Learning and Searching Methods for Robust, Real-Time Visual Odometry.

Accurate position estimation provides a critical foundation for mobile robot perception and control. While well-studied, it remains difficult to provide timely, precise, and robust position estimates for applications that operate in uncontrolled environments, such as robotic exploration and autonomous driving. Continuous, high-rate egomotion estimation is possible using cameras and Visual Odometry (VO), which tracks the movement of sparse scene content known as image keypoints or features. However, high update rates, often 30~Hz or greater, leave little computation time per frame, while variability in scene content stresses robustness. Due to these challenges, implementing an accurate and robust visual odometry system remains difficult. This thesis investigates fundamental improvements throughout all stages of a visual odometry system, and has three primary contributions: The first contribution is a machine learning method for feature detector design. This method considers end-to-end motion estimation accuracy during learning. Consequently, accuracy and robustness are improved across multiple challenging datasets in comparison to state of the art alternatives. The second contribution is a proposed feature descriptor, TailoredBRIEF, that builds upon recent advances in the field in fast, low-memory descriptor extraction and matching. TailoredBRIEF is an in-situ descriptor learning method that improves feature matching accuracy by efficiently customizing descriptor structures on a per-feature basis. Further, a common asymmetry in vision system design between reference and query images is described and exploited, enabling approaches that would otherwise exceed runtime constraints. The final contribution is a new algorithm for visual motion estimation: Perspective Alignment Search~(PAS). Many vision systems depend on the unique appearance of features during matching, despite a large quantity of non-unique features in otherwise barren environments. A search-based method, PAS, is proposed to employ features that lack unique appearance through descriptorless matching. This method simplifies visual odometry pipelines, defining one method that subsumes feature matching, outlier rejection, and motion estimation. Throughout this work, evaluations of the proposed methods and systems are carried out on ground-truth datasets, often generated with custom experimental platforms in challenging environments. Particular focus is placed on preserving runtimes compatible with real-time operation, as is necessary for deployment in the field.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113365/1/chardson_1.pd

Watershed Monitoring in Galicia from UAV Multispectral Imagery Using Advanced Texture Methods

Watershed management is the study of the relevant characteristics of a watershed aimed at the use and sustainable management of forests, land, and water. Watersheds can be threatened by deforestation, uncontrolled logging, changes in farming systems, overgrazing, road and track construction, pollution, and invasion of exotic plants. This article describes a procedure to automatically monitor the river basins of Galicia, Spain, using five-band multispectral images taken by an unmanned aerial vehicle and several image processing algorithms. The objective is to determine the state of the vegetation, especially the identification of areas occupied by invasive species, as well as the detection of man-made structures that occupy the river basin using multispectral images. Since the territory to be studied occupies extensive areas and the resulting images are large, techniques and algorithms have been selected for fast execution and efficient use of computational resources. These techniques include superpixel segmentation and the use of advanced texture methods. For each one of the stages of the method (segmentation, texture codebook generation, feature extraction, and classification), different algorithms have been evaluated in terms of speed and accuracy for the identification of vegetation and natural and artificial structures in the Galician riversides. The experimental results show that the proposed approach can achieve this goal with speed and precisionThis work was supported in part by the Civil Program UAVs Initiative, promoted by the Xunta de Galicia and developed in partnership with the Babcock company to promote the use of unmanned technologies in civil services. We also have to acknowledge the support by the Ministerio de Ciencia e Innovación, Government of Spain (grant number PID2019-104834GB-I00), and Consellería de Educación, Universidade e Formación Profesional (grant number ED431C 2018/19, and accreditation 2019–2022 ED431G-2019/04). All are co-funded by the European Regional Development Fund (ERDF)S

Ecological Impacts of Metallic Starling Colonies in Tropical Queensland, Australia

Animal aggregations – whereby large numbers of animals come together at one place at one time – can have dramatic impacts on individuals, populations and ecosystems. In many cases, they are also unique forms of animal interaction, and include some of the world’s most recognised wildlife spectacles. But despite these traits, the causes and consequences of animal aggregations are often poorly understood. One such case involves large colonies of metallic starlings (Aplonis metallica) that nest together in emergent rainforest trees in northern Queensland, Australia. Starling colonies attract a diverse assemblage of wildlife, which utilise resources dropped by the starlings. Remarkably, these animal aggregations have never been described, and thus broader questions about their influence on ecosystems in tropical Australia remain unanswered. My thesis aims to describe this system, begin to answer some of these questions, and elucidate broad patterns common to other aggregations of animals worldwide. It focuses on the starlings themselves and the reasons for their choice of colonial nest sites. It also explores the lives of the animals using the starling colonies, with specific chapters on native birds and feral pigs, invasive cane toads, and the ways these species mediate the influence of starling colonies on the surrounding ecosystem

