| 1. |
- Granholm, Ann-Helen, et al.
(författare)
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The potential of digital surface models based on aerial images for automated vegetation mapping
- 2015
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Ingår i: International Journal of Remote Sensing. - : Informa UK Limited. - 0143-1161 .- 1366-5901. ; 36, s. 1855-1870
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Tidskriftsartikel (refereegranskat)abstract
- Segmentation of vegetation patches was tested using canopy height models (CHMs) representing the height difference between digital surface models (DSMs), generated by matching digital aerial images from the Z/I Digital Mapping Camera, and a digital elevation model (DEM) based on airborne laser scanner data. Three different combinations of aerial images were used in the production of the CHMs to test the effect of flight altitude and stereo overlap on segmentation accuracy. Segmentation results were evaluated using the standard deviation of photo-interpreted tree height within segments, as well as by visual comparison to existing maps. In addition, height percentiles extracted from the CHMs were used to estimate tree heights. Tree height estimation at the segment level yielded root mean square error (RMSE) values of 2.0 m, or 15.1%, and an adjusted coefficient of determination (adjusted R2) of 0.94 when using a CHM from images acquired at an altitude of 1200 m above ground level (agl) and with an along-track stereo overlap of 80%. When a CHM based on images acquired at 4800 m agl and an overlap of 60% was used, the corresponding results were an RMSE of 2.2 m, or 16.0%, and an adjusted R2 of 0.92. Tree height estimation at the plot level was most accurate for densely forested plots dominated by coniferous tree species (RMSE of 2.1 m, or 9.8%, and adjusted R2 of 0.88). It is shown that CHMs based on aerial images acquired at 4800 m agl and with 60% along-track stereo overlap are useful for the segmentation of vegetation and are at least as good as those based on aerial images collected at a lower flight altitude or with greater overlap.
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| 2. |
- Holmgren, Johan, et al.
(författare)
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Mobile Laser Scanning for Estimating Tree Stem Diameter Using Segmentation and Tree Spine Calibration
- 2019
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- Mobile laser scanning (MLS) could make forest inventories more efficient, by using algorithms that automatically derive tree stem center positions and stem diameters. In this work we present a novel method for calibration of the position for laser returns based on tree spines derived from laser data. A first calibration of positions was made for sequential laser scans and further calibrations of laser returns were possible after segmentation, in which laser returns were associated to individual tree stems. The segmentation made it possible to model tree stem spines (i.e., center line of tree stems). Assumptions of coherent tree spine positions were used for correcting laser return positions on the tree stems, thereby improving estimation of stem profiles (i.e., stem diameters at different heights from the ground level). The method was validated on six 20-m radius field plots. Stem diameters were estimated with a Root-Mean-Square-Error (RMSE) of 1 cm for safely linked trees (maximum link distance of 0.5 m) and with a restriction of a minimum amount of data from height intervals for supporting circle estimates. The accuracy was high for plot level estimates of basal area-weighted mean stem diameter (relative RMSE 3.4%) and basal area (relative RMSE 8.5%) because of little influence of small trees (i.e., aggregation of individual trees). The spine calibration made it possible to derive 3D stem profiles also from 3D laser data calculated from sensor positions with large errors because of disturbed below canopy signals from global navigation satellite systems.
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| 3. |
- Lindberg, Eva, et al.
