Accuracy Assessment of Digital Terrain Models of Lowland Pedunculate Oak Forests Derived from Airborne Laser Scanning and Photogrammetry Article Swipe
Ivan Balenović
,
Mateo Gašparović
,
Anita Šimić Milas
,
Alen Berta
,
Ante Seletković
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.13140/rg.2.2.28926.48962
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.13140/rg.2.2.28926.48962
Digital terrain models (DTMs) present important data source for different applications in environmental disciplines including forestry. At regional level, DTMs are commonly created using airborne digital photogrammetry or airborne laser scanning (ALS) technology. This study aims to evaluate the vertical accuracy of DTMs of different spatial resolutions derived from high-density ALS data and existing photogrammetric (PHM) data in the dense lowland even-aged pedunculate oak forests located in the Pokupsko basin in Central Croatia. As expected, the assessment of DTMs’ vertical accuracy using 22 ground checkpoints shows higher accuracy for ALS-derived than for PHM-derived DTMs. Concerning the different resolutions of ALS-derived (0.5 m, 1 m, 2 m, 5 m) and PHM-derived DTMs (0.5 m, 1 m, 2 m, 5 m, 8 m) compared in this research, the ALS-derived DTM with the finest resolution of 0.5 m shows the highest accuracy. The root mean square error (RMSE) and mean error (ME) values for ALS-derived DTMs range from 0.14 m to 0.15 m and from 0.09 to 0.12 m, respectively, and the values decrease with decreasing spatial resolution. For the PHM-derived DTMs, the RMSE and ME values are almost identical regardless of resolution and they are 0.35 m and 0.17 m, respectively. The findings suggest that the 8 m spatial resolution is optimal for a given photogrammetric data, and no finer than 8 m spatial resolution is required. This research also reveals that the national digital photogrammetric data in the study area contain certain errors (outliers) specific to the terrain type, which could considerably affect the DTM accuracy. Thus, preliminary evaluation of photogrammetric data should be done to eliminate possible outliers prior to the DTM generation in lowland forests with flat terrain. In the absence of ALS data, the finding in this research could be of interests to countries, which still rely on similar photogrammetric data for DTM generation.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doaj.org/article/45fbe83bb01e4035bd4f4a6f6cf1988b
- OA Status
- green
- Cited By
- 16
- References
- 8
- Related Works
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- OpenAlex ID
- https://openalex.org/W2788113738
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2788113738Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.13140/rg.2.2.28926.48962Digital Object Identifier
- Title
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Accuracy Assessment of Digital Terrain Models of Lowland Pedunculate Oak Forests Derived from Airborne Laser Scanning and PhotogrammetryWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2018Year of publication
- Publication date
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2018-01-01Full publication date if available
- Authors
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Ivan Balenović, Mateo Gašparović, Anita Šimić Milas, Alen Berta, Ante SeletkovićList of authors in order
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https://doaj.org/article/45fbe83bb01e4035bd4f4a6f6cf1988bPublisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://doaj.org/article/45fbe83bb01e4035bd4f4a6f6cf1988bDirect OA link when available
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Photogrammetry, Terrain, Laser scanning, Remote sensing, Environmental science, Forestry, Geology, Laser, Geography, Cartography, Optics, PhysicsTop concepts (fields/topics) attached by OpenAlex
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16Total citation count in OpenAlex
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2024: 2, 2022: 1, 2021: 3, 2020: 4, 2019: 4Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.0.5 | 133 |
| abstract_inverted_index.ALS | 50, 283 |
| abstract_inverted_index.DTM | 127, 253, 271, 304 |
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| abstract_inverted_index.The | 139, 199 |
| abstract_inverted_index.and | 52, 108, 145, 160, 167, 181, 190, 195, 215 |
| abstract_inverted_index.are | 20, 184, 192 |
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| abstract_inverted_index.0.09 | 162 |
| abstract_inverted_index.0.12 | 164 |
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| abstract_inverted_index.0.15 | 158 |
| abstract_inverted_index.0.17 | 196 |
| abstract_inverted_index.0.35 | 193 |
| abstract_inverted_index.DTMs | 19, 42, 110, 152 |
| abstract_inverted_index.RMSE | 180 |
| abstract_inverted_index.This | 33, 225 |
| abstract_inverted_index.aims | 35 |
| abstract_inverted_index.also | 227 |
| abstract_inverted_index.area | 238 |
| abstract_inverted_index.data | 6, 51, 56, 234, 260, 302 |
| abstract_inverted_index.done | 263 |
| abstract_inverted_index.flat | 277 |
| abstract_inverted_index.from | 48, 154, 161 |
| abstract_inverted_index.mean | 141, 146 |
| abstract_inverted_index.rely | 298 |
| abstract_inverted_index.root | 140 |
| abstract_inverted_index.than | 90, 218 |
