Historic Low Wall Detection via Topographic Parameter Images Derived from Fine-Resolution DEM

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 la...

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Main Authors: Hone-Jay Chu, Min-Lang Huang, Yu-Ching Tain, Mon-Shieh Yang, Bernhard Höfle
Format: Article
Language:English
Published: MDPI AG 2017-11-01
Series:ISPRS International Journal of Geo-Information
Subjects:
Online Access:https://www.mdpi.com/2220-9964/6/11/346
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spelling doaj-0dd69ea6a515406a9c1fa4470f46c95f2020-11-24T23:56:43ZengMDPI AGISPRS International Journal of Geo-Information2220-99642017-11-0161134610.3390/ijgi6110346ijgi6110346Historic Low Wall Detection via Topographic Parameter Images Derived from Fine-Resolution DEMHone-Jay Chu0Min-Lang Huang1Yu-Ching Tain2Mon-Shieh Yang3Bernhard Höfle4Department of Geomatics, National Cheng Kung University, Tainan 701, TaiwanDepartment of Geomatics, National Cheng Kung University, Tainan 701, TaiwanDepartment of Geomatics, National Cheng Kung University, Tainan 701, TaiwanInstitute of Geography, Heidelberg University, Heidelberg 69117, GermanyInstitute of Geography, Heidelberg University, Heidelberg 69117, GermanyCoral 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.https://www.mdpi.com/2220-9964/6/11/346LiDAR-based DEMtopographic parameterswall detectionfeature identification
collection DOAJ
language English
format Article
sources DOAJ
author Hone-Jay Chu
Min-Lang Huang
Yu-Ching Tain
Mon-Shieh Yang
Bernhard Höfle
spellingShingle Hone-Jay Chu
Min-Lang Huang
Yu-Ching Tain
Mon-Shieh Yang
Bernhard Höfle
Historic Low Wall Detection via Topographic Parameter Images Derived from Fine-Resolution DEM
ISPRS International Journal of Geo-Information
LiDAR-based DEM
topographic parameters
wall detection
feature identification
author_facet Hone-Jay Chu
Min-Lang Huang
Yu-Ching Tain
Mon-Shieh Yang
Bernhard Höfle
author_sort Hone-Jay Chu
title Historic Low Wall Detection via Topographic Parameter Images Derived from Fine-Resolution DEM
title_short Historic Low Wall Detection via Topographic Parameter Images Derived from Fine-Resolution DEM
title_full Historic Low Wall Detection via Topographic Parameter Images Derived from Fine-Resolution DEM
title_fullStr Historic Low Wall Detection via Topographic Parameter Images Derived from Fine-Resolution DEM
title_full_unstemmed Historic Low Wall Detection via Topographic Parameter Images Derived from Fine-Resolution DEM
title_sort historic low wall detection via topographic parameter images derived from fine-resolution dem
publisher MDPI AG
series ISPRS International Journal of Geo-Information
issn 2220-9964
publishDate 2017-11-01
description 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.
topic LiDAR-based DEM
topographic parameters
wall detection
feature identification
url https://www.mdpi.com/2220-9964/6/11/346
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AT yuchingtain historiclowwalldetectionviatopographicparameterimagesderivedfromfineresolutiondem
AT monshiehyang historiclowwalldetectionviatopographicparameterimagesderivedfromfineresolutiondem
AT bernhardhofle historiclowwalldetectionviatopographicparameterimagesderivedfromfineresolutiondem
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