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Spectral and Structural Differences Between Coniferous and Broadleaf Forest derived from LIDAR and AVIRIS
Dar A. Roberts1, Keely L. Roth2, Eliza Bradley3, Geoffrey G. Parker4, Philip E. Dennison5, and Bothaina Natour6
1 Dept of Geography, Univ. Calif. Santa Barbara, 2. Smithsonian Environmental Research Center, Edgewater, MD 21037-0028, 3Dept of Geography, Univ. Utah, Salt Lake City, UH, 94119
[email protected],1 [email protected], 1 [email protected], 2 [email protected], [email protected],3
AbstractCombined LIDAR and hyperspectral measures can improve our ability to estimate carbon stocks and fluxes, through improved maps of forest structure, tree species and biophysical attributes. In past research, we have demonstrated strong relationships between small footprint (discrete) LIDAR and canopy albedo across an age chronosequence in coniferous forest. We have also shown strong relationships between LIDAR-derived height measures and biomass in several coniferous ecosystems. In this poster we extend this analysis to compare LIDAR and AVIRIS-derived measures in a broadleaf deciduous forest (SERC) to those derived from a coniferous forest at Wind River.Analysis at SERC focused on three main sensors, discrete LIDAR, waveform LIDAR (LVIS) and AVIRIS. Discrete LIDAR data were processed to generate a digital terrain model (DTM), then used to create a digital canopy model (DCM). A comparison between canopy height measures derived from LVIS, and height derived from the discrete LIDAR showed both systems to be highly correlated. Height measures from LVIS and discrete LIDAR were validated using canopy height derived from a stem map and by comparison to simulated canopy hemi-ellipsoids generated through a model. Comparison between modeled canopy height and LIDAR derived height were generally good, improving when species-level differences were incorporated into simulated canopy hemi-ellipsoids. Correlation between AVIRIS-derived albedo, and canopy shade at SERC was very high. However, correlation between the standard deviation of LIDAR-height (rugosity) and albedo was low, contrary to previous findings at Wind River. Likely mechanisms accounting for differences between SERC and Wind River include 1) a lack of a large range of age classes at SERC; 2) architectural differences between conifer and broadleaf trees; and 3) differences in the spatial scale of analysis, where correlations are high when the data are aggregated to stand scales, but lower when aggregated over fixed window sizes.
Study Areas
Summary
References
ResultsLVIS, discrete LIDAR and AVIRIS were analyzed at SERC Maryland, a mixed broadleaf forest and compared to similar analysis in a coniferous ecosystem at Wind River. Important findings included
• Comparisons between height measures derived from LVIS and discrete LIDAR at SERC demonstrated strong correlations. The highest correlation was observed for maximum canopy height over a 50 m window size.
• Comparison between field measured canopy height and canopy height derived from discrete LIDAR demonstrated a weak relationship for maximum height, but a strong correlation for average height.
• Regressions between LIDAR-derived average height and field-derived heights improved when species-specific models were used to synthesize crowns from the stem map.
• Similar to prior work, AVIRIS-derived albedo was highly correlated with the GV and shade fractions at SERC.
• Contrary to findings at Wind River, AVIRIS-derived albedo was poorly correlated with LIDAR-derived rugosity at all four spatial scales tested.
Ogunjemiyo, S., Parker, G., and Roberts, D., 2005, Reflections in bumpy terrain: implications of canopy surface variations for the radiation balance of vegetation, IEEE Geoscience and Remote Sensing Letters, 2(1), 90-93.
Purves, DW, Lichstein, JW & Pacala, SW. (2007) Crown plasticity and competition for canopy space: a spatially implicit model parameterized for 250 North American tree species. PLoS-ONE 2(9): e870. doi:10.1371/journal.pone.0000870
Roberts, D.A., Gardner, M., Church, R., Ustin, S., Scheer, G.,and Green, R.O., 1998, Mapping Chaparral in the Santa Monica Mountains using Multiple Endmember Spectral Mixture Models, Rem. Sens. Environ. 65: 267-279.
Roberts, D.A., Ustin, S.L., Ogunjemiyo, S., Greenberg, J., Dobrowski,S.Z., Chen, J. and Hinckley, T.M., 2004, Spectral and structural measures of Northwest forest vegetation at leaf to landscape scales, Ecosystems, 7:545-562
Methods
1. Discrete LIDAR and LVIS 3. AVIRIS Albedo
Acknowledgements: This research was funded in part by “Multisite Integration of LIDAR and Hyperspectral Data for Improved Estimation of Carbon Stocks and Exchanges”, NASA Carbon Cycle Science grant NNG05GE56G
Figure 1) (a) Wind River AVIRIS image showing the general dimensions of the LIDAR data set (orange) (b) SERC, Maryland. Boxes mark the locations of the three main data sets at SERC including LVIS (white), the stem map (magenta) and discrete LIDAR (yellow)
Two study areas are included in this poster (Fig. 1), including western hemlock/Douglas-fir (Wind River) and mixed broadleaf deciduous forest (SERC). AVIRIS data include coarse resolution at Wind River acquired in 1998 and high resolution acquired in 2006 at SERC. LIDAR include discrete return LIDAR at Wind River (Aeroscan) and two LIDAR data sets at SERC, a discrete return system acquired in 2004 and LVIS acquired in 2003. Field data included tree height for individual trees ( ~ 200 Wind River) and a 700x700 m stem map at SERC consisting of 9563 trees with stems > 20 cm DBH.
