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Articles by Lauri Korhonen

Category : Climate resilient and sustainable forest management – Research article

article id 23042, category Climate resilient and sustainable forest management – Research article
Johanna Jääskeläinen, Lauri Korhonen, Mikko Kukkonen, Petteri Packalen, Matti Maltamo. (2024). Individual tree inventory based on uncrewed aerial vehicle data: how to utilise stand-wise field measurements of diameter for calibration? Silva Fennica vol. 58 no. 3 article id 23042. https://doi.org/10.14214/sf.23042
Keywords: laser scanning; calibration; mixed-effects model; single-tree detection
Highlights: A practical scheme to improve the accuracy of predicted tree and stand attributes in an uncrewed aerial vehicle based individual tree inventory; Accuracy was considerably improved with data from 2–4 sample trees from the target stand; Calibrated existing models and the construction of local models performed equally well; The laborious task of constructing a local model can be avoided by using a calibrated transferred model.
Abstract | Full text in HTML | Full text in PDF | Author Info
Uncrewed aerial vehicles (UAV) have great potential for use in forest inventories, but in practice they can be expensive for relatively small inventory areas as a large number of field measurements are needed for model construction. One proposed solution is to transfer previously constructed models to a new inventory area and to calibrate these with a small number of local field measurements. Our objective was to compare calibration of general models and the construction of new models to determine the best approach for UAV-based forest inventories. Our material included field measurements and UAV-based laser scanning data, from which individual trees were automatically identified. A general mixed-effects model for diameter at breast height (DBH) had been formulated earlier based on data from a geographically wider area. It was calibrated to the study area with field measurements from 2–10 randomly selected calibration trees. The calibrated diameters were used to calculate the diameter of a basal area median tree (DGM), tree volumes, and the volume of all trees at plot-level. Next, new DBH-models were formulated based on the 2–10 randomly selected trees and calibrated with plot-level random effects estimated during model construction. Finally, plot-specific height-diameter regression models were formulated by randomly selecting 10 trees from each plot. Calibration reduced the prediction errors of all variables. An increase in the number of calibration trees decreased error rates by 1–6% depending on the variable. Calibrated predictions from the general mixed-effects model were similar to the separately formulated mixed-effects models and plot-specific regression models.

Category : Research article

article id 10515, category Research article
Alwin A. Hardenbol, Anton Kuzmin, Lauri Korhonen, Pasi Korpelainen, Timo Kumpula, Matti Maltamo, Jari Kouki. (2021). Detection of aspen in conifer-dominated boreal forests with seasonal multispectral drone image point clouds. Silva Fennica vol. 55 no. 4 article id 10515. https://doi.org/10.14214/sf.10515
Keywords: Populus tremula; deciduous trees; mixed forest; protected areas; tree species classification; unmanned aerial vehicles
Highlights: Four boreal tree species (Scots pine, Norway spruce, birches and European aspen) classified with an overall accuracy of 95%; Presence of European aspen detected with excellent accuracy (UA: 97%, PA: 96%); Late spring is the best time for species classification by remote sensing; Best time to separate aspen from birch was when birch had leaves, but aspen did not.
Abstract | Full text in HTML | Full text in PDF | Author Info

Current remote sensing methods can provide detailed tree species classification in boreal forests. However, classification studies have so far focused on the dominant tree species, with few studies on less frequent but ecologically important species. We aimed to separate European aspen (Populus tremula L.), a biodiversity-supporting tree species, from the more common species in European boreal forests (Pinus sylvestris L., Picea abies [L.] Karst., Betula spp.). Using multispectral drone images collected on five dates throughout one thermal growing season (May–September), we tested the optimal season for the acquisition of mono-temporal data. These images were collected from a mature, unmanaged forest. After conversion into photogrammetric point clouds, we segmented crowns manually and automatically and classified the species by linear discriminant analysis. The highest overall classification accuracy (95%) for the four species as well as the highest classification accuracy for aspen specifically (user’s accuracy of 97% and a producer’s accuracy of 96%) were obtained at the beginning of the thermal growing season (13 May) by manual segmentation. On 13 May, aspen had no leaves yet, unlike birches. In contrast, the lowest classification accuracy was achieved on 27 September during the autumn senescence period. This is potentially caused by high intraspecific variation in aspen autumn coloration but may also be related to our date of acquisition. Our findings indicate that multispectral drone images collected in spring can be used to locate and classify less frequent tree species highly accurately. The temporal variation in leaf and canopy appearance can alter the detection accuracy considerably.

