Effects of plot size, stand density, and scan density on the relationship between airborne laser scanning metrics and the gini coefficient of tree size inequality

© 2017, Canadian Science Publishing. All rights reserved. Estimation of the Gini coefficient (GC) of tree sizes using airborne laser scanning (ALS) can provide maps of forest structure across the landscape, which can support sustainable forest management. A challenge arise s in determining the optimal spatial resolution that maximizes the stability and precision of GC estimates, which in turn depends on stand density or ALS scan density. By subsampling different plot sizes within large field plots, we evaluated the optimal spatial resolution by observing changes in GC estimation and in its correlation with ALS metrics. We found that plot size had greater effects than either stand density or ALS scan density on the relationship between GC and ALS metrics. Uncertainty in GC estimates fell as plot size increased. Correlation with ALS metrics showed convex curves with maxima at 250–450m 2 , which thus was considered the optimal plot size and, consequently, the optimal spatial resolution. By thinning the density of the ALS point cloud, we deduced that at least 3 points·m −2 were needed for reliable GC estimates. Many nationwide ALS scan densities are sparser than this, so may be unreliable for GC estimation. Ours is a simple approach for evaluating the optimal spatial resolution in remote sensing estimation of any forest attribute

Can models for forest attributes based on airborne laser scanning be generalized for different silvicultural management systems?

In Finland, interest in continuous cover forestry (CCF) has increased rapidly in recent years. During those years CCF has been examined from various viewpoints but not from the perspective of forest inventories. This holds especially true for applications based on remote sensing. Conversely, airborne laser scanning (ALS) data have been widely used to predict forest characteristics such as size distribution and vertical forest structure, which are closely related to the forest information needs of CCF. In this study we used the area-based approach to predict a set of stand attributes from ALS data (5 pulses per m2) in a CCF forest management experiment in Katajama & BULL;ki, eastern Finland. In addition to the CCF stands, the experiment included shelterwood stands and untreated stands. The predicted attributes included volume, biomass, basal area, number of stems, mean diameter, Lorey's height, dominant height, standing dead wood volume, parameters of the theoretical stem diameter distribution model, understory height and number of understory stems. Our main aim was to test whether the same model could be used across different management systems. The accuracy of the attributes predicted for the CCF stands was compared with the predictions for the other management systems in the same experiment. We also compared and discussed our results in relation to the even-aged stand attribute predictions that were conducted by using separate operational forest data collected from sites surrounding Katajama & BULL;ki. The results showed that forest data from the different management systems could be combined into a single model of a stand attribute, i.e., ALS metrics were found to be suitable for comparing different management systems in regard to differences in forest structure. The accuracy of the predicted attributes in the CCF plots was comparable to that of the other management alternatives in the experiment. The accuracy was also comparable to that of even-aged forests. The results of this study were promising; the stand attributes of CCF-managed forests could be predicted analogously to those of other management systems. This indicates that for the purposes of forest inventories there may not be a need to stratify forest lands by management system. It should be noted, however, that the study area was relatively small, that the forest stands were harvested in the 1980 s, and that the attributes may not have been completely exhaustive for CCF

A simple approach to forest structure classification using airborne laser scanning that can be adopted across bioregions

Reliable assessment of forest structural types (FSTs) aids sustainable forest management. We developed a methodology for the identification of FSTs using airborne laser scanning (ALS), and demonstrate its generality by applying it to forests from Boreal, Mediterranean and Atlantic biogeographical regions. First, hierarchal clustering analysis (HCA) was applied and clusters (FSTs) were determined in coniferous and deciduous forests using four forest structural variables obtained from forest inventory data – quadratic mean diameter (QMD), Gini coefficient (GC), basal area larger than mean (BALM) and density of stems (N) –. Then, classification and regression tree analysis (CART) were used to extract the empirical threshold values for discriminating those clusters. Based on the classification trees, GC and BALM were the most important variables in the identification of FSTs. Lower, medium and high values of GC and BALM characterize single storey FSTs, multi-layered FSTs and exponentially decreasing size distributions (reversed J), respectively. Within each of these main FST groups, we also identified young/mature and sparse/dense subtypes using QMD and N. Then we used similar structural predictors derived from ALS – maximum height (Max), L-coefficient of variation (Lcv), L-skewness (Lskew), and percentage of penetration (cover), – and a nearest neighbour method to predict the FSTs. We obtained a greater overall accuracy in deciduous forest (0.87) as compared to the coniferous forest (0.72). Our methodology proves the usefulness of ALS data for structural heterogeneity assessment of forests across biogeographical regions. Our simple two-tier approach to FST classification paves the way toward transnational assessments of forest structure across bioregions

Retrieval of vegetative fluid resistance terms for rigid stems using airborne lidar.

