bluuebunny/crossref_metadata_2025_split huggingface dataset:
Density from pellet groups: Comparing methods for estimating dung persistence time
Authors: Delisle Zackary J. , Swihart Robert K. , Quinby Brandon M. , Sample Richard D. , Kinser‐Mcbee Kevin J. , Tauber Emily K. , Flaherty Elizabeth A. 
Published: July 2022

AbstractEffective wildlife management often relies on estimates of animal density, and cue counting is a viable estimation strategy. A key component of density estimation from dung, a form of cue counting, is estimation of the persistence time, , of dung piles. However, differences between observers on what constitutes a dung pile may alter subsequent density estimates. Additionally, many researchers studying white‐tailed deer (Odocoileus virginianus) have substituted for the number of days between the date in which 98% of deciduous trees shed leaves in autumn and field sampling. To address these 2 concerns, we compared 3 methods for estimating of white‐tailed deer pellet groups: (1) a common modelling approach based on observations from a single observer (single‐observer method), (2) a method that accommodates interobserver variation on the status of dung during field surveys (interobserver method), and (3) the days elapsed since 98% of deciduous trees shed autumn leaves (leaf‐off method). We then applied these 3 estimates to distance‐sampling data on pellet groups from white‐tailed deer that we collected along transects during 3 sampling seasons from 2019–2021 in west‐central Indiana. We estimated habitat‐ and year‐specific deer densities. Persistence probability of pellet groups varied across habitats and years, positively with age and number of pellets, and negatively with precipitation and temperature. In several instances, we found strong or marginal differences between densities estimated using the leaf‐off method and the other 2 methods. The densities using the interobserver and single‐observer methods were similar, with the latter being larger by an average of 8.0% (SE = 1.71). The latter also yielded coefficients of variation (CV) that averaged 16.6% (SE = 4.8) larger, attributable to interobserver discrepancies in scoring dung persistence. Density estimates from the leaf‐off method were 32.6% (SE = 15.3) and 37.8% (SE = 13.0) less than the density estimates from the interobserver and single‐observer methods, respectively. We encourage future researchers estimating density using multiple observers and dung sampling techniques to incorporate interobserver variation. We advocate that biologists relying on dung‐based estimation of density for white‐tailed deer abandon the conventional leaf‐off method and adopt other modelling approaches.
Geographically Weighted Methods to Validate Land Use Cover Maps
Authors: Molinero-Parejo Ramón 
Published: July 2022

AbstractOne of the most commonly used techniques for validating Land Use Cover (LUC) maps are the accuracy assessment statistics derived from the cross-tabulation matrix. However, although these accuracy metrics are applied to spatial data, this does not mean that they produce spatial results. The overall, user’s and producer’s accuracy metrics provide global information for the entire area analysed, but shed no light on possible variations in accuracy at different points within this area, a shortcoming that has been widely criticized. To address this issue, a series of techniques have been developed to integrate a spatial component into these accuracy assessment statistics for the analysis and validation of LUC maps. Geographically Weighted Regression (GWR) is a local technique for estimating the relationship between a dependent variable with respect to one or more independent variables or explanatory factors. However, unlike traditional regression techniques, it considers the distance between data points when estimating the coefficients of the regression points using a moving window. Hence, it assumes that geographic data are non-stationary i.e., they vary over space. Geographically weighted methods provide a non-stationary analysis, which can reveal the spatial relationships between reference data obtained from a LUC map and classified data. Specifically, logistic GWR is used in this chapter to estimate the accuracy of each LUC data point, so allowing us to observe the spatial variation in overall, user’s and producer’s accuracies. A specific tool (Local accuracy assessment statistics) was specially developed for this practical exercise, aimed at validating a Land Use Cover map. The Marqués de Comillas region was selected as the study area for implementing this tool and demonstrating its applicability. For the calculation of the user’s and producer’s accuracy metrics, we selected the tropical rain forest category [50] as an example. Furthermore, a series of maps were obtained by interpolating the results of the tool, so enabling a visual interpretation and a description of the spatial distribution of error and accuracy.
General Land Use Cover Datasets for Europe
Authors: García-Álvarez David , Lara Hinojosa Javier , Jurado Pérez Francisco José , Quintero Villaraso Jaime 
Published: July 2022

