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Joensuu, Finland

Arbonaut Ltd.

Joensuu, Finland
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Kauranne T.,Lappeenranta University of Technology | Kauranne T.,Arbonaut Ltd. | Pyankov S.,Perm State University | Junttila V.,Lappeenranta University of Technology | And 7 more authors.
Forests | Year: 2017

Airborne laser scanning (ALS) based stand level forest inventory has been used in Finland and other Nordic countries for several years. In the Russian Federation, ALS is not extensively used for forest inventory purposes, despite a long history of research into the use of lasers for forest measurement that dates back to the 1970s. Furthermore, there is also no generally accepted ALS-based methodology that meets the official inventory requirements of the Russian Federation. In this paper, a method developed for Finnish forest conditions is applied to ALS-based forest inventory in the Perm region of Russia. Sparse Bayesian regression is used with ALS data, SPOT satellite images and field reference data to estimate five forest parameters for three species groups (pine, spruce, deciduous): total mean volume, basal area, mean tree diameter, mean tree height, and number of stems per hectare. Parameter estimates are validated at both the plot level and stand level, and the validation results are compared to results published for three Finnish test areas. Overall, relative root mean square errors (RMSE) were higher for forest parameters in the Perm region than for the Finnish sites at both the plot and stand level. At the stand level, relative RMSE generally decreased with increasing stand size and was lower when considered overall than for individual species groups. © 2017 by the authors.

Kauranne T.,Lappeenranta University of Technology | Kauranne T.,Arbonaut Ltd. | Joshi A.,University of Minnesota | Gautam B.,Arbonaut Ltd. | And 12 more authors.
Remote Sensing | Year: 2017

Forest measurement for purposes like harvesting planning, biomass estimation and mitigating climate change through carbon capture by forests call for increasingly frequent forest measurement campaigns that need to balance cost with accuracy and precision. Often this implies the use of remote sensing based measurement methods. For any remote-sensing based methods to be accurate, they must be validated against field data. We present a method that combines field measurements with two layers of remote sensing data: sampling of forests by airborne laser scanning (LiDAR) and Landsat imagery. The Bayesian model-based framework presented here is called Lidar-Assisted Multi-source Programme-or LAMP-for Above Ground Biomass estimation. The method has two variants: LAMP2 which splits the biomass estimation task into two separate stages: forest type stratification from Landsat imagery and mean biomass density estimation of each forest type by LiDAR models calibrated on field plots. LAMP3, on the other hand, estimates first the biomass on a LiDAR sample using models calibrated with field plots and then uses these LiDAR-based models to generate biomass density estimates on thousands of surrogate plots, with which a satellite image based model is calibrated and subsequently used to estimate biomass density on the entire forest area. Both LAMP methods have been applied to a 2 million hectare area in Southern Nepal, the Terai Arc Landscape or TAL to calculate the emission Reference Levels (RLs) that are required for the UN REDD+ program that was accepted as part of the Paris Climate Agreement. The uncertainty of these estimates is studied with error variance estimation, cross-validation and Monte Carlo simulation. The relative accuracy of activity data at pixel level was found to be 14 per cent at 95 per cent confidence level and the root mean squared error of biomass estimates to be between 35 and 39 per cent at 1 ha resolution. © 2017 by the authors.

Junttila S.,University of Helsinki | Junttila S.,Finnish Geospatial Research Institute FGI | Vastaranta M.,University of Helsinki | Vastaranta M.,Finnish Geospatial Research Institute FGI | And 8 more authors.
Scandinavian Journal of Forest Research | Year: 2016

