Feasibility Of Terrestrial Laser Scanning Research Articles

Feasibility of terrestrial laser scanning for quantification of vegetation structure parameters of restored sandy land in the southern Qinghai–Tibeten Plateau

AbstractThe efficiency of ecosystem restoration of degraded land is typically assessed using vegetation structure parameters (VSP). However, the field measurement of VSP, particularly in the Qinghai–Tibet Plateau (QTP), which is a problematic area for ecological restoration because of its harsh environment, is generally time‐consuming and labour intensive. Therefore, new methods for VSP measurement should be developed. In this study, VSP were quantified at the individual scale, including their number, height, and crown width. Furthermore, the quadrat scale, such as vegetation coverage (VC) and leaf area index (LAI) on a restored sandy plot (100 m × 100 m) in the middle reaches of the Yarlung Zangbo River basin, is quantified using terrestrial laser scanning. Thereafter, the TLS‐derived VSP are validated by field measurements. To extract the VSP of low‐height vegetation (shrubs) at the individual scale, three approaches are tested: the manual measurement of VSP from TLS data, individual vegetation segmentation (IVS) without specific root locations, and IVS with specific root locations. The results indicate that the VSP of shrubs can be manually derived from the TLS data and are consistent with field measurements. By specifying the root locations before performing the IVS, the identification rate of the number of shrubs can be improved significantly from 40.3% to 100%. The height extracted by the IVS corresponds better with the manual measurement (R2 > 0.99) than with the crown width (R2 < 0.65). At the quadrat scale, the optimal resolution is analyzed to extract the VC. The analysis suggests that the optimal resolution for extracting the VC based on the canopy height model is in the 1‐2 cm range. In extracting the LAI using the voxel‐based method, it is discovered that the appropriate voxel size is linearly dependent on the mean point spacing (y = 2.143x, R2= 0.826). This study provides useful guidance for monitoring the effectiveness of ecological conservation and restoration on the QTP.

Forest Inventory and Aboveground Biomass Estimation with Terrestrial LiDAR in the Tropical Forest of Malaysia

An accurate forest inventory is crucial for forest monitoring and quantifying forest aboveground biomass (AGB). This study aimed to investigate the feasibility of Terrestrial Laser Scanning (TLS) in forest inventory and AGB estimation in the tropical forest of Malaysia. Individual trees were detected using manual and automatic detection methods. An average tree detection rate of 99.55% and 93.75% were achieved using the manual and automatic detection method respectively. The accuracy of the diameter at breast height (DBH) of trees measured from TLS was validated using field DBH as reference. A root means square error (RMSE) of 1.37 cm (6.60%) and 2.36 cm (11.47%), respectively, were obtained for manually and automatically measured TLS DBH. Similarly, TLS based tree height was validated using Airborne Laser Scanner (ALS) height as a reference and resulted in RMSE of 1.74 m (9.30%) and 3.17 m (17.40%) with manual and automatic method respectively. Finally, AGB was calculated using the variables derived from the TLS data. Results show an R2 value of 0.98 and RMSE of 0.08 Mg. The results of this study confirmed that TLS as a nondestructive approach can provide a very good estimation of forest attributes and AGB in the dense tropical forest conditions.

Detecting and characterizing downed dead wood using terrestrial laser scanning

Dead wood is a key forest structural component for maintaining biodiversity and storing carbon. Despite its important role in a forest ecosystem, quantifying dead wood alongside standing trees has often neglected when investigating the feasibility of terrestrial laser scanning (TLS) in forest inventories. The objective of this study was therefore to develop an automatic method for detecting and characterizing downed dead wood with a diameter exceeding 5 cm using multi-scan TLS data. The developed four-stage algorithm included (1) RANSAC-cylinder filtering, (2) point cloud rasterization, (3) raster image segmentation, and (4) dead wood trunk positioning. For each detected trunk, geometry-related quality attributes such as dimensions and volume were automatically determined from the point cloud. For method development and validation, reference data were collected from 20 sample plots representing diverse southern boreal forest conditions. Using the developed method, the downed dead wood trunks were detected with an overall completeness of 33% and correctness of 76%. Up to 92% of the downed dead wood volume were detected at plot level with mean value of 68%. We were able to improve the detection accuracy of individual trunks with visual interpretation of the point cloud, in which case the overall completeness was increased to 72% with mean proportion of detected dead wood volume of 83%. Downed dead wood volume was automatically estimated with an RMSE of 15.0 m3/ha (59.3%), which was reduced to 6.4 m3/ha (25.3%) as visual interpretation was utilized to aid the trunk detection. The reliability of TLS-based dead wood mapping was found to increase as the dimensions of dead wood trunks increased. Dense vegetation caused occlusion and reduced the trunk detection accuracy. Therefore, when collecting the data, attention must be paid to the point cloud quality. Nevertheless, the results of this study strengthen the feasibility of TLS-based approaches in mapping biodiversity indicators by demonstrating an improved performance in quantifying ecologically most valuable downed dead wood in diverse forest conditions.

Feasibility of Terrestrial laser scanning for collecting stem volume information from single trees

Interest in measuring forest biomass and carbon stock has increased as a result of the United Nations Framework Convention on Climate Change, and sustainable planning of forest resources is therefore essential. Biomass and carbon stock estimates are based on the large area estimates of growing stock volume provided by national forest inventories (NFIs). The estimates for growing stock volume based on the NFIs depend on stem volume estimates of individual trees. Data collection for formulating stem volume and biomass models is challenging, because the amount of data required is considerable, and the fact that the detailed destructive measurements required to provide these data are laborious. Due to natural diversity, sample size for developing allometric models should be rather large. Terrestrial laser scanning (TLS) has proved to be an efficient tool for collecting information on tree stems. Therefore, we investigated how TLS data for deriving stem volume information from single trees should be collected. The broader context of the study was to determine the feasibility of replacing destructive and laborious field measurements, which have been needed for development of empirical stem volume models, with TLS. The aim of the study was to investigate the effect of the TLS data captured at various distance (i.e. corresponding 25%, 50%, 75% and 100% of tree height) on the accuracy of the stem volume derived. In addition, we examined how multiple TLS point cloud data acquired at various distances improved the results. Analysis was carried out with two ways when multiple point clouds were used: individual tree attributes were derived from separate point clouds and the volume was estimated based on these separate values (multiple-scan A), and point clouds were georeferenced as a combined point cloud from which the stem volume was estimated (multiple-scan B). This permitted us to deal with the practical aspects of TLS data collection and data processing for development of stem volume equations in boreal forests. The results indicated that a scanning distance of approximately 25% of tree height would be optimal for stem volume estimation with TLS if a single scan was utilized in boreal forest conditions studied here and scanning resolution employed. Larger distances increased the uncertainty, especially when the scanning distance was greater than approximately 50% of tree height, because the number of successfully measured diameters from the TLS point cloud was not sufficient for estimating the stem volume. When two TLS point clouds were utilized, the accuracy of stem volume estimates was improved: RMSE decreased from 12.4% to 6.8%. When two point clouds were processed separately (i.e. tree attributes were derived from separate point clouds and then combined) more accurate results were obtained; smaller RMSE and relative error were achieved compared to processing point clouds together (i.e. tree attributes were derived from a combined point cloud). TLS data collection and processing for the optimal setup in this study required only one sixth of time that was necessary to obtain the field reference. These results helped to further our knowledge on TLS in estimating stem volume in boreal forests studied here and brought us one step closer in providing best practices how a phase-shift TLS can be utilized in collecting data when developing stem volume models.