Factors Influencing the Use of Geospatial Technology with LiDAR for Road Design: Case of Malaysia
Abstract
:1. Introduction
2. Literature Review on Factors Influencing Usage of Geospatial Technology
3. Hypothesis Formulation and Conceptual Framework
3.1. Barrier Factor
3.2. Motivational Factor
3.3. Strategy Factor
3.4. Proposed Conceptual Framework
4. Methodology
4.1. Instrument Design
4.2. Sample Size and Data Collection
4.3. Data Analysis and Tools
4.3.1. Exploratory Factor Analysis (EFA)
4.3.2. Confirmatory Factor Analysis (CFA): Measurement Model & Structural Model
5. Results
5.1. Exploratory Factor Analysis (EFA)
5.2. Confirmatory Factor Analysis (CFA)
5.2.1. Measurement Model
5.2.2. Structural Model
6. Discussion of the Results
6.1. Theoretical Implications
6.2. Practical Implications
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
TS | Total station |
UAV | Unmanned Aerial Vehicle |
DEM | Digital Surface Model |
DTM | Digital Terrain Model |
SEM | Structural Equation Model |
EFA | Exploratory Factor Analysis |
KMO | Kaiser—Meyer—Olkin |
CFA | Confirmatory Factor Analysis |
HTMT | Heterotrait-Monotrait Ratio |
AVE | Average Variance Extracted |
CR | Composite Reliability |
Appendix A
Factors/Items | References | |
---|---|---|
BR | Barrier | Self-created by referring to the research by Kim et al. [21], S.de Gouw et al. [4], and Grohmann et al. [23], Reynard [9], S. Gargoum and El-Basyouny [27], Suleymanoglu and Soycan [28] |
BR1 | Difficulty in getting expert staff | |
BR2 | Adequacy of reference to guide | |
BR3 | High operating cost | |
BR4 | Restricted budget to implement LiDAR technology | |
BR5 | Lack of high-end computers | |
BR6 | Restricted budget on the subscription of paid software to analyse data | |
BR7 | Restricted budget on appointing experts | |
BR8 | Difficulty in filtering data | |
MV | Motivational | Self-created by referring to the research by Häggquist and Nilsson [29], S.de Gouw et al. [4], Peterson et al. [30], Cao et al. [31], Kweon et al. [32], L. Rose et al. [33]. B.Bigdeli et al. [58], B. Babble et al. [59], Z.Zhang et al. [60] |
MV1 | Support from the management is given through exposure to the importance of data application | |
MV2 | Management support to provide specialized staff | |
MV3 | Support from the management is given through providing training | |
MV4 | Support from the management is given by providing computer software | |
MV5 | Stakeholders’ views are considered in enhancing the knowledge | |
MV6 | Appointed an experienced contractor | |
MV7 | Appointed a competent contractor | |
MV8 | Appointed a knowledgeable contractor | |
MV9 | Providing comprehensive information on dense forest and mountain areas. | |
ST | Strategy | Self-created by referring to the research by Olafsson & Skov-Petersen [61], T. Hammond et al. [26], A. Shaker et al. [62], S.de Gouw et al. [4], Aksamitauskas et al. [36], of Peterson et al. [30] and Cao et al. [31], S. Landry et al. [63] |
ST1 | Procedure is developed by those who have the expertise | |
ST2 | Developed procedure must consider the scope of work | |
ST3 | Developed procedure must involve the technical agency | |
ST4 | Developed procedure must solve problems | |
ST5 | Developed procedure must involve an experienced staff | |
ST6 | Procedures developed should identify the knowledge and skills | |
ST7 | Procedures developed should identify the adequacy of training | |
