1,721,170 research outputs found
The causal effect of agricultural landscape simplification on Germany’s grasslands during a compound drought and heatwave in 2018
No full textPreserving ecosystem services and economic and environmental benefits will require future landscape policies to identify and incorporate specific landscape features. In this paper we define the term, agricultural landscape simplification, as the reduced compositional and configurational heterogeneity characterized by lower diversity and smaller numbers, sizes, and simpler arrangements of agricultural land uses, which can impair multiple regulating ecosystem services. To examine the causal effects of agricultural landscape simplification on grassland drought impact, we derive a novel remote-sensing product to measure spatial variation in the impact of drought in grasslands during a prolonged drought and heatwave in 2018, and relate it to a multidimensional index of landscape simplification based on landscape metrics. Our causal identification strategy relies on a spatially explicit fuzzy Regression Discontinuity Design (RDD) and uses Germany’s former inner border as an exogenous predictor of agricultural landscape simplification intensity. We identify that a 10 % increase in agricultural landscape simplification is associated with a 7 % increase in grassland drought impact at the former inner border, and quantify the potential forgone revenues associated with the decrease in grassland productivity at approximately 52 € per ha. Our results suggest that identifying the full range of agricultural landscape simplification’s adverse environmental and economic effects would improve preventive landscape policy designs enhancing drought resistance and fostering climate change adaptation strategies.Keywords: Drought, Grassland, Landscape Simplification, Former Inner German Border, Fuzzy RDDJEL-codes: Q54, Q1
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Annual Landsat time series reveal post-Soviet changes in grazing pressure
No full textTemperate grasslands are globally widespread, play an important role as carbon storage, and harbor unique biodiversity. Livestock grazing is the most widespread land use in temperate grasslands, and understanding the impact of grazing on grassland ecosystems is therefore important. However, monitoring grazing pressure and how it changes is hampered by a lack of adequate tools. The Eurasian steppe belt, extending from Eastern Europe to China has experienced marked changes in grazing pressure. Most notably, livestock numbers in the steppes of Kazakhstan and Russia declined by up to 80% after the breakdown of the Soviet Union in 1991, yet how this impacted spatial patterns of grazing pressure is unclear. To address this research gap, we used all available Landsat data from 1985 to 2017 together with extensive ground reference data on grazing pressure to evaluate a broad range of spectral-temporal metrics regarding their ability to capture grazing pressure. While Tasseled Cap-based disturbance indices performed best, combining all spectral-temporal metrics in a binary random forest classification yielded a grazing class membership probability that strongly outperformed all individual metrics. This new index of grazing pressure correlated well with a range of field-based grazing indicators (e.g., number of dung piles, herbaceous biomass) and yielded highly plausible spatial patterns of grazing pressure. We used this index to reconstruct annual changes in grazing pressure across our 360,000 km2 study region, and used LandTrendr time series segmentation to identify trends in grazing pressure. Aggregated grazing pressure followed closely known trends in total livestock numbers over the time period we studied. The spatial footprint of heavy grazing was very large before 1991, but decreased by 73 (±2) % until 2017. This now leaves large areas virtually ungrazed, even in close vicinity to settlements and agricultural areas, and despite a recent recovery of livestock numbers. Our analyses uncovered previously unknown hot-spots of heavy grazing during Soviet times (e.g., around watering points). Our findings suggest potential for a further revival of the livestock sector as well as for the restoration of steppe ecosystems. More broadly, our study highlights how the Landsat archive, in combination with field data on grazing, can be used to map grazing pressure reliably across large areas and over long time spans.</p
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
A Landsat-based analysis of tropical forest dynamics in the Central Ecuadorian Amazon : Patterns and causes of deforestation and reforestation
