From a tree to a stand in Finnish boreal forests : biomass estimation and comparison of methods

There is an increasing need to compare the results obtained with different methods of estimation of tree biomass in order to reduce the uncertainty in the assessment of forest biomass carbon. In this study, tree biomass was investigated in a 30-year-old Scots pine (Pinus sylvestris) (Young-Stand) and a 130-year-old mixed Norway spruce (Picea abies)-Scots pine stand (Mature-Stand) located in southern Finland (61º50' N, 24º22' E). In particular, a comparison of the results of different estimation methods was conducted to assess the reliability and suitability of their applications. For the trees in Mature-Stand, annual stem biomass increment fluctuated following a sigmoid equation, and the fitting curves reached a maximum level (from about 1 kg/yr for understorey spruce to 7 kg/yr for dominant pine) when the trees were 100 years old. Tree biomass was estimated to be about 70 Mg/ha in Young-Stand and about 220 Mg/ha in Mature-Stand. In the region (58.00-62.13 ºN, 14-34 ºE, ≤ 300 m a.s.l.) surrounding the study stands, the tree biomass accumulation in Norway spruce and Scots pine stands followed a sigmoid equation with stand age, with a maximum of 230 Mg/ha at the age of 140 years. In Mature-Stand, lichen biomass on the trees was 1.63 Mg/ha with more than half of the biomass occurring on dead branches, and the standing crop of litter lichen on the ground was about 0.09 Mg/ha. There were substantial differences among the results estimated by different methods in the stands. These results imply that a possible estimation error should be taken into account when calculating tree biomass in a stand with an indirect approach.Erilaisten puuston biomassa-arviointimenetelmien vertailu on yhä tärkeämpää, jotta metsien biomassan hiilimäärää voidaan arvioida entistä luotettavammin. Tässä tutkimuksessa selvitettiin kahden eri-ikäisen havupuumetsikön puuston biomassa Etelä-Suomessa (61°50' N, 24°22' E). Nuorempi metsikkö oli 30-vuotiasta mäntymetsää (Pinus sylvestris) ja vanhempi metsikkö 130-vuotiasta mänty–kuusi (Picea abies) sekametsää. Tutkimuksessa keskityttiin erityisesti vertailemaan erilaisten arviointimenetelmien luotettavuutta ja soveltuvuutta. Vanhan metsikön runkobiomassan vuosittainen kasvu vaihteli sigmoidaalisen yhtälön mukaisesti. Sovitettu käyrä saavutti huipputason puiden ollessa satavuotiaita, jolloin aluskasvustona olevien kuusten biomassa kasvoi 1 kg/v ja valtapuuston muodostavien mäntyjen biomassa 7 kg/v. Nuoren metsikön puuston biomassaksi arvioitiin noin 70 Mg/ha ja vanhan metsikön puuston biomassaksi noin 220 Mg/ha. Tutkimusmetsiköitä ympäröivän alueen (58.00–61.13° N, 14–34° E, 300 m m.p.y.) havupuumetsiköiden puuston biomassakertymä seurasi sigmoidaalisesti metsikön ikää. Korkeimmillaan puustobiomassa oli 140-vuotiaissa metsiköissä 200 Mg/ha. Vanhassa tutkimusmetsikössä puilla kasvavan jäkälän biomassa oli 1.63 Mg/ha, josta yli puolet oli kuolleilla oksilla. Maassa karikkeella jäkälää kasvoi 0.09 Mg/ha. Vertailun mukaan erilaisilla arviointimenetelmillä saatujen biomassatulosten välillä saattaa olla huomattavia eroja. Sen vuoksi puuston biomassa-arviointimenetelmiä käytettäessä onkin syytä kiinnittää huomioita niiden soveltuvuuteen ja virhearviointiin