The effect of forest structure and health on the relative surface temperature captured by airborne thermal imagery was investigated in Norway Spruce-dominated stands in Southern Finland. Airborne thermal imagery, airborne scanning light detection and ranging (LiDAR) data and 92 field-measured sample plots were acquired at the area of interest. The surface temperature correlated most negatively with the logarithm of stem volume, Lorey’s height and the logarithm of basal area at a resolution of 254 m2 (9 m radius). LiDAR-derived metrics: the standard deviations of the canopy heights, canopy height (upper percentiles and maximum height) and canopy cover percentage were most strongly negatively correlated with the surface temperature. Although forest structure has an effect on the detected surface temperature, higher temperatures were detected in severely defoliated canopies and the difference was statistically significant. We also found that the surface temperature differences between the segmented canopy and the entire plot were greater in the defoliated plots, indicating that thermal images may also provide some additional information for classifying forests health status. Based on our results, the effects of forest structure on the surface temperature captured by airborne thermal imagery should be taken into account when developing forest health mapping applications using thermal imagery. © 2016 Informa UK Limited, trading as Taylor & Francis Group

PubMed | Mzumbe University, Arbonaut Ltd, International Center for Integrated Mountain Development and Lappeenranta University of Technology
Type: Journal Article | Journal: Carbon balance and management | Year: 2015

Participatory forest monitoring has been promoted as a means to engage local forest-dependent communities in concrete climate mitigation activities as it brings a sense of ownership to the communities and hence increases the likelihood of success of forest preservation measures. However, sceptics of this approach argue that local community forest members will not easily attain the level of technical proficiency that accurate monitoring needs. Thus it is interesting to establish if local communities can attain such a level of technical proficiency. This paper addresses this issue by assessing the robustness of biomass estimation models based on air-borne laser data using models calibrated with two different field sample designs namely, field data gathered by professional forester teams and field data collected by local communities trained by professional foresters in two study sites in Nepal. The aim is to find if the two field sample data sets can give similar results (LiDAR models) and whether the data can be combined and used together in estimating biomass.Results show that even though the sampling designs and principles of both field campaigns were different, they produced equivalent regression models based on LiDAR data. This was successful in one of the sites (Gorkha). At the other site (Chitwan), however, major discrepancies remained in model-based estimates that used different field sample data sets. This discrepancy can be attributed to the complex terrain and dense forest in the site which makes it difficult to obtain an accurate digital elevation model (DTM) from LiDAR data, and neither set of data produced satisfactory results.Field sample data produced by professional foresters and field sample data produced by professionally trained communities can be used together without affecting prediction performance provided that the correlation between LiDAR predictors and biomass estimates is good enough.

Maguya A.S.,Lappeenranta University of Technology | Maguya A.S.,Mzumbe University | Tegel K.,Arbonaut Ltd. | Junttila V.,Lappeenranta University of Technology | And 6 more authors.
Remote Sensing | Year: 2015

Canopy base height (CBH) is a key parameter used in forest-fire modeling, particularly crown fires. However, estimating CBH is a challenging task, because normally, it is difficult to measure it in the field. This has led to the use of simple estimators (e.g., the average of individual trees in a plot) for modeling CBH. In this paper, we propose a method for estimating CBH from airborne light detection and ranging (LiDAR) data. We also compare the performance of several estimators (Lorey's mean, the arithmetic mean and the 40th and 50th percentiles) used to estimate CBH at the plot level. The method we propose uses a moving voxel to estimate the height of the gaps (in the LiDAR point cloud) below tree crowns and uses this information for modeling CBH. The advantage of this approach is that it is more tolerant to variations in LiDAR data (e.g., due to season) and tree species, because it works directly with the height information in the data. Our approach gave better results when compared to standard percentile-based LiDAR metrics commonly used in modeling CBH. Using Lorey's mean, the arithmetic mean and the 40th and 50th percentiles as CBH estimators at the plot level, the highest and lowest values for root mean square error (RMSE) and root mean square error for cross-validation (RMSEcv) and R2 for our method were 1.74/2.40, 2.69/3.90 and 0.46/0.71, respectively, while with traditional LiDAR-based metrics, the results were 1.92/2.48, 3.34/5.51 and 0.44/0.65. Moreover, the use of Lorey's mean as a CBH estimator at the plot level resulted in models with better predictive value based on the leave-one-out cross-validation (LOOCV) results used to compute the RMSEcv values. © 2015 by the authors.