Full text linkTropical deforestation constitutes a major threat to the Amazon rainforest. Monitoring forest dynamics is therefore necessary for sustainable management of forest resources in this region. However, cloudiness results in scarce good quality satellite observations, and is therefore a major challenge for monitoring deforestation and for detecting subtle processes such as reforestation. Furthermore, varying human pressure highlights the importance of understanding the underlying forces behind these processes at multiple scales but also from an interand transdisciplinary perspective. Against this background, this study analyzes and recommends different methodologies for accomplishing these goals, exemplifying their use with Landsat timeseries and socioeconomic data. The study cases were located in the Central Ecuadorian Amazon (CEA), an area characterized by different deforestation and reforestation processes and socioeconomic and landscape settings. Three objectives guided this research. First, processing and timeseries analysis algorithms for forest dynamics monitoring in areas with limited Landsat data were evaluated, using an innovative approach based in genetic algorithms. Second, a methodology based in image compositing, multisensor data fusion and postclassification change detection is proposed to address the limitations observed in forest dynamics monitoring with timeseries analysis algorithms. Third, the evaluation of the underlying driving forces of deforestation and reforestation in the CEA are conducted using a novel modelling technique called geographically weight ridge regression for improving processing and analysis of socioeconomic data. The methodology for forest dynamics monitoring demonstrates that despite abundant data gaps in the Landsat archive for the CEA, historical patterns of deforestation and reforestation can still be reported biennially with overall accuracies above 70%. Furthermore, the improved methodology for analyzing underlying driving forces of forest dynamics identified local drivers and specific socioeconomic settings that improved the explanations for the high deforestation and reforestation rates in the CEA. The results indicate that the proposed methodologies are an alternative for monitoring and analyzing forest dynamics, particularly in areas where data scarcity and landscape complexity require approaches that are more specialized.Landsat-basierte Analyse der Dynamik tropischer Wälder im Zentral-Ecuadorianischen Amazonasgebiet: Muster und Ursachen von Abholzung und Wiederaufforstung Die tropische Entwaldung stellt eine große Bedrohung für den AmazonasRegenwald dar. Daher ist die Überwachung von Walddynamiken eine notwendige Maßnahme, um eine nachhaltige Bewirtschaftung der Waldressourcen in dieser Region zu gewährleisten. Jedoch verschlechtert Bewölkung die Qualität der Satellitenaufnahmen und stellt die hauptsächliche Herausforderung für die Überwachung der Entwaldung sowie die Detektierung einhergehender Prozesse, wie der Wiederaufforstung, dar. Darüber hinaus zeigt der unterschiedliche menschliche Nutzungsdruck, wie wichtig es ist, die zugrundeliegenden Kräfte hinter diesen Prozessen auf mehreren Ebenen, aber auch interund transdisziplinär, zu verstehen. Variierender anthropogener Einfluss unterstreicht die Notwendigkeit, unterschwellige Prozesse (oder "Driver") auf multiplen Skalen aus interund transdisziplinärer Sicht zu verstehen. Darauf basierend analysiert und empfiehlt die vorliegende Studie unterschiedliche Methoden, welche unter Verwendung von LandsatZeitreihen und sozioökonomischen Daten zur Erreichung dieser Ziele beitragen. Die Untersuchungsgebiete befinden sich im ZentralEcuadorianischen Amazonasgebiet (CEA). Einem Gebiet, das einerseits durch differenzierte Entwaldungsund Aufforstungsprozesse, andererseits durch seine sozioökonomischen und landschaftlichen Gegebenheiten geprägt ist. Das Forschungsprojekt hat drei Zielvorgaben. Erstens werden auf genetischen Algorithmen basierten Verfahren zur Verarbeitung der Zeitreihenanalyse für die Überwachung der Walddynamik in Gebieten, für die nur begrenzte LandsatDaten vorhanden waren, bewertet. Zweitens soll eine Methode in Anlehnung an Satellitenbildkompositen, Datenfusion von mehreren Satellitenbildern und Veränderungsdetektion gefunden werden, die Einschränkungen der Walddynamik durch Entwaldung mithilfe von ZeitreihenAlgorithmen thematisiert. Drittens werden die Ursachen der Entwaldung/Abholzung im CEA anhand der geographischen gewichteten RidgeRegression, die zur einen verbesserten Analyse der sozioökonomischen Information beiträgt, bewertet. Die Methodik für das WalddynamikMonitoring zeigt, dass trotz umfangreicher Datenlücken im LandsatArchiv für das CEA alle zwei Jahre die historischen Entwaldungsund Wiederaufforstungsmuster mit einer Genauigkeit von über 70% gemeldet werden können. Eine verbesserte Analysemethode trägt außerdem dazu bei, die für die Walddynamik verantwortlichen treibenden Kräfte zu identifizieren, sowie lokale Treiber und spezifische sozioökonomische Rahmenbedingungen auszumachen, die eine bessere Erklärung für die hohen Entwaldungsund Wiederaufforstungsraten im CEA aufzeigen. Die erzielten Ergebnisse machen deutlich, dass die vorgeschlagenen Methoden eine Alternative zum Monitoring und zur Analyse der Walddynamik darstellen; Insbesondere in Gebieten, in denen Datenknappheit und Landschaftskomplexität spezialisierte Ansätze erforderlich machen
- …