ST8 | Observation of survey is faster | |
UL | Use of LiDAR | Self-created by referring to the research by S. Gargoum et al. [37], T. Görüm [64], P. Jagodnik, et al. [65], F. Hatta Antah et al. [66]. B. Matinnia et al. [18] |
UL1 | Data obtained detects assets of roads | |
UL2 | Generation of a computerized model | |
UL3 | Development of landslide risk maps | |
UL4 | Survey data collection is limited to weather factors | |
UL5 | The capabilities of accurate data measurement |
Factors | Code | Kaiser–Meyer–Olkin (KMO) | Bartlett’s Test of Sphericity | Anti-Image Correlation Matrix of Items | Communalities | Factor Loadings | |||||
---|---|---|---|---|---|---|---|---|---|---|---|
Approx. Chi-Squared | df | Sig. | Barrier | Motivational | Strategy | Use of LiDAR | |||||
Barrier (BR) | 0.799 | 665.741 | 28 | 0.000 | |||||||
BR1 | 0.746 | 0.848 | |||||||||
BR2 | 0.750 | 0.794 | 0.848 | ||||||||
BR3 | 0.835 | 0.773 | 0.620 | ||||||||
BR4 | 0.828 | 0.546 | 0.839 | ||||||||
BR5 | 0.820 | 0.742 | 0.859 | ||||||||
BR6 | 0.796 | 0.763 | 0.854 | ||||||||
BR7 | 0.788 | 0.809 | 0.746 | ||||||||
BR8 | 0.877 | 0.671 | 0.633 | ||||||||
0.435 | |||||||||||
0.889 | 1979.794 | 36 | 0.000 | ||||||||
Motivational (MV) | MV1 | 0.899 | 0.874 | 0.887 | |||||||
MV2 | 0.890 | 0.904 | 0.909 | ||||||||
MV3 | 0.830 | 0.938 | 0.938 | ||||||||
MV4 | 0.864 | 0.902 | 0.908 | ||||||||
MV5 | 0.974 | 0.808 | 0.853 | ||||||||
MV6 | 0.881 | 0.941 | 0.916 | ||||||||
MV7 | 0.837 | 0.956 | 0.948 | ||||||||
MV8 | 0.918 | 0.931 | 0.923 | ||||||||
MV9 | 0.838 | 0.878 | 0.891 | ||||||||
0.854 | 951.614 | 28 | 0.000 | ||||||||
Strategy | ST1 | 0.879 | 0.674 | 0.764 | |||||||
(ST) | ST2 | 0.902 | 0.844 | 0.882 | |||||||
ST3 | 0.907 | 0.823 | 0.880 | ||||||||
ST4 | 0.846 | 0.863 | 0.894 | ||||||||
ST5 | 0.907 | 0.773 | 0.847 | ||||||||
ST6 | 0.727 | 0.881 | 0.917 | ||||||||
ST7 | 0.754 | 0.865 | 0.886 | ||||||||
ST8 | 0.923 | 0.452 | 0.630 | ||||||||
0.657 | 268.315 | 10 | 0.000 | ||||||||
Use of LiDAR (UL) | UL1 | 0.818 | 0.643 | 0.791 | |||||||
UL2 | 0.656 | 0.805 | 0.891 | ||||||||
UL3 | 0.669 | 0.827 | 0.904 | ||||||||
UL4 | 0.524 | 0.795 | 0.890 | ||||||||
UL5 | 0.578 | 0.765 | 0.856 |
Factors | Items | Outer Loadings | Average Variance Extracted (AVE) | Composite Reliability (CR) |
---|---|---|---|---|
Barrier (BR) | BR3 | 0.739 | 0.614 | 0.905 |
BR4 | 0.823 | |||
BR5 | 0.798 | |||
BR6 | 0.854 | |||
BR7 | 0.776 | |||
BR8 | 0.704 | |||
Motivational (MV) | MV1 | 0.851 | 0.720 | 0.959 |
MV2 | 0.841 | |||
MV3 | 0.883 | |||
MV4 | 0.839 | |||
MV5 | 0.843 | |||
MV6 | 0.827 | |||
MV7 | 0.849 | |||
MV8 | 0.848 | |||
MV9 | 0.851 | |||
Strategy (ST) | ST1 | 0.729 | 0.636 | 0.924 |
ST2 | 0.831 | |||
ST3 | 0.828 | |||
ST4 | 0.900 | |||
ST5 | 0.854 | |||
ST6 | 0.709 | |||
ST7 | 0.711 | |||
Use of LiDAR (UL) | UL2 | 0.704 | 0.629 | 0.836 |
UL3 | 0.754 | |||
UL5 | 0.793 |
References
- Downs, R.M. Coming of age in the geospatial revolution: The geographic self re-defined. Hum. Dev. 2014, 57, 35–57. [Google Scholar] [CrossRef]
- Halim, N.D.A.; Latif, M.T.; Mohamed, A.F.; Maulud, K.N.A.; Idrus, S.; Azhari, A.; Othman, M.; Sofwan, N.M. Spatial assessment of land use impact on air quality in mega urban regions, Malaysia. Sustain. Cities Soc. 2020, 63, 102436. [Google Scholar] [CrossRef]
- Acharya, T.D.; Lee, D.H. Remote sensing and geospatial technologies for sustainable development: A review of applications. Sensors Mater. 2019, 31, 3931–3945. [Google Scholar] [CrossRef]
