While exploring an unknown environment, an intelligent agent has only its sensors to guide its actions. Each sensor's ability to provide accurate information depends on the environment's characteristics. If the agent does not know these characteristics, how can it determine which sensors to rely on? This problem is exacerbated by sensing anomalies: cases where sensor(s) are working but the readings lead to an incorrect interpretation of the environment, e.g. laser sensors cannot detect glass. This work addresses the following research question: Can an inconsistency-based sensing accuracy indicator, which relies solely on fused sensor readings, be used to detect and characterize sensing anomalies in unknown environments? A novel inconsistency-based approach was investigated for sensing anomaly detection and characterization by a mobile robot using range sensing for mapping. Based on the hypothesis that sensing anomalies manifest as inconsistent sensor readings, the approach employed Dempster-Shafer theory and six metrics from the evidential literature to measure the magnitude of inconsistency. These were applied directly to fused sensor data with a threshold, forming an indicator, used to distinguish minor noise from anomalous readings. Experiments with real sensor data from four indoor and two outdoor environments showed that three of the six evidential inconsistency metrics can partially address the issue of noticing sensing anomalies in unknown environments. Polaroid sonar sensors, SICK laser range finders, and a Canesta range camera were used. Despite extensive training in known environments, the indicators could not reliably detect sensing anomalies, i.e. distinguish them from ordinary noise. However, sensing accuracy could be estimated (correlations with sensor error exceeded 0.8) and regions with suspect readings could be isolated. Trained indicators failed to rank sensors, but improved map quality by resetting suspect regions (up to 57.65%) or guiding sensor selection (up to 75.86%). This work contributes to the robotics and uncertainty in artificial intelligence communities by establishing the use of evidential metrics for adapting a single sensor or identifying the most accurate sensor to optimize the sensing accuracy in unknown environments. Future applications could enable intelligent systems to switch information sources to optimize mission performance and identify the reliability of sources for different environments.

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The effects of landscape structure on wildlife populations have drawn more attention from ecologists and wildlife managers as landscapes have rapidly changed worldwide. The objectives of this study were to (1) conduct a statewide habitat suitability assessment for wild turkeys (Meleagris gallopavo) in Mississippi using machine learning methods; (2) determine landscape-abundance relationships of wild turkeys at 2 spatial scales; and (3) measure genetic distinction of wild turkey populations in Mississippi. I found that habitat suitability for wild turkeys was positively related to amount of forest cover. Wild turkey relative abundance peaked at an optimal hardwood forest proportion of 0.29 and increased with enhanced landscape configuration at the annual dispersal scale, supporting the landscape composition hypothesis. Using microsatellite analysis of 224 birds, I found 3 distinct genetic clusters in Mississippi; however, population genetic differentiation neither fit to the isolation by distance or isolation by resistance models but may have behavioral cues.

Modeling habitat suitability is beneficial for management and conservation of a species. Although data-rich models are commonly used, opinion-based models may be a beneficial alternative to estimate suitable habitat locations. Despite the increasing use of habitat models, few studies have linked habitat model covariates (i.e., land cover, weather, and normalized difference vegetation indexes (NDVI)) to demographic parameters. This study evaluates model performance and transferability of maximum entropy (MaxEnt) and expert opinion models for predicting American beaver (Castor canadensis) distribution in the southeastern US. I also investigated the relationship of environmental and habitat model covariates to beaver survival. The model’s predictive performance and transferability were evaluated using the area under the curve (AUC) index. Both model approaches performed well at predicting beaver presence. While MaxEnt had better performance, the expert models predicted greater areas as suitable for beaver. Beaver survival was estimated for northern Alabama and was found to be influenced by NDVI and weather covariates in this study.

