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Dive into the research topics where Aarne Hovi is active.

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Featured researches published by Aarne Hovi.


European Journal of Forest Research | 2012

Mapping of snow-damaged trees based on bitemporal airborne LiDAR data

Mikko Vastaranta; Ilkka Korpela; Antti Uotila; Aarne Hovi; Markus Holopainen

The use of multitemporal LiDAR data in forest-monitoring applications has been so far largely unexplored. In this work, we aimed to develop and test a simple method for the detection of snow-induced canopy changes by employing bitemporal LiDAR data acquired in 2006–2010. Our study area was located in southern Finland (62°N, 24°E), where snow-induced damage occurred in 10 permanent Scots pine (Pinus sylvestris)-dominated plots in winter 2009–2010. For the detection of snow-damaged crowns, we developed a ∆CHM method by contrasting bitemporal LiDAR canopy height models (CHMs) and analyzing the resulting difference image, using binary image operations to extract the damaged crowns. Furthermore, we examined the structural and spatial factors that could explain snow damage at the individual tree level. The ∆CHM method developed is based on two threshold parameters, i.e., the required height difference in the contrasted CHMs and the minimum plausible area of damage. When testing the performance of ∆CHM method, we found that the plot-level omission error rates were 19–75%, while the commission error rates were 0–21%. Furthermore, the relative estimation accuracy of the damaged crown projection area (DCPA) ranged from −16.4 to 5.4%. The observed damage could be explained at tree level by stem tapering, relative tree size, and local stand density. To conclude, ∆CHM method developed constitutes a potential tool for the monitoring of structural canopy changes in the dominant tree layer if dense multitemporal LiDAR data are available.


Journal of remote sensing | 2013

Estimation of tree crown volume from airborne lidar data using computational geometry

Lauri Korhonen; Jari Vauhkonen; Anni Virolainen; Aarne Hovi; Ilkka Korpela

Although simple geometrical shapes are commonly used to describe tree crowns, computational geometry enables calculation of the individual crown properties directly from airborne lidar point clouds. Our objective was to calculate crown volumes (CVs) using this technique and validate the results by comparing them with field-measured values and modelled ellipsoidal crowns. The CVs of standing trees were obtained by measuring the crown radii at different heights, integrating the obtained crown profiles as solids of revolution, and finally averaging the volumes obtained from the four separate profiles. With the lidar data, the CVs were extracted using 3D alpha shape and 3D convex hull techniques. Crown base heights (CBHs) were also estimated from the lidar data and used to exclude echoes from the understory, which was also done using field-based CBHs to exclude this error source. The results show that the field-measured CVs had a high correlation with lidar-based estimates (best R 2 = 0.83), but the lidar-based estimates were generally smaller than the field values. The best correspondence (root mean square difference (RMSD) = 45.0%, average difference = –24.7%) was obtained using the convex hull of the point data and field-measured CBH. The CBHs were consistently overestimated (RMSD = 37.3%; average difference = –20.0%), especially in spruces with long crowns. Thus using lidar-based CBH also increased the inaccuracy of the CV estimates. While the underestimation of CV is mainly explained by the inadequate number of echoes from the lower regions of the crowns, the CVs obtained from the lidar were better than those obtained with ellipsoids fitted by using general models for crown dimensions. The utility of the estimated CVs in the prediction of stem diameter is also demonstrated.


Remote Sensing | 2018

Spectral Properties of Coniferous Forests: A Review of In Situ and Laboratory Measurements

Miina Rautiainen; Petr Lukes; Lucie Homolová; Aarne Hovi; Jan Pisek; Matti Mõttus

Coniferous species are present in almost all major vegetation biomes on Earth, though they are the most abundant in the northern hemisphere, where they form the northern tree and forest lines close to the Arctic Circle. Monitoring coniferous forests with satellite and airborne remote sensing is active, due to the forests’ great ecological and economic importance. We review the current understanding of spectral behavior of different components forming coniferous forests. We look at the spatial, directional, and seasonal variations in needle, shoot, woody element, and understory spectra in coniferous forests, based on measurements. Through selected case studies, we also demonstrate how coniferous canopy spectra vary at different spatial scales, and in different viewing angles and seasons. Finally, we provide a synthesis of gaps in the current knowledge on spectra of elements forming coniferous forests that could also serve as a recommendation for planning scientific efforts in the future.


Applied Optics | 2017

Theoretical algorithm and application of a double-integrating sphere system for measuring leaf transmittance and reflectance spectra

Matti Mõttus; Aarne Hovi; Miina Rautiainen

We present the theoretical background and analytical equations for calculating spectral reflectance and transmittance factors for plant leaves from data collected by a field spectroradiometer attached to a double-integrating sphere. The sphere constants required for the calculations are derived from measurements of an empty sample port and a diffuse reflectance panel. The new method is applied in measuring the spectra of leaves belonging to 13 tree species. The greatest advantages of a double-sphere system compared with the conventional single-sphere substitution method is the speed of measurements, ease of operation, and increased portability and field-ruggedness.


Remote Sensing of Environment | 2012

Understory trees in airborne LiDAR data — Selective mapping due to transmission losses and echo-triggering mechanisms

Ilkka Korpela; Aarne Hovi; Felix Morsdorf


Remote Sensing of Environment | 2016

LiDAR waveform features for tree species classification and their sensitivity to tree- and acquisition related parameters

Aarne Hovi; Lauri Korhonen; Jari Vauhkonen; Ilkka Korpela


Remote Sensing of Environment | 2014

Real and simulated waveform-recording LiDAR data in juvenile boreal forest vegetation

Aarne Hovi; Ilkka Korpela


Remote Sensing of Environment | 2013

Evaluation of simulated bands in airborne optical sensors for tree species identification

Paras Pant; Ville Heikkinen; Aarne Hovi; Ilkka Korpela; Markku Hauta-Kasari; Timo Tokola


Isprs Journal of Photogrammetry and Remote Sensing | 2013

Backscattering of individual LiDAR pulses from forest canopies explained by photogrammetrically derived vegetation structure

Ilkka Korpela; Aarne Hovi; Lauri Korhonen


ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences | 2012

AREA-BASED SNOW DAMAGE CLASSIFICATION OF FOREST CANOPIES USING BI-TEMPORAL LIDAR DATA

Mikko Vastaranta; Ilkka Korpela; Antti Uotila; Aarne Hovi; Markus Holopainen

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Lauri Korhonen

University of Eastern Finland

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Jari Vauhkonen

University of Eastern Finland

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Petr Lukes

Academy of Sciences of the Czech Republic

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