| 1. |
- Bohlin, Jonas, et al.
(author)
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Species-specific forest variable estimation using non-parametric modeling of multi-spectral photogrammetric point cloud data
- 2012
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Conference paper (other academic/artistic)abstract
- The recent development in software for automatic photogrammetric processing of multispectral aerial imagery, and the growing nation-wide availability of Digital Elevation Model (DEM) data, are about to revolutionize data capture for forest management planning in Scandinavia. Using only already available aerial imagery and ALS-assessed DEM data, raster estimates of the forest variables mean tree height, basal area, total stem volume, and species-specific stem volumes were produced and evaluated. The study was conducted at a coniferous hemi-boreal test site in southern Sweden (lat. 58° N, long. 13° E). Digital aerial images from the Zeiss/Intergraph Digital Mapping Camera system were used to produce 3D point-cloud data with spectral information. Metrics were calculated for 696 field plots (10 m radius) from point-cloud data and used in k-MSN to estimate forest variables. For these stands, the tree height ranged from 1.4 to 33.0 m (18.1 m mean), stem volume from 0 to 829 m3 ha-1 (249 m3 ha-1 mean) and basal area from 0 to 62.2 m2 ha-1 (26.1 m2 ha-1 mean), with mean stand size of 2.8 ha. Estimates made using digital aerial images corresponding to the standard acquisition of the Swedish National Land Survey (Lantmäteriet) showed RMSEs (in percent of the surveyed stand mean) of 7.5% for tree height, 11.4% for basal area, 13.2% for total stem volume, 90.6% for pine stem volume, 26.4 for spruce stem volume, and 72.6% for deciduous stem volume. The results imply that photogrammetric matching of digital aerial images has significant potential for operational use in forestry.
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| 2. |
- Fransson, Johan E.S., et al.
(author)
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Estimation of Forest Stem Volume using ALOS-2 PALSAR-2 Satellite Images
- 2016
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In: Proceedings of 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016; Beijing; China; 10-15 July 2016. - 9781509033324 ; 2016-November, s. Art no 7730388, Pages 5327-5330
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Conference paper (peer-reviewed)abstract
- © 2016 IEEE. A first evaluation of ALOS-2 PALSAR-2 data for forest stem volume estimation has been performed at a coniferous dominated test site in southern Sweden. Both the Fine Beam Dual (FBD) polarization and the Quad-polarimetric mode were investigated. Forest plots with stem volume reaching up to a maximum of about 620 m 3 ha -1 (corresponding to 370 tons ha -1 ) were analyzed by relating backscatter intensity to field data using an exponential model derived from the Water Cloud Model. The estimation accuracy of stem volume at plot level (0.5 ha) was calculated in terms of Root Mean Square Error (RMSE). For the best case investigated an RMSE of 39.8% was obtained using one of the FBD HV-polarized images. The corresponding RMSE for the FBD HH-polarized images was 43.9%. In the Quad-polarimetric mode the lowest RMSE at HV- and HH-polarization was found to be 43.1% and 66.1%, respectively.
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| 3. |
- Lindberg, Eva, et al.
(author)
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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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In: International Journal of Remote Sensing. - : Informa UK Limited. - 0143-1161 .- 1366-5901. ; 31, s. 1175-1192
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Journal article (peer-reviewed)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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| 4. |
- Lindberg, Eva, et al.
(author)
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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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In: Remote Sensing. - : MDPI AG. - 2072-4292. ; 5, s. 1932-1955
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Journal article (peer-reviewed)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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| 5. |
- Lindgren, Nils, et al.
