1,720,973 research outputs found
Environmental Effectiveness of Swine Sewage Management: A Multicriteria AHP-Based Model for a Reliable Quick Assessment
No full textEnvironmental issues related to swine production are still a major concern for the general public and represent a key challenge for the swine industry. The environmental impact of higher livestock concentration is particularly significant where it coincides with weaker policy standards and poor manure management. Effective tools for environmental monitoring of the swine sewage management process become essential for verifying the environmental compatibility of farming facilities and for defining suitable policies aimed at increasing swine production sustainability. This research aims at the development and application of a model for a quick assessment of the environmental effectiveness of the pig farming sewage management process. In order to define the model, multicriteria techniques, and in particular, Saaty’s analytic hierarchy process, were used to develop an iterative process in which the various key factors influencing the process under investigation were analyzed. The model, named EASE (Environmental Assessment of Sewages management Effectiveness), was optimized and applied to the Lake Trasimeno basin (Umbria, Italy), an area of high natural, environmental and aesthetic value. In this context, inadequate disposal of pig sewage represents a potential source of very considerable pollution. The results have demonstrated how the multicriteria model can represent a very effective and adaptable tool also in those decision-making processes aimed at the sustainable management of livestock production
Assessing ecosystem and urban services for landscape suitability mapping
No full textEcosystem services (ES) and urban services (US) can comparably improve human well-being. Models for integrating ES and US with unexpressed and objective needs of defined groups of stakeholders may prove helpful for supporting decisions in landscape planning and manage-ment. In fact, they could be applied for highlighting landscape areas with different characteristics in terms of services provided. From this base, a suitability spatial assessment model (SUSAM) was developed and applied in a study area considering different verisimilar scenarios that policy makers could analyse. Each scenario is based on the prioritization of a set of services considering a defined group of stakeholders. Consistent and comparable ES and US indices of spatial benefiting areas (SBA) of services were calculated using GIS spatialization techniques. These indices were aggre-gated hierarchically with the relevance of services according to a spatial multicriteria decision analysis (S‐MCDA). Results include maps for each scenario showing detailed spatial indices of suitability that integrate the local availability of SBA of ES and US, along with their relevance. The results were compared with known landscape classes identified in previous studies, which made it possible to interpret the spatial variation of suitability in the light of known landscape features. A complete sensitivity analysis was performed to test the sensitiveness of the model’s outputs to variations of judgements and their resistance to the indicators’ variation. The application of the model demonstrated its effectiveness in a landscape suitability assessment. At the same time, the sensitivity analysis and helping to understand the model behaviour in the different landscape classes also suggested possible solutions for simplifying the whole methodology
ECOSYSTEM SERVICES DEMAND, SUPPLY AND BUDGET ALONG THE URBAN-RURAL-NATURAL GRADIENT
No full textLandscapes can be viewed as a continuum and studied using spatial gradients,
along which environmental modifications are ordered in space and determine the
structural and functional components of ecosystems. The anthropogenic land
uses generate specific gradients that can be recognised along the succession of
urban–suburban–cultivated–managed-natural landscapes. From this point of
view, the traditional urban-rural dichotomy can also be considered as a gradient,
produced by a sliding level of human influence on ecosystems. Since the
Millennium Ecosystem Assessment (2005), many scientists are directing their
effort to studying and quantifying the benefits people obtain from ecosystems,
synthesised by the concept of Ecosystem Services (ES). One of the major
approaches for ES assessment is based on the analysis of stock and the condition
of the biodiversity of the usual components of habitat, ecotopes or biomes.
Despite this increasing interest, spatially explicit methods to analyse ES are still
lacking. The research aimed to develop an innovative methodology supporting
landscape analysis and planning processes by means of (a) the identification and
characterisation of the types of landscape along the urban-rural-natural gradient
and (b) the analysis of potential ES demand and supply within said types of
landscape. The Kernel Density Estimation technique was applied to calculate
continuous intensity indicators associated with urbanisation, agriculture, and
natural elements, considered as key components of the formation of the
landscape gradient. A multivariate, spatial analysis enabled the identification of
different landscape structures constituting the gradient of the study area. The
classification highlighted not only specific “pillar” landscapes, dominated by
one of the three components (urban, agricultural, and natural), but also
transitional landscapes, where the most relevant relationships between land uses
were identified. The potential ES demand, supply, and budget within each
landscape area were assessed using specific indices, based on an expertknowledge
approach, retrieved from the bibliography and combined with the
intensity indicators calculated for the landscape components under investigation.