AbstractThe land uses and covers of Europe are the most systematically mapped in the world today, and their associated datasets offer the greatest spatial and thematic detail. Thanks to the work done within the Copernicus Land Monitoring programme run by the European Environmental Agency (EEA) and the Joint Research Centre (JRC) of the European Commission, there are many general LUC datasets covering most of the European continent. These general datasets map all land uses and covers on the ground, without focusing on any specific type. However, whereas some cover the whole of Europe, others only map specific local areas of interest, such as urban or coastal areas, riparian zones or spaces protected under the Nature 2000 network. CORINE Land Cover (CLC) is the flagship European LUC mapping programme and a reference worldwide. It has provided consistent LUC information at a detailed scale (1:100,000) every 6 years since 1990. This is the result of a high degree of coordination between many different organizations and institutions across Europe. The Copernicus programme also includes other European datasets such as Urban Atlas, N2K, Riparian Zones and Coastal Zones, which provide very detailed LUC information at higher levels of spatial detail (scale 1:10,000) for specific geographical area types: Functional Urban Areas, the Natura 2000 network, riparian zones from Strahler level 2–8 rivers and areas 10 km away from the coastline. However, these projects do not cover the same long timeframe as CLC. In addition, their long-term future is far from clear in that updates are only planned for Urban Atlas and Coastal Zones. PELCOM, GlobCorine and the Annual Land Cover Product are the European projects that most resemble the LUC maps available at global and supra-national scales for other parts of the world. They were obtained through classification of satellite imagery. PELCOM and GlobCorine are only available for a few dates and at quite coarse spatial resolutions: 1 km and 300 m respectively. The Annual Land Cover Product consists of a series of LUC maps for the period 2000–2019 at a highly detailed spatial resolution (30 m). It offers information for a large number of different points in time. However, it makes a separate classification of land uses each year, which means that change analysis with this dataset is more uncertain than with CLC or other Copernicus Land Monitoring products. HILDA and S2GLC 2017 are LUC datasets produced within the framework of different research projects, which can be considered reference products in their respective fields. HILDA provides one of the largest time series of LUC maps currently available, spanning the period from 1900 to 2010. S2GLC 2017 is one of the most spatially detailed LUC mapping experiences at a supra-national scale, with a spatial resolution of 10 m.
General Land Use Cover Datasets for America and Asia
Authors: García-Álvarez David , Lara Hinojosa Javier 
Published: July 2022

AbstractIn this chapter we review some examples of general Land Use Cover (LUC) mapping at a supra-national level in America and Asia. These datasets provide a general overview of the land uses and covers in specific American or Asian regions, without focusing on any particular land use or cover. For Asia, we have only identified one dataset mapping the Himalayan region, whereas for America five different datasets were identified. Only three of these are reviewed here, as the other two (SERENA, South America 30 m) are not available for download. The most ambitious project of all those reviewed is NALCMS, which coordinates the production of a LUC map for the whole of North America (Canada, Mexico, USA) at detailed scales (30–250 m) and using the same classification legend. It is the only dataset of all those reviewed that provides a time series of LUC maps (2005, 2010 and 2015). The Himalaya Regional Land Cover database is a vector-based map that provides information on LUC changes over the period 1970/80–2007 at a scale of 1:350,000. The other two American datasets—LBA-ECO LC-08 (1 km, 1987/91) and MERISAM2009 (300 m, 2008/10)—are raster-based and only available for one date, therefore making change detection impossible.
Global Thematic Land Use Cover Datasets Characterizing Agricultural Covers
Authors: García-Álvarez David , Lara Hinojosa Javier 
Published: July 2022