- de Gouw, S.; Morgenroth, J.; Xu, C. An updated survey on the use of geospatial technologies in New Zealand’s plantation forestry sector. N. Z. J. For. Sci. 2020, 50, 1–17. [Google Scholar] [CrossRef]
- Fatma, N.; Yaacob, F.; Rusli, N.; Norashikin Bohari, S.; Razuhanafi, M.; Yazid, M.; Das, A.M. Integrated GIS Tool for Investigating the Relationship of Road Characteristics with Road Traffic Accidents. J. Kejuruter. 2020, 32, 683–691. [Google Scholar]
- Garba, T.E.; Richard, R.L.; Thani, N.E.A.; Majid, M.A.A.; Lawal, M.; Yelwa, N.A. Geological effects on water quality: A review of issues and challenges in Malaysia. Sains Malays. 2021, 50, 1857–1870. [Google Scholar] [CrossRef]
- Mohd, F.A.; Abdul Maulud, K.N.; Karim, O.A.; Ibrahim, M.A.; Benson, Y.A.; Wahab, A.K.A. Integrasi Kaedah Geospatial dan Pemodelan Hidrodinamik untuk Mengkaji Impak Kenaikan Aras Laut Terhadap Kawasan Pantai. J. Kejuruter. 2018, 30, 65–75. [Google Scholar] [CrossRef]
- Ali, A.; Imran, M.; Jabeen, M.; Ali, Z.; Mahmood, S.A. Factors influencing integrated information management: Spatial data infrastructure in Pakistan. Inf. Dev. 2021. [Google Scholar] [CrossRef]
- Reynard, D. Five classes of geospatial data and the barriers to using them. Geogr. Compass 2018, 12, e12364. [Google Scholar] [CrossRef] [Green Version]
- Jozefowicz, S.; Stone, M.; Aravopoulou, E. Geospatial data in the UK. Bottom Line 2020, 33, 27–41. [Google Scholar] [CrossRef]
- Waterman, L.; Casado, M.R.; Bergin, E.; McInally, G. A mixed-methods investigation into barriers for sharing geospatial and resilience flood data in the uk. Water 2021, 13, 1235. [Google Scholar] [CrossRef]
- Eilola, S.; Käyhkö, N.; Fagerholm, N. Lessons learned from participatory land use planning with high-resolution remote sensing images in Tanzania: Practitioners’ and participants’ perspectives. Land Use Policy 2021, 109, 105649. [Google Scholar] [CrossRef]
- Henrico, S.; Coetzee, S.; Cooper, A.; Rautenbach, V. Acceptance of open source geospatial software: Assessing QGIS in South Africa with the UTAUT2 model. Trans. GIS 2021, 25, 468–490. [Google Scholar] [CrossRef]
- Hennig, S. Motivation and Its Consideration in Participatory Spatial Data Contribution. Prof. Geogr. 2020, 72, 238–252. [Google Scholar] [CrossRef] [Green Version]
- Khanal, M.; Hasan, M.; Sterbentz, N.; Johnson, R.; Weatherly, J. Accuracy Comparison of Aerial Lidar, Mobile-Terrestrial Lidar, and UAV Photogrammetric Capture Data Elevations over Different Terrain Types. Infrastructures 2020, 5, 65. [Google Scholar] [CrossRef]
- Buján, S.; Guerra-Hernández, J.; González-Ferreiro, E.; Miranda, D. Forest road detection using LiDAR data and hybrid classification. Remote Sens. 2021, 13, 393. [Google Scholar] [CrossRef]
- Barazzetti, L.; Previtali, M.; Scaioni, M. Roads Detection and Parametrization in Integrated BIM-GIS Using LiDAR. Infrastructures 2020, 5, 55. [Google Scholar] [CrossRef]
- Matinnia, B.; Parsakhoo, A.; Mohamadi, J.; Shataee Jouibary, S. Study of the LiDAR accuracy in mapping forest road alignments and estimating the earthwork volume. J. For. Sci. 2018, 64, 469–477. [Google Scholar] [CrossRef] [Green Version]
- Falamarzi, A.; Moridpour, S.; Nazem, M. A review on existing sensors and devices for inspecting railway in-frastructure. J. Kejuruter. 2019, 31, 1–10. [Google Scholar]
