Helsinki, Finland
Helsinki, Finland

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Vastaranta M.,University of Helsinki | Vastaranta M.,Finnish Geodetic Institute | Niemi M.,University of Helsinki | Niemi M.,Finnish Geodetic Institute | And 7 more authors.
Remote Sensing | Year: 2014

Consistent, detailed and up-to-date forest resource information is required for allocation of forestry activities and national and international reporting obligations. We evaluated the forest stand attribute prediction accuracy when radargrammetry was used to derive height information from TerraSAR-X stereo imagery. Radargrammetric elevations were normalized to heights above ground using an airborne laser scanning (ALS)-derived digital terrain model (DTM). Derived height metrics were used as predictors in the most similar neighbor (MSN) estimation approach. In total, 207 field measured plots were used in MSN estimation, and the obtained results were validated using 94 stands with an average area of 4.1 ha. The relative root mean square errors for Lorey's height, basal area, stem volume, and above-ground biomass were 6.7% (1.1 m), 12.0% (2.9 m2/ha), 16.3% (31.1 m3/ha), and 16.1% (15.6 t/ha). Although the prediction accuracies were promising, it should be noted that the predictions included bias. The respective biases were -4.6% (-0.7 m), -6.4% (-1.6 m2/ha), -9.3% (-17.8 m3/ha), and -9.5% (-9.1 t/ha). With detailed DTM, TerraSAR-X stereo radargrammetry-derived forest information appears to be suitable for providing consistent forest resource information over large areas. © 2014 by the authors; licensee MDPI, Basel, Switzerland.


Niemi M.,University of Helsinki | Niemi M.,Finnish Geospatial Research Institute FGI | Vastaranta M.,University of Helsinki | Vastaranta M.,Finnish Geospatial Research Institute FGI | And 3 more authors.
Silva Fennica | Year: 2015

Nearly 30% of Finland’s land area is covered by peatlands. In Northern parts of the country there is a significant amount of low-productive drained peatlands (LPDPs) where the average annual stem volume growth is less than 1 m3 ha–1. The re-use of LPDPs has been considered thoroughly since Finnish forest legislation was updated and the forest regeneration prerequisite was removed from LPDPs in January 2014. Currently, forestry is one of the re-use alternatives, thus detailed forest resource information is required for allocating activities. However, current forest inventory practices have not been evaluated for sparse growing stocks (e.g., LPDPs). The purpose of our study was to evaluate the suitability of airborne laser scanning (ALS) for mapping forest inventory attributes in LPDPs. We used ALS data with a density of 0.8 pulses per m2, 558 field-measured reference plots (500 from productive forests and 58 from LPDPs) and k nearest neighbour (k-NN) estimation. Our main aim was to study the sensitivity of predictions to the number of LPDP reference plots used in the k-NN estimation. When the reference data consisted of 500 plots from productive forest stands, the root mean square errors (RMSEs) for the prediction accuracy of Lorey’s height, basal area and stem volume were 1.4 m, 2.7 m2 ha–1 and 13.7 m3 ha–1 in LPDPs, respectively. When 30 additional reference plots were allocated to LPDPs, the respective RMSEs were 1.1 m, 1.7 m2 ha–1 and 10.0 m3 ha–1. Additional reference plot allocation did not affect the predictions in productive forest stands. © 2015, Finnish Society of Forest Science. All Rights Reserved.


Packalen P.,University of Eastern Finland | Packalen P.,Oregon State University | Vauhkonen J.,University of Helsinki | Kallio E.,University of Eastern Finland | And 5 more authors.
International Journal of Remote Sensing | Year: 2013

The spatial pattern of trees can be defined as a property of their location in relation to each other. In this study, the spatial pattern was summarized into three categories, regular, random, and clustered, using Ripley's L-function. The study was carried out at 79 sample plots located in a managed forest in Finland. The goal was to study how well the spatial pattern of trees can be predicted by airborne laser scanning (ALS) data. ALS-derived predictions were based upon individual tree detection (ITD), semi-individual tree detection (semi-ITD), and plot-level metrics calculated from the canopy height model, AREA. The kappa value for ITD was almost zero, which indicates no agreement. The semi-ITD and AREA methods performed better, although kappa values were only 0.34 and 0.24, respectively. It appears difficult to detect a particularly clustered spatial pattern. © 2013 Copyright Taylor and Francis Group, LLC.


Junttila V.,Lappeenranta University of Technology | Kauranne T.,Lappeenranta University of Technology | Kauranne T.,Arbonaut Oy Ltd. | Finley A.O.,Michigan State University | Bradford J.B.,U.S. Geological Survey
IEEE Transactions on Geoscience and Remote Sensing | Year: 2015

Modern operational forest inventory often uses remotely sensed data that cover the whole inventory area to produce spatially explicit estimates of forest properties through statistical models. The data obtained by airborne light detection and ranging (LiDAR) correlate well with many forest inventory variables, such as the tree height, the timber volume, and the biomass. To construct an accurate model over thousands of hectares, LiDAR data must be supplemented with several hundred field sample measurements of forest inventory variables. This can be costly and time consuming. Different LiDAR-data-based and spatial-data-based sampling designs can reduce the number of field sample plots needed. However, problems arising from the features of the LiDAR data, such as a large number of predictors compared with the sample size (overfitting) or a strong correlation among predictors (multicollinearity), may decrease the accuracy and precision of the estimates and predictions. To overcome these problems, a Bayesian linear model with the singular value decomposition of predictors, combined with regularization, is proposed. The model performance in predicting different forest inventory variables is verified in ten inventory areas from two continents, where the number of field sample plots is reduced using different sampling designs. The results show that, with an appropriate field plot selection strategy and the proposed linear model, the total relative error of the predicted forest inventory variables is only 5%-15% larger using 50 field sample plots than the error of a linear model estimated with several hundred field sample plots when we sum up the error due to both the model noise variance and the model's lack of fit. © 1980-2012 IEEE.

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