Forests and climate change: adaptation and mitigation

ETFRN news No. 50: Forests and Climate Change: adaptation and mitigation. This newsletter contains interesting materials for those who think about the question how to proceed with forests and climate change after Copenhagen, with or without an agreement. Here below are presented some observations from this newsletter: • Adaptation and mitigation are separate issues in the climate discussions, but in forest practice they are two sides of the same coin. • We need forest management directed at the realization of different objectives at the same time, we do not need pure ‘carbon forests’. Not addressing ‘people’ and ‘planet’ considerations is increasingly seen – by both the public and private sector – as a business risk. • Not all countries will be able to comply with REDD rules in the short term. The voluntary carbon market will remain important. • REDD is an opportunity and a risk for local communities. Risks should be made transparent, and open and equal participation by communities in design and decision-making should be promoted • REDD and other forest-based climate change mitigation measures are likely to be low-cost and effective in the short to medium term. Some stakeholders fear that forests may become a too-cheap mitigation option and corrupt the overall climate agreement. In most calculations, however, the costs of developing, operating and managing the institutional system required to produce credible and sustainable forest carbon credits are not internalized in forest carbon prices. If they were, forest carbon prices would become much higher and more realistic. • The role of forests must be clarified and articulated in National Adaptation Programs of Action (NAPAs). At present most political attention and financing is focused on REDD, and, in general, on climate mitigation. Only recently has the concern for the role of forests in adaptation gained ground; this emanates from the growing recognition that climate change will happen anyway. Moreover, climate change will affect the most vulnerable ecosystems and poorer regions. • There is a clear need for harmonization and coherence in the certification market (SFM, and carbon, fair trade etc.). Certification is not necessarily the only credible basis for payment. As illustrated in this issue, mutual trust can be an alternative, particularly for small-scale initiatives that cannot afford the high transaction costs of certification

Analysis of the Phytoremediation Potential of a Chrysopogon Grass and Pteris Fern in Virginia Soils Affected by Acid Mine Drainage

Pollution from the mining industry is a historic and global environmental issue. The remediation of resulting acid and heavy metal contamination from outdated mining practices is often costly and difficult. Contamination at mine sites can result from acid mine drainage (AMD) and the disruption of underlying geologic formations, as well as improper disposal of mine tailings and other wastes. Phytoremediation, which is the use of plants to treat soil contamination, is an emerging method of reclaiming areas contaminated by toxic heavy metals and AMD. The ecosystem of Contrary Creek, a tributary of Lake Anna in Louisa County, Virginia, has been significantly affected by AMD from abandoned pyrite mining operations. Bioavailable and total recoverable metal concentrations, pH, and organic matter content were analyzed in soil samples from sites along the creek. Soil from two sites along Contrary Creek and an uncontaminated site was collected and used to grow two known hyperaccumulator plants, a grass, Chrysopogon zizanioides, and a fern, Pteris cretica. The plants were grown in controlled conditions similar to regional environmental characteristics and harvested after 21, 80, and 170 days. The shoots, roots, and soil were analyzed for metal concentrations. These results were compared with initial Day 1 concentrations to determine the ability of Chrysopogon zizanioides and Pteris cretica to hyperaccumulate metals from these Virginia AMD-contaminated soils. The Pteris fern and Chrysopogon grass accumulated significant concentrations of aluminum, arsenic, chromium, copper, iron, manganese, and lead after 170 days of growth. The concentrations of these metals in plant biomass were significantly higher than the concentrations in the bioavailable fraction of the soil by Day 170, indicating that both plants are hyperaccumulators of the metals present in the Contrary Creek soil and could serve as phytoremdiators at Contrary Creek

Nutritional ecology of brushtail possums (Trichosurus vulpecula Kerr) in New Zealand

In this thesis, a combination of field observations and laboratory experiments are used to address the question: how do the nutritional strategies of brushtail possums enable this alien pest species to obtain a balance of nutrients at native forest-pasture margins in New Zealand? Possums had a polyphagous diet with a core of leaves from three species, supplemented by flowers in spring and summer, fruits in autumn and invertebrates in winter. Graphical models confirmed that possums were polyphagous in all seasons, although diet width and the extent to which individual species of food dominated the diet varied between seasons. Four nutrient characteristics of potential foods were analysed: total nitrogen, available nitrogen, soluble organic matter and total digestible dry matter. They varied significantly between food species and between seasons but were not significantly associated with either proportion by mass, or frequency of occurrence in the diet. In laboratory experiments relative preferences between leaves of three woody species changed when the basal leaf diet was supplemented by flowers or by pasture species. Intake rates of nutrients also varied according to diet. The target nutrient intake ratio of available nitrogen to digestible dry matter was calculated. Predictions from optimal foraging, nitrogen limitation and mixed diet theories were compared but mixed diet theory was supported most strongly. Home ranges were delineated using data from GPS telemetry. Nocturnal foraging pathways were characterised and divided to distinguish between searching within patches and travelling between them. Possums travelled further per night and travelled faster between search patches in winter than they did in autumn. Selection ratios showed that native forest and native shrub/scrubland were preferred habitats for both travelling and searching. The implications of applying mixed diet theory to possum nutritional ecology, for improving the efficiency of control programmes and reducing interactions between possums and cattle are explored