Mechanical properties of tree roots for soil reinforcement models

Evidence from forestry has shown that part of the forest floor bearing capacity is delivered by tree roots. The beneficial effect however varies and diminishes with increasing number of vehicle passes. Roots potential for reinforcing the soil is known to depend among others on root mechanical properties, distribution, morphology, etc. Rooting intensity and root patterns of forest trees are complicated, but some information is available. The objectives of this study are therefore as follows: (1) addressing the occurrence of field traffic on forest soils, (2) identifying root mechanical properties that play a role in soil reinforcement, (3) measuring root stress-strain relationships, root failure stress and strain and root behaviour under repeated loading and (4) simulating root reinforcement effect using a FEM (Finite Element Method) code capable of accounting for root properties in reinforcement simulations.The repeated loading experiments included repeated loading of tree roots to different loading levels and loading with different loading rates or elongation rates. These studies revealed that tree roots possess stiffness and failure strengths. They show elastic as well as plastic behaviour. They also show fatigue phenomena in repeated loading. Available FEM codes were studied with respect to their capability in dealing with soil reinforcement by roots. PLAXIS which is a commercially available FEM code was used due to its ability to calculate stresses, strains and failure states of soil mechanical problems. It can also cope with unsaturated reinforced soil. The finite element calculations conducted with PLAXIS are intended for soils loaded by forestry vehicles. These involved situations with and without reinforcement by tree roots. The reinforcement effects are, among others, decrease of wheel rut depth and rolling resistance, decrease of damage to soil structure by the wheel load and as a negative effect, physiological damage to the tree root system. The magnitude of these effects depends on a number of parameters: stiffness and strength of the tree roots, soil mechanical properties like cohesion, angle of internal friction, compression index, preconsolidation stress, depth of a hard sublayer (if present), distance between vehicle and tree, rooting patterns, adhesive and frictional properties of the soil-root interface, wheel load and contact surface. The presented simulation results, which are based on realistic input data, show the sensitivity of the reinforcement effect to the listed variables.</p

A comparative analysis of breast cancer detection and diagnosis using data visualization and machine learning applications

In the developing world, cancer death is one of the major problems for humankind. Even though there are many ways to prevent it before happening, some cancer types still do not have any treatment. One of the most common cancer types is breast cancer, and early diagnosis is the most important thing in its treatment. Accurate diagnosis is one of the most important processes in breast cancer treatment. In the literature, there are many studies about predicting the type of breast tumors. In this research paper, data about breast cancer tumors from Dr. William H. Walberg of the University of Wisconsin Hospital were used for making predictions on breast tumor types. Data visualization and machine learning techniques including logistic regression, k-nearest neighbors, support vector machine, naïve Bayes, decision tree, random forest, and rotation forest were applied to this dataset. R, Minitab, and Python were chosen to be applied to these machine learning techniques and visualization. The paper aimed to make a comparative analysis using data visualization and machine learning applications for breast cancer detection and diagnosis. Diagnostic performances of applications were comparable for detecting breast cancers. Data visualization and machine learning techniques can provide significant benefits and impact cancer detection in the decision-making process. In this paper, different machine learning and data mining techniques for the detection of breast cancer were proposed. Results obtained with the logistic regression model with all features included showed the highest classification accuracy (98.1%), and the proposed approach revealed the enhancement in accuracy performances. These results indicated the potential to open new opportunities in the detection of breast cancer.No sponso

