scholarly journals Evaluating the Differences in Modeling Biophysical Attributes between Deciduous Broadleaved and Evergreen Conifer Forests Using Low-Density Small-Footprint LiDAR Data

2017 ◽  
Vol 9 (6) ◽  
pp. 572 ◽  
Author(s):  
Yoshio Awaya ◽  
Tomoaki Takahashi
Keyword(s):  
Lidar Data ◽  
Low Density ◽  
Conifer Forests ◽  
Small Footprint ◽  
Evergreen Conifer ◽  
Biophysical Attributes

Estimation of biophysical characteristics for highly variable mixed-conifer stands using small-footprint lidar

2006 ◽  
Vol 36 (5) ◽  
pp. 1129-1138 ◽  
Author(s):  
Jennifer L. Rooker Jensen ◽  
Karen S Humes ◽  
Tamara Conner ◽  
Christopher J Williams ◽  
John DeGroot
Keyword(s):  
Regression Model ◽  
Large Scale ◽  
Canopy Height ◽  
Training Data ◽  
Lidar Data ◽  
Mixed Conifer ◽  
Wood Volume ◽  
Small Footprint ◽  
Mean Square Errors ◽  
Single Regression

Although lidar data are widely available from commercial contractors, operational use in North America is still limited by both cost and the uncertainty of large-scale application and associated model accuracy issues. We analyzed whether small-footprint lidar data obtained from five noncontiguous geographic areas with varying species and structural composition, silvicultural practices, and topography could be used in a single regression model to produce accurate estimates of commonly obtained forest inventory attributes on the Nez Perce Reservation in northern Idaho, USA. Lidar-derived height metrics were used as predictor variables in a best-subset multiple linear regression procedure to determine whether a suite of stand inventory variables could be accurately estimated. Empirical relationships between lidar-derived height metrics and field-measured dependent variables were developed with training data and acceptable models validated with an independent subset. Models were then fit with all data, resulting in coefficients of determination and root mean square errors (respectively) for seven biophysical characteristics, including maximum canopy height (0.91, 3.03 m), mean canopy height (0.79, 2.64 m), quadratic mean DBH (0.61, 6.31 cm), total basal area (0.91, 2.99 m2/ha), ellipsoidal crown closure (0.80, 0.08%), total wood volume (0.93, 24.65 m3/ha), and large saw-wood volume (0.75, 28.76 m3/ha). Although these regression models cannot be generalized to other sites without additional testing, the results obtained in this study suggest that for these types of mixed-conifer forests, some biophysical characteristics can be adequately estimated using a single regression model over stands with highly variable structural characteristics and topography.

Estimating plot-level tree height and volume of Eucalyptus grandis plantations using small-footprint, discrete return lidar data

2010 ◽  
Vol 34 (4) ◽  
pp. 515-540 ◽  
Author(s):  
S.G. Tesfamichael ◽  
J.A.N. van Aardt ◽  
F. Ahmed
Keyword(s):  
Eucalyptus Grandis ◽  
Tree Height ◽  
Mean Value ◽  
Primary Objective ◽  
Stand Age ◽  
Lidar Data ◽  
Maximum Height ◽  
Density Level ◽  
Local Maxima ◽  
Small Footprint

This study explores the utility of small-footprint, discrete return lidar data in deriving important forest structural attributes with the primary objective of estimating plot-level mean tree height, dominant height, and volume of Eucalyptus grandis plantations. The secondary objectives of the study were related to investigating the effect of lidar point densities (1 point/m2, 3 points/m2, and 5 points/m2) on height and volume estimates. Tree tops were located by applying local maxima (LM) filtering to canopy height surfaces created at each density level, followed by buffering using circular polygons. Maximum and mean height values of the original lidar points falling within each tree polygon were used to generate lidar mean and dominant heights. Lidar mean value was superior to the maximum lidar value approach in estimating mean plot height (R2∼0.95; RMSE∼7%), while the maximum height approach resulted in superior estimates for dominant plot height (R2 ∼0.95; RMSE∼5%). These observations were similar across all lidar point density levels. Plot-level volume was calculated using approaches based on lidar-derived height variables and stems per hectare, as well as stand age. The level of association between estimated and observed volume was relatively high (R2=0.82—0.94) with non-significant differences among estimates at high lidar point densities and field observation. Nearly all estimates, however, exhibited negative biases and RMSE ranging in the order of 20—43%. Overall, the results of the study demonstrate the potential of lidar-based approaches for forest structural assessment in commercial plantations, even though further research is required on improving stems per hectare (SPHA) estimation.

An Automatic Lane Marking Detection Method With Low-Density Roadside LiDAR Data

2021 ◽  
Vol 21 (8) ◽  
pp. 10029-10038
Author(s):  
Ciyun Lin ◽  
Yingzhi Guo ◽  
Wenjun Li ◽  
Hui Liu ◽  
Dayong Wu
Keyword(s):  
Detection Method ◽  
Lidar Data ◽  
Low Density ◽  
Lane Marking

Airborne small-footprint discrete-return LiDAR data in the assessment of boreal mire surface patterns, vegetation, and habitats

2009 ◽  
Vol 258 (7) ◽  
pp. 1549-1566 ◽  
Author(s):  
Ilkka Korpela ◽  
Markku Koskinen ◽  
Harri Vasander ◽  
Markus Holopainen ◽  
Kari Minkkinen
Keyword(s):  
Lidar Data ◽  
Small Footprint ◽  
Surface Patterns

scholarly journals Evaluation of the use of low-density LiDAR data to estimate structural attributes and biomass yield in a short-rotation willow coppice: an example in a field trial

2017 ◽  
Vol 74 (4) ◽  
Author(s):  
María Castaño-Díaz ◽  
Pedro Álvarez-Álvarez ◽  
Brian Tobin ◽  
Maarten Nieuwenhuis ◽  
Elías Afif-Khouri ◽  
...  
Keyword(s):  
Field Trial ◽  
Biomass Yield ◽  
Lidar Data ◽  
Low Density ◽  
Short Rotation
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