A Comparative Study of Three Non-Geostatistical Methods for Optimising Digital Elevation Model Interpolation
It is common to generate digital elevation models (DEMs) from aerial laser scanning (ALS) data. However, cost and lack of knowledge may preclude its use. In contrast, global navigation satellite systems (GNSS) are seldom used to collect and generate DEMs. These receivers have the potential to be con...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-07-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | http://www.mdpi.com/2220-9964/7/8/300 |
| _version_ | 1818821624681463808 |
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| author | Serajis Salekin Jack H. Burgess Justin Morgenroth Euan G. Mason Dean F. Meason |
| author_facet | Serajis Salekin Jack H. Burgess Justin Morgenroth Euan G. Mason Dean F. Meason |
| author_sort | Serajis Salekin |
| collection | DOAJ |
| description | It is common to generate digital elevation models (DEMs) from aerial laser scanning (ALS) data. However, cost and lack of knowledge may preclude its use. In contrast, global navigation satellite systems (GNSS) are seldom used to collect and generate DEMs. These receivers have the potential to be considered as data sources for DEM interpolation, as they can be inexpensive, easy to use, and mobile. The data interpolation method and spatial resolution from this method needs to be optimised to create accurate DEMs. Moreover, the density of GNSS data is likely to affect DEM accuracy. This study investigates three different deterministic approaches, in combination with spatial resolution and data thinning, to determine their combined effects on DEM accuracy. Digital elevation models were interpolated, with resolutions ranging from 0.5 m to 10 m using natural neighbour (NaN), topo to raster (ANUDEM), and inverse distance weighted (IDW) methods. The GNSS data were thinned by 25% (0.389 points m−2), 50% (0.259 points m−2), and 75% (0.129 points m−2) and resulting DEMs were contrast against a DEM interpolated from unthinned data (0.519 points m−2). Digital elevation model accuracy was measured by root mean square error (RMSE) and mean absolute error (MAE). It was found that the highest resolution, 0.5 m, produced the lowest errors in resulting DEMs (RMSE = 0.428 m, MAE = 0.274 m). The ANUDEM method yielded the greatest DEM accuracy from a quantitative perspective (RMSE = 0.305 m and MAE = 0.197 m); however, NaN produced a more visually appealing surface. In all the assessments, IDW showed the lowest accuracy. Thinning the input data by 25% and even 50% had relatively little impact on DEM quality; however, accuracy decreased markedly at 75% thinning (0.129 points m−2). This study showed that, in a time where ALS is commonly used to generate DEMs, GNSS-surveyed data can be used to create accurate DEMs. This study confirmed the need for optimization to choose the appropriate interpolation method and spatial resolution in order to produce a reliable DEM. |
| first_indexed | 2024-12-18T23:11:09Z |
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| id | doaj.art-55accca98b054e07a776622bb4bffc72 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-12-18T23:11:09Z |
| publishDate | 2018-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-55accca98b054e07a776622bb4bffc722022-12-21T20:48:20ZengMDPI AGISPRS International Journal of Geo-Information2220-99642018-07-017830010.3390/ijgi7080300ijgi7080300A Comparative Study of Three Non-Geostatistical Methods for Optimising Digital Elevation Model InterpolationSerajis Salekin0Jack H. Burgess1Justin Morgenroth2Euan G. Mason3Dean F. Meason4New Zealand School of Forestry, University of Canterbury, Christchurch 8140, New ZealandNew Zealand School of Forestry, University of Canterbury, Christchurch 8140, New ZealandNew Zealand School of Forestry, University of Canterbury, Christchurch 8140, New ZealandNew Zealand School of Forestry, University of Canterbury, Christchurch 8140, New ZealandScion, 49 Sala Street, Private Bag 3020, Rotorua 3010, New ZealandIt is common to generate digital elevation models (DEMs) from aerial laser scanning (ALS) data. However, cost and lack of knowledge may preclude its use. In contrast, global navigation satellite systems (GNSS) are seldom used to collect and generate DEMs. These receivers have the potential to be considered as data sources for DEM interpolation, as they can be inexpensive, easy to use, and mobile. The data interpolation method and spatial resolution from this method needs to be optimised to create accurate DEMs. Moreover, the density of GNSS data is likely to affect DEM accuracy. This study investigates three different deterministic approaches, in combination with spatial resolution and data thinning, to determine their combined effects on DEM accuracy. Digital elevation models were interpolated, with resolutions ranging from 0.5 m to 10 m using natural neighbour (NaN), topo to raster (ANUDEM), and inverse distance weighted (IDW) methods. The GNSS data were thinned by 25% (0.389 points m−2), 50% (0.259 points m−2), and 75% (0.129 points m−2) and resulting DEMs were contrast against a DEM interpolated from unthinned data (0.519 points m−2). Digital elevation model accuracy was measured by root mean square error (RMSE) and mean absolute error (MAE). It was found that the highest resolution, 0.5 m, produced the lowest errors in resulting DEMs (RMSE = 0.428 m, MAE = 0.274 m). The ANUDEM method yielded the greatest DEM accuracy from a quantitative perspective (RMSE = 0.305 m and MAE = 0.197 m); however, NaN produced a more visually appealing surface. In all the assessments, IDW showed the lowest accuracy. Thinning the input data by 25% and even 50% had relatively little impact on DEM quality; however, accuracy decreased markedly at 75% thinning (0.129 points m−2). This study showed that, in a time where ALS is commonly used to generate DEMs, GNSS-surveyed data can be used to create accurate DEMs. This study confirmed the need for optimization to choose the appropriate interpolation method and spatial resolution in order to produce a reliable DEM.http://www.mdpi.com/2220-9964/7/8/300GNSSANUDEMIDWNaNresolutioninterpolationDEM |
| spellingShingle | Serajis Salekin Jack H. Burgess Justin Morgenroth Euan G. Mason Dean F. Meason A Comparative Study of Three Non-Geostatistical Methods for Optimising Digital Elevation Model Interpolation ISPRS International Journal of Geo-Information GNSS ANUDEM IDW NaN resolution interpolation DEM |
| title | A Comparative Study of Three Non-Geostatistical Methods for Optimising Digital Elevation Model Interpolation |
| title_full | A Comparative Study of Three Non-Geostatistical Methods for Optimising Digital Elevation Model Interpolation |
| title_fullStr | A Comparative Study of Three Non-Geostatistical Methods for Optimising Digital Elevation Model Interpolation |
| title_full_unstemmed | A Comparative Study of Three Non-Geostatistical Methods for Optimising Digital Elevation Model Interpolation |
| title_short | A Comparative Study of Three Non-Geostatistical Methods for Optimising Digital Elevation Model Interpolation |
| title_sort | comparative study of three non geostatistical methods for optimising digital elevation model interpolation |
| topic | GNSS ANUDEM IDW NaN resolution interpolation DEM |
| url | http://www.mdpi.com/2220-9964/7/8/300 |
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