Role of Temperature in the Biological Activity of a Boreal Forest

In northern latitudes, temperature is the key factor driving the temporal scales of biological activity, namely the length of the growing season and the seasonal efficiency of photosynthesis. The formation of atmospheric concentrations of biogenic volatile organic compounds (BVOCs) are linked to the intensity of biological activity. However, interdisciplinary knowledge of the role of temperature in the biological processes related to the annual cycle and photosynthesis and atmospheric chemistry is not fully understood. The aim of this study was to improve understanding of the role of temperature in these three interlinked areas: 1) onset of growing season, 2) photosynthetic efficiency and 3) BVOC air concentrations in a boreal forest. The results present a cross-section of the role of temperature on different spatial (southern northern boreal), structural (tree forest stand - forest) and temporal (day-season- year) scales. The fundamental status of the Thermal Time model in predicting the onset of spring recovery was confirmed. However, it was recommended that sequential models would be more appropriate tools when the onset of the growing season is estimated under a warmer climate. A similar type of relationship between photosynthetic efficiency and temperature history was found in both southern and northern boreal forest stands. This result draws attention to the critical question of the seasonal efficiency of coniferous species to emit organic compounds under a warmer climate. New knowledge about the temperature dependence of the concentrations of biogenic volatile organic compounds in a boreal forest stand was obtained. The seasonal progress and the inter-correlation of BVOC concentrations in ambient air indicated a link to biological activity. Temperature was found to be the main driving factor for the concentrations. However, in addition to temperature, other factors may play a significant role here, especially when the peak concentrations are studied. There is strong evidence that the spring recovery and phenological events of many plant species have already advanced in Europe. This study does not fully support this observation. In a boreal forest, changes in the annual cycle, especially the temperature requirement in winter, would have an impact on the atmospheric BVOC composition. According to this study, more joint phenological and BVOC field observations and laboratory experiments are still needed to improve these scenarios.Lämpötila on keskeinen biologisista aktiivisuutta, kuten kasvukauden pituutta ja fotosynteesiä, säätelevä ympäristötekijä pohjoisella havumetsävyöhykkeellä. Ilmakehän säätelymekanismien kannalta keskeisten orgaanisia hiilivety-yhdisteiden muodostuminen on yhteydessä biologiseen aktiivisuuteen. Tämän tutkimuksen tavoitteena on lisätä ymmärrystä lämpötilan merkityksestä näillä kolmella toisiinsa liittyvillä alueilla: 1) kasvukauden alkaminen, 2) fotosynteesin tehokkuus sekä 3) ilmakehän orgaanisten hiilivetyjen pitoisuudet. Tutkimustuloksia tarkastellaan orgaanisten hiilivetyjen näkökulmasta ja pohditaan, mitä mahdollisia vaikutuksia ilman lämpötilan muutoksilla voisi olla boreaalisen metsän orgaanisiin hiilivety pitoisuuksiin. Tutkimustulokset esittelevät lämpötilan vaikutusta eri aika (päivä vuodenaika- vuosi) ja alueskaaloilla (etelä-pohjoisboreaalinen metsä). Tutkimuksen perusteella voidaan todeta, että ns. yksinkertaistettu lämpösumma-malli ennustaa parhaiten lehteentulon päivämäärän. Sen sijaan fenologinen malli, joka huomio lepokauden aikaisen lämpötilavaatimuksen, on todennäköisesti tarkempi ilmaston muuttuessa. Laaja SMEAR mittausaineisto vahvistaa käsitystä, että fotosynteesitehokkuuden yhteys lämpötilaan on hyvin samantyyppinen niin etelä- kuin pohjoisboreaalisessa metsässä. Tätä tietoa voidaan hyödyntää arvioitaessa orgaanisten hiilivety päästöjä ja ilmakehäpitoisuuksia muuttuneissa ilmasto-olosuhteissa. Lämpötila ohjaa voimakkaasti ilmakehän orgaanisia hiilivetypitoisuuksia boreaalisessa metsässä, mutta hiilivedyille tyypillisten korkeiden pitoisuuspiikkien kuvaamisen tarvitaan lisätutkimusta. Aikasarja-analyysien perusteella on havaittu, että kasvillisuuden keväänherääminen on keskimäärin aikaistunut Euroopassa. Tämä tutkimus ei suoraan vahvista tätä yleistä käsitystä. Erityispiirteenä boreaalisille puulajeille on lepokaudenaikainen lämpötilavaatimus, jolla voi olla tulevaisuudessa vaikutusta biologisen aktiivisuuden käynnistymisen ajankohtaan ja tätä kautta aina ilmakehän orgaanisten hiilivetyjen pitoisuuksiin asti. Tämän tutkimuksen perusteella tarvitsemme lisää samanaikaisia fenologisia ja BVOC yhdisteisiin liittyviä havaintoja ja mittauksia näiden arvioiden tueksi

