Deriving and validating Leaf Area Index (LAI) at multiple spatial scales through lidar remote sensing: A case study in Sierra National Forest, CA

Tang, Hao; Brolly, Matthew; Zhao, Feng; Strahler, Alan H.; Schaaf, Crystal B.; Ganguly, Sangram; Zhang, Gong; Dubayah, Ralph

doi:10.1016/j.rse.2013.12.007

Cited by 156 publications

(107 citation statements)

References 53 publications

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“…Studies using large-footprint, full waveform data for forest inventory in the Sierra Nevada forests have demonstrated the ability of full waveform Lidar to retrieve accurate canopy fuel maps needed for fire behavior modeling and to provide an accurate estimate of leaf area index at multiple spatial scales (Tang et al 2014). Zhao et al (2013) compared the abilities of an airborne and a ground-based full waveform system to retrieve foliage profiles in the Sierra National Forest and showed the benefits of integrating terrestrial and airborne Lidar data for a detailed description of forest canopy structure.…”

Section: Sierra Nevada Adaptive Management Projectmentioning

confidence: 99%

Mapping forests with Lidar provides flexible, accurate data with many uses

Kelly¹,

Tommaso²

2015

Cal Ag

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Section: Sierra Nevada Adaptive Management Projectmentioning

confidence: 99%

Mapping forests with Lidar provides flexible, accurate data with many uses

Kelly¹,

Tommaso²

2015

Cal Ag

View full text Add to dashboard Cite

“…Whilst this applies a more empirically-based method to estimating canopy cover, the need for extensive field data limits the spatial reach of such analysis. As follow-up studies, Tang et al [19] and Tang et al [20] built on previous findings and employed a recursive algorithm to retrieve estimates of Leaf Area Index (LAI) via refined GF estimates from GLAS; however, this method required the use of some locally applicable initial conditions by which scaling factor outputs were governed (i.e. expected minimum and maximum values).…”

Section: Introductionmentioning

confidence: 99%

Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems

et al. 2017

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Spaceborne laser altimetry waveform estimates of canopy Gap Fraction (GF) vary with respect to discrete return airborne equivalents due to their greater sensitivity to reflectance differences between canopy and ground surfaces resulting from differences in footprint size, energy thresholding, noise characteristics and sampling geometry. Applying scaling factors to either the ground or canopy portions of waveforms has successfully circumvented this issue, but not at large scales. This study develops a method to scale spaceborne altimeter waveforms by identifying which remotely-sensed vegetation, terrain and environmental attributes are best suited to predicting scaling factors based on an independent measure of importance. The most important attributes were identified as: soil phosphorus and nitrogen contents, vegetation height, MODIS vegetation continuous fields product and terrain slope. Unscaled and scaled estimates of GF are compared to corresponding ALS data for all available data and an optimized subset, where the latter produced most encouraging results (R 2 = 0.89, RMSE = 0.10). This methodology shows potential for successfully refining estimates of GF at large scales and identifies the most suitable attributes for deriving appropriate scaling factors. Large-scale active sensor estimates of GF can establish a baseline from which future monitoring investigations can be initiated via upcoming Earth Observation missions.

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“…Lidar pulses are able to penetrate through the vegetation canopy, enabling simultaneous point cloud collection from multiple vegetation layers as well as ground level. Therefore, airborne, terrestrial and spaceborne scanning Lidar are widely used to collect forest structural parameters [16][17][18][19][20]. However, most of the current Lidar sensors provide only a 3D point cloud collected using a single wavelength and with an uncalibrated intensity information.…”

Section: Introductionmentioning

confidence: 99%

Investigating Bi-Temporal Hyperspectral Lidar Measurements from Declined Trees—Experiences from Laboratory Test

et al. 2015

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Global warming is posing a threat to the health and condition of forests as the amount and length of biotic and abiotic disturbances increase. Most methods for detecting disturbances and measuring forest health are based on multi-and hyperspectral imaging. We conducted a test with spruce and pine trees using a hyperspectral Lidar instrument in a laboratory to determine the capability of combined range and reflectance measurements to investigate forest health. A simple drought treatment was conducted by leaving the harvested trees outdoors without a water supply for 12 days. The results showed statistically significant variation in reflectance after the drought treatment for both species. However, the changes differed between the species, indicating that drought-induced alterations in spectral characteristics may be species-dependent. Based on our results, hyperspectral Lidar has the potential to detect drought in spruce and pine trees.

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Deriving and validating Leaf Area Index (LAI) at multiple spatial scales through lidar remote sensing: A case study in Sierra National Forest, CA

Cited by 156 publications

References 53 publications

Mapping forests with Lidar provides flexible, accurate data with many uses

Mapping forests with Lidar provides flexible, accurate data with many uses

Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems

Investigating Bi-Temporal Hyperspectral Lidar Measurements from Declined Trees—Experiences from Laboratory Test

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