(författare)
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Classification of tree species classes in a hemi-boreal forest from multispectral airborne laser scanning data using a mini raster cell method
- 2021
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 0303-2434 .- 1569-8432. ; 100
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Tidskriftsartikel (refereegranskat)abstract
- Classification of tree species or species classes is still a challenge for remote sensing-based forest inventory. Operational use of Airborne Laser Scanning (ALS) data for prediction of forest variables has this far been dominated by area-based methods where laser scanning data have been used for estimation of forest variables within raster cells. Classification of tree species has however not been achieved with sufficient accuracy with area-based methods using only ALS data. Furthermore, analysis of tree species at the level of raster cells with typical size of 15 m ? 15 m is not ideal in the case of mixed species stands. Most ALS systems for terrestrial mapping use only one wavelength of light. New multispectral ALS systems for terrestrial mapping have recently become operational, such as the Optech Titan system with wavelengths 1550 nm, 1064 nm, and 532 nm. This study presents an alternative type of area-based method for classification of tree species classes where multispectral ALS data are used in combination with small raster cells. In this ?mini raster cell method? features for classification are derived from the intensity of the different wavelengths in small raster cells using a moving window average approach to allow for a heterogeneous tree species composition. The most common tree species in the Nordic countries are Pinus sylvestris and Picea abies, constituting about 80% of the growing stock volume. The remaining 20% consists of several deciduous species, mainly Betula pendula and Betula pubescens, and often grow in mixed forest stands. Classification was done for pine (Pinus sylvestris), spruce (Picea abies), deciduous species and mixed species in middle-aged and mature stands in a study area located in hemi-boreal forest in the southwest of Sweden (N 58?27?, E 13?39?). The results were validated at plot level with the tree species composition defined as proportion of basal area of the tree species classes. The mini raster cell classification method was slightly more accurate (75% overall accuracy) than classification with a plot level area-based method (68% overall accuracy). The explanation is most likely that the mini raster cell method is successful at classifying homogenous patches of tree species classes within a field plot, while classification based on plot level analysis requires one or several heterogeneous classes of mixed species forest. The mini raster cell method also results in a high-resolution tree species map. The small raster cells can be aggregated to estimate tree species composition for arbitrary areas, for example forest stands or area units corresponding to field plots.
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| 4. |
- Lindberg, Eva, et al.
(författare)
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Estimation of 3D vegetation structure from waveform and discrete return airborne laser scanning data
- 2012
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Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257 .- 1879-0704. ; 118, s. 151-161
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Tidskriftsartikel (refereegranskat)abstract
- This study presents and compares new methods to describe the 3D canopy structure with Airborne Laser Scanning (ALS) waveform data as well as ALS point data. The ALS waveform data were analyzed in three different ways; by summing the intensity of the waveforms in height intervals (a); by first normalizing the waveforms with an algorithm based on Beer-Lambert law to compensate for the shielding effect of higher vegetation layers on reflection from lower layers and then summing the intensity (b); and by deriving points from the waveforms (c). As a comparison, conventional, discrete return ALS point data from the laser scanning system were also analyzed (d). The study area was located in hemi-boreal, spruce dominated forest in the southwest of Sweden (Lat. 58° N, Long. 13° E). The vegetation volume profile was defined as the volume of all tree crowns and shrubs in 1 dm height intervals in a field plot and the total vegetation volume as the sum of the vegetation volume profile in the field plot. The total vegetation volume was estimated for 68 field plots with 12 m radius from the proportion between the amount of ALS reflections from the vegetation and the total amount of ALS reflections based on Beer-Lambert law. ALS profiles were derived from the distribution of the ALS data above the ground in 1 dm height intervals. The ALS profiles were rescaled using the estimated total vegetation volume to derive the amount of vegetation at different heights above the ground. The root mean square error (RMSE) for cross validated regression estimates of the total vegetation volume was 31.9% for ALS waveform data (a), 27.6% for normalized waveform data (b), 29.1% for point data derived from the ALS waveforms (c), and 36.5% for ALS point data from the laser scanning system (d). The correspondence between the estimated vegetation volume profiles was also best for the normalized waveform data and the point data derived from the ALS waveforms and worst for ALS point data from the laser scanning system as demonstrated by the Reynolds error index. The results suggest that ALS waveform data describe the volumetric aspects of vertical vegetation structure somewhat more accurately than ALS point data from the laser scanning system and that compensation for the shielding effect of higher vegetation layers is useful. The new methods for estimation of vegetation volume profiles from ALS data could be used in the future to derive 3D models of the vegetation structure in large areas.
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| 5. |
- Lindberg, Eva, et al.
(författare)
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Estimation of stem attributes using a combination of terrestrial and airborne laser scanning
- 2010
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- A new method to combine terrestrial laser scanning (TLS) with airborne laser scanning (ALS) has been evaluated for estimation of stem diameters (DBH) and stem volume at single tree level. The aim is to measure tree stems in field plots with TLS to use as training data for wall-to-wall estimation of forest variables based on ALS data. DBH and tree positions were estimated from TLS data in six field plots. Trees estimated from TLS data and tree heights and positions estimated from ALS data were co-registered to automatically link TLS and ALS measured trees. DBH estimated from TLS data and tree height measured in field for a sub-sample of trees were used to train regression models based on ALS derived tree crown segments. At tree level, the root mean square error (RMSE) was 46.6 mm (15.6%) for DBH, 9.8 dm (3.8%) for tree height, and 200.6 dm3 (34.4%) for stem volume
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| 6. |
- Lindberg, Eva, et al.
