Mapping of summer crops in the State of Paraná, Brazil, through the 10-day spot vegetation NDVI composites

The search for low subjectivity area estimates has increased the use of remote sensing for agricultural monitoring and crop yield prediction, leading to more flexibility in data acquisition and lower costs comparing to traditional methods such as census and surveys. Low spatial resolution satellite images with higher frequency in image acquisition have shown to be adequate for cropland mapping and monitoring in large areas. The main goal of this study was to map the Summer crops in the State of Paraná, Brazil, using 10-day composition of NDVI SPOT Vegetation data for 2005/2006, 2006/2007 and 2007/2008 cropping seasons. For this, a supervised digital classification method with Parallelepiped algorithm in multitemporal RGB image composites was used, in order to generate masks of Summer cultures for each 10-day composition. Accuracy assessment was performed using Kappa index, overall accuracy and Willmott's concordance index, resulting in good levels of accuracy. This methodology allowed the accomplishment, with free and low resolution data, of the mapping of Summer cultures at State level.

Spatial variability of yield and other parameters associated with pear trees

In the last few years, precision agriculture has become commonly used with many crops, particularly cereals, and there is also interest in precision horticulture. Pear is a seasonal fruit and well appreciated by Brazilian people, although it is mostly imported. Brazilian farmers are nowadays trying to increase pear production. Thus, this research aimed at mapping the yield of pear trees in order to study the spatial variability of yield as well as its comparison with spatial variability of soil and plant attributes. The experimental field had 146 pear trees, variety 'Pêra d'água', distributed on a 1.24 ha. Four harvests were performed according to the fruit ripening and from each tree; only the ripe fruits were harvested. In each harvest, all the fruits were weighed and the total yield was obtained based on the sum of each harvest. The soil attributes analyzed were P, K, Ca, Mg, pH in CaCl2, C, Cu, Zn, Fe, Mn and base saturation, and the plant attributes were fruit length, diameter and yield. Yield had low correlation with soil and plant attributes. An index of spatial variability was suggested in this study and helped in classifying levels of spatial dependence of the various soil and plant attributes: very low (fruit length); low (P, fruit diameter), medium (Mg, pH, Cu, Zn, Fe), high (Ca, K, base saturation and yield), and very high (Mn and C).

Mapping of use and occupation of the soil and irrigation water quality in the city of Salto do Lontra-Paraná, Brazil

The objective of this study consisted on mapping the use and soil occupation and evaluation of the quality of irrigation water used in Salto do Lontra, in the state of Paraná, Brazil. Images of the satellite SPOT-5 were used to perform the supervised classification of the Maximum Likelihood algorithm - MAXVER, and the water quality parameters analyzed were pH, EC, HCO3-, Cl-, PO4(3-), NO3-, turbidity, temperature and thermotolerant coliforms in two distinct rainfall periods. The water quality data were subjected to statistical analysis by the techniques of PCA and FA, to identify the most relevant variables in assessing the quality of irrigation water. The characterization of soil use and occupation by the classifier MAXVER allowed the identification of the following classes: crops, bare soil/stubble, forests and urban area. The PCA technique applied to irrigation water quality data explained 53.27% of the variation in water quality among the sampled points. Nitrate, thermotolerant coliforms, temperature, electrical conductivity and bicarbonate were the parameters that best explained the spatial variation of water quality.

Using fraction images derived from modis data for coffee crop mapping

Coffee production was closely linked to the economic development of Brazil and, even today, coffee is an important product of the national agriculture. The State of Minas Gerais currently accounts for 52% of the whole coffee area in Brazil. Remote sensing data can provide information for monitoring and mapping of coffee crops, faster and cheaper than conventional methods. In this context, the objective of this study was to assess the effectiveness of coffee crop mapping in Monte Santo de Minas municipality, Minas Gerais State, Brazil, from fraction images derived from MODIS data, in both dry and rainy seasons. The Spectral Linear Mixing Model was used to derive fraction images of soil, coffee, and water/shade. These fraction images served as input data for the supervised automatic classification using the SVM - Support Vector Machine approach. The best results concerning Overall Accuracy and Kappa Index were obtained in the classification of the dry season, with 67% and 0.41, respectively.

