LiDAR-Based Regional Inventory of Tall Trees—Wellington, New Zealand
AbstractIndigenous forests cover 23.9% of New Zealand’s land area and provide highly valued ecosystem services, including climate regulation, habitat for native biota, regulation of soil erosion and recreation. Despite their importance, information on the number of tall trees and the tree height distribution across different forest classes is scarce. We present the first region-wide spatial inventory of tall trees (>30 m) based on airborne LiDAR (Light Detection and Ranging) measurements in New Zealand—covering the Greater Wellington region. This region has 159,000 ha of indigenous forest, primarily on steep mountainous land. We implement a high-performance tree mapping algorithm that uses local maxima in a canopy height model (CHM) as initial tree locations and accurately identifies the tree top positions by combining a raster-based tree crown delineation approach with information from the digital surface and terrain models. Our algorithm includes a check and correction for over-estimated heights of trees on very steep terrain such as on cliff edges. The number of tall trees (>30 m) occurring in indigenous forest in the Wellington Region is estimated to be 286,041 (±1%) and the number of giant trees (>40 m tall) is estimated to be 7340 (±1%). Stereo-analysis of aerial photographs was used to determine the accuracy of the automated tree mapping. The giant trees are mainly in the beech-broadleaved-podocarp and broadleaved-podocarp forests, with density being 0.04 and 0.12 (trees per hectare) respectively. The inventory of tall trees in the Wellington Region established here improves the characterization of indigenous forests for management and provides a useful baseline for long-term monitoring of forest conditions. Our tree top detection scheme provides a simple and fast method to accurately map overstory trees in flat as well as mountainous areas and can be directly applied to improve existing and build new tree inventories in regions where LiDAR data is available. View Full-Text
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Description: PyCrown - Fast raster-based individual tree segmentation for LiDAR data
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Zörner, J.; Dymond, J.R.; Shepherd, J.D.; Wiser, S.K.; Jolly, B. LiDAR-Based Regional Inventory of Tall Trees—Wellington, New Zealand. Forests 2018, 9, 702.
Zörner J, Dymond JR, Shepherd JD, Wiser SK, Jolly B. LiDAR-Based Regional Inventory of Tall Trees—Wellington, New Zealand. Forests. 2018; 9(11):702.Chicago/Turabian Style
Zörner, Jan; Dymond, John R.; Shepherd, James D.; Wiser, Susan K.; Jolly, Ben. 2018. "LiDAR-Based Regional Inventory of Tall Trees—Wellington, New Zealand." Forests 9, no. 11: 702.
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