  • Hardenbol, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland ORCID https://orcid.org/0000-0002-0615-505X E-mail: alwin.hardenbol@uef.fi (email)
  • Kuzmin, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland; University of Eastern Finland, Department of Geographical and Historical Studies, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: anton.kuzmin@uef.fi
  • Korhonen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.korhonen@uef.fi
  • Korpelainen, University of Eastern Finland, Department of Geographical and Historical Studies, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: pasi.korpelainen@uef.fi
  • Kumpula, University of Eastern Finland, Department of Geographical and Historical Studies, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: timo.kumpula@uef.fi
  • Maltamo, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: matti.maltamo@uef.fi
  • Kouki, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: jari.kouki@uef.fi
article id 10360, category Research article
Mikko Kukkonen, Eetu Kotivuori, Matti Maltamo, Lauri Korhonen, Petteri Packalen. (2021). Volumes by tree species can be predicted using photogrammetric UAS data, Sentinel-2 images and prior field measurements. Silva Fennica vol. 55 no. 1 article id 10360. https://doi.org/10.14214/sf.10360
Keywords: forest inventory; satellite image; open data; drone; stereo matching; unmanned aircraft system
Highlights: A UAS-based species-specific forest inventory approach that avoids new field measurements is presented; Models were constructed using previously measured training plots and remotely sensed data; Bi-seasonal Sentinel-2 data were beneficial in the prediction of species-specific volumes; RMSE values associated with the prediction of volumes by tree species and total volume at the validation plot level were 33.4–62.6% and 9.0%, respectively.
Abstract | Full text in HTML | Full text in PDF | Author Info

Photogrammetric point clouds obtained with unmanned aircraft systems (UAS) have emerged as an alternative source of remotely sensed data for small area forest management inventories (FMI). Nonetheless, it is often overlooked that small area FMI require considerable field data in addition to UAS data, to support the modelling of forest attributes. In this study, we propose a method whereby tree volumes by species are predicted with photogrammetric UAS data and Sentinel-2 images, using models fitted with airborne laser scanning data. The study area is in a managed boreal forest area in Eastern Finland. First, we predicted total volume with UAS point cloud metrics using a prior regression model fitted in another area with ALS data. Tree species proportions were then predicted by k nearest neighbor (k-NN) imputation based on bi-seasonal Sentinel-2 images without measuring new field plot data. Species-specific volumes were then obtained by multiplying the total volume by species proportions. The relative root mean square error (RMSE) values for total and species-specific volume predictions at the validation plot level (30 m × 30 m) were 9.0%, and 33.4–62.6%, respectively. Our approach appears promising for species-specific small area FMI in Finland and in comparable forest conditions in which suitable field plots are available.

  • Kukkonen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: mikko.kukkonen@uef.fi (email)
  • Kotivuori, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: eetu.kotivuori@uef.fi
  • Maltamo, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: matti.maltamo@uef.fi
  • Korhonen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.korhonen@uef.fi
  • Packalen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: petteri.packalen@uef.fi
article id 10179, category Research article
Lauri Korhonen, Jaakko Repola, Tomi Karjalainen, Petteri Packalen, Matti Maltamo. (2019). Transferability and calibration of airborne laser scanning based mixed-effects models to estimate the attributes of sawlog-sized Scots pines. Silva Fennica vol. 53 no. 3 article id 10179. https://doi.org/10.14214/sf.10179
Keywords: Pinus sylvestris; LIDAR; crown base height; hierarchical data; individual tree detection; sawlog quality
Highlights: Attributes of individual sawlog-sized pines estimated by transferring ALS-based models between sites; Mixed effects models were more accurate than k-NN imputation tested earlier; Calibration with a small number of field measured trees improved the accuracy.
Abstract | Full text in HTML | Full text in PDF | Author Info

Airborne laser scanning (ALS) data is nowadays often available for forest inventory purposes, but adequate field data for constructing new forest attribute models for each area may be lacking. Thus there is a need to study the transferability of existing ALS-based models among different inventory areas. The objective of our study was to apply ALS-based mixed models to estimate the diameter, height and crown base height of individual sawlog sized Scots pines (Pinus sylvestris L.) at three different inventory sites in eastern Finland. Different ALS sensors and acquisition parameters were used at each site. Multivariate mixed-effects models were fitted at one site and the models were validated at two independent test sites. Validation was carried out by applying the fixed parts of the mixed models as such, and by calibrating them using 1–3 sample trees per plot. The results showed that the relative RMSEs of the predictions were 1.2–6.5 percent points larger at the test sites compared to the training site. Systematic errors of 2.4–6.2 percent points also emerged at the test sites. However, both the RMSEs and the systematic errors decreased with calibration. The results showed that mixed-effects models of individual tree attributes can be successfully transferred and calibrated to other ALS inventory areas in a level of accuracy that appears suitable for practical applications.