Mapping the translation needs and practices in development co-operation projects of NGOs

Tämän pro gradu -tutkielman aiheena on kääntäminen ja viestintä vieraalla kielellä Suomen ulkoasiainministeriön rahoittamissa kansalaisjärjestöjen kehitysyhteistyöhankkeissa. Tutkielman on tarkoitus kartoittaa kyseisten kehitysyhteistyöhankkeiden kääntämiseen ja vieraskieliseen viestintään liittyviä tarpeita ja -käytäntöjä. Aihetta ei ole tutkittu laajasti muutoin, joten empiirinen osuus on erityisen merkittävässä roolissa. Tutkielman alussa esitellään ulkoasiainministeriön laatimia suomalaisten kansalaisjärjestöjen kehitysyhteistyöhankkeiden ehtoja ja ohjeita, jotka ovat kyseiselle tutkimukselle olennaisia. Tutkielman teoreettinen osuus koostuu lähinnä metodologisesta pohdinnasta, koska aiempaa tutkimustietoa on vähän. Tutkimusmetodien analysointia on painotettu myös siksi, että empiirisellä osuudella on niin suuri merkitys. Tutkielman empiirinen aineisto on yhtäältä peräisin tutkijan havainnoista, jotka saatiin omia varhaisempia hankehallinnointikokemuksia hyödyntäen sekä osallistuvan havainnoinnin menetelmää käyttäen kahden opiskelijajärjestön kehitysyhteistyöhankkeista vuosien 2009 ja 2012 välisenä aikana. Toisaalta aineisto on kerätty kyselytutkimuksen vastauksista. Kysely lähetettiin 35 suomalaiselle kansalaisjärjestölle, joiden kehitysyhteistyöhankkeita Suomen ulkoasiainministeriö rahoittaa. Järjestöt valittiin satunnaisotannalla. Empiirisen aineiston analyysistä ilmeni muun muassa, että tutkijan alkuperäinen hypoteesi kehitysyhteistyöhön liittyvien tekstien kääntämisen yleisyydestä pitää osittain paikkansa, vaikka tekstejä käännettiinkin kansalaisjärjestöissä vähemmän kuin tutkija odotti. Tutkimusaineisto ei ole kuitenkaan tarpeeksi laaja, jotta tuloksia voitaisiin yleistää, sillä kansalaisjärjestöt, niiden hankkeet, käännöstarpeet ja asiantuntemus osoittautuivat odotetusti hyvin erilaisiksi. Tulokset sisältävät asiaa myös esimerkiksi järjestöissä kääntävien ja vieraalla kielellä viestivien taustoista. Jatkotutkimukset ovat kuitenkin tarpeen, ja mahdollisia tutkimusaiheita on runsaasti.

Mapping financial stability

The ongoing global financial crisis has demonstrated the importance of a systemwide, or macroprudential, approach to safeguarding financial stability. An essential part of macroprudential oversight concerns the tasks of early identification and assessment of risks and vulnerabilities that eventually may lead to a systemic financial crisis. Thriving tools are crucial as they allow early policy actions to decrease or prevent further build-up of risks or to otherwise enhance the shock absorption capacity of the financial system. In the literature, three types of systemic risk can be identified: i ) build-up of widespread imbalances, ii ) exogenous aggregate shocks, and iii ) contagion. Accordingly, the systemic risks are matched by three categories of analytical methods for decision support: i ) early-warning, ii ) macro stress-testing, and iii ) contagion models. Stimulated by the prolonged global financial crisis, today's toolbox of analytical methods includes a wide range of innovative solutions to the two tasks of risk identification and risk assessment. Yet, the literature lacks a focus on the task of risk communication. This thesis discusses macroprudential oversight from the viewpoint of all three tasks: Within analytical tools for risk identification and risk assessment, the focus concerns a tight integration of means for risk communication. Data and dimension reduction methods, and their combinations, hold promise for representing multivariate data structures in easily understandable formats. The overall task of this thesis is to represent high-dimensional data concerning financial entities on lowdimensional displays. The low-dimensional representations have two subtasks: i ) to function as a display for individual data concerning entities and their time series, and ii ) to use the display as a basis to which additional information can be linked. The final nuance of the task is, however, set by the needs of the domain, data and methods. The following ve questions comprise subsequent steps addressed in the process of this thesis: 1. What are the needs for macroprudential oversight? 2. What form do macroprudential data take? 3. Which data and dimension reduction methods hold most promise for the task? 4. How should the methods be extended and enhanced for the task? 5. How should the methods and their extensions be applied to the task? Based upon the Self-Organizing Map (SOM), this thesis not only creates the Self-Organizing Financial Stability Map (SOFSM), but also lays out a general framework for mapping the state of financial stability. This thesis also introduces three extensions to the standard SOM for enhancing the visualization and extraction of information: i ) fuzzifications, ii ) transition probabilities, and iii ) network analysis. Thus, the SOFSM functions as a display for risk identification, on top of which risk assessments can be illustrated. In addition, this thesis puts forward the Self-Organizing Time Map (SOTM) to provide means for visual dynamic clustering, which in the context of macroprudential oversight concerns the identification of cross-sectional changes in risks and vulnerabilities over time. Rather than automated analysis, the aim of visual means for identifying and assessing risks is to support disciplined and structured judgmental analysis based upon policymakers' experience and domain intelligence, as well as external risk communication.

