Discrete return lidar-based prediction of leaf area index in two conifer forests
Introduction
The foliage component of a forest canopy is the primary surface that controls mass, energy, and gas exchange between photosynthetically active vegetation and the atmosphere (Fournier et al., 2003). A thorough characterization of leaf area index (LAI; the ratio of half of the total needle surface area per unit ground area) can therefore provide valuable information about nutrient cycling, hydrologic forecasting, and biogeochemical processes in a forested ecosystem. As a key vegetation structural characteristic that drives many vegetation functions, LAI is a primary parameter used in ecophysiological and biogeochemical models to describe plant canopies (Chen et al., 1997). For example, process-based models such as BIOMASS (McMurtrie & Landsberg, 1992), FOREST-BGC (Running & Coughlan, 1988) and RHESSys (Band et al., 1991) use LAI as a primary or intermediate variable for forest growth and productivity. Additionally, LAI is often employed as a critical calibration variable for remote sensing datasets to differentiate vegetation characteristics over a wide range of biomes (Coops et al., 2004). LAI has also been used to characterize forest radiation regimes and the amount of light available to the understory in tropical (e.g. Rich et al., 1993, Vierling and Wessman, 2000) and temperate conifer (e.g. Law et al., 2001a) and deciduous forests (e.g. Ellsworth & Reich, 1993). Given the role of LAI in determining many forest ecosystem processes, several techniques have been developed for rapid LAI estimation.
The most commonly employed methods for estimating LAI across landscapes rely on the relationships between LAI and various manipulations of spectral information from aircraft or satellite-based imagery. A significant amount of research has been dedicated to quantifying the connections between spectral vegetation indices (SVIs) that associate foliar composition in the visible red waveband, which is absorbed by chlorophyll a and b, and the near-infrared waveband, which is scattered by plant cellular structures. The normalized difference vegetation index (NDVI) (Rouse et al., 1974) and the simple ratio (SR) (Birth & McVey, 1968) are the most frequently used SVIs to estimate LAI for a variety of ecosystem types including coniferous forests (Chen et al., 1997, Curran et al., 1992), grasslands (Friedl et al., 1994) and deciduous forests (Coops et al., 2004). Recent studies have incorporated more complex vegetation indices by including spectral response from additional wavelengths in an effort to minimize the influences of atmospheric disparities and canopy background noise. For example, a mid-infrared correction proposed by Nemani et al. (1993) to NDVI and SR have been found by White et al. (1997) and Pocewicz et al. (2004) to improve LAI estimates in montane and temperate coniferous forests. Lymburner et al. (2000) developed the specific leaf area vegetation index (SLAVI) to account for mid-infrared sensitivity to varying canopy structure for heterogeneous forest/woodland compositions. Chen et al. (2004) examined the use of the enhanced vegetation index (EVI; Huete et al., 1997) to improve LAI and vegetation cover estimates in a ponderosa pine forest. The reduced simple ratio (RSR) has demonstrated success for estimating LAI in pine and spruce stands (Stenberg et al., 2004) and for a post-fire chronosequence in Siberia (Chen et al., 2005b).
Overall, commonly used SVIs serve as suitable surrogates to approximate LAI for canopies with relatively low LAI (e.g. LAI = 3–5) (Chen and Cihlar, 1996, Turner et al., 1999). However, for values above this LAI threshold, many SVIs tend to saturate such that LAI estimates for high biomass forests may be grossly underestimated. For most temperate coniferous forests, the ability to discriminate higher LAI values from optical remote sensing data has been a major challenge.
Lidar data provide an alternative approach for estimating LAI across the landscape. Throughout the past decade, many researchers have reported the utility of lidar data to estimate a suite of forest biophysical characteristics such as canopy height, basal area, crown closure, wood volume, stem density, and biomass (Maclean and Krabill, 1986, Means et al., 2000, Naesset and Bjerknes, 2001, Nelson et al., 1988, Popescu et al., 2003) over a range of forest structural types and regional (Lefsky et al., 2005a) to sub-regional scales (Jensen et al., 2006). More recently, researchers have attempted to relate the three-dimensional structural information captured with lidar data to both direct and indirect estimates of LAI based on various analytical methods. For instance, Magnussen and Boudewyn (1998) found that the proportion of lidar returns corresponding to calculated canopy heights was correlated with the fractional leaf area above canopy-specific height thresholds. Lefsky et al. (1999) explored a three-dimensional (volumetric) analysis of waveform lidar data to estimate leaf area index within a multiple regression framework. Chen et al. (2004) investigated the relationships between trees identified with lidar data tree cover response obtained by a discrete-return system to spectrally-derived vegetation indices and LAI. Riano et al. (2004) and Morsdorf et al. (2006) assessed the capacity of lidar and variable-radius plots to estimate LAI. Lefsky et al. (2005a) developed robust empirical estimates based on waveform lidar and regional LAI measurements for the U.S. Pacific Northwest and Koetz et al. (2006) inverted both actual and simulated 3-D lidar waveform models to estimate LAI and other biophysical parameters within a radiative transfer model.