The aim in this study was to develop a model for the probability of finding active golden eagle nests during their breeding season. It was done by using environmental variables derived from expert models which were tested against empirical data. This resulted in a habitat suitability index (HSI), which in this case is assumed to indicate the probability of active nests of golden eagles. The study was conducted together with the County Administrative Board of Västernorrland with the purpose to improve golden eagle’s ecological status.To develop the model, different combinations of several explanatory variables were tested in a model selection process, where the most optimal and parsimonious model was chosen. The tested variables have earlier been shown to affect golden eagles, as slope, aspect, forest age, foraging habitat, suitable flight routes, human population density, roads, railways, power lines, wind power plants, hiking trails and clear cuts. The variables where applied in in ArcMAP at three different scales: nest scale (25 x 25 meter), proximate scale (a circle with the radius of 500 meter) and home range scale (a circle with the radius of 8253 meter). A preliminary test of the variables showed that all golden eagle nests were found in slopes with at least 5֯ degreesas well as in home ranges with human population density not more than 8 people/km2. Due to that a stratified analysis wasperformed. The variables where analysed by multiple logistic regression in R, where the occurrence of golden eagles’ nestswas compared towards random points in the landscape. All variables were also tested one by one by logistic regression. Afterperforming the multiple logistic regression, it was possible to apply its equation into ArcMap to obtain suitability maps withHSI values over Västernorrland’s county.The comparisons of different models show that it is better to combine different spatial scales in the model than only using one spatial scale. The result indicate that three different models might be the best, which all had different combinations of slope and aspect at nest scale and power lines at the proximate scale. Two of these models also include hiking trails and human population density, both at home range scale, in their equation. Since it was some unclarity about the causality between hiking trails and human population density, the conclusion was not to choose any of these as the final model. The final model was more parsimonious and had an additive effect from slope and southern aspect at the nest scale and an antagonistic effect from power lines at the proximate scale.This study clarifies that golden eagles’ habitat preferences for nesting sites during their breeding period is steep slopes (at minimum 5֯ degrees) in more southern aspects with few power lines in the proximate area surrounding the nest. Their homeranges are also situated in areas with less than 8 people/km2. The study also pinpoints a potential conflict between golden eagleand wind power planning, as golden eagles prefer steep slopes and remote areas, which also are valuable areas for wind powerplants. Golden eagles’ preference of remote areas also indicate that they might be affected by human persecution, why certainconservation effort should be focused into this issue. Out from the final model, you can find cluster in the landscape where youcan focus conservation management and restrict exploitation. Due to low number of wind power plants in the landscape, nothingcould be concluded about their effect on golden eagle in this study. An advice from the golden eagle’s perspective is to use theprecautionary principle and further plan wind power plants in areas which already have high disturbance, as for example closeto power lines or roads. The result also indicates that forest age from SLU Forest Map is not suitable for telling where to findgolden eagle nests. GIS-data over forest age would facilitate conservation management for plenty of species connected to theforest.Although good statistical results for the final model, cautions need to be taken in general, since neither population viability analysis have been included, nor changes over time in the landscape. Another issue is the low sample size, where a larger sample size would make it possible to perform profound calibration and validation of the data. To develop a more robust model, the advice is to include these into the model and use a larger sample size.

Conservation managers and planners need the ‘best available science’ to support robust and defensible decisions, ensuring that public resources are appropriately allocated. Spatial planning products and decision-support tools developed for this purpose should enable partner organizations to achieve focus, coordination, and increased effectiveness in their investments and actions. Whereas conservation partnerships have historically created distinct planning tools, there is increasing interest for improved coordination, communication, and unifying biological datasets to improve the cohesiveness of regional management activities. We sought to inform spatial conservation planning efforts in the southeastern United States through the development of species distribution models for focal avian species to prioritize open pine habitat management and a methodological framework to assess alignment between spatial planning products. We offer insight that can help managers maximize data-informed decision making and contribute to more synergistic and effective conservation actions in the future.