| abstract_inverted_index.that | 202, 229 |
| abstract_inverted_index.they | 191 |
| abstract_inverted_index.this | 123, 288 |
| abstract_inverted_index.with | 128, 171, 276 |
| abstract_inverted_index.(ALS) | 31 |
| abstract_inverted_index.(PHM) | 55 |
| abstract_inverted_index.DTMs, | 178 |
| abstract_inverted_index.DTMs. | 93 |
| abstract_inverted_index.Thus, | 255 |
| abstract_inverted_index.basin | 69 |
| abstract_inverted_index.could | 249, 290 |
| abstract_inverted_index.data, | 214, 284 |
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| abstract_inverted_index.finer | 217 |
| abstract_inverted_index.given | 212 |
| abstract_inverted_index.laser | 29 |
| abstract_inverted_index.prior | 268 |
| abstract_inverted_index.range | 153 |
| abstract_inverted_index.shows | 85, 135 |
| abstract_inverted_index.still | 297 |
| abstract_inverted_index.study | 34, 237 |
| abstract_inverted_index.type, | 247 |
| abstract_inverted_index.using | 23, 81 |
| abstract_inverted_index.which | 248, 296 |
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| abstract_inverted_index.(RMSE) | 144 |
| abstract_inverted_index.affect | 251 |
| abstract_inverted_index.almost | 185 |
| abstract_inverted_index.errors | 241 |
| abstract_inverted_index.finest | 130 |
| abstract_inverted_index.ground | 83 |
| abstract_inverted_index.higher | 86 |
| abstract_inverted_index.level, | 18 |
| abstract_inverted_index.models | 2 |
| abstract_inverted_index.should | 261 |
| abstract_inverted_index.source | 7 |
| abstract_inverted_index.square | 142 |
| abstract_inverted_index.values | 149, 169, 183 |
| abstract_inverted_index.Central | 71 |
| abstract_inverted_index.DTMs’ | 78 |
| abstract_inverted_index.Digital | 0 |
| abstract_inverted_index.absence | 281 |
| abstract_inverted_index.certain | 240 |
| abstract_inverted_index.contain | 239 |
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| abstract_inverted_index.derived | 47 |
| abstract_inverted_index.digital | 25, 232 |
| abstract_inverted_index.finding | 286 |
| abstract_inverted_index.forests | 64, 275 |
| abstract_inverted_index.highest | 137 |
| abstract_inverted_index.located | 65 |
| abstract_inverted_index.lowland | 60, 274 |
| abstract_inverted_index.optimal | 209 |
| abstract_inverted_index.present | 4 |
| abstract_inverted_index.reveals | 228 |
| abstract_inverted_index.similar | 300 |
| abstract_inverted_index.spatial | 45, 173, 206, 221 |
| abstract_inverted_index.suggest | 201 |
| abstract_inverted_index.terrain | 1, 246 |
| abstract_inverted_index.Croatia. | 72 |
| abstract_inverted_index.Pokupsko | 68 |
| abstract_inverted_index.accuracy | 40, 80, 87 |
| abstract_inverted_index.airborne | 24, 28 |
| abstract_inverted_index.commonly | 21 |
| abstract_inverted_index.compared | 121 |
| abstract_inverted_index.decrease | 170 |
| abstract_inverted_index.evaluate | 37 |
| abstract_inverted_index.existing | 53 |
| abstract_inverted_index.findings | 200 |
| abstract_inverted_index.national | 231 |
| abstract_inverted_index.outliers | 267 |
| abstract_inverted_index.possible | 266 |
| abstract_inverted_index.regional | 17 |
| abstract_inverted_index.research | 226, 289 |
| abstract_inverted_index.scanning | 30 |
| abstract_inverted_index.specific | 243 |
| abstract_inverted_index.terrain. | 278 |
| abstract_inverted_index.vertical | 39, 79 |
| abstract_inverted_index.accuracy. | 138, 254 |
| abstract_inverted_index.different | 9, 44, 96 |
| abstract_inverted_index.eliminate | 265 |
| abstract_inverted_index.even-aged | 61 |
| abstract_inverted_index.expected, | 74 |
| abstract_inverted_index.forestry. | 15 |
| abstract_inverted_index.identical | 186 |
| abstract_inverted_index.important | 5 |
| abstract_inverted_index.including | 14 |
| abstract_inverted_index.interests | 293 |
| abstract_inverted_index.required. | 224 |
| abstract_inverted_index.research, | 124 |
| abstract_inverted_index.(outliers) | 242 |
| abstract_inverted_index.Concerning | 94 |
| abstract_inverted_index.assessment | 76 |
| abstract_inverted_index.countries, | 295 |
| abstract_inverted_index.decreasing | 172 |
| abstract_inverted_index.evaluation | 257 |
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| abstract_inverted_index.regardless | 187 |
| abstract_inverted_index.resolution | 131, 189, 207, 222 |
| abstract_inverted_index.ALS-derived | 89, 99, 126, 151 |
| abstract_inverted_index.PHM-derived | 92, 109, 177 |
| abstract_inverted_index.checkpoints | 84 |
| abstract_inverted_index.disciplines | 13 |
| abstract_inverted_index.generation. | 305 |
| abstract_inverted_index.pedunculate | 62 |
| abstract_inverted_index.preliminary | 256 |
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| abstract_inverted_index.resolutions | 46, 97 |
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| abstract_inverted_index.applications | 10 |
| abstract_inverted_index.considerably | 250 |
| abstract_inverted_index.high-density | 49 |
| abstract_inverted_index.environmental | 12 |
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| abstract_inverted_index.respectively. | 198 |
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| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.699999988079071 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.81457896 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |