Calibration of laser-derived tree height estimates by means of photogrammetric techniques

Rönnholm, Petri; Hyyppä, Juha; Hyyppä, Hannu; Haggrén, Henrik; Yu, Xiaowei; Kaartinen, Harri

doi:10.1080/02827580410019436

Cited by 41 publications

(16 citation statements)

References 8 publications

(9 reference statements)

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“…Hence, the limiting factor of the image-based point clouds is the vertical penetration, which is provided by ALS. Thus the idea of combining photogrammetric image-based DSM with pre-existing historical ALS DTM could be a promising solution for the generation of CHM (Rönnholm et al 2004, St-Onge et al 2004, Jan 2005, Vé-ga & St-Onge 2008, Bohlin et al 2012.…”

Section: Introductionmentioning

confidence: 99%

Comparing image-based point clouds and airborne laser scanning data for estimating forest heights

Ullah¹,

Adler²,

Dees³

et al. 2017

iForest

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Accurate and updated knowledge of forest tree heights is fundamental in the context of forest management. However, measuring canopy height over large forest areas using traditional inventory techniques is laborious, time-consuming and excessively expensive. In this study, image-based point clouds produced from stereo aerial photographs (AP) were used to estimate forest height, and compared to Airborne Laser Scanning (ALS) data. We generated image-based Canopy Height Models (CHM) using different image-matching algorithms (SGM: Semi-Global Matching; eATE: enhanced Automatic Terrain Extraction), which were compared with a pure ALS-derived CHM. Additionally, plotlevel height and density metrics were extracted from CHMs and used as explanatory variables for predicting the Lorey's mean height (LMH), which was measured at 296 reference points on the ground. CHMSGM and CHMALS showed similar results in predicting LMH at sample plot locations (RMSE% = 8.54 vs. 7.92, respectively), while CHMeATE had lower accuracy (RMSE% = 13.23). Similarly, CHMSGM showed a lower normalized median absolute deviation (NMAD) from CHMALS (0.68 m) compared to CHMeATE (1.1 m). Our study revealed that image-based point clouds using SGM in the presence of high-resolution ALSderived digital terrain model (DTM) provide comparable results with ALS data, while the performance of image-based point clouds using eATE is poorer than ALS for forest height estimation. The findings of this study provide a viable and cost-effective option for assessing height-related forest structural parameters. The proposed methodology can be usefully applied in all those countries where AP are updated on a regular basis and pre-existing historical ALS-derived DTMs are available.

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Section: Introductionmentioning

confidence: 99%

Comparing image-based point clouds and airborne laser scanning data for estimating forest heights

Ullah¹,

Adler²,

Dees³

et al. 2017

iForest

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“…Further, tree height determination from CHM is highly accurate but is prone to underestimation (e.g. Rönnholm et al 2004). If the ground elevation and the uppermost proportion of a crown are not detected, then the tree height is automatically underestimated.…”

Section: Estimation Of Stand and Tree Variables With Individual Tree mentioning

confidence: 99%

“…Rönnholm et al 2004) and usually calibrated using field data. Tree dbh cannot be measured directly from the point cloud and must be predicted.…”

Section: Individual Tree Detectionmentioning

confidence: 99%

Forest mapping and monitoring using active 3D remote sensing

Vastaranta¹

2012

Diss. For.

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The main aim in forest mapping and monitoring is to produce accurate information for forest managers with the use of efficient methodologies. For example, it is important to locate harvesting sites and stands where forest operations should be carried out as well as to provide updates regarding forest growth, among other changes in forest structure. In recent years, remote sensing (RS) has taken a significant technological leap forward. It has become possible to acquire three-dimensional (3D), spatially accurate information from forest resources using active RS methods. In practical applications, mainly 3D information produced by airborne laser scanning (ALS) has opened up groundbreaking potential in natural resource mapping and monitoring. In addition to ALS, new satellite radars are also capable of acquiring spatially accurate 3D information. The main objectives of the present study were to develop 3D RS methodologies for large-area forest mapping and monitoring applications. In substudy I, we aim to map harvesting sites, while in substudy II, we monitor changes in the forest canopy structure. In studies III-V, efficient mapping and monitoring applications were developed and tested.In substudy I, we predicted plot-level thinning maturity within the next 10-year planning period. Stands requiring immediate thinning were located with an overall accuracy of 83%-86% depending on the prediction method applied. The respective prediction accuracy for stands reaching thinning maturity within the next 10 years was 70%-79%.Substudy II addressed natural disturbance monitoring that could be linked to forest management planning when an ALS time series is available. The accuracy of the damaged canopy cover area estimate varied between -16.4% to 5.4%. Substudy II showed that changes in the forest canopy structure can be monitored with a rather straightforward method by contrasting bi-temporal canopy height models.In substudy III, we developed a RS-based forest inventory method where single-tree RS is used to acquire modelling data needed in area-based predictions. The method uses ALS data and is capable of producing accurate stand variable estimates even at the sub-compartment level. The developed method could be applied in areas with sparse road networks or when the costs of fieldwork must be minimized. The method is especially suitable for largearea biomass or stem volume mapping.Based on substudy IV, the use of stereo synthetic aperture radar (SAR) satellite data in the prediction of plotlevel forest variables appears to be promising for large-area applications. In the best case, the plot-level stem volume (VOL) was predicted with a relative error (RMSE%) of 34.9%. Typically, such a high level of prediction accuracy cannot be obtained using spaceborne RS data. Then, in substudy V, we compared the aboveground biomass and VOL estimates derived by radargrammetry to the ALS estimates. The difference between the estimation accuracy of ALS-based and TerraSAR X-based features was smaller than in any previous study in which...

