Small-footprint Lidar Estimations of Sagebrush Canopy Characteristics

Mitchell, Jessica J.; Glenn, Nancy F.; Sankey, Temuulen Tsagaan; Derryberry, DeWayne R.; Anderson, Matthew; Hruska, Ryan

doi:10.14358/pers.77.5.521

Cited by 53 publications

(32 citation statements)

References 47 publications

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“…Eight (n = 8) vegetation height metrics (Table 3) were rasterized at seven resolutions (0.5, 1, 3, 5, 10, 15, 30 m) from both the TLS and ALS data. The ALS-derived mean height had up to a 30 percent underestimation of the mean vegetation heights from TLS data, which is in agreement with previous studies in sagebrush-steppe communities from airborne lidar (Streutker and Glenn, 2006;Glenn et al, 2011;Mitchell et al, 2011). Therefore, the ALS-derived mean height was calibrated and scaled with the TLS-derived mean height.…”

Section: June2017supporting

confidence: 87%

Aerodynamic Roughness Length Estimation with Lidar and Imaging Spectroscopy in a Shrub-Dominated Dryland

Li¹,

Zhao²,

Mitchell³

et al. 2017

photogramm eng remote sensing

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Section: June2017supporting

confidence: 87%

Aerodynamic Roughness Length Estimation with Lidar and Imaging Spectroscopy in a Shrub-Dominated Dryland

Li¹,

Zhao²,

Mitchell³

et al. 2017

photogramm eng remote sensing

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“…Vegetation Lidar returns are also more likely to be mixed with those of annual grasses, perennial bunchgrasses, litter, or bare ground in our study area. Hence, shrub height underestimation is likely more pronounced in this study due to constraints related to the laser pulse length [24,26,65,66]. Yet the variability of height may still be sufficiently captured by the Lidar to represent the spatial pattern of biomass with smaller shrub canopies in our study site.…”

Section: Rf Regression Model Variablesmentioning

confidence: 85%

Lidar Aboveground Vegetation Biomass Estimates in Shrublands: Prediction, Uncertainties and Application to Coarser Scales

Dhakal

Glenn

et al. 2017

Remote Sensing

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Abstract:Our study objectives were to model the aboveground biomass in a xeric shrub-steppe landscape with airborne light detection and ranging (Lidar) and explore the uncertainty associated with the models we created. We incorporated vegetation vertical structure information obtained from Lidar with ground-measured biomass data, allowing us to scale shrub biomass from small field sites (1 m subplots and 1 ha plots) to a larger landscape. A series of airborne Lidar-derived vegetation metrics were trained and linked with the field-measured biomass in Random Forests (RF) regression models. A Stepwise Multiple Regression (SMR) model was also explored as a comparison. Our results demonstrated that the important predictors from Lidar-derived metrics had a strong correlation with field-measured biomass in the RF regression models with a pseudo R 2 of 0.76 and RMSE of 125 g/m 2 for shrub biomass and a pseudo R 2 of 0.74 and RMSE of 141 g/m 2 for total biomass, and a weak correlation with field-measured herbaceous biomass. The SMR results were similar but slightly better than RF, explaining 77-79% of the variance, with RMSE ranging from 120 to 129 g/m 2 for shrub and total biomass, respectively. We further explored the computational efficiency and relative accuracies of using point cloud and raster Lidar metrics at different resolutions (1 m to 1 ha). Metrics derived from the Lidar point cloud processing led to improved biomass estimates at nearly all resolutions in comparison to raster-derived Lidar metrics. Only at 1 m were the results from the point cloud and raster products nearly equivalent. The best Lidar prediction models of biomass at the plot-level (1 ha) were achieved when Lidar metrics were derived from an average of fine resolution (1 m) metrics to minimize boundary effects and to smooth variability. Overall, both RF and SMR methods explained more than 74% of the variance in biomass, with the most important Lidar variables being associated with vegetation structure and statistical measures of this structure (e.g., standard deviation of height was a strong predictor of biomass). Using our model results, we developed spatially-explicit Lidar estimates of total and shrub biomass across our study site in the Great Basin, U.S.A., for monitoring and planning in this imperiled ecosystem.

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“…ALS mapping of vegetation structure is operational in forests [37][38][39], rapidly developing in shrublands [40][41][42][43][44] but applications to riparian vegetation types remain rare [45,46] …”

Section: Passive Remote Sensing Of Wetland Vegetationmentioning

confidence: 99%

Categorizing Wetland Vegetation by Airborne Laser Scanning on Lake Balaton and Kis-Balaton, Hungary

et al. 2012

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Outlining patches dominated by different plants in wetland vegetation provides information on species succession, microhabitat patterns, wetland health and ecosystem services. Aerial photogrammetry and hyperspectral imaging are the usual data acquisition methods but the application of airborne laser scanning (ALS) as a standalone tool also holds promises for this field since it can be used to quantify 3-dimensional vegetation structure. Lake Balaton is a large shallow lake in western Hungary with shore wetlands that have been in decline since the 1970s. In August 2010, an ALS survey of the shores of Lake Balaton was completed with 1 pt/m 2 discrete echo recording. The resulting ALS dataset was processed to several output rasters describing vegetation and terrain properties, creating a sufficient number of independent variables for each raster cell to allow for basic multivariate classification. An expert-generated decision tree algorithm was applied to outline wetland areas, and within these, patches dominated by Typha sp. Carex sp., and Phragmites australis. Reed health was mapped into four categories: healthy, stressed, ruderal and die-back. The output map was tested against a set of 775 geo-tagged ground photographs and had a user's accuracy of >97% for detecting non-wetland features (trees, artificial surfaces and low density Scirpus stands), >72% for dominant genus detection and >80% for most reed health categories (with 62% for one category). Overall classification OPEN ACCESSRemote Sens. 2012, 4 1618 accuracy was 82.5%, Cohen's Kappa 0.80, which is similar to some hyperspectral or multispectral-ALS fusion studies. Compared to hyperspectral imaging, the processing chain of ALS can be automated in a similar way but relies directly on differences in vegetation structure and actively sensed reflectance and is thus probably more robust. The data acquisition parameters are similar to the national surveys of several European countries, suggesting that these existing datasets could be used for vegetation mapping and monitoring.

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Small-footprint Lidar Estimations of Sagebrush Canopy Characteristics

Cited by 53 publications

References 47 publications

Aerodynamic Roughness Length Estimation with Lidar and Imaging Spectroscopy in a Shrub-Dominated Dryland

Aerodynamic Roughness Length Estimation with Lidar and Imaging Spectroscopy in a Shrub-Dominated Dryland

Lidar Aboveground Vegetation Biomass Estimates in Shrublands: Prediction, Uncertainties and Application to Coarser Scales

Categorizing Wetland Vegetation by Airborne Laser Scanning on Lake Balaton and Kis-Balaton, Hungary

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