) neural networks with multi vegetation index combination,the best-fit method between ground measured LAI and vegetation indices with the highest accuracy of LAI estimation was obtained and used to estimate LAI from TM image in the urban area of Beijing. Results show that the accuracy of LAI estimated by using non-linear statistical model is higher than that estimated by using linear statistical model based on single vegetation index. The neural network with NDVI,RSR and SAVI as inputs outperforms the other methods in estimating LAI with the highest R2( coefficient of determination) value of 0.827 and the lowest RMSE( root mean square error) value of 0. 189. The neural network does not need to determine many coefficients and is applicable for estimatingforest leaf area index in urban areas using remote sensing data.

Estimate of Leaf Area Index in an Old-Growth Mixed Broadleaved-Korean Pine Forest in Northeastern China

Leaf area index (LAI) is an important variable in the study of forest ecosystem processes, but very few studies are designed to monitor LAI and the seasonal variability in a mixed forest using non-destructive sampling. In this study, first, true LAI from May 1st and November 15th was estimated by making several calibrations to LAI as measured from the WinSCANOPY 2006 Plant Canopy Analyzer. These calibrations include a foliage element (shoot, that is considered to be a collection of needles) clumping index measured directly from the optical instrument, TRAC (Tracing Radiation and Architecture of Canopies); a needle-to-shoot area ratio obtained from shoot samples; and a woody-to-total area ratio. Second, by periodically combining true LAI (May 1st) with the seasonality of LAI for deciduous and coniferous species throughout the leaf-expansion season (from May to August), we estimated LAI of each investigation period in the leaf-expansion season. Third, by combining true LAI (November 15th) with litter trap data (both deciduous and coniferous species), we estimated LAI of each investigation period during the leaf-fall season (from September to mid-November). Finally, LAI for the entire canopy then was derived from the initial leaf expansion to the leaf fall. The results showed that LAI reached its peak with a value of 6.53 m2 m−2 (a corresponding value of 3.83 m2 m−2 from optical instrument) in early August, and the mean LAI was 4.97 m2 m−2 from May to November using the proposed method. The optical instrument method underestimated LAI by an average of 41.64% (SD = 6.54) throughout the whole study period compared to that estimated by the proposed method. The result of the present work implied that our method would be suitable for measuring LAI, for detecting the seasonality of LAI in a mixed forest, and for measuring LAI seasonality for each species.

Global clumping index map derived from the MODIS BRDF product

Clumping index (CI), quantifying the level of foliage grouping within distinct canopy structures relative to a random distribution, is a key structural parameter of plant canopies and very useful in ecological and meteorological models. Previously, a Normalized Difference between Hotspot and Darkspot (NDHD) index has been proposed to accurately retrieve the global CI using POLarization and Directionality of the Earth's Reflectances (POLDER) data at ~6km resolution (Chen et al., 2005). In this study, we show for the first time a global CI map at 500m resolution derived using the Bidirectional Reflectance Distribution Function (BRDF) product from Moderate Resolution Imaging Spectroradiometer (MODIS), which currently provides the finest pseudo multi-angular data for the global land surface. In computing the NDHD we found that the hotspot calculated from the MODIS BRDF product is underestimated in comparison with POLDER measurements very near the hotspot. Without correcting the bias in MODIS data, the MODIS-derived CI could be overestimated. We have developed an approach to correct the MODIS hotspot magnitude with co-registered POLDER-3 data acquired at about the same time. Field-measured element-CIs in zenith angles of 30–60° by the Tracing Radiation and Architecture of Canopies (TRAC) instrument from 38 sites together with corresponding needle-to-shoot area ratios were used to evaluate the performance of hotspot-corrected NDHD and to tune the parameters to get accurate CI. It is found that the MODIS-derived CI has a dependence on the solar zenith angle, and this dependence in the red band is smaller than that in the near infrared (NIR) band. The highest correlation between field measurements and MODIS-derived CI is achieved when hotspot at nadir is used. The correct estimation of forest species (and therefore the crown shapes) from land cover is very important to the accuracy of MODIS-derived CI. For the red band, the coefficient of determination (R2) between MODIS-derived CI and field-measured CI is 0.76 for all sites and R2 is 0.53 for only the needle-leaved forest sites after the hotspot correction, indicating that a strong correlation between MODIS-derived NDHD and CI indeed exists. The MODIS-derived CI is regularly smaller than the field measurement. The red band in the MODIS BRDF product is found to be better than the NIR band for deriving CI for the vegetation covers investigated (mostly dense forests). Therefore a global clumping index map has been produced using the corrected MODIS hot\\spot in the red band and optimized parameters. However there were 10 broad-leaved forest sites used in the validation, which are too few for a reliable validation. For the broad-leaved forest area, where the same crown shape (ellipsoid) is used in the algorithm, the global CI spatial pattern is interestingly very similar to the pattern of the land cover distribution, suggesting that the MODIS-derived map has effectively captured the structural differences among cover types.