- Fareed, N.; Wang, C. Accuracy Comparison on Culvert-Modified Digital Elevation Models of DSMA and BA Methods Using ALS Point Clouds. Int. J. Geo-Inf. 2021, 10, 254. [Google Scholar] [CrossRef]
- Kim, D.; Zhang, Y.; Lee, C.K. Understanding needs and barriers to using geospatial tools for public health policymaking in China. Geospat. Health 2018, 13, 79–85. [Google Scholar] [CrossRef] [PubMed]
- Jeppesen, J.H.; Ebeid, E.; Jacobsen, R.H.; Toftegaard, T.S. Open geospatial infrastructure for data management and analytics in interdisciplinary research. Comput. Electron. Agric. 2018, 145, 130–141. [Google Scholar] [CrossRef]
- Grohmann, C.H.; Garcia, G.P.B.; Affonso, A.A.; Albuquerque, R.W. Dune migration and volume change from airborne LiDAR, terrestrial LiDAR and Structure from Motion-Multi View Stereo. Comput. Geosci. 2020, 143, 104569. [Google Scholar] [CrossRef]
- Pellicani, R.; Argentiero, I.; Manzari, P.; Spilotro, G.; Marzo, C.; Ermini, R.; Apollonio, C. UAV and airborne LiDAR data for interpreting kinematic evolution of landslide movements: The case study of the montescaglioso landslide (Southern Italy). Geosciences 2019, 9, 248. [Google Scholar] [CrossRef] [Green Version]
- Schumann, G.J.P.; Moller, D.K.; Mentgen, F. High-accuracy elevation data at large scales from airborne single-pass SAR interferometry. Front. Earth Sci. 2016, 3, 88. [Google Scholar] [CrossRef] [Green Version]
- Hammond, T.C.; Bodzin, A.; Anastasio, D.; Holland, B.; Popejoy, K.; Sahagian, D.; Rutzmoser, S.; Carrigan, J.; Farina, W. “You know you can do this, right?”: Developing geospatial technological pedagogical content knowledge and enhancing teachers’ cartographic practices with socio-environmental science investigations. Cartogr. Geogr. Inf. Sci. 2018, 45, 305–318. [Google Scholar] [CrossRef]
- Gargoum, S.A.; El Basyouny, K. A literature synthesis of LiDAR applications in transportation: Feature extraction and geometric assessments of highways. GIScience Remote Sens. 2019, 56, 864–893. [Google Scholar] [CrossRef]
- Suleymanoglu, B.; Soycan, M. Comparison of filtering algorithms used for dtm production from airborne lidar data: A case study in Bergama, Turkey. Geod. Vestn. 2019, 63, 395–414. [Google Scholar] [CrossRef]
- Häggquist, E.; Nilsson, I. Factors influencing the adoption of geological information in Swedish municipalities. J. Environ. Plan. Manag. 2017, 60, 1112–1126. [Google Scholar] [CrossRef]
- Peterson, B.; Nelson, K.J.; Seielstad, C.; Stoker, J.; Jolly, W.M.; Parsons, R. Automated integration of lidar into the LANDFIRE product suite. Remote Sens. Lett. 2015, 6, 247–256. [Google Scholar] [CrossRef]
- Cao, V.H.; Chu, K.X.; Le-Khac, N.A.; Kechadi, M.T.; Laefer, D.; Truong-Hong, L. Toward a new approach for massive LiDAR data processing. In Proceedings of the 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, ICSDM 2015, Fuzhou, China, 8–10 July 2015; pp. 135–140. [Google Scholar] [CrossRef] [Green Version]
- Kweon, H.; Kim, M.; Lee, J.W.; Seo, J., II; Rhee, H. Comparison of horizontal accuracy, shape similarity and cost of three different road mapping techniques. Forests 2019, 10, 452. [Google Scholar] [CrossRef] [Green Version]
- Rose, L.S.; Seong, J.C.; Ogle, J.; Beute, E.; Indridason, J.; Hall, J.D.; Nelson, S.; Jones, T.; Humphrey, J. Challenges and lessons from a wetland LiDAR project: A case study of the Okefenokee Swamp, Georgia, USA. Geocarto Int. 2013, 28, 210–226. [Google Scholar] [CrossRef]
