Tuomarin päätöksenteko ja vankeusrangaistuksen perusteleminen

Tutkielma käsittelee rangaistuksen määräämistä monitieteisesti rikosoikeuden, prosessioikeuden ja kognitiivisen psykologian näkökulmista. Tutkielmassa esitetään päätöksenteon tutkimuksen alaan kuuluvan tutkimuskirjallisuuden pohjalta psykologisesti realistinen kuva rangaistuksen määräämisestä kognitiivisena prosessina. Rangaistusseuraamuksen perustelemista tarkastellaan käyttäen aineistona käräjäoikeuksien seuraamusperusteluja. Tuomarit ja monet muut ammattilaiset ovat siinä käsityksessä, että heidän ammattitaitonsa kehittyy kokemuksen myötä. Esimerkiksi juuri tuomarin päätöksentekoympäristö on kuitenkin sellainen, että vaikka itsevarmuus kasvaakin kokemuksen karttuessa, ns. implisiittistä oppimista eli intuitioiden kehittymistä ei tapahdu. Tämä edellyttäisi päätöksentekoympäristöltä riittävää ennustettavuutta ja välitöntä palautetta onnistumisista ja epäonnistumisista. Tuomari, erityisesti rangaistuksen määrääjä, ei tee työtään tällaisessa ympäristössä vaan varsin vaihtelevassa ympäristössä, jossa palaute on erittäin harvinaista ja epäsäännöllistä. Tällainen ympäristö altistaa päinvastoin epäolennaisten seikkojen hiipivälle vaikutukselle ja väärien heuristiikkojen kehittymiselle. Tuomarin täytyy kehittää ammattitaitoaan tietoisella, systemaattisella opiskelulla eikä luottaa siihen, että päätöksenteko kehittyisi kokemuksen myötä kuin itsestään. Tutkitut perustelut ovat törkeästä pahoinpitelystä tuomittujen rangaistusseuraamusten perusteluja. Törkeän pahoinpitelyn rangaistusasteikko mahdollistaa normaalisti vain ehdollisen ja ehdottoman vankeusrangaistuksen, joten tuomarin päätöksenteko muodostuu näissä tapauksissa vankeusrangaistuksen keston mittaamista ja vankeuslajin valinnasta. Rikoslain järjestelmässä nämä osaratkaisut pitäisi tehdä erikseen ja painottaen osittain eri perusteita. Mittaamisratkaisu tulisi tehdä ensin. Perusteluista kuitenkin havaitaan, että näitä rangaistuksen aspekteja ei aina perustella erikseen vaan vankeusrangaistuksen kesto ja laji (ehdollinen tai ehdoton) perustellaan varsin usein samalla kertaa ja samoin perustein. Tämä on tutkielmassa kutsuttu holistiseksi perustelutavaksi. Silloin kun mainitut osaratkaisut perustellaan erikseen, vankeuslajia perustellaan erityisesti rikostaustalla. Tällöin jää epäselväksi, onko teonpiirteitä otettu enää tässä vaiheessa huomioon, kuten rikoslain mukaan pitäisi. Lisäksi vankeuslajin valinta perustellaan ja siis oletettavasti tehdään joskus ennen mittaamisratkaisua, joskin tämä vaihtoehto on melko harvinainen. Suomessa ei ole tehty kokeellista tutkimusta tuomarin päätöksenteosta, ja tutkielmassa esitetäänkin useita mahdollisia koeasetelmia tätä tarkoitusta varten. Rikosoikeudellisessa ja prosessilainsäädännössä tulee ottaa huomioon päätöksenteon kognitiiviset reunaehdot. Rangaistuksen määräämisen löyhä sääntely yhdistettynä mittaamisprejudikaatteihin vaikuttaa jokseenkin toimivalta ratkaisulta

Yhteisen rangaistuksen mittaamisen ennakoitavuus

Foreseeability of joint sentencesCourts are to apply criminal law in a foreseeable manner. According to the European Court of Human Rights, unforeseeability in either convictions or sentencing violates the principle of legality in criminal law. In this article, the scope of the foreseeability requirement in sentencing is analysed with particular focus on joint sentences, i.e. the sentencing for multiple offences under Finnish law. The analysis shows that the concept of foreseeability has next to no doctrinal significance in sentencing because truly unforeseeable sentences would also fly in the face of the wording of the law. In addition, the proportionality principle and the uniformity goal of the sentencing practice set more stringent limits for sentencing than does the foreseeability requirement associated with the principle of legality.The Supreme Court regularly hands out precedents with the aim of improving the uniformity of the sentencing practice. In joint sentence precedents, the court of last resort typically considers the seriousness of the gravest offence, the seriousness of the other offences, and the mutual connection of the offences. To study the effect of the precedents and to put the foreseeability of sentencingto a demanding test, a small set of recent appeals court judgments (N = 37) in joint sentence cases were analysed for the article. The result was that the appeals court practice seems beyond reproach from the point of view of the principle of legality although oftentimes lacking from the point of view of reasoning models promoted by the Supreme Court.Lower courts could improve their reasoning and perhaps their decision-making in multiple offences cases by more regularly taking the systematic approach followed by the Supreme Court. Such a technique would elevate the clarity of court reasoning and most likely the uniformity of the sentencing practice. As a by-product, the foreseeability of joint sentences would be upgraded from its already adequate level