Spatial Enhancement of MODIS-based Images of Leaf Area Index: Application to the Boreal Forest Region of Northern Alberta, Canada

Leaf area index (LAI) is one of the most commonly used ecological variables in describing forests. Since 2000, 1-km resolution Moderate Resolution Imaging Spectroradiometer (MODIS)-based 8-day composites of LAI have been operationally available from the National Aeronautics and Space Administration (NASA), USA, at no cost to the user. In this paper, we present a simple protocol to enhance the spatial resolution of NASA-produced LAI composites to 250-m resolution. This is done by fusing MODIS-based estimates of enhanced vegetation index (EVI), consisting of 16-day 250-m resolution composites (also from NASA), with estimates of LAI. We apply the protocol to derive 250-m resolution maps of LAI for the boreal forest region of northern Alberta, Canada. Data fusion was possible in this study because of the inherent linear correlation that exists between EVI and LAI for the April to October growing period of 2005–2008, producing r2-values of 0.85–0.95 and p-values < 0.0001. Comparison of MODIS-based LAI with field-based measurements using the Tracing Radiation and Architecture of Canopies (TRAC) sensor and LAI-2000 Plant Canopy Analyzer showed reasonable agreement across values; statistical comparison of LAI data points produced an r2-value of 0.71 and a p-value < 0.0001. Seventy one percent of MODIS-based LAI were within ±20% of field estimates.

METHODS FOR DETERMINING CANOPY LEAF AREA INDEX OF PICEA CRASSIFOLIA FOREST IN QILIAN MOUNTAINS, CHINA

Aims There is increasing need for regional estimates of leaf area index (LAI) because it is an essential input for many eco-hydrological processes models; however,there has been a lack of effec-tive methods for its estimation. Picea crassifolia is the dominant species in the forest ecosystem of Qi-lian Mountains and is critical to the eco-hydrological processes of the ecosystem. Our objective is to compare methods for determining canopy LAI of this P. crassifolia forest. Methods We investigated canopy LAI with an LAI-2000 canopy analyzer,hemispherical photography and allometric regression on tree height and diameter at breast height (DBH). The value was underestimated by the two instruments because of clumping in the conifer forest. In order to adjust the LAI measured and obtain the spatial distribution of LAI,we first measured the clumping index by Tracing Radiation and Architecture of Canopies (TRAC). Then we calculated the adjusting coefficient by the clumping index,which was used to adjust the LAI value measured by hemispherical photography. Then,we determined the relationship between adjusted LAI and vegetation indexes retrieved by high resolution remote sensing data (QuickBird) and estimated the spatial distribution of canopy LAI. Important findings The values of canopy LAI were 1.03–3.70 by LAI-2000 canopy analyzer,0.48–2.26 by hemispherical photography,and 2.27–8.20 by allometric regression. LAI value by allometric regression on tree height and DBH was used for assessing the measurement accuracy by the other two indirect measurement techniques. We found the two instruments (LAI-2000 canopy analyzer and hemispherical photography) under estimated the canopy LAI of the forest by about 3.14–3.86 times. We built statistical models between adjusted LAI and vegetation indexes and selected the optimal model,i.e.,correlation between normalized difference vegetation index and LAI,through validating.

Leaf Area Index (LAI) Change Detection Analysis on Loblolly Pine (<I>Pinus taeda</I>) Following Complete Understory Removal