- Lin, Z.; Kaneda, H.; Mukoyama, S.; Asada, N.; Chiba, T. Detection of subtle tectonic-geomorphic features in densely forested mountains by very high-resolution airborne LiDAR survey. Geomorphology 2013, 182, 104–115. [Google Scholar] [CrossRef]
- Enwright, N.M.; Wang, L.; Borchert, S.M.; Day, R.H.; Feher, L.C.; Osland, M.J. The impact of lidar elevation uncertainty on mapping intertidal habitats on barrier Islands. Remote Sens. 2018, 10, 5. [Google Scholar] [CrossRef] [Green Version]
- Aksamitauskas, V.Č.; Kriaučiūnaitė-Neklejonovienė, V.; Rekus, D.; Ruzgienė, B.; Puodžiukas, V.; Stanionis, A. Advantages of laser scanning systems for topographical surveys in roads engineering. Balt. J. Road Bridg. Eng. 2016, 11, 153–159. [Google Scholar] [CrossRef]
- Gargoum, S.; El-Basyouny, K. Transportation Infrastructure Asset Management using LiDAR Remote Sensing Technology. Int. J. Trend Res. Dev. 2018, 143, 143–152. [Google Scholar]
- Guo, F.; Jahren, C.T.; Hao, J.; Zhang, C. Implementation of CIM-related technologies within transportation projects. Int. J. Constr. Manag. 2020, 20, 510–519. [Google Scholar] [CrossRef]
- Morgenroth, J.; Visser, R. Uptake and barriers to the use of geospatial technologies in forest management. N. Z. J. For. Sci. 2013, 43, 16. [Google Scholar] [CrossRef] [Green Version]
- Roussel, J.R.; Auty, D.; Coops, N.C.; Tompalski, P.; Goodbody, T.R.H.; Meador, A.S.; Bourdon, J.F.; de Boissieu, F.; Achim, A. lidR: An R package for analysis of Airborne Laser Scanning (ALS) data. Remote Sens. Environ. 2020, 251, 112061. [Google Scholar] [CrossRef]
- Zahid, M.; Ur Rahman, H.; Ullah, Z.; Muhammad, A. Sustainability and branchless banking : The development and validation of a distinct measurement scale. Technol. Soc. 2021, 67, 101764. [Google Scholar] [CrossRef]
- Dwi, T.; Aljoza, M. Individual Acceptance of e-Government Services in a Developing Country: Dimensions of Perceived Usefulness and Perceived Ease of Use and the Importance of Trust and Social Influence. Procedia Comput. Sci. 2015, 72, 622–629. [Google Scholar] [CrossRef] [Green Version]
- Almanasreh, E.; Moles, R.; Chen, T.F. Evaluation of methods used for estimating content validity. Res. Soc. Adm. Pharm. 2019, 15, 214–221. [Google Scholar] [CrossRef] [PubMed]
- Zinbarg, R.E.; Pinsof, W.; Quirk, K.; Kendall, A.; Goldsmith, J.; Hardy, N.; He, Y.; Sabey, A.; Latta, T. Testing the convergent and discriminant validity of the Systemic Therapy Inventory of Change Initial scales. Psychother. Res. 2018, 28, 734–749. [Google Scholar] [CrossRef]
- Sekaran, U.; Bougie, R. Research Methods for Business; Wiley & Sons: Hoboken, NJ, USA, 2016. [Google Scholar]
- Krejcie, R.V.; Morgan, D.W. Determining sample Size for Research Activities”, Educational and Psychological Measurement. Int. J. Employ. Stud. 1970, 30, 607–610. [Google Scholar]
- Henseler, J.; Dijkstra, T.K.; Sarstedt, M.; Ringle, C.M.; Diamantopoulos, A.; Straub, D.W.; Ketchen, D.J.; Hair, J.F.; Hult, G.T.M.; Calantone, R.J. Common Beliefs and Reality About PLS: Comments on Rönkkö and Evermann (2013). Organ. Res. Methods 2014, 17, 182–209. [Google Scholar] [CrossRef] [Green Version]
- Baruch, Y.; Holtom, B.C. Survey response rate levels and trends in organizational research. Hum. Relat. 2008, 61, 1139–1160. [Google Scholar] [CrossRef] [Green Version]