This method enabled a large group of ES to be quantified simultaneously by
means of comparable demand, supply and budget indicators. Results showed a
complex organisation of pillar and transitional landscapes along the identified
urban-rural-natural gradient, which match different bundles of ES demand and
supply. The research findings contribute to a new interpretation of ES demand
and supply on the landscape scale and can support a better spatial
contextualisation of the ecological and socio-economic issues characterising
landscape gradients
Urban-rural gradient detection using multivariate spatial analysis and landscape metrics
Full text linkThe gradient approach allows for an innovative representation of landscape composition and configuration not presupposing spatial discontinuities typical of the conventional methods of analysis. Also the
urban-rural dichotomy can be better understood through a continuous landscape gradient whose characterization changes accordingly to natural and anthropic variables taken into account and to the spatio-temporal scale adopted for the study. The research was aimed at the analysis of an urban-rural gradient within a study area located in central Italy, using spatial indicators associated with urbanization, agriculture and natural elements. A multivariate spatial analysis (MSA) of such indicators enabled the identification of urban, agricultural and natural
dominated areas, as well as specific landscape transitions where the most relevant relationships between agriculture and other landscape components were detected. Landscapes derived from MSA were studied by a set of key landscape pattern metrics within a framework oriented to the structural characterization of the whole urban-rural gra-
dient. The results showed two distinct sub-gradients: one urban-agricultural and one agricultural-natural, both characterized by different fringe areas. This application highlighted how the proposed methodology can represent a reliable approach supporting modern landscape
planning and management
A revised model of anatomically modern human expansions out of africa through a machine learning approximate bayesian computation approach
No full textThere is a wide consensus in considering Africa as the birthplace of anatomically modern humans (AMH), but the dispersal pattern and the main routes followed by our ancestors to colonize the world are still matters of debate. It is still an open question whether AMH left Africa through a single process, dispersing almost simultaneously over Asia and Europe, or in two main waves, first through the Arab Peninsula into southern Asia and Australo-Melanesia, and later through a northern route crossing the Levant. The development of new methodologies for inferring population history and the availability of worldwide high-coverage whole-genome sequences did not resolve this debate. In this work, we test the two main out-of-Africa hypotheses through an Approximate Bayesian Computation approach, based on the Random-Forest algorithm. We evaluated the ability of the method to discriminate between the alternative models of AMH out-of-Africa, using simulated data. Once assessed that the models are distinguishable, we compared simulated data with real genomic variation, from modern and archaic populations. This analysis showed that a model of multiple dispersals is four-fold as likely as the alternative single-dispersal model. According to our estimates, the two dispersal processes may be placed, respectively, around 74,000 and around 46,000 years ago
Predictive Modelling of Maize Yield Using Sentinel 2 NDVI
No full textAccurate yield prediction is essential for precision agriculture as it
enables farmers to optimize their inputs and manage their resources more efficiently,
ultimately leading to higher profitability and sustainable farming practices.
The aim of this research is to verify whether satellite images can be a valid tool
for predicting yield in summer maize (Zea mays L.). The study was conducted in
2022 in two adjacent fields in Umbria, Italy. The approach adopted is based on
the use of NDVI (Normalized Difference Vegetation Index) data from Sentinel 2
(S2) satellites and yield data from combine harvesters to estimate the grain yield
of maize. The NDVI data, derived from S2 images acquired at different growth
cycle stages, were used for a Principal Component Analysis (PCA). Subsequently,
a multiple linear regression (MLR) model was created. The results show a significant
correlation between NDVI and grain yield, suggesting that the central stages
of the maize phenological cycle are more indicative for this purpose. Further
experiments are necessary to confirm the effectiveness of the proposed approach,
using more specific vegetation indices or including soil and climate data in the
procedure, thus obtaining more comprehensive responses
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
Object-Based Informal Settlement Mapping in Google Earth Engine Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data
No full textMapping informal settlements’ diverse morphological patterns remains intricate due to the unavailability and huge costs of high-resolution data, as well as the spatial heterogeneity of urban environments. The accessibility to high-spatial-resolution PlanetScope imagery, coupled with the convenience of simple non-iterative clustering (SNIC) algorithm within the Google Earth Engine (GEE), presents the potential for Geographic Object-Based Image Analysis (GEOBIA) to map the spatial morphology of deprivation pockets in a complex built-up environment of Durban. Such advances in multi-sensor satellite image inventories on GEE also afford the possibility to integrate data from sensors with different spectral characteristics and spatial resolutions for effective abstraction of informal settlement diversity. The main objective is to exploit Sentinel-1 radar data, Sentinel-2 and PlanetScope optical data fusion for more accurate and precise localization of informal settlements using GEOBIA, within GEE. The findings reveal that the Random Forests classification model achieved informal settlement identification accuracy of 87% (F-score) and overall accuracy of 96%. An assessment of agreement between observed informal settlement extents and ground truth dimensions was conducted through regression analysis, yielding root mean square log error (RMSLE) = 0.69 and mean absolute percent error (MAPE) = 0.28. The results demonstrate reliability of the classification model in capturing variability of spatial characteristics of informal settlements. The research findings confirm efficacy of combined advantages of GEOBIA within GEE, and integrated datasets for more precise capturing of characteristic morphologic informal settlement features. The outcomes suggest a shift from standard static conventional approaches towards more dynamic, on-demand informal settlement mapping through cloud computing, a powerful analysis platform that simplifies access to and the processing of voluminous data. The study has important implications for identifying the most effective ways to map informal settlements in a complex urban landscape, thus providing a benchmark for other regions with significant landscape heterogeneity
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
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