The confounding effect of understory vegetation contributions to satellite-derived estimates of leaf area index (LAI) was investigated on two loblolly pine (Pinus taeda) forest stands located in Virginia and North Carolina. In order to separate NDVI contributions of the dominant-codominate crown class from that of the understory, two P. taeda 1 ha plots centered in planted stands of ages 19 and 23 years with similar crown closures (71 percent) were analyzed for in situ LAI and NDVI differences following a complete understory removal at the peak period of LAI. Understory vegetation was removed from both stands using mechanical harvest and herbicide application in late July and early August 2002. Ikonos data was acquired both prior and subsequent to understory removal and were evaluated for NDVI response. Total vegetative biomass removed under the canopies was estimated using the Tracing Radiation and Architecture of Canopies (TRAC) instrument combined with digital hemispherical photography (DHP). Within-image NDVI change detection analysis (CDA) on the Virginia site showed that the percentage of removed understory (LAI) detected by the Ikonos sensor was 5.0 percent when compared to an actual in situ LAI reduction of 10.0 percent. The North Carolina site results showed a smaller percentage of reduced understory LAI detected by the Ikonos sensor (1.8 percent) when compared to the actual LAI reduction as measured in situ (17.4 percent). Image-to-image NDVI CDA proved problematic due to the time period between the Ikonos image collections (2.5 to 3 months). Sensor and solar position differences between the two collections, along with pine LAI increases through multiple needle flush, exaggerated NDVI reductions when compared to in situ data.

Validation of an Integrated Estimation of Loblolly Pine (Pinus taeda L.) Leaf Area Index (LAI) Using Two Indirect Optical Methods in the Southeastern United States

Abstract Quality assessment of satellite-derived leaf area index (LAI) products requires appropriate ground measurements for validation. Since the National Aeronautics and Space Administration launch of Terra (1999) and Aqua (2001), 1-km, 8-day composited retrievals of LAI have been produced for six biome classes worldwide. The evergreen needle leaf biome has been examined at numerous validation sites, but the dominant commercial species in the southeastern United States, loblolly pine (Pinus taeda), has not been investigated. The objective of this research was to evaluate an in situ optical LAI estimation technique combining measurements from the Tracing Radiation and Architecture of Canopies (TRAC) optical sensor and digital hemispherical photography (DHP) in the southeastern US P.taeda forests. Stand-level LAI estimated from allometric regression equations developed from whole-tree harvest data were compared to TRAC–DHP optical LAI estimates at a study site located in the North Carolina Sandhills Region. Within-shoot clumping, (i.e., the needle-to-shoot area ratio [γE]) was estimated at 1.21 and fell within the range of previously reported values for coniferous species (1.2–2.1). The woody-to-total area ratio (α = 0.31) was within the range of other published results (0.11–0.34). Overall, the indirect optical TRAC–DHP method of determining LAI was similar to LAI estimates that had been derived from allometric equations from whole-tree harvests. The TRAC–DHP yielded a value 0.14 LAI units below that retrieved from stand-level whole-tree harvest allometric equations. DHP alone yielded the best LAI estimate, a 0.04 LAI unit differential compared with the same allometrically derived LAI.

Vertical integration of leaf area index in a Japanese deciduous broad-leaved forest

Leaf area index (LAI) is an important quantity in the study of forest ecosystems, but field measurements of LAI often contain errors because of the vertical complexity of the forest canopy. In this study, we established a practical method for field measurement of LAI in the canopy of a deciduous broadleaved forest by accounting for its vertical complexity. First, we produced a semi-empirical model for the vertical integration of leaf dry mass per unit leaf area. We also quantified the litterfall for each tree species. These data enabled us to estimate the LAI of each species in autumn. By periodic in situ monitoring of some fixed sample shoots throughout the growing season, we were able to estimate the seasonality of leaf area (as a proportion of the annual maximum value at each point in time) of each species. By using this seasonality to extrapolate LAI values as a proportion of the LAI data in the leaf-fall season, we were able to estimate LAI throughout the year. We applied this method in a cool-temperate deciduous forest in central Japan (Takayama) in 2006 and validated conventional methods of LAI measurement: the plant canopy analyzer (LAI-2000) and the Tracing Radiation and Architecture of Canopies (TRAC) approach. LAI estimated by TRAC was in good agreement with our results, but LAI estimated using the LAI-2000 was only half the value estimated using our method. The use of basal area data as a proxy for species-specific leaf areas may save labor and time. Our method will be useful for studying the dynamics and interactions of multiple species because it can estimate LAI and its seasonal changes for each species.

The effect of spatial resolution on the accuracy of leaf area index estimation for a forest planted in the desert transition zone

An approach is presented for determining leaf area index (LAI) of a forest located at the desert fringe by using high spatial resolution imagery and by implementing values from a moderate spatial but high temporal resolution sensor. A 4-m spatial resolution multi-spectral IKONOS image was acquired under clear sky conditions on March 25, 2004. Normalized differences vegetation index (NDVI) and a linear mixture model were applied to calculate fractional vegetation cover (FVC). LAI was calculated using a non-linear relationship to FVC and then compared with ground truth measurements made in ten 1000 m 2 plots using the tracing radiation and architecture of canopies (TRAC) canopy analyzer under bright and clear sky conditions during March and April, 2004. Calculated LAI, corrected with a measured clumping index, was highly correlated with measured LAI ( R 2 = 0.79, p < 0.01). This approach was used to produce a 4-m resolution LAI map of the forest. The procedure was then applied to the MODIS 250-m resolution surface reflectance product, where MODIS LAI and VI products were used to calculate the extinction coefficient by inversion of the LAI-FVC relationship, and the extinction coefficient was then used to calculate LAI for moderate resolution. Histograms of resulting LAI distributions and descriptive statistics at the different spatial resolutions are compared. LAI spatial distribution at lower resolution was similar to that obtained at higher resolution and remained close to being normally distributed.

Physically based inversion modeling for unsupervised cluster labeling, independent forest classification, and LAI estimation using MFM-5-Scale

Unsupervised clustering is important for regional- to national-scale forest inventories where supervised training data are impractical or unavailable. However, labeling clusters in terms of land-cover classes can be labour intensive and problematic, and clustering and related methods do not provide biophysical-structural information (BSI). Canopy reflectance models such as 5-Scale are powerful forest remote sensing tools; however, 5-Scale can only be run in forward mode and is not invertible to obtain the required forest information. This problem was solved using multiple-forward-mode (MFM) coupled with 5-Scale to enable MFM-5-Scale inversion of land cover and BSI using a look-up table (MFM-LUT) approach that matches satellite image reflectance values with modeled reflectance values that have associated land cover and BSI, such as density, leaf area index (LAI), and crown dimensions, as well as subpixel-scale component fractions. MFM requires no training data or a priori BSI and can optionally be stratified (generalized) by species, structural, hierarchical, mixed forest, and other class definitions. In this paper, MFM-5-Scale was used with Landsat thematic mapper (TM) imagery at the Boreal Ecosystem-Atmosphere Study (BOREAS) southern study area (SSA) modeling subarea (MSA) in Saskatchewan, Canada. MFM-5-Scale was used to label unsupervised cluster sets (n = 17 and 97) from a previous land-cover classification by progressive generalization (CPG), with the best results obtained from independent, stand-alone MFM classification (87%, 76%, and 71% for the three hierarchies of 16 forest type, species, and density classes) validated against the provincial (SERM) forest inventory map and also compared with a standard maximum likelihood (ML) classification. Further, MFM-5-Scale estimated LAI at 24 BOREAS plots within ±0.57 LAI compared with ground-based tracing radiation and architecture of canopies (TRAC) LAI validation data. BSI is not provided by CPG clustering or ML. Based on this and other studies, we conclude that MFM provides an inversion modeling context for sophisticated forest radiative transfer models to retrieve a higher level of land cover and BSI, with detailed LUTs providing a rich set of forest information suitable for query, analysis, and follow-on simulation studies. These methods can augment existing regional- to national-scale remote sensing based inventories by providing a robust cluster labeling and BSI capability or can provide stand-alone capabilities over a variety of applications and scales.

Methodology comparison for canopy structure parameters extraction from digital hemispherical photography in boreal forests

The retrieval of canopy architectural parameters using off-the-shelf digital cameras with fish-eye lens is investigated. The technique used takes advantage of the sensor's linear response to light of these cameras to improve the estimation of gap fraction using: (1) the digital numbers of mixed sky-canopy pixels to estimate the within-pixel gap fraction; and (2) this process is done considering the variation in view zenith angle to take into account the sky radiance distribution and the canopy multiple scattering effects. The foliage element clumping index is retrieved over a wide range of view zenith angles using: (1) the accumulated gap size distribution theory developed for the TRAC by Chen and Cihlar (1995a); (2) the Lang and Xiang (1986) finite-length averaging method; and (3) a method combining the gap size distribution and the Lang and Xiang finite-length methods. Using data from Canadian and Russian boreal forests, comparisons of gap fraction, clumping index and plant area index measured with the tracing radiation and architecture of canopies (TRAC) and digital hemispherical photography are presented. Evaluation of the LAI estimated from digital hemispherical photography with allometric LAI of two boreal forest stands suggest that that the clumping index combined method may be more accurate.

