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480 results on '"Holopainen, Markus"'

153. Comparison of Laser and Stereo Optical, SAR and InSAR Point Clouds from Air- and Space-Borne Sources in the Retrieval of Forest Inventory Attributes

Author

Yu, Xiaowei, primary, Hyyppä, Juha, additional, Karjalainen, Mika, additional, Nurminen, Kimmo, additional, Karila, Kirsi, additional, Vastaranta, Mikko, additional, Kankare, Ville, additional, Kaartinen, Harri, additional, Holopainen, Markus, additional, Honkavaara, Eija, additional, Kukko, Antero, additional, Jaakkola, Anttoni, additional, Liang, Xinlian, additional, Wang, Yunsheng, additional, Hyyppä, Hannu, additional, and Katoh, Masato, additional

Published
2015
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156. Accuracy of Kinematic Positioning Using Global Satellite Navigation Systems under Forest Canopies

Author

Kaartinen, Harri, primary, Hyyppä, Juha, additional, Vastaranta, Mikko, additional, Kukko, Antero, additional, Jaakkola, Anttoni, additional, Yu, Xiaowei, additional, Pyörälä, Jiri, additional, Liang, Xinlian, additional, Liu, Jingbin, additional, Wang, Yungshen, additional, Kaijaluoto, Risto, additional, Melkas, Timo, additional, Holopainen, Markus, additional, and Hyyppä, Hannu, additional

Published
2015
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168. Effect of forest structure and health on the relative surface temperature captured by airborne thermal imagery – Case study in Norway Spruce-dominated stands in Southern Finland.

Author

Junttila, Samuli, Vastaranta, Mikko, Hämäläinen, Jarno, Latva-käyrä, Petri, Holopainen, Markus, Hernández Clemente, Rocío, Hyyppä, Hannu, and Navarro-Cerrillo, Rafael M.

Subjects
FORESTS & forestry, SURFACE temperature, HEAT convection, OPTICAL radar, DEFOLIATION
Abstract

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. [ABSTRACT FROM PUBLISHER]

Published
2017
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169. Single-Sensor Solution to Tree Species Classification Using Multispectral Airborne Laser Scanning.

Author

Xiaowei Yu, Hyyppä, Juha, Litkey, Paula, Kaartinen, Harri, Vastaranta, Mikko, and Holopainen, Markus

Subjects
AIRBORNE lasers, FOREST mapping, FOREST management, FORESTS & forestry, TAIGAS
Abstract

This paper investigated the potential of multispectral airborne laser scanning (ALS) data for individual tree detection and tree species classification. The aim was to develop a single-sensor solution for forest mapping that is capable of providing species-specific information, required for forest management and planning purposes. Experiments were conducted using 1903 ground measured trees from 22 sample plots and multispectral ALS data, acquired with an Optech Titan scanner over a boreal forest, mainly consisting of Scots pine (Pinus Sylvestris), Norway spruce (Picea Abies), and birch (Betula sp.), in southern Finland. ALS-features used as predictors for tree species were extracted from segmented tree objects and used in random forest classification. Different combinations of features, including point cloud features, and intensity features of single and multiple channels, were tested. Among the field-measured trees, 61.3% were correctly detected. The best overall accuracy (OA) of tree species classification achieved for correctly-detected trees was 85.9% (Kappa = 0.75), using a point cloud and single-channel intensity features combination, which was not significantly different from the ones that were obtained either using all features (OA = 85.6%, Kappa = 0.75), or single-channel intensity features alone (OA = 85.4%, Kappa = 0.75). Point cloud features alone achieved the lowest accuracy, with an OA of 76.0%. Field-measured trees were also divided into four categories. An examination of the classification accuracy for four categories of trees showed that isolated and dominant trees can be detected with a detection rate of 91.9%, and classified with a high overall accuracy of 90.5%. The corresponding detection rate and accuracy were 81.5% and 89.8% for a group of trees, 26.4% and 79.1% for trees next to a larger tree, and 7.2% and 53.9% for trees situated under a larger tree, respectively. The results suggest that Channel 2 (1064 nm) contains more information for separating pine, spruce, and birch, followed by channel 1 (1550 nm) and channel 3 (532 nm) with an overall accuracy of 81.9%, 78.3%, and 69.1%, respectively. Our results indicate that the use of multispectral ALS data has great potential to lead to a single-sensor solution for forest mapping. [ABSTRACT FROM AUTHOR]

Published
2017
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170. Measuring Leaf Water Content with Dual-Wavelength Intensity Data from Terrestrial Laser Scanners.