Kolesnikov A.,Arbonaut Ltd. | Trichina E.,Kudelski Group
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

In this paper we consider a problem of an unsupervised clustering of multidimensional numerical data. We propose a new method for determining an optimal number of clusters in a data set which is based on a parametric model of a Rate-Distortion curve. Theproposed method can be used in conjunction with any suitable clustering algorithm. It was tested with artificial and real numerical data sets and the results of experiments demonstrate empirically not only effectiveness of the method but also its ability to cope with "difficult" cases where other known methods failed. © 2012 Springer-Verlag.

Eivazi A.,Lappeenranta University of Technology | Kolesnikov A.,Arbonaut Ltd. | Junttila V.,Lappeenranta University of Technology | Kauranne T.,Lappeenranta University of Technology
ISPRS Journal of Photogrammetry and Remote Sensing | Year: 2015

Measuring, Reporting and Verification (MRV) systems of the United Nations programme on Reducing Emissions from Deforestation and forest Degradation (REDD+) aim to provide robust and reliable data on carbon credits over large areas. Multitemporal satellite mosaics are often the only cost-effective remote sensing data that allow such coverage. Although a number of methods for producing mosaics has been proposed, most of them are dependent on the order in which tiles to normalized are presented to the algorithm and suffer from loss of input scenes' variance which can substantially reduce the carbon credits. In this study we propose a variance-preserving mosaic (VPM) algorithm that considers all images at the same time, minimizes overall error of the normalization and aims to preserve average variance of input images. We have compared the presented method with a popular relative normalization algorithm commonly used nowadays. The proposed algorithm allows to avoid iterative pair-wise normalization, results in visually uniform mosaics while maintaining also the original image variance. © 2015 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).

Kolesnikov A.,Arbonaut Ltd. | Trichina E.,University of Eastern Finland | Kauranne T.,Lappeenranta University of Technology
Pattern Recognition | Year: 2015

In this paper, we consider the problem of unsupervised clustering (vector quantization) of multidimensional numerical data. We propose a new method for determining an optimal number of clusters in the data set. The method is based on parametric modeling of the quantization error. The model parameter can be treated as the effective dimensionality of the data set. The proposed method was tested with artificial and real numerical data sets and the results of the experiments demonstrate empirically not only the effectiveness of the method but its ability to cope with difficult cases where other known methods fail. © 2014 Elsevier Ltd. All rights reserved.

Kolesnikov A.,Arbonaut Ltd. | Kauranne T.,Lappeenranta University of Technology
Pattern Recognition | Year: 2014

This paper considers the problem of unsupervised segmentation and approximation of digital curves and trajectories with a set of geometrical primitives (model functions). An algorithm is proposed based on a parameterized model of the Rate-Distortion curve. The multiplicative cost function is then derived from the model. By analyzing the minimum of the cost function, a solution is defined that produces the best possible balance between the number of segments and the approximation error. The proposed algorithm was tested for polygonal approximation and multi-model approximation (circular arcs and line segments for digital curves, and polynomials for trajectory). The algorithm demonstrated its efficiency in comparisons with known methods with a heuristic cost function. The proposed method can additionally be used for segmentation and approximation of signals and time series. © 2013 Elsevier Ltd.

Rana P.,University of Eastern Finland | Korhonen L.,University of Eastern Finland | Gautam B.,Arbonaut Ltd. | Tokola T.,University of Eastern Finland
ISPRS Journal of Photogrammetry and Remote Sensing | Year: 2014

The prediction of tropical forest attributes using airborne laser scanning (ALS) is becoming attractive as an alternative to traditional field measurements. Area-based ALS inventories require a set of representative field plots from the study area, which may be difficult to obtain in tropical forests with limited accessibility. This study investigates the effect of sample-plot selection in Nepal, based on two accessibility factors: distance to road and degree of slope. The sparse Bayesian method was employed in the model to estimate above-ground biomass (AGB) with an independent validation dataset for model validation. Study findings showed that the sample plot distance and slope had a considerable effect on the accuracy of the AGB estimation, because the forest structure varied according to the level of accessibility. Thus, the field sample plots that are used in model construction should cover the full range of sample plot distances and slopes occurring within the area. © 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).

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