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“…ALS tends to underestimate tree height (see e.g. Rönnholm et al 2004) due to pulse interactions within the canopy. Rönnholm et al (2004) showed that single tree height was underestimated, on average, by 0.76 m to 1.46 m depending on the tree species.…”

Section: Tree Species Dbh Height and Stem Curvementioning

confidence: 99%

“…Rönnholm et al 2004) due to pulse interactions within the canopy. Rönnholm et al (2004) showed that single tree height was underestimated, on average, by 0.76 m to 1.46 m depending on the tree species. Similar underestimations where found by Maltamo et al (2004), where 46% of the trees were underestimated by > 1 m and 37.4% by < 1 m. Tree DBH can be modelled based on allometric relationship between DBH and height (see e.g.…”

Section: Tree Species Dbh Height and Stem Curvementioning

confidence: 99%

The prediction of single-tree biomass, logging recoveries and quality attributes with laser scanning techniques

Kankare¹

2015

Diss. For.

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To be presented with the permission of the Faculty of Agriculture and Forestry, University of Helsinki, for public criticism in the Walter Auditorium of the EE-building, Agnes Sjöbergin katu 2, Helsinki on June 5 th , 2015 at 12 o'clock noon. ABSTRACTThe precise knowledge of forest structural attributes, such as biomass, logging recoveries and quality of the available timber, play an essential role in decision-making, forest management procedure planning and in wood supply chain optimization. Remote sensing-aided mapping applications are used intensively to acquire required forest resource information. Laser scanning (LS) is one of the most promising remote sensing techniques, which can be used to estimate forest attributes at all levels, from single trees to global applications. The main objectives of the present thesis were to develop LS-based methodologies for mapping and measuring single trees. More specifically, new high-density LS-based models and methodologies were developed for the prediction of aboveground biomass (AGB), logging recovery, stem curve and external tree quality estimation. Multisource remote sensing methodologies were additionally introduced for the detailed next generation forest-inventory process. Substudies I and II concentrated on developing LS-based biomass models. Total AGB was estimated with the relative root mean squared errors (RMSE%) of 12.9% and 11.9% for Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) H.Karst.), respectively using terrestrial LS (TLS) -derived predictors in multiple regression modelling. TLS-based AGB models significantly improved the estimation accuracy of AGB components compared to state-of-the-art allometric biomass models. Airborne LS (ALS) resulted in slightly higher RMSE% values of 26.3% and 36.8% for Scots pine and Norway spruce compared to results obtained with TLS. The goal of substudies III and IV was to predict timber assortment and tree quality information using high-density LS data. Sawlog volumes were estimated with RMSE% of 17.5% and 16.8% with TLS and a combination of TLS and ALS, respectively. Tree quality is an important factor for accurate and successful timber assortment estimation. The use of TLS data showed high potential for tree quality assessment. Results in IV showed that trees could be successfully classified in different quality classes based on TLS-measured attributes with accuracies between 76.4% and 83.6% depending on the amount of quality classes. Substudies V and VI presented new automatic processing tools for TLS data and a multisource approach for the more detailed prediction of diameter distribution. Automatic processing of TLS data was demonstrated to be effective and accurate and could be utilized to make future TLS measurements more efficient. Accuracies of ~1 cm were achieved using the automatic stem curve procedure. The multisource single-tree inventory approach combined accurate treemaps produced automatically from the TLS data, and ALS individual tree detection technique for predicting forest preh...

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Calibration of laser-derived tree height estimates by means of photogrammetric techniques

Cited by 41 publications

References 8 publications

Comparing image-based point clouds and airborne laser scanning data for estimating forest heights

Comparing image-based point clouds and airborne laser scanning data for estimating forest heights

Forest mapping and monitoring using active 3D remote sensing

The prediction of single-tree biomass, logging recoveries and quality attributes with laser scanning techniques

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