AbstractThere is a wide variety of global thematic Land Use Cover (LUC) datasets characterizing agricultural covers. Most of them focus on cropland areas, providing information on their extent or the percentage of cropland cover on the ground. In some cases, the focus is more specific and they provide information on cropland irrigation practices. In other cases, specific maps charting the extension of different crops are also available. In this chapter, we review 8 different datasets with a spatial resolution of at least 1 km. There are many other datasets characterizing agricultural covers at coarser resolutions, such as the Historic Croplands Dataset, GMRCA or GIAM. Their coarse resolution hampers their potential application in practice, which is why they are not described in detail in this chapter. Nor do we analyse FROM-GC, a dataset mapping the extent of global cropland at 30 m, because it is not currently accessible. GFSAD30 has the highest resolution of all the datasets reviewed (30 m). It also provides some of the most up-to-date information (2015). However, it only charts the extent of cropland. As part of an associated project, GFSAD1KCD and GFSAD1KCM characterize cropland areas in 9 and 7 categories respectively at 1 km for 2010. They provide information on the irrigation status of the crops. GFSAD1KCD and GFSAD1KCM were obtained from data fusion. This method is commonly used in the production of many of the cropland datasets reviewed: IIASA-IFPRI cropland map, Global Synergy Cropland Map, Unified Cropland Layer (UCL) and ASAP Land Cover Masks. The IIASA-IFPRI (2005) and ASAP maps provide information on the proportion of cropland at a spatial resolution of 1 km. ASAP also includes a map on rangeland covers, and as such is the only dataset described in this chapter that maps a cover other than croplands. The Global Synergy Cropland Map (2010) and the Unified Cropland Layer (2014) also map cropland proportions, although they have been produced at higher spatial resolutions: 500 and 250 m respectively. The Global Cropland Extent product maps the extent of cropland at 250 m based on imagery from 2000-2008. Although thematically limited, this dataset is less affected by time variability, as it is based on imagery taken over a long period (8 years). Finally, GRIPC maps the extent of three types of cropland area (irrigated, rainfed and paddy crops) at 500 m for 2005.
Supra-National Thematic Land Use Cover Datasets
Authors: García-Álvarez David , Jurado Pérez Francisco José , Lara Hinojosa Javier 
Published: July 2022

AbstractSupra-national thematic Land Use Cover (LUC) datasets are not very common. While there are several general datasets mapping all the land uses or covers in different supra-national areas across the world, LUC datasets with a similar extent that focus on the mapping of specific land covers in greater thematic detail are scarce. In this chapter, we review six different supra-national thematic LUC datasets. Three others were also found in the literature, but are not fully available for download, namely the TREES Vegetation Map of Tropical South America, the Central Africa—Vegetation map and FACET. The Circumpolar Arctic Region Vegetation dataset was also excluded from this review because of its specificity and coarse scale (1:7,500,000). Europe is the continent with the most relevant, most updated and most detailed LUC thematic datasets at supra-national scales. This is due to the work being done by the European Commission through its Joint Research Centre (JRC) and the Copernicus Land Monitoring Programme. The High-Resolution Layers (HRL) provide very detailed information, both thematically and spatially (from 10 m), for five different themes: imperviousness, tree cover, grasslands, water and wet covers, and small woody features. The European Settlement Map also provides information on built-up areas at very detailed scales (from 2.5 m). HRL and ESM are recently launched datasets which, therefore, do not provide a long series of historical data. In addition, ESM is an experimental dataset produced within the framework of a research project funded by the European Commission and no updates are expected. The datasets reviewed in this chapter for other parts of the world focus on vegetation covers of tropical forests and other relevant areas in terms of biodiversity and environmental studies. These datasets were produced within projects funded by the European Commission and the United States Agency for International Development. Unlike the previous datasets for Europe, they are already outdated and are usually produced at coarser spatial resolutions: Insular Southeast Asia—Forest Cover Map (1 km, 1998/00); Continental Southeast Asia—Forest Cover Map (1 km, 1998/02). For its part, the Congo Basin Monitoring dataset, although outdated, provides information at a higher resolution (57 m) for two different dates: 1990, 2000. The Joint Research Centre of the European Commission also produced an African cropland mask as a source of information for policy-makers. Of all the datasets reviewed in this chapter, it is the only one to focus on agricultural covers. It was obtained from data fusion at 250 m. Consequently, it does not show the cropland areas of Africa for a specific date across the whole continent.
Land Use Cover Datasets: A Review
Authors: García-Álvarez David , Nanu Sabina Florina 
Published: July 2022

AbstractThis chapter presents a review of Land Use Cover (LUC) datasets at global and supranational scales. To this end, we differentiate between LUC maps (Sect. 3) and reference LUC datasets (Sect. 4). The former map how different land uses or covers are distributed across the Earth’s surface. The latter provides a sample of LUC data for specific points on Earth and are normally used in LUC mapping and modelling calibration and validation exercises. We also include a brief presentation of the main producers of LUC datasets (Sect. 2). The LUC maps reviewed here are classified according to different criteria. First, we differentiate between general LUC maps (Sect. 3.2), which provide information about all land uses and covers on Earth, and thematic LUC maps (Sect. 3.3), which focus on the mapping of a specific land use or cover. Second, we classify general and thematic LUC maps according to their extent, distinguishing between global and supra-national LUC maps. The general maps are classified according to the continent for which they provide information, either fully or partially, while the thematic maps are classified according to the type of land use or cover they focus on. Most of the datasets reviewed in this chapter are characterized in detail in Part IV of this book, to which this chapter acts as an introduction. This chapter includes a series of tables with all the datasets, indicating those for which a detailed description is provided in Part IV.
Pontius Jr. Methods Based on a Cross-Tabulation Matrix to Validate Land Use Cover Maps
Authors: Paegelow Martin , Mas Jean-François , Gallardo Marta , Camacho Olmedo María Teresa , García-Álvarez David 
Published: July 2022

AbstractSeveral validation techniques based on the cross-tabulation matrix can be applied to validate Land Use Cover (LUC) maps. The exercises in this chapter focus, in particular, on the cross-tabulation techniques proposed by Robert Gilmore Pontius Jr., who has developed many indices and techniques in this field. Given his major contribution to this family of validation techniques, we have associated his name here with cross-tabulation techniques without this in any way implying that his scientific activity is limited to this field. The null model (Sect. 1) is especially useful for validating simulations, comparing the modelled map to a reference map with full persistence. LUCC budget (Sect. 2) only focusses on changes, which it splits into different components. This method can be used to compare the changes we want to validate with a reference set of changes, so providing interesting information as to how well our maps capture the dynamics of the landscape. Quantity and allocation disagreement (Sect. 3) analyse the differences between the reference map and the map being validated using two indices: disagreement in quantity and disagreement in allocation. The Figure of Merit (FoM) (Sect. 4) technique is used to validate a set of LUC changes by comparing them with a reference, distinguishing between different components of agreement: correctly simulated change, wrongly simulated or missing change. Incidents and States (Sect. 5) allows us to identify illogical transitions in a time series of maps by providing the number of states and transitions that a cell undergoes over the course of the series. Intensity analysis (Sect. 6) and Flow matrix (Sect. 7) also enable us to validate the logic of LUC changes in a time series of maps. Intensity analysis provides information on the speed of changes, identifying those transitions or changes that do not follow a logical trend, while the flow matrix enables us to spot unstable changes in a series of maps. In this chapter, we present examples of how these techniques can be used in different cases: to validate single LUC maps, to validate a series of maps with two or more time points, to validate simulated changes against a reference map of changes and to validate changes simulated by various models. All these techniques are illustrated by exercises using datasets from the Asturias Central Area and the Ariège Valley.
Policy Framework for Hydropower Mitigation
Authors: Kampa Eleftheria 
Published: July 2022

AbstractHydropower is at the cross-road of different policies on renewable energy, climate change, water and nature. Knowledge of current policies and regulations at different levels is crucial for understanding the framework conditions for more sustainable hydropower. This chapter examines the role of policy in planning and implementing mitigation actions for European hydropower. The overarching framework is set by European Union legislation in particular the Water Framework Directive, EU policies on nature/biodiversity and the EU agenda for energy and climate. All these different policies should be taken into account in a balanced way considering synergies and trade-offs. EU policies are complemented by national legislation that sets the regulatory framework for hydropower mitigation measures in each country. The impacts of hydropower for which mitigation is most commonly required by legislation are the disruption of upstream fish migration and the modification of flow conditions. Supported by a policy analysis of eight European countries, this chapter shows that recent changes in environmental legislation and societal pressure have reduced permit duration for hydropower plants. Further, the Water Framework Directive and national policy revisions have triggered modifications in authorisation procedures for hydropower at the benefit of implementing mitigation measures.
Impacts and Risks of Hydropower
Authors: van Treeck Ruben , Geist Juergen , Pander Joachim , Tuhtan Jeffrey , Wolter Christian 
Published: July 2022