- Ullman, J.B. Structural Equation Modeling: Reviewing the Basics and Moving Forward. J. Pers. Assess. 2006, 87, 116–117. [Google Scholar] [CrossRef]
- Mâţă, L.; Clipa, O.; Tzafilkou, K. The development and validation of a scale to measure university teachers’ attitude towards ethical use of information technology for a sustainable education. Sustainability 2020, 12, 6268. [Google Scholar] [CrossRef]
- Jasmine Adela, M.; Lailawati, M.; Ferlis, B.; Bee Sok, C. Analisis faktor, kesahan dan keboleh percayaan instrumen Inventori Stereotaip Multi Etnik Malaysia (ISMEM). J. Psikol. Malays. 2015, 29, 106–122. [Google Scholar]
- Osborne, J.; Costello, A.; Kellow, T. Best Practice in Exploratory Factor Analysis; Osborne, J., Ed.; SAGE: New York, NY, USA, 2008; ISBN 9781412940658. [Google Scholar]
- Hair, J.F., Jr.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 2nd ed.; Sage Publications: Los Angeles, CA, USA, 2017; ISBN 9781483377445. [Google Scholar]
- Kline, R.B. Principles and Practices of Structural Equation Modelling, 4th ed.; Guilford Press: New York, NY, USA, 2016; ISBN 9781609182304. [Google Scholar]
- Teo, T.S.H.; Srivastava, S.C.; Jiang, L. Trust and electronic government success: An empirical study. J. Manag. Inf. Syst. 2008, 25, 99–132. [Google Scholar] [CrossRef]
- Cohen, J. Statistical Power Analysis for the Behavioral Sciences, 2nd ed.; Lawrence Erlbaum Associates: Mahwah, NJ, USA, 1988; ISBN 9788490225370. [Google Scholar]
- Schindler, M.; Dionisio, R.; Kingham, S. Challenges of Spatial Decision-Support Tools in Urban Planning: Lessons from New Zealand’s Cities. J. Urban Plan. Dev. 2020, 146, 04020012. [Google Scholar] [CrossRef]
- Bigdeli, B.; Amini Amirkolaee, H.; Pahlavani, P. DTM extraction under forest canopy using LiDAR data and a modified invasive weed optimization algorithm. Remote Sens. Environ. 2018, 216, 289–300. [Google Scholar] [CrossRef]
- Babbel, B.J.; Olsen, M.J.; Che, E.; Leshchinsky, B.A.; Simpson, C.; Dafni, J. Evaluation of Uncrewed Aircraft Systems’ LiDAR data Quality. ISPRS Int. J. Geo-Inf. 2019, 8, 532. [Google Scholar] [CrossRef] [Green Version]
- Zhang, Z.; Zhang, X.; Sun, Y.; Zhang, P. Road centerline extraction from very-high-resolution aerial image and LiDAR data based on road connectivity. Remote Sens. 2018, 10, 1284. [Google Scholar] [CrossRef] [Green Version]
- Olafsson, A.S.; Skov-Petersen, H. The use of GIS-based support of recreational trail planning by local governments. Appl. Spat. Anal. Policy 2014, 7, 149–168. [Google Scholar] [CrossRef]
- Shaker, A.; Yan, W.Y.; LaRocque, P.E. Automatic land-water classification using multispectral airborne LiDAR data for near-shore and river environments. ISPRS J. Photogramm. Remote Sens. 2019, 152, 94–108. [Google Scholar] [CrossRef]
- Landry, S.; St-Laurent, M.H.; Pelletier, G.; Villard, M.A. The best of both worlds? Integrating sentinel-2 images and airborne LiDAR to characterize forest regeneration. Remote Sens. 2020, 12, 2440. [Google Scholar] [CrossRef]
- Görüm, T. Landslide recognition and mapping in a mixed forest environment from airborne LiDAR data. Eng. Geol. 2019, 258, 105155. [Google Scholar] [CrossRef]
- Jagodnik, P.; Gazibara, S.B.; Jagodnik, V.; Arbanas, S.M. Types and distribution of quaternary deposits originating from carbonate rock slopes in the vinodol valley, croatia—New insight using airborne lidar data. Min. Geol. Pet. Eng. Bull. 2020, 35, 57–77. [Google Scholar] [CrossRef]
- Hatta Antah, F.; Muhamad Azry, K.; Khairul Nizam, A.M.; Azlina, A. Perceived Usefulness of Airborne LiDAR Technology in Road Design and Management: A Review. Sustainability 2021, 13, 11773. [Google Scholar] [CrossRef]
Author(s)/ Years | Country | Geospatial Technology | Instruments | Factors | Findings |
---|---|---|---|---|---|
D. Reynard [9] 2018 |
|
|
|
|
|
S. Jozefowicz et al. [10] 2019 |
|
|
|
|
|
S. de Gouw et al. [4] 2020 |
|
|
|
|
|
S. Hennig [14] 2020 |
|
|
|
|
|
S. Henrico et al. [13] 2021 |
|
|
|
|
|
L. Waterman et al. [11] 2021 |
|
|
|
|
|
A. Ali et al. [8] 2021 |
|
|
|
|
|
S. Eilola et al. [12] 2021 |
|
|
|
|
|
Factors | Cronbach’s Alpha |
---|---|
Barrier | 0.885 |
Motivational | 0.952 |
Strategy | 0.923 |
Use of LiDAR | 0.853 |
Characteristics | Items | Numbers | Percentage (%) |
---|---|---|---|
Gender | Male | 92 | 59.4 |
Female | 63 | 40.6 | |
Educational | Diploma | 12 | 7.7 |
Degree | 96 | 61.9 | |
Master | 44 | 28.4 | |
Ph.D | 3 | 1.9 | |
Working experience | <1 year | 1 | 6 |
1–10 years | 42 | 27.1 | |
11–20 years | 78 | 50.3 | |
>20 years | 34 | 21.9 | |
Working sector | Government | 89 | 57.4 |
Private | 66 | 42.6 | |
Site engineer | 14 | 9 | |
Position | Design engineer | 63 | 40.6 |
Project engineer | 78 | 50.3 | |
Use of surveying data | Total station/GPS | 89 | 57.4 |
UAV/Drone | 6 | 3.9 | |
LiDAR | 20 | 12.9 | |
Total station/GPS & UAV/Drone | 11 | 7.1 | |
Total station/GPS & LiDAR | 8 | 5.3 | |
Total station/GPS, UAV/Drone & LiDAR | 18 | 11.6 | |
UAV/Drone & LiDAR | 3 | 1.9 |
Factors | Eliminated Items | Outer Loadings |
---|---|---|
Strategy | ST8 | 0.579 |
Barrier | BR1 | 0.588 |
BR2 | 0.597 | |
Use of LiDAR | UL1 | 0.626 |
UL4 | 0.458 |
Factors | Barrier | Strategy | Motivational | Use of LiDAR |
---|---|---|---|---|
Barrier | ||||
Strategy | 0.583 | |||
Motivational | 0.540 | 0.785 | ||
Use of LiDAR | 0.664 | 0.745 | 0.676 |
Path Coefficients (β) | T Statistics | p Values | Results | |
---|---|---|---|---|
H1: Barrier factor->Use of LiDAR | 0.331 | 5.668 | 0.000 | Significant |
H2: Strategy factor->Use of LiDAR | 0.306 | 2.542 | 0.011 | Significant |
H3: Motivational factor->Use of LiDAR | 0.182 | 1.587 | 0.113 | Not significant |
R Squared | Result | |
---|---|---|
Use of LiDAR | 0.471 | moderate |
Q Squared | Result | |
---|---|---|
Use of LiDAR | 0.261 | relevant impact predictions |
Dependent Variable | Independent Variable | f Squared | Results |
---|---|---|---|
Barrier factor | 0.148 | Small to medium effect | |
Use of LiDAR | Strategy factor | 0.079 | Small to medium effect |
Motivational factor | 0.029 | Small to medium effect |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Hatta Antah, F.; Khoiry, M.A.; Abdul Maulud, K.N.; Ibrahim, A.N.H. Factors Influencing the Use of Geospatial Technology with LiDAR for Road Design: Case of Malaysia. Sustainability 2022, 14, 8977. https://doi.org/10.3390/su14158977
Hatta Antah F, Khoiry MA, Abdul Maulud KN, Ibrahim ANH. Factors Influencing the Use of Geospatial Technology with LiDAR for Road Design: Case of Malaysia. Sustainability. 2022; 14(15):8977. https://doi.org/10.3390/su14158977
Chicago/Turabian StyleHatta Antah, Fazilah, Muhamad Azry Khoiry, Khairul Nizam Abdul Maulud, and Ahmad Nazrul Hakimi Ibrahim. 2022. "Factors Influencing the Use of Geospatial Technology with LiDAR for Road Design: Case of Malaysia" Sustainability 14, no. 15: 8977. https://doi.org/10.3390/su14158977