LAI can be estimated from a variety of remote sensing datasets, warranting the exploration of lidar and multispectral data integration. Lidar/multispectral data integration (also referred to as data fusion or synergy) has been explored for retrieval of other forest characteristics such as canopy height (Hudak et al., 2002, Popescu and Wynne, 2004, Wulder and Seemann, 2003), volume and biomass (Hudak et al., 2006, Popescu et al., 2004), stand density (McCombs et al., 2003), forest productivity (Lefsky et al., 2005b), canopy change detection (Wulder et al., 2007) and characterization of foliage pigments (Blackburn, 2002). However, the potential for spatial and spectral data integration remains significantly unaddressed in terms of quantifying and mapping LAI in moderate to high biomass coniferous forests.
Previous studies of LAI in northern Idaho conifer forests have reported LAI ranging from 0 to 13, with the majority of observations exceeding LAI = 4 (Duursma et al., 2003, Pocewicz et al., 2004). In terms of geographic significance, the northern Idaho mountain ranges may represent the region of highest carbon uptake in the Rocky Mountain range, and thus the most substantial carbon sink between the Cascade Mountains and the Midwestern U.S. (Schimel et al., 2002). Therefore, accurate and reliable estimates of LAI are vital to adequately characterize ecosystem processes and monitor trajectories of change. Currently, operational LAI products from the MODIS sensor and SPOT VEGETATION provide repeat spatial and temporal coverage of biophysical variables used to describe vegetation structure (Baret et al., 2007, Yang et al., 2006), but at a much coarser spatial resolution such that heterogeneity of fine-to-medium scale landscape features is lost.
The specific objectives of our research are to determine 1) the capability of lidar-derived covariates to estimate measured and corrected LAI quantities, 2) the extent to which SPOT 5 spectral data may improve lidar-based LAI estimates, and 3) the applicability of a regional model to quantify LAI in northern Rocky Mountain forests.
Section snippets
Study areas
Forested regions of northern Idaho exhibit a wide range of stand characteristics representative of conifer forests in the Northern Rocky mountains, and more generally, the western United States. A diverse range of topographic and climatic conditions combined with forest management practices serve to determine species composition and land-use patterns in the Intermountain West. To meet our research objectives, two distinct forested areas were selected to represent the broader range of forest
Results
Exploratory data analysis indicated that LAI quantities were not normally distributed; thus, response data were transformed to satisfy the normality assumption for linear regression. A natural log transformation was used for the SJW and a square root transformation for the NPR. The combined dataset used the square root and natural log transformation for LAIe and corrected LAI quantities, respectively. Different transformations were required for the combined dataset because a single
Regression analysis
Lidar-derived covariates explained the largest proportion of variation in LAI and corrected quantities among the three datasets used in this analysis. Although existing methods to estimate LAI often rely on a single optically-derived SVI, the relationships are often asymptotic and can result in unreliable estimates for moderate to high biomass forests. The number of lidar covariates selected for each model was a balance between parsimony and relevance, or the explanatory value of individual
Conclusion
The two selected study areas represent a diverse assemblage of ecoregional characteristics, climatic conditions, and anthropogenic influences including management ideology and implementation. Such factors control the type, density, and location of vegetation both within an individual stand and the region as a whole. Despite this matrix of variable forest conditions, lidar data were able to account for a significant amount of variation in measured LAI for both individual study areas and when
Acknowledgements
Funding for this research supported by NSF Idaho EPSCoR program grant EPS-447689; by NASA Idaho Space Grant Consortium grant NGG-05GG29H, a NASA Earth Sciences Enterprise Application Division grant BAA-01-OES-01, and NASA EPSCoR grant NCC5-588. We would like to particularly thank the Potlatch Corporation and the Nez Perce Tribe for allowing this research to be conducted on their lands. The authors acknowledge Drs. Jing Chen and Sylvain Leblanc for their communications regarding LAI measurements
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