Estimation of wood volume and height of olive tree plantations using airborne discrete-return LiDAR data

The aim of this study is to analyze methodologies based on airborne LiDAR (light detection and ranging) technology of low pulse density points (0.5m(-2)) for height and volume quantification of olive trees in Viver (Spain). A total of 29 circular plots, each with a radius of 20m, were sampled and their volumes and heights were obtained by dendrometric methods. For these estimations, several statistics derived from LiDAR data were calculated in each plot. Regression models were used to predict volume and height. The results showed good performance for estimating volume (R-2=0.70) and total height (R-2=0.67).The authors appreciate the financial support provided by the Spanish Ministerio de Ciencia e Innovacion (Ministry for Science & Innovation) within the framework of the project AGL2010-15334 and by the Vice-Rectorate for Research of the Universitat Politecnica de Valencia [Grant PAID-06-12-3297; SP20120534].Estornell Cremades, J.; Velázquez Martí, B.; López Cortés, I.; Salazar Hernández, DM.; Fernández-Sarría, A. (2014). Estimation of wood volume and height of olive tree plantations using airborne discrete-return LiDAR data. GIScience and Remote Sensing. 51(1):17-29. https://doi.org/10.1080/15481603.2014.883209S1729511Estornell, J., Ruiz, L. A., Velázquez-Martí, B., & Fernández-Sarría, A. (2011). Estimation of shrub biomass by airborne LiDAR data in small forest stands. Forest Ecology and Management, 262(9), 1697-1703. doi:10.1016/j.foreco.2011.07.026García, M., Riaño, D., Chuvieco, E., & Danson, F. M. (2010). Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data. Remote Sensing of Environment, 114(4), 816-830. doi:10.1016/j.rse.2009.11.021Hyyppa, J., Kelle, O., Lehikoinen, M., & Inkinen, M. (2001). A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners. IEEE Transactions on Geoscience and Remote Sensing, 39(5), 969-975. doi:10.1109/36.921414Kim, Y., Yang, Z., Cohen, W. B., Pflugmacher, D., Lauver, C. L., & Vankat, J. L. (2009). Distinguishing between live and dead standing tree biomass on the North Rim of Grand Canyon National Park, USA using small-footprint lidar data. Remote Sensing of Environment, 113(11), 2499-2510. doi:10.1016/j.rse.2009.07.010Moorthy, I., Miller, J. R., Berni, J. A. J., Zarco-Tejada, P., Hu, B., & Chen, J. (2011). Field characterization of olive (Olea europaea L.) tree crown architecture using terrestrial laser scanning data. Agricultural and Forest Meteorology, 151(2), 204-214. doi:10.1016/j.agrformet.2010.10.005Næsset, E. (2004). Accuracy of forest inventory using airborne laser scanning: evaluating the first nordic full-scale operational project. Scandinavian Journal of Forest Research, 19(6), 554-557. doi:10.1080/02827580410019544Popescu, S. C. (2007). Estimating biomass of individual pine trees using airborne lidar. Biomass and Bioenergy, 31(9), 646-655. doi:10.1016/j.biombioe.2007.06.022Popescu, S. C., Wynne, R. H., & Nelson, R. F. (2002). Estimating plot-level tree heights with lidar: local filtering with a canopy-height based variable window size. Computers and Electronics in Agriculture, 37(1-3), 71-95. doi:10.1016/s0168-1699(02)00121-7Velázquez-Martí, B., Estornell, J., López-Cortés, I., & Martí-Gavilá, J. (2012). Calculation of biomass volume of citrus trees from an adapted dendrometry. Biosystems Engineering, 112(4), 285-292. doi:10.1016/j.biosystemseng.2012.04.011Velázquez-Martí, B., Fernández-González, E., Estornell, J., & Ruiz, L. A. (2010). Dendrometric and dasometric analysis of the bushy biomass in Mediterranean forests. Forest Ecology and Management, 259(5), 875-882. doi:10.1016/j.foreco.2009.11.027Velázquez-Martí, B., Fernández-González, E., López-Cortés, I., & Salazar-Hernández, D. M. (2011). Quantification of the residual biomass obtained from pruning of trees in Mediterranean olive groves. Biomass and Bioenergy, 35(7), 3208-3217. doi:10.1016/j.biombioe.2011.04.042Yu, X., Hyyppä, J., Kaartinen, H., & Maltamo, M. (2004). Automatic detection of harvested trees and determination of forest growth using airborne laser scanning. Remote Sensing of Environment, 90(4), 451-462. doi:10.1016/j.rse.2004.02.00

Accuracy assessment of LiDAR elevation data using survey marks

Airborne LiDAR has become the preferred technology for digital elevation data acquisition in a wide range of applications. The vertical accuracy with respect to a specified vertical datum is the principal criterion in specifying the quality of LiDAR elevation data. The quantitative assessment of LiDAR elevation data is usually conducted by comparing high-accuracy checkpoints with elevations estimated from the LiDAR ground data. However, the collection of a sufficient number of checkpoints by field surveying is a time-consuming task. This study used survey marks to assess the vertical accuracy of LiDAR data for different land covers in a rural area and explored the performance of different methods for deriving elevations from LiDAR data corresponding to the locations of heckpoints. Normality tests using both frequency histograms and quantile-quantile plots were performed for vertical differences between the LiDAR data and the checkpoints, so the appropriate measures (the formula 1.96×RMSE or the 95th percentile) can be used for the vertical accuracy ssessment of the LiDAR data for different land covers. The results demonstrated the suitability of using survey marks as checkpoints for the assessment of the vertical accuracy of LiDAR data

Monitoring selective logging in western Amazonia with repeat lidar flights.

The objective of this study was to test the use of repeat flight, airborne laser scanning data (lidar) for estimating changes associatedwith low-impact selective logging (approx. 10?15 m3 ha−1 = 5?7% of total standing volume harvested) in natural tropical forests in the Western Brazilian Amazon. Specifically, we investigated change in area impacted by selective logging, change in tall canopy (30 m+) area, change in lidar canopy structuremetrics, and change in above ground biomass (AGB) using a model-based statistical framework. Ground plot measurements were only available from the time of the 2010 lidar acquisition. A simple differencing of the 2010 and 2011 lidar canopy height models identified areas where canopy over 30 m tall had been removed. Area of tall canopy dropped from 22.8% in 2010 to 18.7% in 2011, a reduction of 4.1%. Using a relative density model (RDM) technique the increase in area of roads, skidtrails, landings, and felled tree gaps was estimated to be 17.1%. A lidar-based regression model for estimating AGB was developed using lidar metrics from the 2010 lidar acquisition and corresponding AGB ground plot measurements. The estimator was then used to compute AGB estimates for the site in 2010 and 2011 using the 2010 and 2011 lidar acquisition data, respectively. A model-based statistical approach was then used to estimate the uncertainty of the changes in AGB between the acquisitions. Change in RDMs between lidar acquisitions was used to classify each 50 m cell in the study area as impacted or non-impacted by logging. The change in mean AGB for the entire study area was −9.1 Mg ha−1 ± 1.9 (mean ± SD) (P-value b 0.0001). The change in mean AGB for areas newly impacted in 2011 was −17.9 ± 3.1 Mg ha−1 (P-value b 0.0001) while the change in mean AGB for non-impacted areaswas significantly less at−2.6 ± 1.1 Mg ha−1 (P-value = 0.009). These results provide corroborating evidence of the spatial extent and magnitude of change due to low-intensity logging in tropical forests with heavy residual canopy cover.201