Changing Human Impact On The Montane Forests Of The Eastern Andean Flank, Ecuador

The montane cloud forests of South America are some of the most biodiverse habitats in the world, whilst also being especially vulnerable to climate change and human disturbance. Today much of this landscape has been transformed into a mosaic of secondary forest and agricultural fields. This thesis uses palaeoecological proxies (pollen, non-pollen palynomorphs, charcoal, organic content) to interpret ecosystem dynamics during the late Quaternary, unravelling the vegetation history of the landscape and the relationship between people and the montane cloud forest of the eastern Andean flank of Ecuador. Two new sedimentary records are examined from the montane forest adjacent to the Río Cosanga (Vinillos) and in the Quijos Valley (Huila). These sites characterise the natural dynamics of a pre-human arrival montane forest and reveal how vegetation responded during historical changes in local human populations. Non-pollen palynomorphs (NPPs) are employed in a novel approach to analyse a forest cover gradient across these sites. The analysis identifies a distinctive NPP assemblage connected to low forest cover and increased regional burning. Investigation into the late Pleistocene Vinillos sediments show volcanic activity to be the primary landscape-scale driver of ecosystem dynamics prior to human arrival, influencing montane forest populations but having little effect on vegetation composition. Lake sediments at Huila from the last 700 years indicate the presence of pre-Hispanic peoples, managing and cultivating an open landscape. The subsequent colonization of the region by Europeans in the late 1500’s decimated the indigenous population, leading to the abandonment of the region in conjunction with an expansion in forest cover ca. 1588 CE. After approximately 130 years of vegetation recovery, montane cloud forest reached a stage of structural maturity comparable to that seen in the pre-human arrival forest. The following 100 years (1718-1822 CE) of low human population and minimal human impact in the region is proposed as a shifted ecological baseline for future restoration and conservation goals. This ‘cultural ecological baseline’ features a landscape that retains many of the ecosystem service provided by a pristine montane forest, while retaining the cultural history of its indigenous people within the vegetation

PREDICTING TROPICAL RAINFOREST DEFORESTATION USING MACHINE LEARNING, REMOTE SENSING & GIS: CASE STUDY OF THE CROSS RIVER NATIONAL PARK, NIGERIA

Population growth, urban sprawl, agricultural expansion, and illegal logging has led to losses in forested land in most parts of the world, especially in a highly populated country like Nigeria. The Cross River National Park (CRNP) in southeastern Nigeria with an area just above 4000km2 is designated a biodiverse hotspot and one of the oldest rainforests in Africa. As with all other tropical forests spread across the globe the CRNP is not immune to these factors that threaten its existence. The focus of this study is to analyze the change of forest cover at the Oban division of the Cross River National Park using multi-temporal remotely sensed data to predict and model the future probability of deforestation within the area of interest. This study made use of the Landsat West Africa Land Use/Land Cover Time Series dataset for the years 1975, 2000 and 2013 and Landsat 8 operational land imager (OLI) imagery for the year 2020 in a post classification change detection model to determine the extent of change in forest cover classes. Random forest decision tree machine learning algorithm was used to predict the future risk of forest cover loss using the datasets produced from the post classification change detection. The model related deforestation probabilities with several physical and anthropogenic factors such as elevation, slope angle, solar radiation, aspect, topographic roughness, soil type, distance from roads, distance from towns, distance from rivers, distance from plantations and population density. The results from the change detection analysis showed that from 1975 to 2020 the forest cover declined by 1909km2 a rate of 42km2 per year. The random forest regression analysis predicted areas of the forest with modest to high deforestation probabilities and indicated that socio-economic factors are major drivers of deforestation in the region rather than physical factors

Wild NYC: Building Biodiversity in Fresh Kills and City Parks

This dissertation is an anthropological field study of the work of urban ecological maintenance being conducted in New York City through the analysis of the reclamation and biotic restoration of the Fresh Kills landfill, located in the borough of Staten Island. This landfill was once the largest urban dump in the United States. Its 2,200 acres of trash buried in four mounds have polluted an area historically noted for its natural beauty as a collection of marshes and woodlands bordering the Kill Van Kull, a tidal strait that flows into the New York Harbor. The current plan for park and nature reserve introduces rolling grassland habitats otherwise extirpated in the region and re-introduces native plants to enhance the area\u27s biotic diversity. The site\u27s large acreage will also link up with and expand the Staten Island Greenbelt. Fresh Kills, once transformed, will become one of the largest urban nature preserves in the city. This dissertation also explores the essential maintenance work performed by researchers, city workers, and volunteers alike for creating and preserving wild spaces in New York City. Despite the ecological benefits envisioned in the Fresh Kills conversion, there are challenges ahead for implementing sustainability. Chief among them is the scarce funding for land reclamation in light of competing urban priorities. The substantial commitment to convert the world\u27s largest landfill into an urban park and nature preserve, however, holds important lessons for public and non-profit agencies interested in urban environmental improvement