(author)
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Data assimilation in stand level forest inventory – first results
- 2015
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In: Natural resources and bioeconomy studies. - 2342-7639. ; 29, s. 37-37
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Conference paper (other academic/artistic)abstract
- Data assimilation in stand-level forest inventory – first results Nils Lindgren 1 , Mattias Nyström1 , Jörgen Wallerman 1 , Sarah Ehlers 1 , Anton Grafström1 , Anders Muszta 1 , Kenneth Nyström1 , Erik Willen 2 , Johan Fransson 1 , Jonas Bohlin 1 , Håkan Olsson 1 , Göran Ståhl 1 1Swedish University of Agricultural Sciences, Umeå, Sweden 2Skogforsk, Uppsala, Sweden As we are entering an era of increased supply of remote sensing data, we believe that data assimilation has a large potential for keeping forest stand registers up to date (Ehlers et al. 2013). Data assimilation combines forecasts of previous estimates with new observations of the current state in an optimal way based on the uncertainties in the forecast and the observations. These forecasting and updating steps can be repeated with new available observations to get improved estimations. In the present study, we use canopy height models obtained from matching of digital aerial photos over the test site Remningstorp in Sweden, acquired 2003, 2005, 2007, 2009, 2010 and 2012 and normalized with a DEM from airborne laser scanning. Stem volume was estimated for each data acquisition and stand, using regression functions based on field reference data from sample plots. Forecasting was done with growth functions constructed from National Forest Inventory plots. The remote sensing estimates for each time point were assimilated with the forecasts of the previous estimates, using extended Kalman filtering. Validation was done on 40 m radius sample plots dominated by Norway spruce. Early results for three stands show that the variances were lower when using assimilation of new estimates and there was less fluctuation compared to repeated remote sensing estimates. The results for the assimilated data at year 2011 were also consistently closer to the validation data measured in 2011 compared to the remote sensing estimates from year 2011.
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| 6. |
- Montaghi, Alessandro, et al.
(author)
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Analysis of effects of scanning angle on ALS-derived vegetation metrics in a nationwide airborne ALS acquisition
- 2012
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Conference paper (other academic/artistic)abstract
- In the summer of 2009, the Swedish government, under the co-ordination of Lantmäteriet (the Swedish mapping, cadastral and land registration authority), started a five-year project (2009- 2013) using airborne laser scanning for production of the New National Elevation Model (in Swedish: Ny Nationell Höjdmodell, NNH) for all of Sweden (450,000 km2). The primary aim of this project is to produce a 2 meter grid DEM (Digital Elevation Model) in which the standard error is better than 0.5 m. A total of about four hundred scanning areas, with a size of 25 km by 50 km, are being scanned with a nominal density of 0.5-1 point per square meter, and with a maximum scanning angle of ± 20 degrees (Petersen and Burman Rost, 2011). The acquisition is primarily done using Leica Geosystems ALS50-II and ALS60 sensors, with Optech ALTM Gemini as a complementary sensor. In each scanning area, 21 parallel flight lines were flown, with a nominal overlap of 20%, in addition also three perpendicular crossing lines where acquired. This flight line arrangement was designed to allow strip adjustment techniques, based on sensor parameter calibration, to be used for creation of a seamless final product (Toth, 2009). Finally, main and cross strips were merged together and delivered in blocks of 2.5 km by 2.5 km. The ALS data acquired for the NNH project is a resource of interest also for forest estimation. However, since the ALS survey is being carried out for a purpose other than measuring forest parameters, there are a number of issues that need to be considered. These include the effect of different time points for the scanning, the relatively large view angles used, and the positional accuracy for the NFI plots. In this paper, the effect of one of these issues, the view angle influence on LiDAR forest metrics, will be reported. At the conference also initial results from predictions based on LiDAR data and SPOT HRG satellite data, trained with national forest inventory sample plots, will be presented.
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| 7. |
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| 8. |
- Nilsson, Mats, et al.
(author)
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A nationwide forest attribute map of Sweden predicted using airborne laser scanning data and field data from the National Forest Inventory
- 2017
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In: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257 .- 1879-0704. ; 194, s. 447-454
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Journal article (peer-reviewed)abstract
- The National Mapping Agency in Sweden has conducted an airborne laser scanning (ALS) campaign covering almost the entire country for the purpose of creating a new national Digital Elevation Model (DEM). The ALS data were collected between 2009 and 2015 using Leica, Optech, Riegi, and Trimble scanners and have a point density of 0.5-1.0 pulses/m(2). A high resolution national raster database (12.5 m x 12.5 m cell size) with forest variables was produced by combining the ALS data with field data from the Swedish National Forest Inventory (NFI). Approximately 11500 NFI plots (10 meter radius) located on productive forest land, inventoried between 2009 and 2013, were used to create linear regression models relating selected forest variables, or transformations of the variables, to metrics derived from the ALS data. The resulting stand level relative RMSEs for predictions of stem volume, basal area, basal-area weighted mean tree height, and basal-area weighted mean stem diameter were in the ranges of 17.2-22.0%, 13.9-18.2%, 5.4-9.5%, and 8.7-13.1%, respectively. It was concluded that the predictions had an accuracy that were at least as good as data typically used in forest management planning. Above ground tree biomass was also included in the national raster database but not validated on a stand -level. An important part of the project was to make the raster database available to private forest owners, forest associations, forest companies, authorities, researchers, and the general public. Thus, all predicted forest variables can be viewed and downloaded free of charge at the Swedish Forest Agency's homepage (http://www. skogsstyrelsen.se/skogligagrunddata). (C) 2016 Elsevier Inc. All rights reserved.