Análise e previsão de acidentes rodoviários usando data mining

Road traffic crashes is an impactful problem in nowadays society, causing significant life and property losses. Due to the urbanization process across the world and the population’s growth, the number of crashes is also increasing. Predicting a crash severity and cost is an important step to better understand which causative variables have more influence and therefore, implement prevention measures that can reduce the number of crashes. Road traffic crashes predictions is a complex problem due to the high number of independent causative variables that contribute to the event. The used dataset contains crashes occurred in the State of Iowa in the recent years. Feature selection and data cleaning techniques are applied to improve the data quality and enhance the learning process. Previous research on the road safety field applied approaches that led to unsatisfactory results. Recent studies based on more complex approaches like neural networks had better results. This document’s work is based on deep learning, studying how the usage of deep neural networks can enhance previous results on road traffic crashes predictions taking causative variables as input. Various models are built using different optimization and activation functions. The evaluation is based on the comparison of these models.Os acidentes rodoviários representam um dos maiores problemas da comunidade atual, tendo um grande impacto social e económico. Além da enorme quantidade de feridos e mortos resultantes deste tipo de eventos (sendo mesmo considerada uma das maiores causas de morte a nível global, a maior em jovens adultos), a prevenção e consequentes custos de um acidente rodoviário representam também uma parte respeitável dos orçamentos de estado. Existe, um conjunto de variáveis envolvidas neste tipo de eventos que os tornam possíveis de prever e evitar, como por exemplo a existência de álcool, luminosidade no local e estado da estrada. Entender o impacto destas variáveis permite criar relações lógicas entre os seus valores e a gravidade e custos inerentes a um acidente, tornando possível a implementação de medidas de prevenção mais eficientes. Contudo e devido ao elevado número de variáveis a considerar, este é um problema complexo. Apesar de ser um problema global, este documento foca-se num contexto mais específico, o do estado de Iowa nos Estados Unidos da América. O conjunto de dados utilizados foi recolhido pelo departamento de transportes do estado de Iowa e contém variáveis ambiente, gravidade e custo dos acidentes rodoviários ocorridos nos últimos anos. O número de registos é elevado, o que permite a existência de diversificados cenários. No entanto, estes dados contêm algumas falhas (valores não recolhidos) e, em alguns cenários, não se encontram balanceados. Diversas técnicas de pré-processamento de dados como limpeza e transformação destes são aplicadas de forma a ultrapassar este problema. A partir da análise dos dados é possível ainda identificar quais os campos que não representam interesse no contexto deste problema, procedendo-se com a sua remoção e consequente redução do tamanho do conjunto de dados. A área de prevenção e previsão de acidentes rodoviários utilizando técnicas de data mining já foi explorada anteriormente. A aplicação de modelos mais clássicos (como modelos probabilísticos e baseados em procura) não obteve resultados totalmente satisfatórios. Nos estudos mais recentes, onde técnicas com maior poder computacional foram aplicadas (métodos baseados em otimização), os resultados foram melhores. Desta forma e tendo em consideração as conclusões dos estudos referidos na literatura, este documento pretende abordar como a utilização de deep learning, uma técnica de redes neuronais profundas e de elevado poder computacional, pode melhorar os resultados previamente obtidos. Para tal, são implementados diversos modelos para prever a gravidade e custo de um acidente com recurso a redes neuronais. A configuração dos modelos varia, sendo utlizados diferentes funções de custo e de ativação, de forma a explorar quais são as melhores abordagens a estes problemas. De forma a otimizar o processo de desenvolvimento é também utilizada uma framework de deep learning, o Tensorflow. Esta framework, além de primar pela flexibilidade e capacidade de implementação de arquiteturas variadas, permite uma elevada abstração do processo de treino das redes neuronais, calculando dinamicamente qual a profundidade e largura da rede mais indicada. A sua utilização teve também por base a comunidade open-source, que garante a manutenção e otimização desta framework no futuro. Os resultados da utilização de frameworks no processo de treino de redes neuronais no contexto de acidentes rodoviários não são ainda conclusivos, sendo este um fator a ter em conta no desenvolvimento do projeto. Os modelos desenvolvidos são depois comparados, utilizando métricas como Exatidão e AUC (Area Under the Curve), e com recurso a validação do tipo Holdout de forma a perceber se os resultados obtidos são válidos. São utilizados dois conjuntos de dados, um de treino e um outro de teste, para a avaliação da solução