  • Korhonen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland ORCID http://orcid.org/0000-0002-9352-0114 E-mail: lauri.korhonen@uef.fi (email)
  • Repola, Natural Resources Institute of Finland (Luke), Natural resources, Eteläranta 55, FI-96300 Rovaniemi, Finland E-mail: jaakko.repola@luke.fi
  • Karjalainen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: tomikar@uef.fi
  • Packalen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: petteri.packalen@uef.fi
  • Maltamo, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: matti.maltamo@uef.fi
article id 1567, category Research article
Eetu Kotivuori, Lauri Korhonen, Petteri Packalen. (2016). Nationwide airborne laser scanning based models for volume, biomass and dominant height in Finland. Silva Fennica vol. 50 no. 4 article id 1567. https://doi.org/10.14214/sf.1567
Keywords: forest inventory; LIDAR; regression analysis; remote sensing; calibration; area-based approach; mixed-effect models
Highlights: Pooled data from nine inventory projects in Finland were used to create nationwide laser-based regression models for dominant height, volume and biomass; Volume and biomass models provided regionally different means than real means, but for dominant height the mean difference was small; The accuracy of general volume predictions was nevertheless comparable to relascope-based field inventory by compartments.
Abstract | Full text in HTML | Full text in PDF | Author Info

The aim of this study was to examine how well stem volume, above-ground biomass and dominant height can be predicted using nationwide airborne laser scanning (ALS) based regression models. The study material consisted of nine practical ALS inventory projects taken from different parts of Finland. We used field sample plots and airborne laser scanning data to create nationwide and regional models for each response variable. The final models had one or two ALS predictors, which were chosen based on the root mean square error (RMSE), and cross-validated. Finally, we tested how much predictions would improve if the nationwide models were calibrated with a small number of regional sample plots. Although forest structures differ among different parts of Finland, the nationwide volume and biomass models performed quite well (leave-inventory-area-out RMSE 22.3% to 33.8%, mean difference [MD] –13.8% to 18.7%) compared with regional models (leave-plot-out RMSE 20.2% to 26.8%). However, the nationwide dominant height model (RMSE 5.4% to 7.7%, MD –2.0% to 2.8%, with the exception of the Tornio region – RMSE 11.4%, MD –9.1%) performed nearly as well as the regional models (RMSE 5.2% to 6.7%). The results show that the nationwide volume and biomass models provided different means than real means at regional level, because forest structure and ALS device have a considerable effect on the predictions. Large MDs appeared especially in northern Finland. Local calibration decreased the MD and RMSE of volume and biomass models. However, the nationwide dominant height model did not benefit much from calibration.

  • Kotivuori, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: eetu.kotivuori@uef.fi (email)
  • Korhonen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.korhonen@uef.fi
  • Packalen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: petteri.packalen@uef.fi
article id 1405, category Research article
Lauri Korhonen, Daniela Ali-Sisto, Timo Tokola. (2015). Tropical forest canopy cover estimation using satellite imagery and airborne lidar reference data. Silva Fennica vol. 49 no. 5 article id 1405. https://doi.org/10.14214/sf.1405
Keywords: logistic regression; beta regression; forest area; international forest definition; ALOS AVNIR-2; vegetation index
Highlights: The fusion of airborne lidar data and satellite images enables accurate canopy cover mapping; The zero-and-one inflated beta regression is demonstrated in large area estimation; Forest/non-forest classification should be done directly, for example by using logistic regression.
Abstract | Full text in HTML | Full text in PDF | Author Info

The fusion of optical satellite imagery, strips of lidar data and field plots is a promising approach for the inventory of tropical forests. Airborne lidars also enable an accurate direct estimation of the forest canopy cover (CC), and thus a sample of lidar strips can be used as reference data for creating CC maps which are based on satellite images. In this study, our objective was to validate CC maps obtained from an ALOS AVNIR-2 satellite image wall-to-wall, against a lidar-based CC map of a tropical forest area located in Laos. The reference CC values which were needed for model training were obtained from a sample of four lidar strips. Zero-and-one inflated beta regression (ZOINBR) models were applied to link the spectral vegetation indices derived from the ALOS image with the lidar-based CC estimates. In addition, we compared ZOINBR and logistic regression models in the forest area estimation by using >20% CC as a forest definition. Using a total of 409 217 30 × 30 m population units as validation, our model showed a strong correlation between lidar-based CC and spectral satellite features (root mean square error = 12.8%, R2 = 0.82). In the forest area estimation, a direct classification using logistic regression provided better accuracy than the estimation of CC values as an intermediate step (kappa = 0.61 vs. 0.53). It is important to obtain sufficient training data from both ends of the CC range. The forest area estimation should be done before the CC estimation, rather than vice versa.

  • Korhonen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland; (current) University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland ORCID http://orcid.org/0000-0002-9352-0114 E-mail: lauri.z.korhonen@helsinki.fi (email)
  • Ali-Sisto, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: dheikkil@student.uef.fi
  • Tokola, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland. E-mail: timo.tokola@uef.fi
article id 943, category Research article
Terje Gobakken, Lauri Korhonen, Erik Næsset. (2013). Laser-assisted selection of field plots for an area-based forest inventory. Silva Fennica vol. 47 no. 5 article id 943. https://doi.org/10.14214/sf.943
Keywords: forest inventory; LIDAR; airborne laser scanning; stratified sampling; area-based approach
Highlights: Using laser data as auxiliary information in the selection of field plot locations helps to decrease costs in forest inventories based on airborne laser scanning; Two independent, differently selected sets of field plots were used for model fitting, and third for validation; Using partial instead of ordinary least squares had no major influence on the results; Forty well placed plots produced fairly reliable volume estimates.
Abstract | Full text in HTML | Full text in PDF | Author Info
Field measurements conducted on sample plots are a major cost component in airborne laser scanning (ALS) based forest inventories, as field data is needed to obtain reference variables for the statistical models. The ALS data also provides an excellent source of prior information that may be used in the design phase of the field survey to reduce the size of the field data set. In the current study, we acquired two independent modeling data sets: one with ALS-assisted and another with random plot selection. A third data set was used for validation. One canopy height and one canopy density variable were used as a basis for the ALS-assisted selection. Ordinary and partial least squares regressions for stem volume were fitted for four different strata using the two data sets separately. The results show that the ALS-assisted plot selection helped to decrease the root mean square error (RMSE) of the predicted volume. Although the differences in RMSE were relatively small, models based on random plot selection showed larger mean differences from the reference in the independent validation data. Furthermore, a sub-sampling experiment showed that 40 well placed plots should be enough for fairly reliable predictions.
  • Gobakken, Norwegian University of Life Sciences, Department of Ecology and Natural Resource Management, Ås, Norway E-mail: terje.gobakken@umb.no
  • Korhonen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.korhonen@uef.fi (email)
  • Næsset, Norwegian University of Life Sciences, Department of Ecology and Natural Resource Management, Ås, Norway E-mail: erik.naesset@umb.no
article id 952, category Research article
Lauri Korhonen, Inka Pippuri, Petteri Packalén, Ville Heikkinen, Matti Maltamo, Juho Heikkilä. (2013). Detection of the need for seedling stand tending using high-resolution remote sensing data. Silva Fennica vol. 47 no. 2 article id 952. https://doi.org/10.14214/sf.952
Keywords: forest management; airborne laser scanning; logistic regression; seedling stand; tending; support vector machine
Abstract | Full text in HTML | Full text in PDF | Author Info
Seedling stands are problematic in airborne laser scanning (ALS) based stand level forest management inventories, as the stem density and species proportions are difficult to estimate accurately using only remotely sensed data. Thus the seedling stands must still be checked in the field, which results in an increase in costs. In this study we tested an approach where ALS data and aerial images are used to directly classify the seedling stands into two categories: those that involve tending within the next five years and those which involve no tending. Standard ALS-based height and density features, together with texture and spectral features calculated from aerial images, were used as inputs to two classifiers: logistic regression and the support vector machine (SVM). The classifiers were trained using 208 seedling plots whose tending need was estimated by a local forestry expert. The classification was validated on 68 separate seedling stands. In the training data, the logistic model’s kappa coefficient was 0.55 and overall accuracy (OA) 77%. The SVM did slightly better with a kappa = 0.71 and an OA = 86%. In the stand level validation data, the performance decreased for both the logistic model (kappa = 0.38, OA = 71%) and the SVM (kappa = 0.37, OA = 72%). Thus our approach cannot totally replace the field checks. However, in considering the stands where the logistic model predictions had high reliability, the number of misclassifications reduced drastically. The SVM however, was not as good at recognizing reliable cases.
  • Korhonen, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.korhonen@uef.fi (email)
  • Pippuri, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: inka.pippuri@uef.fi
  • Packalén, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: petteri.packalen@uef.fi
  • Heikkinen, School of Computing, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: ville.heikkinen@uef.fi
  • Maltamo, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: matti.maltamo@uef.fi
  • Heikkilä, Finnish Forest Centre, Public Services, Maistraatinportti 4 A, FI-00240 Helsinki, Finland E-mail: juho.heikkila@metsakeskus.fi
article id 275, category Research article
Lauri Korhonen, Kari T. Korhonen, Pauline Stenberg, Matti Maltamo, Miina Rautiainen. (2007). Local models for forest canopy cover with beta regression. Silva Fennica vol. 41 no. 4 article id 275. https://doi.org/10.14214/sf.275
Keywords: beta regression; canopy cover; forest canopy
Abstract | View details | Full text in PDF | Author Info
Accurate field measurement of the forest canopy cover is too laborious to be used in extensive forest inventories. A possible alternative to the separate canopy cover measurements is to utilize the correlations between the percent canopy cover and easier-to-measure forest variables, especially the basal area. A fairly new analysis technique, the beta regression, is specially designed for modelling percentages. As an extension to the generalized linear models, the beta regression takes into account the distribution of the model residuals, and uses a logistic link function to ensure logical predictions. In this study, the beta regression method was found to perform well in conifer dominated study area located in central Finland. The same model shape, with basal area, tree height and an additional predictor (Scots pine: site fertility, Norway spruce: percentage of hardwoods) as independent variables, produced good results for both pine and spruce dominated sites. The models had reasonably high pseudo R-squared values (pine: 0.91, spruce: 0.87) and low standard errors (pine: 6.3%, spruce: 5.9%) for the fitting data, and also performed well in a cross validation test. The models were also tested on separate test plots located in a different geographical area, where the prediction errors were slightly larger (pine: 8.8%, spruce: 7.4%). In pine plots, the model fit was further improved by introducing additional predictors such as stand age and density. This improved also the performance of the models in the cross validation test, but weakened the results for the external data set. Our results indicated that the beta regression method offers a noteworthy alternative to separate canopy cover measurements, especially if time is limited and the models can be applied in the same region where the modelling data were collected.
  • Korhonen, University of Joensuu, Faculty of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.korhonen@joensuu.fi (email)
  • Korhonen, Finnish Forest Research Institute, Joensuu Research Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: ktk@nn.fi
  • Stenberg, Univ. of Helsinki, Dept of Forest Resource Management, P.O. BOX 27, FI-00014 University of Helsinki, Finland E-mail: ps@nn.fi
  • Maltamo, University of Joensuu, Faculty of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: mm@nn.fi
  • Rautiainen, Univ. of Helsinki, Dept of Forest Resource Management, P.O. BOX 27, FI-00014 University of Helsinki, Finland E-mail: mr@nn.fi
article id 315, category Research article
Lauri Korhonen, Kari T. Korhonen, Miina Rautiainen, Pauline Stenberg. (2006). Estimation of forest canopy cover: a comparison of field measurement techniques. Silva Fennica vol. 40 no. 4 article id 315. https://doi.org/10.14214/sf.315
Keywords: canopy cover; forest canopy; canopy closure; Cajanus tube; line intersect sampling; spherical densiometer; digital photographs
Abstract | View details | Full text in PDF | Author Info
Estimation of forest canopy cover has recently been included in many forest inventory programmes. In this study, after discussing how canopy cover is defined, different ground-based canopy cover estimation techniques are compared to determine which would be the most feasible for a large scale forest inventory. Canopy cover was estimated in 19 Scots pine or Norway spruce dominated plots using the Cajanus tube, line intersect sampling, modified spherical densiometer, digital photographs, and ocular estimation. The comparisons were based on the differences in values acquired with selected techniques and control values acquired with the Cajanus tube. The statistical significance of the differences between the techniques was tested with the nonparametric Kruskall-Wallis analysis of variance and multiple comparisons. The results indicate that different techniques yield considerably different canopy cover estimates. In general, labour intensive techniques (the Cajanus tube, line intersect sampling) provide unbiased and more precise estimates, whereas the estimates provided by fast techniques (digital photographs, ocular estimation) have larger variances and may also be seriously biased.
  • Korhonen, University of Joensuu, P.O. Box 68, FI-68101 Joensuu, Finland E-mail: lauri.korhonen@joensuu.fi (email)
  • Korhonen, University of Joensuu, P.O. Box 68, FI-68101 Joensuu, Finland E-mail: ktk@nn.fi
  • Rautiainen, University of Joensuu, P.O. Box 68, FI-68101 Joensuu, Finland E-mail: mr@nn.fi
  • Stenberg, University of Joensuu, P.O. Box 68, FI-68101 Joensuu, Finland E-mail: ps@nn.fi

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