2. Comparison to Plot data
Tree height measures from discrete LIDAR and LVIS (RH100) proved to be highly correlated (Fig. 3). Two window sizes were used for comparison: 30 m and 50m (Fig. 4). Highest correlations were observed between LVIS and discrete LIDAR for maximum tree height at the 50 m scale (Fig. 4a). Mean tree height was poorly correlated at both scales (Fig. 4b).
Figure 5) Comparison of LVIS waveforms and synthetic waveforms generated from discrete LIDAR. Some waveforms are nearly identical, while others differ markedly.
Figure 9) Showing the relationship between the GV fraction, shade fraction, and albedo as a function of stand age (From Ogunjemiyo et al., 2005).
• In prior research at Wind River, we demonstrated a strong relationship between LIDAR-derived roughness, calculated as the standard deviation of LIDAR height within a fixed window size (rugosity) and albedo and spectral fractions (Fig. 9). As stands aged, rugosity tended to increase, the GVF fraction and albedo decreased and shade fraction increased.
• LIDAR height metrics, including maximum and mean height derived from discrete LIDAR at 50 and 100 m were compared to height measures derived from the SERC stem map. Overall, mean height showed a higher correlation to field data than maximum height with highest correlations for a 100 m window size (Fig. 6).
• Comparison between canopy height derived from allometry and canopies synthesized using the ideal crown distribution of Purves et al. (2007) demonstrated improved correlation when species specific crown morphology is taken into account (Fig. 7).
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Figure 2) Generation of DCM from first and last return LIDAR at SERC.
Figure 3) DCM from discrete LIDAR and canopy height from LVIS
4. AVIRIS Albedo and Rugosity
• As shown previously by Ogunjemiyo et al., (2005) albedo derived from AVIRIS was negatively correlated with the shade fraction and positively correlated with the GV fraction (Fig. 8). Brightness differences within these stands are a product of spectral differences in crowns, crown architecture and forest gaps.
LVIS and discrete LIDAR produced similar waveforms in most cases showing a range in correspondence from a near perfect match in many instances to significant differences (Fig. 5).
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AVIRIS Processing
1) AVIRIS data were processed to surface reflectance using radiance modeled by MODTRAN allowing for variable amounts of column water vapor and liquid water
2) AVIRIS reflectance data were modeled using Multiple Endmember Spectral Mixture Analysis (MESMA; Roberts et al., 1998) to calculate fractions of GV, NPV and shade
3) MODTRAN modeled irradiance were convolved against surface reflectance then integrated across wavelengths to estimate surface albedo (Roberts et al., 2004)
4) AVIRIS fractions and albedo were compared to LIDAR rugosity over a range of window sizes of 8, 12, 16 and 20 m.
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LIDAR Processing
1) Discrete LIDAR were processed first to determine a Digital Terrain Model (DTM) using a 16 m fixed window to locate height minima followed by linear interpolation between minima. A digital canopy model (DCM) was developed for each site as the difference between LIDAR first return (DSM) and the DTM (Fig. 2)
2) Discrete LIDAR were reprocessed to synthesize LVIS waveforms over footprints that matched LVIS. Discrete LIDAR were also processed to calculate rugosity at 8, 12, 16 and 20 m
3) Height metrics from LVIS and discrete LIDAR were compared at SERC
4) Height metrics from both sensors were compared to estimated tree height from allometry and modeled crowns using Purves et al., 2007
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Figure 4) Regression between LVIS and discrete LIDAR at 30 m (a) and 50 m (b) for maximum height (left) and average height (right)
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Figure 7) Correlation between maximum and mean tree height calculated from discrete LIDAR at 100 m compared to standard allometry (a) and species-specific growth forms from Purves et al. (2007) (b)
20m: Albedo vs. GV Fraction
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20m: Albedo vs. Shade Fraction
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• Contrary to prior work at Wind River, albedo was poorly correlated to LIDAR-derived rugosity at all spatial scales of 8, 12, 16 and 20 m (Fig. 10). Differences between Wind River and SERC are hypothesized to be due to several factors including:
a) The spatial scale of analysis. Albedo-rugosity relationships at Wind River were calculated for discrete stands defined by the USFS, thus suppressing within stand variability. Albedo-rugosity at SERC was calculated from stem maps.
b) Wind river includes a wide range of stand ages ranging from recent clear cuts to over 400 years old. SERC consists primarily of young and intermediate aged stands.
c) Wind river analysis focused exclusively on conifers with well defined, conical crowns. SERC analysis includes a diversity of crown forms including numerous overlapping crowns a majority of which are from broadleaf deciduous trees.
20m: Albedo vs. Rugosity
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Figure 10) Showing the relationship between the albedo and rugosity at Wind River (a) and SERC (b)
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Figure 6) Correlation between maximum and mean tree height calculated from discrete LIDAR at 50 (a) and 100 m (b) and field allometry at the same spatial scales.
Figure 8) Showing plots of shade and GV (x) plotted against albedo (y).