Taistele tai pakene – hätävarjelun liioittelun rangaistavuus

Peer reviewe

Kaksinkertaisen kvalifioinnin kiellon vaikutus rangaistuksen määräämiseen

Peer reviewe

Assessing Precision in Conventional Field Measurements of Individual Tree Attributes

Forest resource information has a hierarchical structure: individual tree attributes are summed at the plot level and then in turn, plot-level estimates are used to derive stand or large-area estimates of forest resources. Due to this hierarchy, it is imperative that individual tree attributes are measured with accuracy and precision. With the widespread use of different measurement tools, it is also important to understand the expected degree of precision associated with these measurements. The most prevalent tree attributes measured in the field are tree species, stem diameter-at-breast-height (dbh), and tree height. For dbh and height, the most commonly used measuring devices are calipers and clinometers, respectively. The aim of our study was to characterize the precision of individual tree dbh and height measurements in boreal forest conditions when using calipers and clinometers. The data consisted of 319 sample trees at a study area in Evo, southern Finland. The sample trees were measured independently by four trained mensurationists. The standard deviation in tree dbh and height measurements was 0.3 cm (1.5%) and 0.5 m (2.9%), respectively. Precision was also assessed by tree species and tree size classes; however, there were no statistically significant differences between the mensurationists for dbh or height measurements. Our study offers insights into the expected precision of tree dbh and height as measured with the most commonly used devices. These results are important when using sample plot data in forest inventory applications, especially now, at a time when new tree attribute measurement techniques based on remote sensing are being developed and compared to the conventional caliper and clinometer measurements.Peer reviewe

Assessing the Effects of Sample Size on Parametrizing a Taper Curve Equation and the Resultant Stem-Volume Estimates

Large and comprehensive datasets, traditionally based on destructive stem analysis or other labor-intensive approaches, are commonly considered as a necessity in developing stem-volume equations. The aim here was to investigate how a decreasing number of sample trees affects parametrizing an existing taper curve equation and resultant stem-volume estimates. Furthermore, the potential of terrestrial laser scanning (TLS) in producing taper curves was examined. A TLS-based taper curve was derived for 246 Scots pines (Pinus sylvestris L.) from southern Finland to parametrize an existing taper curve equation. To assess sensitivity of the parametrization regarding sample size, the number of Scots pines included in the parametrization varied between full census and 1 Scots pine at a time. Root mean square error of stem-volume estimates remained ≤20.9% and the mean absolute difference was relatively constant (≤9.0%) between stem-volume estimates when the sample size included ≥46 Scots pines. Thus, it can be concluded that, with a rather small sample size, a taper curve equation can be re-parametrized for local conditions using point clouds from TLS to produce consistent stem-volume estimates

Assessing the Effects of Sample Size on Parametrizing a Taper Curve Equation and the Resultant Stem-Volume Estimates

Large and comprehensive datasets, traditionally based on destructive stem analysis or other labor-intensive approaches, are commonly considered as a necessity in developing stem-volume equations. The aim here was to investigate how a decreasing number of sample trees affects parametrizing an existing taper curve equation and resultant stem-volume estimates. Furthermore, the potential of terrestrial laser scanning (TLS) in producing taper curves was examined. A TLS-based taper curve was derived for 246 Scots pines (Pinus sylvestris L.) from southern Finland to parametrize an existing taper curve equation. To assess sensitivity of the parametrization regarding sample size, the number of Scots pines included in the parametrization varied between full census and 1 Scots pine at a time. Root mean square error of stem-volume estimates remained ≤20.9% and the mean absolute difference was relatively constant (≤9.0%) between stem-volume estimates when the sample size included ≥46 Scots pines. Thus, it can be concluded that, with a rather small sample size, a taper curve equation can be re-parametrized for local conditions using point clouds from TLS to produce consistent stem-volume estimates

Landsat archive holdings for Finland : opportunities for forest monitoring

There is growing interest in the use of Landsat data to enable forest monitoring over large areas. Free and open data access combined with high performance computing have enabled new approaches to Landsat data analysis that use the best observation for any given pixel to generate an annual, cloud-free, gap-free, surface reflectance image composite. Finland has a long history of incorporating Landsat data into its National Forest Inventory to produce forest information in the form of thematic maps and small area statistics on a variety of forest attributes. Herein we explore the spatial and temporal characteristics of the Landsat archive in the context of forest monitoring in Finland. The United States Geological Survey Landsat archive holds a total of 30 076 images (1972-2017) for 66 scenes (each 185 km by 185 km in size) representing the terrestrial area of Finland, of which 93.6% were acquired since 1984 with a spatial resolution of 30 m. Approximately 16.3% of the archived images have desired compositing characteristics (acquired within August 1 +/- 30 days,Peer reviewe

