Historic low wall detection via topographic parameter images derived from fine-resolution DEM

• Verfasst von: Chu, Hone-Jay [VerfasserIn]
• Verfasst von: Yang, Mon-Shieh [VerfasserIn]
• Verfasst von: Höfle, Bernhard [VerfasserIn]
• Titel: Historic low wall detection via topographic parameter images derived from fine-resolution DEM
• Verf.angabe: Hone-Jay Chu, Min-Lang Huang, Yu-Ching Tain, Mon-Shieh Yang and Bernhard Höfle
• E-Jahr: 2017
• Jahr: 7 November 2017
• Umfang: 13 S.
• Fussnoten: Gesehen am 23.07.2018
• Titel Quelle: Enthalten in: International Society for Photogrammetry and Remote SensingISPRS International Journal of Geo-Information
• Ort Quelle: Basel : MDPI, 2012
• Jahr Quelle: 2017
• Band/Heft Quelle: 6(2017), 11, Seite 1-13
• ISSN Quelle: 2220-9964
• DOI: doi:10.3390/ijgi6110346
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: feature identification
• Sach-SW: LiDAR-based DEM
• Sach-SW: topographic parameters
• Sach-SW: wall detection
• K10plus-PPN: 1577820274

Abstract

Coral walls protect vegetation gardens from strong winds that sweep across Xiji Island, Taiwan Strait for half the year. Topographic parameters based on light detection and ranging (LiDAR)-based high-resolution digital elevation model (DEM) provide obvious correspondence with the expected form of landscape features. The information on slope, curvature, and openness can help identify the location of landscape features. This study applied the automatic landscape line detection to extract historic vegetable garden wall lines from a LiDAR-derived DEM. The three rapid processes used in this study included the derivation of topographic parameters, line extraction, and aggregation. The rules were extracted from a decision tree to check the line detection from multiple topographic parameters. Results show that wall line detection with multiple topographic parameter images is an alternative means of obtaining essential historic wall feature information. Multiple topographic parameters are highly related to low wall feature identification. Furthermore, the accuracy of wall feature detection is 74% compared with manual interpretation. Thus, this study provides rapid wall detection systems with multiple topographic parameters for further historic landscape management.