I fastighetsbildningslagens tredje kapitel hittas de allmänna villkoren som måste vara uppfyllda vid en fastighetsbildning. 3 kap. 9 § FBL är en undantagsregel som gör det möjligt att utföra en fastighetsbildning även om villkoren i 3 kap. 1 eller 5 §§ FBL inte är uppfyllda. Regeln kallas även för successiv fastighetsbildning. Trots att lagtexten behandlar regeln och Lantmäterimyndigheten har upprättat en handbok för FBL, lämnas utrymme för egna tolkningar när tillämpning får ske. Rapporten syftar till att tydliggöra innebörden av successiv fastighetsbildning samt ta fram riktlinjer för vid vilka åtgärder det kan tillämpas. Rapporten undersöker inte successiv fastighetsbildning för skogs- och jordbruksfastigheter. Utifrån syftet togs fem frågeställningar fram. En fråga avser att tydliggöra uttrycket successiv fastighetsbildning, en annan fråga syftar till att ta fram de åtgärder som successiv fastighetsbildning kan tillämpas på. Genom en rättsfallsanalys, fallstudier, en intervju samt litteratur- och propositionsstudier kunde ett resultat antas. Däribland framkom det att ingen tydlig bild av successiv fastighetsbildning finns. Litteraturstudien gav en förståelse om att successiv fastighetsbildning är ett annat ord för 3 kap. 9 § FBL som gör det möjligt att, i vissa fall, frångå villkoren i 3 kap. 1 eller 5 §§ FBL. Rättsfallsanalysen och fallstudierna gjorde det möjligt att ta fram riktlinjer för vid vilka åtgärder successiv fastighetsbildning kan tillämpas på. Därefter kunde det konstateras att successiv fastighetsbildning går att tillämpa vid fastighetsbildning av befintliga fastigheter som inte uppfyller lämplighetsvillkoren i 3 kap. 1 § FBL, vid skapande av exploateringsfastigheter samt vid bildande av fastigheter inom ett utvecklingsområde. Resultatet presenteras slutligen i en slutsats där alla fem frågeställningar besvaras. / In the third chapter of the property formation act the general conditions concerning property formation is described. In Chapter 3, section 9 Real Property Formation Act, there is an excepting rule that makes it possible to perform a property formation even if the conditions in the real property formation act 3 chap. 1 or 5 §§ is not fulfilled. This rule is called Successive property formation. Although the legal text treats the rule and the cadastral authority have made a hand book for the Real Property Formation Act, there still is uncertainties about when it can be applied. The aim of this thesis is to clarify the meaning of successive property formation and to present guidelines for when it is possible to apply it. This thesis will not focus on successive property formation for forest and agriculture property. Based on the aim, five issues were made. One of the issues intends to clarify the term successive property formation, and another issue aims at identifying the actions that successive property formation can be applied to. Through a court cases analysis, case study, interview and literature and propositions study, a result could be assumed. Among other things, it emerged that no clear view of successive property formation existed. The literature study gave an understanding of that successive property formation is another word for the real property formation act 3 chap. 9 § that makes it possible to, in some cases, make an exception from the conditions in the real property formation 3 chap. 1 or 5 §§. The court case analysis and the case study made it possible to draw guidelines for actions where successive property formation can be applied. Thereafter it could be noted that successive property formation can be applied on existing properties which does not meet the suitability conditions in the real property formation act 3 chap. 1 §, for the creation of exploitation properties and for the formation of properties within a development area. Conclusively, the result is presented were all five issues are addressed.

Research in sustainable macroalgae aquaculture has been gaining hype (e.g. Seafarm) to provide advancements in new significant sources of food, feed, materials and bio-energy. Despite the fact that in Asia the offshore production of macroalgae is established for many decades, in Europe is still in its infancy. The issue on where to find suitable cultivation areas without conflicting with current uses to respect the environment and the socio-economic activities is a great challenge. This Master of Science thesis aimed to find suitable offshore areas in order to facilitate implementations of macroalgae cultivations in the Swedish West Coast. Thirteen criteria in environmental, economic and social sustainability aspects were identified and employed (e.g. Depth, Distance to Ports, and Natural and Preserved areas (NPAs)), and the tools Geographic Information Systems (GIS) and Multi-criteria Analysis (MCA) were used in the form of GIS-MCDA, an integrated method available in Idrisi. To aggregate the criteria, Boolean and Weighted Linear Combination (WLC) techniques were applied. The results showed that Boolean areas cover 537 km2 (6,98% of the study area). The best suitable areas, possessing the maximum suitability index 10, that resulted from two WLC models comprise 5 km2 (0,07% of the study area) and 26 km2 (0,34% of the study area) including and excluding the criterion NPAs as constraint, respectively. The results further indicated that GIS-MCDA models excelled in providing an overview for effective spatial decision-making. Both techniques play a role in suitability analysis and complement each other in finding an optimal site which could be carefully selected out of the identified areas. It is recommended that areas with a suitability index 10 be chosen inside Boolean suitable areas. Moreover, this study could act as a driving force to build a resilient planning framework that would boost sustainable placement and development of offshore macroalgae cultivations.