Mapping the sites of latency and reactivation by bovine herpesvirus 5 (BoHV-5) and a thymidine kinase-deleted BoHV-5 in lambs

A thymidine kinase (tk)-deleted bovine herpesvirus 5 (BoHV-5tkΔ) was previously shown to establish latent infection and reactivate - even poorly - in a sheep model (Cadore et al. 2013). As TK-negative alphaherpesviruses are unlike to reactivate in neural tissue, this study investigated the sites of latency and reactivation by this recombinant in lambs. For this, groups of lambs were inoculated intranasally with the parental BoHV-5 strain (SV-507/99) or with the recombinant BoHV-5tkΔ. During latent infection (40 days post-inoculation, pi), the distribution of recombinant virus DNA in neural and non-neural tissues was similar to that of the parental virus. Parental and recombinant virus DNA was consistently detected by PCR in trigeminal ganglia (TGs); frequently in palatine and pharyngeal tonsils and, less frequently in the retropharyngeal lymph nodes. In addition, latent DNA of both viruses was detected in several areas of the brain. After dexamethasone (Dx) administration (day 40pi), the recombinant virus was barely detected in nasal secretions contrasting with marked shedding of the parental virus. In tissues of lambs euthanized at day 3 post-Dx treatment (pDx), reverse-transcription-PCR (RT-PCR) for a late viral mRNA (glycoprotein D gene) demonstrated reactivation of parental virus in neural (TGs) and lymphoid tissues (tonsils, lymph node). In contrast, recombinant virus mRNA was detected only in lymphoid tissues. These results demonstrate that BoHV-5 and the recombinant BoHV-5tkΔ do establish latent infection in neural and non-neural sites. Reactivation of the recombinant BoHV-5tkΔ, however, appeared to occur only in non-neural sites. In anyway, the ability of a tk-deleted strain to reactivate latent infection deserves attention in the context of vaccine safety.

Real-time Image-based RGB-D Camera Motion Tracking and Environment Mapping

In this work, image based estimation methods, also known as direct methods, are studied which avoid feature extraction and matching completely. Cost functions use raw pixels as measurements and the goal is to produce precise 3D pose and structure estimates. The cost functions presented minimize the sensor error, because measurements are not transformed or modified. In photometric camera pose estimation, 3D rotation and translation parameters are estimated by minimizing a sequence of image based cost functions, which are non-linear due to perspective projection and lens distortion. In image based structure refinement, on the other hand, 3D structure is refined using a number of additional views and an image based cost metric. Image based estimation methods are particularly useful in conditions where the Lambertian assumption holds, and the 3D points have constant color despite viewing angle. The goal is to improve image based estimation methods, and to produce computationally efficient methods which can be accomodated into real-time applications. The developed image-based 3D pose and structure estimation methods are finally demonstrated in practise in indoor 3D reconstruction use, and in a live augmented reality application.