Estimating LAI in deciduous forest stands

The leaf area index (LAI), was estimated in deciduous forests of southern Sweden using different estimation techniques. The effective LAI ( L e) was estimated optically by measurements with the gap fraction instrument LAI-2000 and corrections for the aggregation of leaves were achieved by using the TRAC (Tracing Radiation and Architecture of Canopies) instrument. Two methods for correcting the contribution of the woody material were tested. The aim was to find the most reliable estimation technique, and therefore the measurements were compared with LAI estimated from litter fall collection. Optically estimated L e was correlated with litter trap LAI, indicating that L e can be used as an estimate of LAI in deciduous forests of the same characteristics as tested here. The litter fall collection technique involves knowledge about the specific leaf area (SLA). Since, we found that SLA varied between leaves picked from top positioned branches and leaves picked from low positioned branches as well as between different species, we suggest some caution when interpreting the values.

Overstory and understory leaf area index as indicators of forest response to ice storm damage

Leaf area index (LAI) was measured with the tracing radiation and architecture of canopies (TRAC) optical instrument in three consecutive summers from 1999 to 2001 in sugar maple forests across eastern Ontario to monitor recovery from ice storm damage suffered in January 1998. The study sites were experimental blocks of the Ontario Ministry of Natural Resources (OMNR) designed for measurement and monitoring of the effects of fertilizer and lime treatments on maple recovery. Understory vegetation survey data, collected in 1999 and again in 2001, were converted to understory gap fraction, and processed in the same manner as the TRAC data to obtain understory LAI. Subtraction of understory LAI from total LAI measured with the TRAC allowed monitoring of productive, overstory trees tapped for syrup production. Annual LAI measurements and LAI change were evaluated in relation to percent crown loss estimates and plot treatments. Understory, overstory and total LAI increased from year-to-year in most plots. Of the single year measurements, total LAI measured in 1999 was significantly related to damage, while 2000 and 2001 LAI accounted for progressively less of the variation in damage, indicating a change in canopy condition between seasons. Understory LAI increased dramatically in response to overstory crown loss, while overstory increased more in absolute terms, but less in relative terms. Between-season LAI change was more significantly related to damage than any of the annual LAI measurements to which it was compared, indicating that LAI response is a better indicator of damage than single year LAI. LAI change was not, however, significantly related to plot treatment.

Ground and remote estimation of leaf area index in Rocky Mountain forest stands, Kananaskis, Alberta

Leaf area index (LAI) is an important measure of canopy structure that is related to biomass, carbon and energy exchange, and is an important input to ecological and climate change models. LAI can be estimated using algorithms applied to airborne and satellite imagery, with ground-based measurements of LAI being required for calibration and validation. A variety of methods exist for ground-based and remote estimation of LAI, and this can lead to confusion and uncertainty regarding selection of methods, experimental design, and instrumentation. As a contribution towards clarifying these protocols, this paper investigated and compared three optical methods and an allometric technique for ground-based estimation of LAI, and these were related to remote LAI estimates derived from the compact airborne spectrographic imager (casi) using three vegetation indices (normalized difference, weighted difference, and soil-adjusted vegetation indices, or NDVI, WDVI, and SAVI, respectively) and subpixel-scale spectral mixture analysis (SMA). The study was conducted in the Kananaskis region of Alberta in the Canadian Rocky Mountains and considered four species compositions within a montane ecological subregion: lodgepole pine, white spruce, composite deciduous (aspen and balsam poplar), and mixedwood (mixture of deciduous and lodgepole pine or white spruce). LAI data were obtained in the field using a LI-COR, Inc. LAI-2000 instrument, a tracing radiation and architecture of canopies (TRAC) system, an integrated (LAI-2000 and TRAC) method, and an allometric technique that used the ratio of sapwood basal area to leaf area. A subsample of plots was assessed with hemispherical photographs and LAI-2000 data from which similar effective leaf area index (eLAI) values were derived for two of the four species analyzed. The results highlight the importance of ensuring that samples represent the range of stand structures and canopy architecture inherent in the species group being assessed. Foliage clumping was observed to be similar in both coniferous and deciduous species and an important element to measure. LAI estimates were influenced by the field methods used to estimate LAI, species and their canopy architecture, and the form of the vegetation index or subpixel-scale mixing derived from the casi image. Of the three vegetation indices, the SAVI was the statistically strongest predictor of LAI for mixedwood species, but all were poor LAI estimators for lodgepole pine and deciduous species. The subpixel-scale scene fractions from SMA provided the best prediction of LAI for white spruce compared with the three vegetation indices. The result for white spruce provides an encouraging basis for further investigation of SMA as a sampling tool to scale from field to high-resolution airborne and satellite imagery for local to landscape-level biophysical estimation.