Remote Sensing | |
Using Tree Detection Algorithms to Predict Stand Sapwood Area, Basal Area and Stocking Density in |
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Dominik Jaskierniak1  George Kuczera2  Richard Benyon1  Luke Wallace3  Lars T. Waser4  | |
[1] Department of Forest and Ecosystem Science, University of Melbourne, Parkville, VIC 3010, Australia; E-Mail:;School of Engineering, University of Newcastle, Callaghan, NSW 2308, Australia; E-Mail:;School of Land and Food, University of Tasmania, Sandy Bay, TAS 7005, Australia; E-Mail:;Department of Forest and Ecosystem Science, University of Melbourne, Parkville, VIC 3010, Australia; E-Mail | |
关键词: LiDAR; normalised cut; local maximum filtering; tree detection; forest hydrology; stand sapwood area; basal area; stocking density; forest inventory; | |
DOI : 10.3390/rs70607298 | |
来源: mdpi | |
【 摘 要 】
Managers of forested water supply catchments require efficient and accurate methods to quantify changes in forest water use due to changes in forest structure and density after disturbance. Using Light Detection and Ranging (LiDAR) data with as few as 0.9 pulses m−2, we applied a local maximum filtering (LMF) method and normalised cut (NCut) algorithm to predict stocking density (
【 授权许可】
CC BY
© 2015 by the authors; licensee MDPI, Basel, Switzerland.
【 预 览 】
Files | Size | Format | View |
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RO202003190011545ZK.pdf | 31881KB | download |