Abstract
The objective of this study was to analyze the efficiency of individual tree identification and stand volume estimation from LiDAR data. The study was located in Norway spruce [Picea abies (L.) Karst.] stands in southwestern Romania and linked airborne laser scanning (ALS) with terrestrial measurements through empirical modelling. The proposed method uses the Canopy Maxima algorithm for individual tree detection together with biometric field measurements and individual trees positioning. Field data was collected using Field-Map real-time GIS-laser equipment, a high-accuracy GNSS receiver and a Vertex IV ultrasound inclinometer. ALS data were collected using a Riegl LMS-Q560 instrument and processed using LP360 and Fusion software to extract digital terrain, surface and canopy height models. For the estimation of tree heights, number of trees and tree crown widths from the ALS data, the Canopy Maxima algorithm was used together with local regression equations relating field-measured tree heights and crown widths at each plot. When compared to LiDAR detected trees, about 40-61% of the field-measured trees were correctly identified. Such trees represented, in general, predominant, dominant and co-dominant trees from the upper canopy. However, it should be noted that the volume of the correctly identified trees represented 60-78% of the total plot volume. The estimation of stand volume using the LiDAR data was achieved by empirical modelling, taking into account the individual tree heights (as identified from the ALS data) and the corresponding ground reference stem volume. The root mean square error (RMSE) between the individual tree heights measured in the field and the corresponding heights identified in the ALS data was 1.7-2.2 meters. Comparing the ground reference estimated stem volume (at trees level) with the corresponding ALS estimated tree stem volume, an RMSE of 0.5-0.7 m3 was achieved. The RMSE was slightly lower when comparing the ground reference stem volume at plot level with the ALS-estimated one, taking into account both the identified and unidentified trees in the LiDAR data (0.4-0.6 m3).
Highlights
The use of airborne LiDAR technology, as a part of modern active sensor remote sensing, has grown rapidly and continuously, boosted by the necessity of obtaining more precise geospatial information about topography, such as digital terrain model (DTM) and digital surface model (DSM) (Popescu et al, 2002; Dorren et al, 2006; Maguya et al, 2014)
After analyzing, based on the results obtained in this study, the possibility of individual tree extraction and stand volume estimation for the considered Norway spruce plots, the following conclusions can be made:
The automatic tree height automatically identified based on the LiDAR data is smaller than the corresponding field-measured tree height
Summary
The use of airborne LiDAR technology (a term first used by Middleton and Spilhaus, 1953), as a part of modern active sensor remote sensing, has grown rapidly and continuously, boosted by the necessity of obtaining more precise geospatial information about topography, such as digital terrain model (DTM) and digital surface model (DSM) (Popescu et al, 2002; Dorren et al, 2006; Maguya et al, 2014). In forestry, LiDAR data are used to monitor changes in forest vegetation and forest inventory, to assess the risks of disasters caused by wind or fire and to map forest ecosystems and habitats (Hyde et al, 2005; Skowronski and Lister, 2012; Montealegre et al, 2014). Forest stand parameters can be estimated from LiDAR data using..one..of..two..major approaches: an area-based approach (using stand mean height and density) or a single tree-based approach (detecting and measuring individual trees). If the objective is to predict forest attributes at plot and stand level in large areas, the area-based approach using low pulse density LiDAR (
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