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Articles by Johan Holmgren

Category : Research article

article id 10712, category Research article
Kenneth Olofsson, Johan Holmgren. (2022). Co-registration of single tree maps and data captured by a moving sensor using stem diameter weighted linking. Silva Fennica vol. 56 no. 3 article id 10712. https://doi.org/10.14214/sf.10712
Keywords: airborne laser scanning; terrestrial laser scanning; field plot; mobile laser scanning; simultaneous location and mapping; stem map
Highlights: A stem diameter weighted linking algorithm for tree maps was introduced which improves linking accuracy; A new simultaneous location and mapping-based co-registration method for stem maps measured with moving sensors was introduced that operates with high linking accuracy.
Abstract | Full text in HTML | Full text in PDF | Author Info

A new method for the co-registration of single tree data in forest stands and forest plots applicable to static as well as dynamic data capture is presented. This method consists of a stem diameter weighted linking algorithm that improves the linking accuracy when operating on diverse diameter stands with stem position errors in the single tree detectors. A co-registration quality metric threshold, QT, is also introduced which makes it possible to discriminate between correct and incorrect stem map co-registrations with high probability (>99%). These two features are combined to a simultaneous location and mapping-based co-registration method that operates with high linking accuracy and that can handle sensors with drifting errors and signal bias. A test with simulated data shows that the method has an 89.35% detection rate. The statistics of different settings in a simulation study are presented, where the effect of stem density and position errors were investigated. A test case with real sensor data from a forest stand shows that the average nearest neighbor distances decreased from 1.90 m to 0.51 m, which indicates the feasibility of this method.

  • Olofsson, Section of Forest Remote Sensing, Department of Forest Resource Management, Swedish University of Agricultural Sciences, Umeå, Sweden ORCID https://orcid.org/0000-0002-2836-2316 E-mail: kenneth.olofsson@slu.se (email)
  • Holmgren, Section of Forest Remote Sensing, Department of Forest Resource Management, Swedish University of Agricultural Sciences, Umeå, Sweden ORCID https://orcid.org/0000-0002-7112-8015 E-mail: johan.holmgren@slu.se
article id 1414, category Research article
Rami Saad, Jörgen Wallerman, Johan Holmgren, Tomas Lämås. (2016). Local pivotal method sampling design combined with micro stands utilizing airborne laser scanning data in a long term forest management planning setting. Silva Fennica vol. 50 no. 2 article id 1414. https://doi.org/10.14214/sf.1414
Keywords: LIDAR; forest management planning; local pivotal method (LPM); segmentation; most similar neighbor (MSN) imputation; suboptimal loss; Heureka; decision support system
Highlights: Most similar neighbor imputation was used to estimate forest variables using airborne laser scanning data as auxiliary data; For selecting field reference plots the local pivotal method (LPM) was compared to systematic sampling design; The LPM sampling design combined with a micro stand approach showed potential for improvement and has the potential to be a competitive method when considering cost efficiency.
Abstract | Full text in HTML | Full text in PDF | Author Info

A new sampling design, the local pivotal method (LPM), was combined with the micro stand approach and compared with the traditional systematic sampling design for estimation of forest stand variables. The LPM uses the distance between units in an auxiliary space – in this case airborne laser scanning (ALS) data – to obtain a well-spread sample. Two sets of reference plots were acquired by the two sampling designs and used for imputing data to evaluation plots. The first set of reference plots, acquired by LPM, made up four imputation alternatives (varying number of reference plots) and the second set of reference plots, acquired by systematic sampling design, made up two alternatives (varying plot radius). The forest variables in these alternatives were estimated using the nonparametric method of most similar neighbor imputation, with the ALS data used as auxiliary data. The relative root mean square error (RelRMSE), stem diameter distribution error index and suboptimal loss were calculated for each alternative, but the results showed that neither sampling design, i.e. LPM vs. systematic, offered clear advantages over the other. It is likely that the obtained results were a consequence of the small evaluation dataset used in the study (n = 30). Nevertheless, the LPM sampling design combined with the micro stand approach showed potential for improvement and might be a competitive method when considering the cost efficiency.

  • Saad, Swedish University of Agricultural Sciences (SLU), Department of Forest Resource Management, Skogsmarksgränd, SE-901 83 Umeå, Sweden E-mail: rami.saad@slu.se (email)
  • Wallerman, Swedish University of Agricultural Sciences (SLU), Department of Forest Resource Management, Skogsmarksgränd, SE-901 83 Umeå, Sweden E-mail: jorgen.wallerman@slu.se
  • Holmgren, Swedish University of Agricultural Sciences (SLU), Department of Forest Resource Management, Skogsmarksgränd, SE-901 83 Umeå, Sweden E-mail: johan.holmgren@slu.se
  • Lämås, Swedish University of Agricultural Sciences (SLU), Department of Forest Resource Management, Skogsmarksgränd, SE-901 83 Umeå, Sweden E-mail: tomas.lamas@slu.se
article id 56, category Research article
Johan Holmgren, Andreas Barth, Henrik Larsson, Håkan Olsson. (2012). Prediction of stem attributes by combining airborne laser scanning and measurements from harvesters. Silva Fennica vol. 46 no. 2 article id 56. https://doi.org/10.14214/sf.56
Keywords: ALS data; pre-harvest inventory; tree detection
Abstract | View details | Full text in PDF | Author Info
In this study, a new method was validated for the first time that predicts stem attributes for a forest area without any manual measurements of tree stems by combining harvester measurements and Airborne Laser Scanning (ALS) data. A new algorithm for automatic segmentation of tree crowns from ALS data based on tree crown models was developed. The test site was located in boreal forest (64°06’N, 19°10’E) dominated by Norway spruce (Picea abies) and Scots Pine (Pinus sylvestris).The trees were harvested on field plots, and each harvested tree was linked to the nearest tree crown segment derived from ALS data. In this way, a reference database was created with both stem data from the harvester and ALS derived features for linked tree crowns. To estimate stem attributes for a tree crown segment in parts of the forest where trees not yet have been harvested, tree stems are imputed from the most similar crown segment in the reference database according to features extracted from ALS data. The imputation of harvester data was validated on a sub-stand-level, i.e. 2–4 aggregated 10 m radius plots, and the obtained RMSE of stem volume, mean tree height, mean stem diameter, and stem density (stems per ha) estimates were 11%, 8%, 12%, and 19%, respectively. The imputation of stem data collected by harvesters could in the future be used for bucking simulations of not yet harvested forest stands in order to predict wood assortments.
  • Holmgren, Swedish University of Agricultural Sciences, Forest Resource Management, Umeå, Sweden E-mail: johan.holmgren@slu.se (email)
  • Barth, The Forestry Research Institute of Sweden, Uppsala, Sweden E-mail: ab@nn.se
  • Larsson, Swedish University of Agricultural Sciences, Forest Resource Management, Umeå, Sweden E-mail: hl@nn.se
  • Olsson, Swedish University of Agricultural Sciences, Forest Resource Management, Umeå, Sweden E-mail: ho@nn.se

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