Author

Junttila, Samuli, Vastaranta, Mikko, Xinlian Liang, Kaartinen, Harri, Kukko, Antero, Kaasalainen, Sanna, Holopainen, Markus, Hyyppä, Hannu, and Hyyppä, Juha

Subjects
MOISTURE content of leaves, FOREST health, OPTICAL scanners, MULTISPECTRAL imaging, SPECULAR reflectance, FOREST ecology
Abstract

Decreased leaf moisture content, typically measured as equivalent water thickness (EWT), is an early signal of tree stress caused by drought, disease, or pest insects. We investigated the use of two terrestrial laser scanners (TLSs) employing different wavelengths for improving the understanding how EWT can be retrieved in a laboratory setting. Two wavelengths were examined for normalizing the effects of varying leaf structure and geometry on the measured intensity. The relationship between laser intensity features, using red (690 nm) and shortwave infrared (1550 nm) wavelengths, and the EWT of individual leaves or groups of needles were determined with and without intensity corrections. To account for wrinkles and curvatures of the leaves and needles, a model describing the relationship between incidence angle and backscattered intensity was applied. Additionally, a reflectance model describing both diffuse and specular reflectance was employed to remove the fraction of specular reflectance from backscattered intensity. A strong correlation (R² = 0.93, RMSE = 0.004 g/cm²) was found between a normalized ratio of the two wavelengths and the measured EWT of samples. The applied intensity correction methods did not significantly improve the results of the study. The backscattered intensity responded to changes in EWT but more investigations are needed to test the suitability of TLSs to retrieve EWT in a forest environment. [ABSTRACT FROM AUTHOR]

Published
2017
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189. Classification of Needle Loss of Individual Scots Pine Trees by Means of Airborne Laser Scanning

Author

University of Helsinki, Department of Forest Sciences, University of Helsinki, Department of Forest Resource Management (-2009), Kantola, Tuula, Vastaranta, Mikko, Lyytikäinen-Saarenmaa, Päivi, Holopainen, Markus, Kankare, Ville, Talvitie, Mervi, Hyyppä, Juha, University of Helsinki, Department of Forest Sciences, University of Helsinki, Department of Forest Resource Management (-2009), Kantola, Tuula, Vastaranta, Mikko, Lyytikäinen-Saarenmaa, Päivi, Holopainen, Markus, Kankare, Ville, Talvitie, Mervi, and Hyyppä, Juha

Abstract

Forest disturbances caused by pest insects are threatening ecosystem stability, sustainable forest management and economic return in boreal forests. Climate change and increased extreme weather patterns can magnify the intensity of forest disturbances, particularly at higher latitudes. Due to rapid responses to elevating temperatures, forest insect pests can flexibly change their survival, dispersal and geographic distributions. The outbreak pattern of forest pests in Finland has evidently changed during the last decade. Projection of shifts in distributions of insect-caused forest damages has become a critical issue in the field of forest research. The Common pine sawfly (Diprion pini L.) (Hymenoptera, Diprionidae) is regarded as a significant threat to boreal pine forests. Defoliation by D. pini has resulted in severe growth loss and mortality of Scots pine (Pinus sylvestris L.) (Pinaceae) in eastern Finland. In this study, tree-wise defoliation was estimated for five different needle loss category classification schemes and for 10 different simulated airborne laser scanning (ALS) pulse densities. The nearest neighbor (NN) approach, a nonparametric estimation method, was used for estimating needle loss of 701 Scots pines, using the means of individual tree features derived from ALS data. The Random Forest (RF) method was applied in NN-search. For the full dense data (~20 pulses/m2), the overall estimation accuracies for tree-wise defoliation level varied between 71.0% and 86.5% (kappa-values of 0.56 and 0.57, respectively), depending on the classification scheme. The overall classification accuracies for two class estimation with different ALS pulse densities varied between 82.8% and 83.7% (kappa-values of 0.62 and 0.67, respectively). We conclude that ALS-based estimation of needle losses may be of acceptable accuracy for individual trees. Our method did not appear sensitive to the applied pulse densities.