Assessing spectral measures of post-harvest forest recovery with field plot data

Information regarding the nature and rate of forest recovery is required to inform forest management, monitoring, and reporting activities. Delayed establishment or return of forests has implications to harvest rotations and carbon uptake, among others, creating a need for spatially-explicit, large-area, characterizations of forest recovery. Landsat time series (LTS) has been demonstrated as a means to quantitatively relate forest recovery, noting that there are gaps in our understanding of the linkage between spectral measures of forest recovery and manifestations of forest structure and composition. Field plots provide a means to better understand the linkage between forest characteristics and spectral recovery indices. As such, from a large set of existing field plots, we considered the conditions present for the year in which the co-located pixel was considered spectrally recovered using the Years to Recovery (Y2R) metric. Y2R is a long-term metric of spectral recovery that indicates the number of years required for a pixel to return to 80% of its pre-disturbance Normalized Burn Ratio value. Absolute and relative metrics of recovery at 5 years post-disturbance were also considered. We used these three spectral recovery metrics to predict the stand development class assigned by the field crew for 284 seedling plots with an overall accuracy of 73.59%, with advanced seedling stands more accurately discriminated (omission error, OE = 15.74%) than young seedling stands (OE = 49.84%). We then used field-measured attributes (e.g. height, stem density, dominant species) from the seedling plots to classify the plots into three spectral recovery groups, which were defined using the Y2R metric: spectral recovery in (1) 1–5 years, (2) 6–10 years, or (3) 11–15 years. Overall accuracy for spectral recovery groups was 61.06%. Recovery groups 1 and 3 were discriminated with greater accuracy (producer’s and user’s accuracies > 66%) than recovery group 2 ( 66%) than recovery group 2 ( 66%) than recovery group 2 (<50%). The top field-measured predictors of spectral recovery were mean height, dominant species, and percentage of stems in the plot that were deciduous. Variability in stand establishment and condition make it challenging to accurately discriminate among recovery rates within 10 years post-harvest. Our results indicate that the long-term metric Y2R relates to forest structure and composition attributes measured in the field and that spectral development post-disturbance corresponds with expectations of structural development, particularly height, for different species, site types, and deciduous abundance. These results confirm the utility of spectral recovery measures derived from LTS data to augment landscape-level assessments of post-disturbance recovery.Peer reviewe

Aboveground forest biomass derived using multiple dates of WorldView-2 stereo-imagery : quantifying the improvement in estimation accuracy

The aim of this study was to investigate the capabilities of two date satellite-derived image-based point clouds (IPCs) to estimate forest aboveground biomass (AGB). The data sets used include panchromatic WorldView-2 stereo-imagery with 0.46 m spatial resolution representing 2014 and 2016 and a detailed digital elevation model derived from airborne laser scanning data. Altogether, 332 field sample plots with an area of 256 m(2) were used for model development and validation. Predictors describing forest height, density, and variation in height were extracted from the IPC 2014 and 2016 and used in k-nearest neighbour imputation models developed with sample plot data for predicting AGB. AGB predictions for 2014 (AGB(2014)) were projected to 2016 using growth models (AGB(Projected_2016)) and combined with the AGB estimates derived from the 2016 data (AGB(2016)). AGB prediction model developed with 2014 data was also applied to 2016 data (AGB(2016_pred2014)). Based on our results, the change in the 90(th) percentile of height derived from the WorldView-2 IPC was able to characterize forest height growth between 2014 and 2016 with an average growth of 0.9 m. Features describing canopy cover and variation in height derived from the IPC were not as consistent. The AGB(2016) had a bias of -7.5% (-10.6 Mg ha(-1)) and root mean square error (RMSE) of 26.0% (36.7 Mg ha(-1)) as the respective values for AGB(Projected_2016) were 7.0% (9.9 Mg ha(-1)) and 21.5% (30.8 Mg ha(-1)). AGB(2016_pred2014) had a bias of -19.6% (-27.7 Mg ha(-1)) and RMSE of 33.2% (46.9 Mg ha(-1)). By combining predictions of AGB(2016) and AGB(Projected_2016) at sample plot level as a weighted average, we were able to decrease the bias notably compared to estimates made on any single date. The lowest bias of -0.25% (-0.4 Mg ha(-1)) was obtained when equal weights of 0.5 were given to the AGB(Projected_2016) and AGB(2016) estimates. Respectively, RMSE of 20.9% (29.5 Mg ha(-1)) was obtained using equal weights. Thus, we conclude that combination of two date WorldView-2 stereo-imagery improved the reliability of AGB estimates on sample plots where forest growth was the only change between the two dates.Peer reviewe