Application and comparison of different classification methods based on symptom analysis with traditional classification technique for breast cancer diagnosis

Novel approach for classification technique such as Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA) and Random Forest (RF) using factor or dichotomic variables has been introduced. This study searches for the highly informative finitely linear combinations (symptoms) of variables in the finite field on the based of the Fisher’s exact test and accurately predict the target class for each case in the data. There are several super symptoms have comparable p-values. In this case, it becomes possible to choose as a nominative representative the factor which is more accessible for interpretation. The super symptom means a linear combination of various multiplications of k dichotomous variables over a field of characteristic 2 without repeating. In algebra, such functions are called Zhegalkin polynomials or algebraic normal forms

Random forest age estimation model based on length of left hand bone for Asian population

In forensic anthropology, age estimation is used to ease the process of identifying the age of a living being or the body of a deceased person. Nonetheless, the specialty of the estimation models is solely suitable to a specific people. Commonly, the models are inter and intra-observer variability as the qualitative set of data is being used which results the estimation of age to rely on forensic experts. This study proposes an age estimation model by using length of bone in left hand of Asian subjects range from newborn up to 18-year-old. One soft computing model, which is Random Forest (RF) is used to develop the estimation model and the results are compared with Artificial Neural Network (ANN) and Support Vector Machine (SVM), developed in the previous case studies. The performance measurement used in this study and the previous case study are R-square and Mean Square Error (MSE) value. Based on the results produced, the RF model shows comparable results with the ANN and SVM model. For male subjects, the performance of the RF model is better than ANN, however less ideal than SVM model. As for female subjects, the RF model overperfoms both ANN and SVM model. Overall, the RF model is the most suitable model in estimating age for female subjects compared to ANN and SVM model, however for male subjects, RF model is the second best model compared to the both models. Yet, the application of this model is restricted only to experimental purpose or forensic practice

Assessing spring phenology of a temperate woodland : a multiscale comparison of ground, unmanned aerial vehicle and Landsat satellite observations

PhD ThesisVegetation phenology is the study of plant natural life cycle stages. Plant phenological events are related to carbon, energy and water cycles within terrestrial ecosystems, operating from local to global scales. As plant phenology events are highly sensitive to climate fluctuations, the timing of these events has been used as an independent indicator of climate change. The monitoring of forest phenology in a cost-effective manner, at a fine spatial scale and over relatively large areas remains a significant challenge. To address this issue, unmanned aerial vehicles (UAVs) appear to be a potential new platform for forest phenology monitoring. The aim of this research is to assess the potential of UAV data to track the temporal dynamics of spring phenology, from the individual tree to woodland scale, and to cross-compare UAV results against ground and satellite observations, in order to better understand characteristics of UAV data and assess potential for use in validation of satellite-derived phenology. A time series of UAV data were acquired in tandem with an intensive ground campaign during the spring season of 2015, over Hanging Leaves Wood, Northumberland, UK. The radiometric quality of the UAV imagery acquired by two consumer-grade cameras was assessed, in terms of the ability to retrieve reflectance and Normalised Difference Vegetation Index (NDVI), and successfully validated against ground (0.84≤R2≥0.96) and Landsat (0.73≤R2≥0.89) measurements, but only NDVI resulted in stable time series. The start (SOS), middle (MOS) and end (EOS) of spring season dates were estimated at an individual tree-level using UAV time series of NDVI and Green Chromatic Coordinate (GCC), with GCC resulting in a clearer and stronger seasonal signal at a tree crown scale. UAV-derived SOS could be predicted more accurately than MOS and EOS, with an accuracy of less than 1 week for deciduous woodland and within 2 weeks for evergreen. The UAV data were used to map phenological events for individual trees across the whole woodland, demonstrating that contrasting canopy phenological events can occur within the extent of a single Landsat pixel. This accounted for the poor relationships found between UAV- and Landsat-derived phenometrics (R2<0.45) in this study. An opportunity is now available to track very fine scale land surface changes over contiguous vegetation communities, information which could improve characterization of vegetation phenology at multiple scales.The Science without Borders program, managed by CAPES-Brazil (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior)