Applying a Robust Empirical Method for Comparing Repeated LiDAR Data with Different Point Density
A key aspect of vegetation monitoring from LiDAR is concerned with the use of comparable data acquired from multitemporal surveys and from different sensors. Accurate digital elevation models (DEMs) to derive vegetation products, are required to make comparisons among repeated LiDAR data. Here, we a...
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MDPI AG
2022-02-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/13/3/380 |
| _version_ | 1797471617492713472 |
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| author | Olga Viedma |
| author_facet | Olga Viedma |
| author_sort | Olga Viedma |
| collection | DOAJ |
| description | A key aspect of vegetation monitoring from LiDAR is concerned with the use of comparable data acquired from multitemporal surveys and from different sensors. Accurate digital elevation models (DEMs) to derive vegetation products, are required to make comparisons among repeated LiDAR data. Here, we aimed to apply an improved empirical method based on DEMs of difference, that adjust the ground elevation of a low-density LiDAR dataset to that of a high-density LiDAR one for ensuring credible vegetation changes. The study areas are a collection of six sites over the Sierra de Gredos in Central Spain. The methodology consisted of producing “the best DEM of difference” between low- and high-density LiDAR data (using the classification filter, the interpolation method and the spatial resolution with the lowest vertical error) to generate a local “pseudo-geoid” (i.e., continuous surfaces of elevation differences) that was used to correct raw low-density LiDAR ground points. The vertical error of DEMs was estimated by the 50th percentile (P<sub>50</sub>), the normalized median absolute deviation (NMAD) and the root mean square error (RMSE) of elevation differences. In addition, we analyzed the effects of site-properties (elevation, slope, vegetation height and distance to the nearest geoid point) on DEMs accuracy. Finally, we assessed if vegetation height changes were related to the ground elevation differences between low- and high-density LiDAR datasets. Before correction and aggregating by sites, the vertical error of DEMs ranged from 0.02 to −2.09 m (P<sub>50</sub>), from 0.39 to 0.85 m (NMDA) and from 0.54 to 2.5 m (RMSE). The segmented-based filter algorithm (CSF) showed the highest error, but there were not significant differences among interpolation methods or spatial resolutions. After correction and aggregating by sites, the vertical error of DEMs dropped significantly: from −0.004 to −0.016 m (P<sub>50</sub>), from 0.10 to 0.06 m (NMDA) and from 0.28 to 0.46 m (RMSE); and the CSF filter algorithm continued showing the greatest vertical error. The terrain slope and the distance to the nearest geoid point were the most important variables for explaining vertical accuracy. After corrections, changes in vegetation height were decoupled from vertical errors of DEMs. This work showed that using continuous surfaces with the lowest elevation differences (i.e., the best DEM of difference) the raw elevation of low-density LiDAR was better adjusted to that of a benchmark for being adapted to site-specific conditions. This method improved the vertical accuracy of low-density LiDAR elevation data, minimizing the random nature of vertical errors and decoupling vegetation changes from those errors. |
| first_indexed | 2024-03-09T19:50:32Z |
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| spelling | doaj.art-f1d6ce21f46b425393c8077d66ef1ca62023-11-24T01:12:17ZengMDPI AGForests1999-49072022-02-0113338010.3390/f13030380Applying a Robust Empirical Method for Comparing Repeated LiDAR Data with Different Point DensityOlga Viedma0Department of Environmental Sciences, University of Castilla-La Mancha, Avda. Carlos III, 45071 Toledo, SpainA key aspect of vegetation monitoring from LiDAR is concerned with the use of comparable data acquired from multitemporal surveys and from different sensors. Accurate digital elevation models (DEMs) to derive vegetation products, are required to make comparisons among repeated LiDAR data. Here, we aimed to apply an improved empirical method based on DEMs of difference, that adjust the ground elevation of a low-density LiDAR dataset to that of a high-density LiDAR one for ensuring credible vegetation changes. The study areas are a collection of six sites over the Sierra de Gredos in Central Spain. The methodology consisted of producing “the best DEM of difference” between low- and high-density LiDAR data (using the classification filter, the interpolation method and the spatial resolution with the lowest vertical error) to generate a local “pseudo-geoid” (i.e., continuous surfaces of elevation differences) that was used to correct raw low-density LiDAR ground points. The vertical error of DEMs was estimated by the 50th percentile (P<sub>50</sub>), the normalized median absolute deviation (NMAD) and the root mean square error (RMSE) of elevation differences. In addition, we analyzed the effects of site-properties (elevation, slope, vegetation height and distance to the nearest geoid point) on DEMs accuracy. Finally, we assessed if vegetation height changes were related to the ground elevation differences between low- and high-density LiDAR datasets. Before correction and aggregating by sites, the vertical error of DEMs ranged from 0.02 to −2.09 m (P<sub>50</sub>), from 0.39 to 0.85 m (NMDA) and from 0.54 to 2.5 m (RMSE). The segmented-based filter algorithm (CSF) showed the highest error, but there were not significant differences among interpolation methods or spatial resolutions. After correction and aggregating by sites, the vertical error of DEMs dropped significantly: from −0.004 to −0.016 m (P<sub>50</sub>), from 0.10 to 0.06 m (NMDA) and from 0.28 to 0.46 m (RMSE); and the CSF filter algorithm continued showing the greatest vertical error. The terrain slope and the distance to the nearest geoid point were the most important variables for explaining vertical accuracy. After corrections, changes in vegetation height were decoupled from vertical errors of DEMs. This work showed that using continuous surfaces with the lowest elevation differences (i.e., the best DEM of difference) the raw elevation of low-density LiDAR was better adjusted to that of a benchmark for being adapted to site-specific conditions. This method improved the vertical accuracy of low-density LiDAR elevation data, minimizing the random nature of vertical errors and decoupling vegetation changes from those errors.https://www.mdpi.com/1999-4907/13/3/380multitemporal LiDARDEMs of differencefiltering algorithmvertical errorpseudo-geoidvegetation growth |
| spellingShingle | Olga Viedma Applying a Robust Empirical Method for Comparing Repeated LiDAR Data with Different Point Density Forests multitemporal LiDAR DEMs of difference filtering algorithm vertical error pseudo-geoid vegetation growth |
| title | Applying a Robust Empirical Method for Comparing Repeated LiDAR Data with Different Point Density |
| title_full | Applying a Robust Empirical Method for Comparing Repeated LiDAR Data with Different Point Density |
| title_fullStr | Applying a Robust Empirical Method for Comparing Repeated LiDAR Data with Different Point Density |
| title_full_unstemmed | Applying a Robust Empirical Method for Comparing Repeated LiDAR Data with Different Point Density |
| title_short | Applying a Robust Empirical Method for Comparing Repeated LiDAR Data with Different Point Density |
| title_sort | applying a robust empirical method for comparing repeated lidar data with different point density |
| topic | multitemporal LiDAR DEMs of difference filtering algorithm vertical error pseudo-geoid vegetation growth |
| url | https://www.mdpi.com/1999-4907/13/3/380 |
| work_keys_str_mv | AT olgaviedma applyingarobustempiricalmethodforcomparingrepeatedlidardatawithdifferentpointdensity |