Snowmelt progression drives habitat selection and vegetation disturbance by an Arctic avian herbivore

Arctic tundra vegetation is affected by rapid climatic change and fluctuating herbivore population sizes. Broad-billed geese, after their arrival in spring, feed intensively on belowground rhizomes, thereby disturbing soil, mosses, and vascular plant vegetation. Understanding of how springtime snowmelt patterns drive goose behavior is thus key to better predict the state of Arctic tundra ecosystems. Here, we analyzed how snowmelt progression affected springtime habitat selection and vegetation disturbance by pink-footed geese (Anser brachyrhynchus) in Svalbard during 2019. Our analysis, based on GPS telemetry data and field observations of geese, plot-based assessments of signs of vegetation disturbance, and drone and satellite images, covered two spatial scales (fine scale: extent 0.3 km2, resolution 5 cm; valley scale: extent 30 km2, resolution 10 m). We show that pink-footed goose habitat selection and signs of vegetation disturbance were correlated during the spring pre-breeding period; disturbances were most prevalent in the moss tundra vegetation class and areas free from snow early in the season. The results were consistent across the spatial scales and methods (GPS telemetry and field observations). We estimated that 23.4% of moss tundra and 11.2% of dwarf-shrub heath vegetation in the valley showed signs of disturbance by pink-footed geese during the study period. This study demonstrates that aerial imagery and telemetry can provide data to detect disturbance hotspots caused by pink-footed geese. Our study provides empirical evidence to general notions about implications of climate change and snow season changes that include increased variability in precipitation.</p

That Bird is Singing Us an Invitation to Meet Our Future: Listening to the Call of Sankofa to Develop Memory Practice Methodologies for Performative Practice

This thesis explores the potential of the philosophical, and practical, application of the Akan Adinkra principle of Sankofa, as a memory practice methodology that offers otherwise readings of remembering for lives omitted from or obscured from archives and official documents of history. The common meaning of Sankofa has been defined as get up and go get it” or “go back and get it,” in African Diaspora cultural and memorial practices with other meanings that refer to returning to the source to recover the past and it is not taboo to fetch what is at risk of being left behind. Represented by a pictograph of a bird with its head turned backwards, gently caring for an egg balanced on its back, Sankofa has been called on to think about how to reconstruct a “fragmented past” (Temple 2010: 127) This thesis explores Sankofa as a memory practice methodology, presenting a reading of the Akan philosophical principle through a black feminist/ decolonial lens. The research considers the potential of Sankofa as a guide in developing methodologies for a performative artistic practice that involves collective learning, remembering, storytelling and the development of communities of support, recovery, and transformation. The intent is to bring a perspective to Sankofa that thinks about how this wise bird holds and nurtures the egg on its back, caring for it and how the birds innate embodied wisdom can help us “to meet the future, undeterred” (Kayper-Mensah 1978:4). This research draws on an ongoing artistic projects Declaration of Independence and The Queen and the Black-Eyed Squint, both of which use methods of remembering to explore and undo memories of the continuing legacies of colonialism and enslavement

Glacier Monitoring Based on Multi-Spectral and Multi-Temporal Satellite Data: A Case Study for Classification with Respect to Different Snow and Ice Types

Remote sensing techniques are frequently applied for the surveying of remote areas, where the use of conventional surveying techniques remains difficult and impracticable. In this paper, we focus on one of the remote glacier areas, namely the Tyndall Glacier area in the Southern Patagonian Icefield in Chile. Based on optical remote sensing data in the form of multi-spectral Sentinel-2 imagery, we analyze the extent of different snow and ice classes on the surface of the glacier by means of pixel-wise classification. Our study comprises three main steps: (1) Labeled Sentinel-2 compliant data are obtained from theoretical spectral reflectance curves, as there are no training data available for the investigated area; (2) Four different classification approaches are used and compared in their ability to identify the defined five snow and ice types, thereof two unsupervised approaches (k-means clustering and rule-based classification via snow and ice indices) and two supervised approaches (Linear Discriminant Analysis and Random Forest classifier); (3) We first focus on the pixel-wise classification of Sentinel-2 imagery, and we then use the best-performing approach for a multi-temporal analysis of the Tyndall Glacier area. While the achieved classification results reveal that all of the used classification approaches are suitable for detecting different snow and ice classes on the glacier surface, the multi-temporal analysis clearly reveals the seasonal development of the glacier. The change of snow and ice types on the glacier surface is evident, especially between the end of ablation season (April) and the end of accumulation season (September) in Southern Chile

