| 1. |
- Granholm, Ann-Helen, et al.
(författare)
-
Estimating vertical canopy cover using dense image-based point cloud data in four vegetation types in southern Sweden
- 2017
-
Ingår i: International Journal of Remote Sensing. - : Informa UK Limited. - 0143-1161 .- 1366-5901. ; 38, s. 1820-1838
-
Tidskriftsartikel (refereegranskat)abstract
- This study had the aim of investigating the utility of image-based point cloud data for estimation of vertical canopy cover (VCC). An accurate measure of VCC based on photogrammetric matching of aerial images would aid in vegetation mapping, especially in areas where aerial imagery is acquired regularly. The test area is located in southern Sweden and was divided into four vegetation types with sparse to dense tree cover: unmanaged coniferous forest; pasture areas with deciduous tree cover; wetland; and managed coniferous forest. Aerial imagery with a ground sample distance of 0.24 m was photogrammetrically matched to produce dense image-based point cloud data. Two different image matching software solutions were used and compared: MATCH-T DSM by Trimble and SURE by nFrames. The image-based point clouds were normalized using a digital terrain model derived from airborne laser scanner (ALS) data. The canopy cover metric vegetation ratio was derived from the image-based point clouds, as well as from raster-based canopy height models (CHMs) derived from the point clouds. Regression analysis was applied with vegetation ratio derived from near nadir ALS data as the dependent variable and metrics derived from image-based point cloud data as the independent variables. Among the different vegetation types, vegetation ratio derived from the image-based point cloud data generated by using MATCH-T resulted in relative root mean square errors (rRMSE) of VCC ranging from 6.1% to 29.3%. Vegetation ratio based on point clouds from SURE resulted in rRMSEs ranging from 7.3% to 37.9%. Use of the vegetation ratio based on CHMs generated from the image-based point clouds resulted in similar, yet slightly higher values of rRMSE.
|
|
| 2. |
- Granholm, Ann-Helen, et al.
(författare)
-
Estimating vertical canopy cover with dense point cloud data from matching of digital aerial photos
- 2015
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- This study aims to explore the use of dense point clouds from matching of aerial photos for estimation of vertical canopy cover (VCC), defined as the proportion of the forest floor covered by the vertical projection of the tree crowns. VCC is commonly estimated using vegetation ratio (VR) derived from airborne laser scanner (ALS) data. A reliable measure of VCC from matching aerial photos would aid in vegetation mapping and reduce the need for repeated ALS data acquisition. The test area is located in southern Sweden and covers a variety of vegetation types. In total 367 sample plots were placed in parts of the study area representing VCC ranging from 0 % up to close to 100 %. ALS data with a density of 20 returns per m2 was used for calculating the VR as the proportion of first returns above a threshold. Aerial imagery with a ground sample distance of 0.25 m was matched to produce dense point cloud data, which was used to derive digital surface models (DSMs) with grid size from 0.25 m up to 2.0 m. Local maxima (LM) detection was applied to the DSMs with search windows of 0.5 m size up to 2.0 m. The heights of the LM were normalized using a digital elevation model (DEM) derived from ALS data. Regression analysis was applied with the VR as dependent variable and the sum of the height of LM within sample plots as independent variable. Results from linear regression using heights of LM detected in a DSM of 0.25 m resolution with a 0.5 m search window gave an root mean square error (RMSE) of 5.5 % and relative RMSE (rRMSE) of 9.3 % in forest on rocky outcrops and boulders, while wooded pasture gave RMSE = 6.3 % and rRMSE = 19 %.
|
|
| 3. |
- Lindberg, Eva, et al.
(författare)
-
Estimation of 3D vegetation structure from waveform and discrete return airborne laser scanning data
- 2012
-
Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257 .- 1879-0704. ; 118, s. 151-161
-
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.
|
|
| 4. |
- Lindberg, Eva, et al.
(författare)
-
Estimation of stem attributes using a combination of terrestrial and airborne laser scanning
- 2010
-
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
|
|
| 5. |
- Lindberg, Eva, et al.
(författare)
-
Estimation of stem attributes using a combination of terrestrial and airborne laser scanning
- 2012
-
Ingår i: European Journal of Forest Research. - : Springer Science and Business Media LLC. - 1612-4669 .- 1612-4677. ; 131, s. 1917-1931
-
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.
|
|
| 6. |
- Lindberg, Eva, et al.
(författare)
-
Estimation of tree lists from airborne laser scanning by combining single-tree and area-based methods
- 2010
-
Ingår i: International Journal of Remote Sensing. - : Informa UK Limited. - 0143-1161 .- 1366-5901. ; 31, s. 1175-1192
-
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.
|
|
| 7. |
- Lindberg, Eva, et al.
(författare)
-
Estimation of Tree Lists from Airborne Laser Scanning Using Tree Model Clustering and k-MSN Imputation
- 2013
-
Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 5, s. 1932-1955
-
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.
|
|
| 8. |
|
|
| 9. |
- Olofsson, Kenneth, et al.
(författare)
-
Estimating tree stem density and diameter distribution in single-scan terrestrial laser measurements of field plots: a simulation study
- 2018
-
Ingår i: Scandinavian Journal of Forest Research. - : Informa UK Limited. - 0282-7581 .- 1651-1891. ; 33, s. 365-377
-
Tidskriftsartikel (refereegranskat)abstract
- The single-scan setup of terrestrial laser scanning of a forest field plot has advantages compared to the multi-scan setup: the speed of operation and that there is no need of a co-registration of the different scans. However in a single-scan setup some of the trees are shaded by others and therefore not detected in the scan. A field inventory solution must take this fact into account. This simulation study shows how different plot sizes and tree stand densities influence the stem visibility giving nonlinear effects especially for large trees and high stem numbers. These effects can be counteracted by using an edge or center stem point detection criteria when analyzing the results or by weighting the detected trees by their visibility. It is shown that the stem density and diameter distribution can be estimated from the visible areas of the plot in case the stem positions are Poisson distributed.
|
|
| 10. |
|
|
