Abstract
We present the point cloud slicing (PCS) algorithm, to post process point cloud data (PCD) from terrestrial laser scanning (TLS). We then test this tool for forest inventory application in urban heterogeneous forests. The methodology was based on a voxel data structure derived from TLS PCD. We retrieved biophysical tree parameters including diameter at breast height (DBH), tree height, basal area, and volume. Our results showed that TLS-based metrics explained 91.17% (RMSE = 9.1739 cm, p < 0.001) of the variation in DBH at individual tree level. Though the scanner generated a high-density PCD, only 57.27% (RMSE = 0.7543 m, p < 0.001) accuracy was achieved for predicting tree heights in these very heterogeneous stands. Furthermore, we developed a voxel-based TLS volume estimation method. Our results showed that PCD generated from TLS single location scans only captures 18% of the total tree volume due to an occlusion effect; yet there are significant relationships between the TLS data and field measured parameters for DBH and height, giving promise to the utility of a side scanning approach. Using our method, a terrestrial LiDAR-based inventory, also applicable to mobile- or vehicle-based laser scanning (MLS or VLS), was produced for future calibration of Aerial Laser Scanning (ALS) data and urban forest canopy assessments.
Highlights
Forest inventory information such as diameter at breast height (DBH), tree height (h), species, basal area and volume are critical to assessing the potential of wild fire hazard [1], obtaining and validating aboveground biomass, calculating forest ecosystem services and assessing carbon sequestration strategies for sustainable management [2,3,4]
DBH and Stem Location Estimation In Figure 6 we show the stem center location and DBH approximation results obtained from horizontally sliced point cloud data (PCD) at breast height, and their comparison with the results from field measurement using the Trimble total station
We demonstrated the correlation between the terrestrial laser scanning (TLS)-based and manually measured DBH results in Overall, TLS captured 91.17% (RMSE = 9.1739 cm, p < 0.001) of the variation of DBH, by visually interpreting the point cloud for stems with lowest results we conclude that occlusion is an issue, especially for large stems where parts of the stems are blocked by trees in between the stem and the scanner
Summary
Forest inventory information such as diameter at breast height (DBH), tree height (h), species, basal area and volume are critical to assessing the potential of wild fire hazard [1], obtaining and validating aboveground biomass, calculating forest ecosystem services and assessing carbon sequestration strategies for sustainable management [2,3,4]. Forest inventory has facilitated studies and research regarding the economic aspects of forest management, such as timber product sale or revenue earnings [5]; and the ecological aspects including wildlife habitat [6,7], forest stability, ecosystem services [8,9] and natural biodiversity conservation [10]. It can aid in better understanding the role of forest ecosystem play in climate change, carbon and water cycling [11,12]. Most of traditional forest structure mensuration methods using digital hemisphere photographs [19] and range finders cannot capture the 3-D structural information for the single tree and forest stand
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