The Need for Accurate Pre-processing and Data Integration for the Application of Hyperspectral Imaging in Mineral Exploration

Die hyperspektrale Bildgebung stellt eine Schlüsseltechnologie in der nicht-invasiven Mineralanalyse dar, sei es im Labormaßstab oder als fernerkundliche Methode. Rasante Entwicklungen im Sensordesign und in der Computertechnik hinsichtlich Miniaturisierung, Bildauflösung und Datenqualität ermöglichen neue Einsatzgebiete in der Erkundung mineralischer Rohstoffe, wie die drohnen-gestützte Datenaufnahme oder digitale Aufschluss- und Bohrkernkartierung. Allgemeingültige Datenverarbeitungsroutinen fehlen jedoch meist und erschweren die Etablierung dieser vielversprechenden Ansätze. Besondere Herausforderungen bestehen hinsichtlich notwendiger radiometrischer und geometrischer Datenkorrekturen, der räumlichen Georeferenzierung sowie der Integration mit anderen Datenquellen. Die vorliegende Arbeit beschreibt innovative Arbeitsabläufe zur Lösung dieser Problemstellungen und demonstriert die Wichtigkeit der einzelnen Schritte. Sie zeigt das Potenzial entsprechend prozessierter spektraler Bilddaten für komplexe Aufgaben in Mineralexploration und Geowissenschaften.Hyperspectral imaging (HSI) is one of the key technologies in current non-invasive material analysis. Recent developments in sensor design and computer technology allow the acquisition and processing of high spectral and spatial resolution datasets. In contrast to active spectroscopic approaches such as X-ray fluorescence or laser-induced breakdown spectroscopy, passive hyperspectral reflectance measurements in the visible and infrared parts of the electromagnetic spectrum are considered rapid, non-destructive, and safe. Compared to true color or multi-spectral imagery, a much larger range and even small compositional changes of substances can be differentiated and analyzed. Applications of hyperspectral reflectance imaging can be found in a wide range of scientific and industrial fields, especially when physically inaccessible or sensitive samples and processes need to be analyzed. In geosciences, this method offers a possibility to obtain spatially continuous compositional information of samples, outcrops, or regions that might be otherwise inaccessible or too large, dangerous, or environmentally valuable for a traditional exploration at reasonable expenditure. Depending on the spectral range and resolution of the deployed sensor, HSI can provide information about the distribution of rock-forming and alteration minerals, specific chemical compounds and ions. Traditional operational applications comprise space-, airborne, and lab-scale measurements with a usually (near-)nadir viewing angle. The diversity of available sensors, in particular the ongoing miniaturization, enables their usage from a wide range of distances and viewing angles on a large variety of platforms. Many recent approaches focus on the application of hyperspectral sensors in an intermediate to close sensor-target distance (one to several hundred meters) between airborne and lab-scale, usually implying exceptional acquisition parameters. These comprise unusual viewing angles as for the imaging of vertical targets, specific geometric and radiometric distortions associated with the deployment of small moving platforms such as unmanned aerial systems (UAS), or extreme size and complexity of data created by large imaging campaigns. Accurate geometric and radiometric data corrections using established methods is often not possible. Another important challenge results from the overall variety of spatial scales, sensors, and viewing angles, which often impedes a combined interpretation of datasets, such as in a 2D geographic information system (GIS). Recent studies mostly referred to work with at least partly uncorrected data that is not able to set the results in a meaningful spatial context. These major unsolved challenges of hyperspectral imaging in mineral exploration initiated the motivation for this work. The core aim is the development of tools that bridge data acquisition and interpretation, by providing full image processing workflows from the acquisition of raw data in the field or lab, to fully corrected, validated and spatially registered at-target reflectance datasets, which are valuable for subsequent spectral analysis, image classification, or fusion in different operational environments at multiple scales. I focus on promising emerging HSI approaches, i.e.: (1) the use of lightweight UAS platforms, (2) mapping of inaccessible vertical outcrops, sometimes at up to several kilometers distance, (3) multi-sensor integration for versatile sample analysis in the near-field or lab-scale, and (4) the combination of reflectance HSI with other spectroscopic methods such as photoluminescence (PL) spectroscopy for the characterization of valuable elements in low-grade ores. In each topic, the state of the art is analyzed, tailored workflows are developed to meet key challenges and the potential of the resulting dataset is showcased on prominent mineral exploration related examples. Combined in a Python toolbox, the developed workflows aim to be versatile in regard to utilized sensors and desired applications

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

WHISPERS OF MEMORY, MURMURS OF HISTORY ACOUSTIC MONU-MEMORIALS IN PUBLIC SPACES. Exploratory research of strategies used to create acoustic experiences of commemoration, remembrance, mourning and memory

This research seeks to make an exploratory study of the strategies used by the creators of monuments, memorials, and commemorative places located in the public spaces that use sound as one of the primary raw material in their design. The term acoustic monu-memorials was coined in this research to encircle these structures and places. In order to achieve the goal of this research, it was necessary to compile a number of samples, primarily after the digital recording era of captured sound around 1971 to the present. The compilation was relevant because such a compendium was not found in the literature, and to the author's knowledge, a comprehensive investigation of the strategies used in planning acoustic monu-memorials in the urban spaces does not exist. The method used to create such compendium was to send a question to people with different background identities, such as visual and sound artists, musicians, art curators, and heritage scholars among others. This question produced a selection of 51 examples of acoustic monu-memorials located in public spaces. Subsequently, the examples were classified into four major categories according to their form and nature. Additionally, two examples from the main categories were chosen as case studies: The Sinti and Roma Memorial in Berlin, Germany and the Niche monument in Cali, Colombia. These study cases were presented, described, and analysed in detail as they represent the type of what could be defined as an acoustic monu-memorial in general. Lynch’s (1960) five elements that help individuals build the image of the city were transferred and used as a tool to help to build this image into acoustic terms. A thorough analysis of the acquired data yielded found the strategies used by the designers to shape, modify, transform, and structure public space. These strategies are entitled Sound Spaces. Moreover, a list entitled Urban Acoustic Commemoration Code was compiled. This list of suggestions addresses urban planners, architects, artists, designers, and general public interested in the aspects involved when creating acoustic commemoration phenomena in public spaces

Development and Applications of Machine Learning Methods for Hyperspectral Data

Die hyperspektrale Fernerkundung der Erde stützt sich auf Daten passiver optischer Sensoren, die auf Plattformen wie Satelliten und unbemannten Luftfahrzeugen montiert sind. Hyperspektrale Daten umfassen Informationen zur Identifizierung von Materialien und zur Überwachung von Umweltvariablen wie Bodentextur, Bodenfeuchte, Chlorophyll a und Landbedeckung. Methoden zur Datenanalyse sind erforderlich, um Informationen aus hyperspektralen Daten zu erhalten. Ein leistungsstarkes Werkzeug bei der Analyse von Hyperspektraldaten ist das Maschinelle Lernen, eine Untergruppe von Künstlicher Intelligenz. Maschinelle Lernverfahren können nichtlineare Korrelationen lösen und sind bei steigenden Datenmengen skalierbar. Jeder Datensatz und jedes maschinelle Lernverfahren bringt neue Herausforderungen mit sich, die innovative Lösungen erfordern. Das Ziel dieser Arbeit ist die Entwicklung und Anwendung von maschinellen Lernverfahren auf hyperspektrale Fernerkundungsdaten. Im Rahmen dieser Arbeit werden Studien vorgestellt, die sich mit drei wesentlichen Herausforderungen befassen: (I) Datensätze, welche nur wenige Datenpunkte mit dazugehörigen Ausgabedaten enthalten, (II) das begrenzte Potential von nicht-tiefen maschinellen Lernverfahren auf hyperspektralen Daten und (III) Unterschiede zwischen den Verteilungen der Trainings- und Testdatensätzen. Die Studien zur Herausforderung (I) führen zur Entwicklung und Veröffentlichung eines Frameworks von Selbstorganisierten Karten (SOMs) für unüberwachtes, überwachtes und teilüberwachtes Lernen. Die SOM wird auf einen hyperspektralen Datensatz in der (teil-)überwachten Regression der Bodenfeuchte angewendet und übertrifft ein Standardverfahren des maschinellen Lernens. Das SOM-Framework zeigt eine angemessene Leistung in der (teil-)überwachten Klassifikation der Landbedeckung. Es bietet zusätzliche Visualisierungsmöglichkeiten, um das Verständnis des zugrunde liegenden Datensatzes zu verbessern. In den Studien, die sich mit Herausforderung (II) befassen, werden drei innovative eindimensionale Convolutional Neural Network (CNN) Architekturen entwickelt. Die CNNs werden für eine Bodentexturklassifikation auf einen frei verfügbaren hyperspektralen Datensatz angewendet. Ihre Leistung wird mit zwei bestehenden CNN-Ansätzen und einem Random Forest verglichen. Die beiden wichtigsten Erkenntnisse lassen sich wie folgt zusammenfassen: Erstens zeigen die CNN-Ansätze eine deutlich bessere Leistung als der angewandte nicht-tiefe Random Forest-Ansatz. Zweitens verbessert das Hinzufügen von Informationen über hyperspektrale Bandnummern zur Eingabeschicht eines CNNs die Leistung im Bezug auf die einzelnen Klassen. Die Studien über die Herausforderung (III) basieren auf einem Datensatz, der auf fünf verschiedenen Messgebieten in Peru im Jahr 2019 erfasst wurde. Die Unterschiede zwischen den Messgebieten werden mit qualitativen Methoden und mit unüberwachten maschinellen Lernverfahren, wie zum Beispiel Principal Component Analysis und Autoencoder, analysiert. Basierend auf den Ergebnissen wird eine überwachte Regression der Bodenfeuchte bei verschiedenen Kombinationen von Messgebieten durchgeführt. Zusätzlich wird der Datensatz mit Monte-Carlo-Methoden ergänzt, um die Auswirkungen der Verschiebung der Verteilungen des Datensatzes auf die Regression zu untersuchen. Der angewandte SOM-Regressor ist relativ robust gegenüber dem Rauschen des Bodenfeuchtesensors und zeigt eine gute Leistung bei kleinen Datensätzen, während der angewandte Random Forest auf dem gesamten Datensatz am besten funktioniert. Die Verschiebung der Verteilungen macht diese Regressionsaufgabe schwierig; einige Kombinationen von Messgebieten bilden einen deutlich sinnvolleren Trainingsdatensatz als andere. Insgesamt zeigen die vorgestellten Studien, die sich mit den drei größten Herausforderungen befassen, vielversprechende Ergebnisse. Die Arbeit gibt schließlich Hinweise darauf, wie die entwickelten maschinellen Lernverfahren in der zukünftigen Forschung weiter verbessert werden können

Assessing the influence of different validation protocols on Ocean Colour match-up analyses

Abstract Multiple approaches have been used by the Ocean Colour community for validating satellite-derived products using in situ data, with most of them derived from mainly two approaches, one suggested by Bailey and Werdell (2006) (BW06) and one suggested by Zibordi et al. (2009a) (Z09), each with a different set of quality checking and spatiotemporal collocation criteria. The question remains what sort of information is added or missed when choosing one over the other. In this work, the differences among validation approaches were determined by using a common dataset of in situ and satellite data. The match-up exercise was separated into two groups of datasets based on the spatial resolution of the sensors to be validated. Sentinel-3A/OLCI data were selected as a representation of medium spatial resolution sensors, and two validation approaches were selected to this match-up dataset. The high spatial resolution sensors were represented by Sentinel-2A/MSI data, and three validation approaches were tested. Data from the AERONET-OC network were chosen as the common in situ dataset. For Sentinel-3A/OLCI, the number of match-ups varies depending on the validation approach used. Bailey and Werdell (2006) produces 20% more match-ups for Sentinel-3A/OLCI due to its more relaxed filtering criteria compared to the criteria applied by Zibordi et al. (2009a) . The validation metrics vary between different validation methods giving a different impression of accuracy of the satellite products. Also, the satellite data selected by BW06 have a statistical distribution with a higher median and standard deviation when compared to Z09. Similar findings are also confirmed for the match-up analysis conducted for Sentinel-2A/MSI. Therefore, although a common reference dataset was used, the validation statistical results were influenced by the validation approach selected. This does not suggest that one validation protocol is better than the other, but it implies that validation statistics reported in different studies may not always be directly comparable. Additionally, it was determined that BW06 could be a better fit when trying to obtain a sufficient number of match-ups for calibration purposes in the shortest time