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| 9. |
- Nilsson, Mats, et al.
(author)
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Estimating annual cuttings using multi-temporal satellite data and field data from the Swedish NFI
- 2009
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In: International Journal of Remote Sensing. - : Informa UK Limited. - 0143-1161 .- 1366-5901. ; 30, s. 5109-5116
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Journal article (peer-reviewed)abstract
- Many countries have ongoing national forest inventories (NFIs) that provide reliable information on current forest conditions and changes in the forest landscape. These inventories are often based on data collected using field inventory procedures and the results are presented in terms of forest statistics for different geographical areas. The Swedish NFI has decided to combine their field data with optical satellite data by using post-stratification to obtain improved and unbiased estimates of forest variables. The method has been shown to reduce the sampling error (standard error) by 10-35% for variables such as stem volume and forest area. The objective of this study is to investigate the effect on sampling error for the estimated annual clear-felled area when the NFI plots are post-stratified by cuttings mapped from multi-temporal satellite images. Clear-felled areas mapped by the Swedish Forest Agency using image pairs (SPOT and Landsat) from the years 2001/2002, 2002/2003, 2003/2004, and 2004/2005 were used to post-stratify the NFI plots. The study area covers approximately a 1.3 Mha forest land area in Coastal Vasterbotten. It was found that the sampling error (standard error) for the annually clear-felled area was reduced by 31% using post-stratification compared to use of field data alone.
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| 10. |
- Nyström, Mattias, et al.
(author)
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Assimilating remote sensing data with forest growth models
- 2015
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Conference paper (peer-reviewed)abstract
- As we are entering an era of increased supply of remote sensing data, we believe that dataassimilation that combines growth forecasts of previous estimates with new observations of thecurrent state has a large potential for keeping forest stand registers up to date (Ehlers et al. 2013).The data assimilation will update a forest model e in an optimal way based on the uncertainties inthe forecast and the observations, each time new data becomes available. These forecasting andupdating steps can be repeated with new available observations to get improved estimations. In thisstudy we present the first practical results from data assimilation of mean tree height, basal area andgrowing stock. The remote sensing data used were canopy height models obtained from matching ofdigital aerial photos over the test site Remningstorp in Sweden. The photos were acquired 2003,2005, 2007, 2009, 2010 and 2012 and normalized with a DEM from airborne laser scanning.The procedure for the data assimilation was as follows: mean tree height, basal area and growingstock were predicted on 18 m × 18 m raster cells using the area based method. Ten meter radiussample plots were used as field calibration data. For each photo year, the field data were adjustedfor growth to have the same state year as each acquisition year of the photos. Growth models wereconstructed from National Forest Inventory plot data. Data assimilation could then be performed onraster cell level by initially start with the estimates from 2003 year´s photos. This prediction was thenforecasted to year 2005 by calculating the growth for the raster cell. This forecasted value is thenblended with the new remote sensing estimation collected 2005. The process was then repeated forthe following years where new measurements were available. In this study, extended Kalmanfiltering was used to blend the forecasted values with the new remote sensing measurements.Validation was done for 40 m radius field plots. Further, the results were also compared with twoalternative approaches: the first was to forecast the first remote sensing estimate to the endpointand the second was to use remote sensing data acquired at the endpoint only.The preliminary results for the eight forest stands show that the variances were lower when usingassimilation of new estimates and there were less fluctuation compared to only using remote sensingdata from the endpoint. However, the mean deviation from the measured value 2011 was lowerwhen only data from the endpoint were used. The assimilated values 2011 were consistently closerto the validation data compared to only forecasting the starting estimate from 2003 to 2011.
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