(författare)
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Estimation of stem attributes using a combination of terrestrial and airborne laser scanning
- 2012
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Ingår i: European Journal of Forest Research. - : Springer Science and Business Media LLC. - 1612-4669 .- 1612-4677. ; 131, s. 1917-1931
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Tidskriftsartikel (refereegranskat)abstract
- Properties of individual trees can be estimated from airborne laser scanning (ALS) data provided that the scanning is dense enough and the positions of field-measured trees are available as training data. However, such detailed manual field measurements are laborious. This paper presents new methods to use terrestrial laser scanning (TLS) for automatic measurements of tree stems and to further link these ground measurements to ALS data analyzed at the single tree level. The methods have been validated in six 80 × 80 m field plots in spruce-dominated forest (lat. 58°N, long. 13°E). In a first step, individual tree stems were automatically detected from TLS data. The root mean square error (RMSE) for DBH was 38.0 mm (13.1 %), and the bias was 1.6 mm (0.5 %). In a second step, trees detected from the TLS data were automatically co-registered and linked with the corresponding trees detected from the ALS data. In a third step, tree level regression models were created for stem attributes derived from the TLS data using independent variables derived from trees detected from the ALS data. Leave-one-out cross-validation for one field plot at a time provided an RMSE for tree level ALS estimates trained with TLS data of 46.0 mm (15.4 %) for DBH, 9.4 dm (3.7 %) for tree height, and 197.4 dm3 (34.0 %) for stem volume, which was nearly as accurate as when data from manual field inventory were used for training.
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| 7. |
- Lindberg, Eva, et al.
(författare)
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Estimation of tree lists from airborne laser scanning by combining single-tree and area-based methods
- 2010
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Ingår i: International Journal of Remote Sensing. - : Informa UK Limited. - 0143-1161 .- 1366-5901. ; 31, s. 1175-1192
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Tidskriftsartikel (refereegranskat)abstract
- Individual tree crown segmentation from airborne laser scanning (ALS) data often fails to detect all trees depending on the forest structure. This paper presents a new method to produce tree lists consistent with unbiased estimates at area level. First, a tree list with height and diameter at breast height (DBH) was estimated from individual tree crown segmentation. Second, estimates at plot level were used to create a target distribution by using a k-nearest neighbour (k-NN) approach. The number of trees per field plot was rescaled with the estimated stem volume for the field plot. Finally, the initial tree list was calibrated using the estimated target distribution. The calibration improved the estimates of the distributions of tree height (error index (EI) from 109 to 96) and DBH (EI from 99 to 93) in the tree list. Thus, the new method could be used to estimate tree lists that are consistent with unbiased estimates from regression models at field plot level.
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| 8. |
- Lindberg, Eva, et al.
(författare)
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Estimation of Tree Lists from Airborne Laser Scanning Using Tree Model Clustering and k-MSN Imputation
- 2013
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 5, s. 1932-1955
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Tidskriftsartikel (refereegranskat)abstract
- Individual tree crowns may be delineated from airborne laser scanning (ALS) data by segmentation of surface models or by 3D analysis. Segmentation of surface models benefits from using a priori knowledge about the proportions of tree crowns, which has not yet been utilized for 3D analysis to any great extent. In this study, an existing surface segmentation method was used as a basis for a new tree model 3D clustering method applied to ALS returns in 104 circular field plots with 12 m radius in pine-dominated boreal forest (64 degrees 14'N, 19 degrees 50'E). For each cluster below the tallest canopy layer, a parabolic surface was fitted to model a tree crown. The tree model clustering identified more trees than segmentation of the surface model, especially smaller trees below the tallest canopy layer. Stem attributes were estimated with k-Most Similar Neighbours (k-MSN) imputation of the clusters based on field-measured trees. The accuracy at plot level from the k-MSN imputation (stem density root mean square error or RMSE 32.7%; stem volume RMSE 28.3%) was similar to the corresponding results from the surface model (stem density RMSE 33.6%; stem volume RMSE 26.1%) with leave-one-out cross-validation for one field plot at a time. Three-dimensional analysis of ALS data should also be evaluated in multi-layered forests since it identified a larger number of small trees below the tallest canopy layer.
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| 9. |
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| 10. |
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