Hydraulic resistance of riparian forests is an unknown but important term in flood conveyance modeling. Lidar has proven to be a very important new data source to physically characterize floodplain vegetation. This research outlines a recent campaign that aims to retrieve vegetation fluid resistance terms from airborne laser scanning to parameterize trunk roughness. Information on crown characteristics and vegetation spacing can be extracted for individual trees to aid in the determining of trunk stem morphology. Airborne lidar data were used to explore the potential to characterize some of the prominent tree morphometric properties from natural and planted riparian poplar zones such as tree position, tree height, trunk location, and tree spacing. Allometric equations of tree characteristics extrapolated from ground measurements were used to infer below-canopy morphometric variables. Results are presented from six riparian-forested zones on the Garonne and Allier rivers in southern and central France. The tree detection and crown segmentation (TDCS) method identified individual trees with 85% accuracy, and the TreeVaW method detected trees with 83% accuracy. Tree heights were overall estimated at both river locations with an RMSE error of around 19% for both methods, but crown diameter at the six sites produced large deviations from ground-measured values of above 40% for both methods. Total height-derived trunk diameters using the TDCS method produced the closest roughness coefficient values to the ground-derived roughness coefficients. The stem roughness values produced from this method fell within guideline values

Different methodologies for calculating crown volume of Platanus hispanica trees by terrestial laser scanner and comparison with classical dendrometric measurements

Terrestrial laser scanners (TLSs) are used in forestry and fruit culture applications to perform a threedimensional geometrical characterization of trees and so make it easier to develop management systems based on that information. In addition, this data can improve the accuracy of dendrometric variable estimations, such as crown volume, obtained by standard methods. The main objective of this paper is to compare classical methods for crown volume estimation with the volumes obtained from the processing of point clouds obtained using a terrestrial laser scanner (TLS) on urban Platanus hispanica trees. This will allow faster quantification of residual biomass from pruning and therefore an improved management in future. The methods applied using TLS data were also evaluated in terms of processing speed. A set of 30 specimens were selected and their main dendrometric parameters (such as diameter breast height, crown diameter, total height, and distance from the crown base to the soil) were manually measured using classical methods. From these dendrometric parameters, the apparent crown volumes were calculated using three geometric models: cone, hemisphere, and paraboloid. Simultaneously, these trees were scanned with a Leica ScanStation2. A laser point cloud was registered for each tree and processed to obtain the crown volumes. Four processing methods were analyzed: (a) convex hull (an irregular polyhedral surface formed by triangles that surround the crown) applied to the whole point cloud that forms the crown; (b) convex hull using slices of 10 cm in height from the top to the base of the crown; (c) XY triangulation in horizontal sections; and (d) voxel discretization. All the obtained volumes (derived from classical methods and TLS) were assessed and compared. The regression equations that compare the volumes obtained by dendrometry and those derived from TLS data showed coefficients of determination (R2) greater than 0.78. The highest R2 (0.89) was obtained in the comparison between the volume calculated using a paraboloid and flat sections, which was also the fastest method. These results show the potential of TLS for predicting the crown volumes of urban trees, such as P. hispanica, to help improve their management, especially the quantification of residual biomass.The authors appreciate the financial support provided by the Spanish Ministry of Science and Innovation in the framework of the Project AGL2010-15334 and by the Generalitat Valenciana in the framework of the Project GV/2012/003.Fernández-Sarría, A.; Martínez, L.; Velázquez Martí, B.; Sajdak, M.; Estornell Cremades, J.; Recio Recio, JA. (2013). Different methodologies for calculating crown volume of Platanus hispanica trees by terrestial laser scanner and comparison with classical dendrometric measurements. Computers and Electronics in Agriculture. 90(1):176-185. https://doi.org/10.1016/j.compag.2012.09.017S17618590

The use of airborne laser scanning to develop a pixel-based stratification for a verified carbon offset project

Background The voluntary carbon market is a new and growing market that is increasingly important to consider in managing forestland. Monitoring, reporting, and verifying carbon stocks and fluxes at a project level is the single largest direct cost of a forest carbon offset project. There are now many methods for estimating forest stocks with high accuracy that use both Airborne Laser Scanning (ALS) and high-resolution optical remote sensing data. However, many of these methods are not appropriate for use under existing carbon offset standards and most have not been field tested. Results This paper presents a pixel-based forest stratification method that uses both ALS and optical remote sensing data to optimally partition the variability across an ~10,000 ha forest ownership in Mendocino County, CA, USA. This new stratification approach improved the accuracy of the forest inventory, reduced the cost of field-based inventory, and provides a powerful tool for future management planning. This approach also details a method of determining the optimum pixel size to best partition a forest. Conclusions The use of ALS and optical remote sensing data can help reduce the cost of field inventory and can help to locate areas that need the most intensive inventory effort. This pixel-based stratification method may provide a cost-effective approach to reducing inventory costs over larger areas when the remote sensing data acquisition costs can be kept low on a per acre basis