During the last couple of years, mobile augmented reality (MAR) has seen an increase in its potential due to improved available hardware and software. Applications such as Pokémon GO and IKEA Place are just a few of the successful ways MAR can be used in. Smartphone user’s tendency to look up information and data, such as live sport feeds, present unexplored opportunities for augmented reality (AR). This thesis aims to explore the usability and suitability using AR for live information visualization. For this purpose, an Android MAR application has been written in Java with Android Studio, using ARCore to visualize live events and statistics from a hockey game. The user can place a virtual hockey rink in a real-world environment and then toggle between a live event feed where current match events are being displayed, and a statistics view, where the user can see the statistics of the match, all in AR. The application was tested by nine participants between the age 1957 years old, which also answered a system usability scale test (SUS). The SUS yielded a score with the mean 76.6 out of 100, which is above the mean of studies using SUS. The user testing yielded mostly positive feedback regarding the concept of using AR to visualize information, and seven out of the nine participants thought it was a fun way of visualizing a hockey game. The result of the user testing also revealed possible problems of using AR to visualize data, such as information occlusion. In conclusion, MAR is a diverse technology which use has much undiscovered potential. The study indicates that it is a suitable and usable method for visualizing information and live data. / Under de senaste åren så har AR (augmented reality) för mobil sett en ökning i dess potential till följd av förbättrad hårdvara och mjukvara. Applikationer som Pokémon GO och IKEA Place är bara ett fåtal exempel på framgångsrika sätt som mobil AR kan användas på. Smartphoneanvändares tendens att kolla upp information och data, till exempel sportsändningar, öppnar möjligheter för AR som än inte har utforskats. Denna uppsats har som mål att undersöka användbarhet och lämplighet för mobil AR som ett verktyg för att visualisera information. För detta ändamål så har en Android mobil AR applikation skapats som använder ARCore för att visualisera livedata och statistik från en ishockeymatch, skriven i Java med Android Studio. Applikationen låter användaren placera en virtuell hockeyrink i verkligheten, och sedan kan byta mellan ett liveflöde där händelser i matchen visas, samt en statistikvy, där användaren kan se statistik från hela matchen, allt i AR. Applikationen testades av nio användare mellan åldrarna 19-57, som också svarade på ett användbarhetstest (SUS). Användbarhetstestet gav ett resultat med medelvärdet 76.6 utav 100, vilket är över medelvärdet av studier som använder SUS. Användartestet gav mestadels positivt resultat angående konceptet att använda AR för att visualisera information och sju utav nio personer ansåg att det var ett roligt sätt att visualisera en hockeymatch. Resultatet av användartestet avslöjade också eventuella problem med att använda AR för att visualisera data, till exempel att information skyms. Sammanfattningsvis så är mobil AR en mångsidig teknik som har mycket outforskad potential. Denna studie indikerar att det är en lämplig metod för att visualisera liveinformation och data.

Green space fragmentation is one reason for the failing ecological networks around our planet. It has been argued that the driving force behind the loss of natural habitats by fragmentation is urban development. This essay aims to look at the subject of green space fragmentation in Uppsala municipality and how it affects both humans and roe deer living in the area. This essay will also explore how the efforts to stop wildlife-vehicle collisions impact roe deer’s ability to roam through the area divided thanks to human induced green space fragmentation. This has been done by using a descriptive quantitative analysis combined with a connectivity model, the least-cost approach. Remote sensing was used to collect the data used for the analysis. The results of this essay indicated that green space fragmentation impacts both humans and roe deer in Uppsala municipality since the wildlife-vehicle collisions can be traced to spots where the roe deer are forced to cross the road because of green space fragmentation. The introduced form of wildlife crossing structures, wildlife fences, does not help minimize the effects of the fragmentation, instead it steers the animals into certain points where the collisions occur. To truly minimize the consequences of human induced green space fragmentation, green infrastructure needs to be taken into account whenever planning future developments.