AbstractThis chapter very briefly outlines site and constellation specific direct and indirect impacts of a hydropower scheme primarily on fishes. It describes potential effects of single elements of a hydropower scheme, such as available migration routes up- and downstream, impoundment, hydraulic head, turbine type and mode of operation. It summarises the state of knowledge, points out knowledge gaps and indicates potential mitigation options.
Academic Achievement and Subjective Well-being: A Representative Cross-sectional Study
Authors: Wollschläger Rachel , Esch Pascale , Keller Ulrich , Fischbach Antoine , Pit-ten Cate Ineke M. 
Published: July 2022

AbstractFormal education is a very important, time-intensive, and highly consequential aspect of adolescents’ everyday life. School as well as education can influence adolescents’ well-being in both the short- and long-term. In return, adolescents’ well-being in- and outside school may affect their educational achievement. The objective of the present study is to investigate how self-reported dimensions of adolescents’ subjective well-being (SWB) in an educational context (i.e., academic self-concept, school anxiety, social and emotional inclusion) relate to educational pathways (regular vs. irregular school transitions; attendance of more vs. less prestigious secondary school tracks) and standardized assessment scores in key academic areas (i.e., mathematics and languages). Drawing on representative data emerging from the Luxembourg School Monitoring Programme “Épreuves Standardisées” (academic year 2018/2019), the relationship between academic achievement and students’ self-reported well-being was analysed cross-sectionally for the entire student cohorts of 5th and 9th graders. Result indicated that grades and educational pathways affect SWB, whereby in general lower ratings of SWB were observed in older students, students that experienced grade retention and students in lower secondary school tracks. Furthermore, ratings of SWB explained a significant proportion of variance in academic achievement in bot Grade 5 and Grade 9. These findings highlight the importance of student´ SWB in education. SWB may not only affect academic achievement, but also impact motivation and engagement and hence long-term educational success. Implications of the findings for research and educational debate are discussed.
Ungleichheiten in schulischen Gesundheitsproblemen und subjektivem Wohlbefinden bei luxemburgischen Grund- und Sekundarschüler/innen
Authors: Hadjar Andreas , de Moll Frederick 
Published: July 2022

ZusammenfassungSubjektives Wohlbefinden und Gesundheit sind grundlegende Voraussetzungen für erfolgreiche Bildungsprozesse in der Schule. Inwieweit gesundheitliche Probleme und mangelndes Wohlbefinden im Zuge der Schullaufbahn bei bestimmten Risikogruppen wie Schülerinnen und Schülern aus benachteiligten Schichten und Einwanderungsgruppen gehäuft auftreten, ist speziell für Luxemburg vergleichsweise wenig erforscht. Gerade im Vergleich zu sozialen Differenzen hinsichtlich kognitiver Variablen und Schulleistungen geraten Ungleichheiten im Wohlbefinden häufig aus dem Blick. Im Beitrag begegnen wir dieser Forschungslücke, indem Gesundheitsprobleme, allgemeine Lebenszufriedenheit und Wohlbefinden in der Grund- und Sekundarschule in Abhängigkeit von sozialer Schicht, Migrationshintergrund und Geschlecht analysiert werden. Im Ergebnis zeigen sich differenzielle Effekte für die unterschiedlichen Risikogruppen. So ist ein Migrationshintergrund mit einer geringeren Lebenszufriedenheit assoziiert, Jungen fühlen sich jahrgangsunabhängig in der Schule weniger wohl als Mädchen, und Schüler/innen aus privilegierten Verhältnissen berichten tendenziell weniger Gesundheitsprobleme in Zusammenhang mit der Schule.