Mapping the Value Network Potential in Cross-sector Collaboration: Case welfare Services for Elderly in Finland

Value network has been studied greatly in the academic research, but a tool for value network mapping is missing. The objective of this study was to design a tool (process) for value network mapping in cross-sector collaboration. Furthermore, the study addressed a future perspective of collaboration, aiming to map the value network potential. During the study was investigated and pondered how to get the full potential of collaboration, by creating new value in collaboration process. These actions are parts of mapping process proposed in the study. The implementation and testing of the mapping process were realized through a case study of cross-sector collaboration in welfare services for elderly in the Eastern Finland. Key representatives in elderly care from public, private and third sectors were interviewed and a workshop with experts from every sector was also conducted in this regard. The value network mapping process designed in this study consists of specific steps that help managers and experts to understand how to get a complex value network map and how to enhance it. Furthermore, it make easier the understanding of how new value can be created in collaboration process. The map can be used in order to motivate participants to be engaged with responsibility in collaboration and to be fully committed in their interactions. It can be also used as a motivator tool for those organizations that intend to engage in collaboration process. Additionally, value network map is a starting point in many value network analyses. Furthermore, the enhanced value network map can be used as a performance measurement tool in cross-sector collaboration.

Weed mapping using a machine vision system

Weed mapping is a useful tool for site-specific herbicide applications. The objectives of this study were (1) to determine the percentage of land area covered by weeds in no-till and conventionally tilled fields of common bean using digital image processing and geostatistics, and (2) to compare two types of cameras. Two digital cameras (color and infrared) and a differential GPS were affixed to a center pivot structure for image acquisition. Sample field images were acquired in a regular grid pattern, and the images were processed to estimate the percentage of weed cover. After calculating the georeferenced weed percentage values, maps were constructed using geostatistical techniques. Based on the results, color images are recommended for mapping the percentage of weed cover in no-till systems, while infrared images are recommended for weed mapping in conventional tillage systems.

Weed mapping using techniques of precision agriculture

The aim of this study was to identify and map the weed population in a no-tillage area. Geostatistical techniques were used in the mapping in order to assess this information as a tool for the localized application of herbicides. The area of study is 58.08 hectares wide and was sampled in a fixed square grid (which point spaced 50 m, 232 points) using a GPS receiver. In each point the weeds species and population were analyzed in a square with a 0.25 m2 fixed area. The species Ipomoea grandifolia, Gnaphalium spicatum, Richardia spp. and Emilia sonchifolia have presented no spatial dependence. However, the species Conyza spp., C. echinatus and E. indica have shown a spatial correlation. Among the models tested, the spherical model has shown had a better fit for Conyza spp. and Eleusine indica and the Gaussian model for Cenchrus echinatus. The three species have a clumped spatial distribution. The mapping of weeds can be a tool for localized control, making herbicide use more rational, effective and economical.

Intelligent mapping of stresses in a hydraulic crane

Continuous loading and unloading can cause breakdown of cranes. In seeking solution to this problem, the use of an intelligent control system for improving the fatigue life of cranes in the control of mechatronics has been under study since 1994. This research focuses on the use of neural networks as possibilities of developing algorithm to map stresses on a crane. The intelligent algorithm was designed to be a part of the system of a crane, the design process started with solid works, ANSYS and co-simulation using MSc Adams software which was incorporated in MATLAB-Simulink and finally MATLAB neural network (NN) for the optimization process. The flexibility of the boom accounted for the accuracy of the maximum stress results in the ADAMS model. The flexibility created in ANSYS produced more accurate results compared to the flexibility model in ADAMS/View using discrete link. The compatibility between.ADAMS and ANSYS softwares was paramount in the efficiency and the accuracy of the results. Von Mises stresses analysis was more suitable for this thesis work because the hydraulic boom was made from construction steel FE-510 of steel grade S355 with yield strength of 355MPa. Von Mises theory was good for further analysis due to ductility of the material and the repeated tensile and shear loading. Neural network predictions for the maximum stresses were then compared with the co-simulation results for accuracy, and the comparison showed that the results obtained from neural network model were sufficiently accurate in predicting the maximum stresses on the boom than co-simulation.

Mapping of a novel viviparous unstable mutant of maize (vp 12)

A new viviparous mutant of maize (Zea mays L.), associated with genetic instability and designated viviparous-12 (vp12), was identified in a synthetic Tuxpeño adapted to tropical regions. In the present work, the linkage group of this new locus was determined. Progenies of inbred line L477 segregating for the vp12 mutant were crossed with waxy-marked reciprocal translocation stocks. The phenotypic frequencies of the wx and vp12 mutants were analyzed in F2 progenies. The results demonstrated that the Viviparous-12 locus of maize is located on the long arm of chromosome 6.