Published
2013

190. Area-Based Mapping of Defoliation of Scots Pine Stands Using Airborne Scanning LiDAR

Author

University of Helsinki, Department of Forest Sciences, Vastaranta, Mikko, Kantola, Tuula, Lyytikäinen-Saarenmaa, Päivi, Holopainen, Markus, Kankare, Ville, Wulder, Michael, Hyyppa, Juha, Hyyppa, Hannu, University of Helsinki, Department of Forest Sciences, Vastaranta, Mikko, Kantola, Tuula, Lyytikäinen-Saarenmaa, Päivi, Holopainen, Markus, Kankare, Ville, Wulder, Michael, Hyyppa, Juha, and Hyyppa, Hannu

Published
2013

192. An International Comparison of Individual Tree Detection and Extraction using Airborne Laser Scanning

Author

University of Helsinki, Department of Forest Sciences, Kaartinen, Harri, Hyyppä, Juha, Yu, Xiaowei, Vastaranta, Mikko, Hyyppä, Hannu, Kukko, Antero, Holopainen, Markus, Heipke, Christian, Hirschmugl, Manuela, Monsdorf, Felix, Naesset, Erik, Pitkänen, Juho, Popescu, Sorin, Solberg, Svein, Wolf, Bernd Michael, Wu, Jee-Cheng, University of Helsinki, Department of Forest Sciences, Kaartinen, Harri, Hyyppä, Juha, Yu, Xiaowei, Vastaranta, Mikko, Hyyppä, Hannu, Kukko, Antero, Holopainen, Markus, Heipke, Christian, Hirschmugl, Manuela, Monsdorf, Felix, Naesset, Erik, Pitkänen, Juho, Popescu, Sorin, Solberg, Svein, Wolf, Bernd Michael, and Wu, Jee-Cheng

Abstract

The objective of the “Tree Extraction” project organized by EuroSDR (European Spatial data Research) and ISPRS (International Society of Photogrammetry and Remote Sensing) was to evaluate the quality, accuracy, and feasibility of automatic tree extraction methods mainly based on laser scanner data. In the final report of the project, Kaartinen and Hyyppä (2008) reported a high variation in the quality of the published methods under boreal forest conditions and with varying laser point densities. This paper summarizes the findings beyond the final report after analyzing the results obtained in different tree height classes. Omission / Commission statistics as well as neighborhood relations are taken into account. Additionally four automatic tree detection and extraction techniques were added to the test. Several methods in this experiment were superior to manual processing in the dominant, co-dominant and suppressed tree storeys. In general, as expected, the taller the tree, the better the location accuracy. The accuracy of tree height, after removing gross errors, was better than 0.5m in all tree height classes with the best methods investigated in this experiment. For forest inventory, minimum curvature-based tree detection accompanied by point cloud -based cluster detection for suppressed trees is a solution that deserves attention in the future.

Published
2012

193. An international comparison of individual tree detection and extraction using airborne laser scanning

Author

Kaartinen, Harri, Hyyppä, Juha, Yu, Xiaowei, Vastaranta, Mikko, Hyyppä, Hannu, Kukko, Antero, Holopainen, Markus, Heipke, Christian, Hirschmugl, Manuela, Morsdorf, Felix, Næsset, Erik, Pitkänen, Juho, Popescu, Sorin, Solberg, Svein, Wolf, Bern Michael, Wu, Jee-Cheng, Kaartinen, Harri, Hyyppä, Juha, Yu, Xiaowei, Vastaranta, Mikko, Hyyppä, Hannu, Kukko, Antero, Holopainen, Markus, Heipke, Christian, Hirschmugl, Manuela, Morsdorf, Felix, Næsset, Erik, Pitkänen, Juho, Popescu, Sorin, Solberg, Svein, Wolf, Bern Michael, and Wu, Jee-Cheng

Abstract

The objective of the "Tree Extraction" project organized by EuroSDR (European Spatial data Research) and ISPRS (International Society of Photogrammetry and Remote Sensing) was to evaluate the quality, accuracy, and feasibility of automatic tree extraction methods, mainly based on laser scanner data. In the final report of the project, Kaartinen and Hyyppä (2008) reported a high variation in the quality of the published methods under boreal forest conditions and with varying laser point densities. This paper summarizes the findings beyond the final report after analyzing the results obtained in different tree height classes. Omission/Commission statistics as well as neighborhood relations are taken into account. Additionally, four automatic tree detection and extraction techniques were added to the test. Several methods in this experiment were superior to manual processing in the dominant, co-dominant and suppressed tree storeys. In general, as expected, the taller the tree, the better the location accuracy. The accuracy of tree height, after removing gross errors, was better than 0.5 m in all tree height classes with the best methods investigated in this experiment. For forest inventory, minimum curvature-based tree detection accompanied by point cloud-based cluster detection for suppressed trees is a solution that deserves attention in the future.

Published
2012

195. Effects of individual tree detection error sources on forest management planning calculations

Author

University of Helsinki, Department of Forest Sciences, Vastaranta, Mikko, Holopainen, Markus, Yu, Xiaowei, Hyyppä, Juha, Mäkinen, Antti, Rasinmäki, Jussi, Melkas, Timo, Kaartinen, Harri, Hyyppä, Hannu, University of Helsinki, Department of Forest Sciences, Vastaranta, Mikko, Holopainen, Markus, Yu, Xiaowei, Hyyppä, Juha, Mäkinen, Antti, Rasinmäki, Jussi, Melkas, Timo, Kaartinen, Harri, and Hyyppä, Hannu

Published
2011

196. Classification of Defoliated Trees Using Tree-Level Airborne Laser Scanning Data Combined with Aerial Images

Author

University of Helsinki, Department of Forest Sciences, University of Helsinki, Department of Forest Resource Management (-2009), Kantola, Tuula, Vastaranta, Mikko, Yu, Xiaowei, Lyytikäinen-Saarenmaa, Päivi, Holopainen, Markus, Talvitie, Mervi, Kaasalainen, Sanna, Solberg, Svein, Hyyppä, Juha, University of Helsinki, Department of Forest Sciences, University of Helsinki, Department of Forest Resource Management (-2009), Kantola, Tuula, Vastaranta, Mikko, Yu, Xiaowei, Lyytikäinen-Saarenmaa, Päivi, Holopainen, Markus, Talvitie, Mervi, Kaasalainen, Sanna, Solberg, Svein, and Hyyppä, Juha

Abstract

Climate change and rising temperatures have been observed to be related to the increase of forest insect damage in the boreal zone. The common pine sawfly (Diprion pini L.) (Hymenoptera, Diprionidae) is regarded as a significant threat to boreal pine forests. Defoliation by D. pini can cause severe growth loss and tree mortality in Scots pine (Pinus sylvestris L.) (Pinaceae). In this study, logistic LASSO regression, Random Forest (RF) and Most Similar Neighbor method (MSN) were investigated for predicting the defoliation level of individual Scots pines using the features derived from airborne laser scanning (ALS) data and aerial images. Classification accuracies from 83.7% (kappa 0.67) to 88.1% (kappa 0.76) were obtained depending on the method. The most accurate result was produced using RF with a combination of data from the two sensors, while the accuracies when using ALS and image features separately were 80.7% and 87.4%, respectively. Evidently, the combination of ALS and aerial images in detecting needle losses is capable of providing satisfactory estimates for individual trees.

Published
2010

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