1,722,382 research outputs found
Land consumption estimation using Landsat satellite data and change detection techniques in the Google Earth Engine cloud environment
Full text linkIl consumo di suolo rappresenta la trasformazione permanente o reversibile della copertura del suolo da non artificiale ad artificiale, che comporta l’espansione e la densificazione urbana, così come la perdita della risorsa suolo. La copertura impermeabile della superficie terrestre causa degradazione del suolo, poichè contribuisce ad incrementare il rischio ambientale e territoriale. La mappatura ed il monitoraggio della crescita urbana è pertanto essenziale per lo sviluppo sostenibile. Il telerilevamento per l’osservazione della Terra è un potente strumento per fornire continue informazioni sui cambiamenti di copertura del suolo. Tra vari datasets satellitari, l’archivio multi-decennale di immagini Landsat è particolarmente adatto per molti studi di change detection a scala urbana. L’obiettivo della tesi è di estrarre informazioni sul consumo di suolo da immagini satellitari open Landsat applicando differenti algoritmi, anche con alcuni elementi innovativi, in ambiente cloud Google Earth Engine (GEE). Il primo metodo di change detection ha implicato lo sviluppo e l’applicazione di un nuovo approccio di classificazione delle immagini index-based, con multiple regole decisionali, da dati Landsat 8. Il “post-classification comparison” tra mappe binarie “Urban/Non-urban”, seguito da “image-differencing” tra mappe di albedo superficiale multi-temporali, è stato testato con successo sull’area di studio di Bitritto e in seguito applicato al territorio di Bari (2015-2023), con risultati molto buoni. Il secondo metodo di change detection ha riguardato l’implementazione dell’algoritmo di Continuous Change Detection and Classification su GEE, utilizzando uno stack di immagini Landsat multi-temporali prive di nuvole per individuare il comportamento spettrale nel tempo di ogni pixel. Le mappe multi-temporali “Urban/Non-urban”, ottenute utilizzando i coefficienti del modello pixel per pixel come variabili di input per classificazioni Random Forest, sono state in seguito sottoposte a “post-classification comparison” e hanno permesso di ricavare informazioni sulla crescita urbana con risultati molto soddisfacenti per tutti i capoluoghi di Regione italiani (2006-2023). Entrambi i metodi hanno leggermente sottostimato i cambiamenti realmente avvenuti a causa del problema dei “pixel misti” e delle tecniche di filtraggio, mentre GEE ha permesso una elaborazione relativamente rapida dei dati telerilevati.Land consumption is the permanent or reversible transformation of non-artificial into artificial land cover, that leads to urban sprawl and densification, as well as the loss of the soil resource. The impervious cover of the land surface causes land degradation, as it contributes to increasing environmental and territorial risks. Mapping and monitoring the urban growth is therefore essential for sustainable development. Earth observation remote sensing is a powerful way to provide continuous information on land cover changes. Among various satellite datasets, the multi-decadal archive of Landsat images is particularly suitable for many urban change detection studies. The aim of this thesis is to extract land consumption information from open Landsat satellite imagery by applying different algorithms, also with some innovative elements, in the Google Earth Engine (GEE) cloud environment. The first change detection method implied the development and application of a novel index-based image classification approach, involving multiple decision rules, from Landsat 8 data. The “post-classification comparison” between binary “Urban/Non-urban” maps, followed by the “image differencing” between multi-date land surface albedo maps, was successfully tested for the study area of Bitritto and subsequently applied to the Bari territory (2015-2023), with very good results. The second change detection method consisted in the implementation of the Continuous Change Detection and Classification algorithm in GEE, involving a stack of multitemporal cloud-free Landsat images to identify temporal spectral behavior, pixel-by-pixel. The multitemporal “Urban/Non-urban” maps, produced using per-pixel model coefficients as input variables for Random Forest classifications, subsequently underwent “post-classification comparison” and allowed to retrieve urban growth information, with very satisfactory results, for all the capitals of Italian regions (2006-2023).
Both methods slightly underestimated the changes that really occurred, due to the “mixed pixels” issue and the filtering techniques, while GEE allowed relatively rapid processing of remote sensing data
Women as decision makers in community forest management: Evidence from Nepal
No full textIn many developing countries women are responsible for the collection and management of forest products essential to the daily lives of their household. However, women are often neglected in the decision-making process within community level institutions devoted to the management of natural resources. This paper looks at whether and how an increased participation of women in the Executive Committee (EC) of Community Forest User Groups (FUG) in Nepal affects forest protection. I exploit an amendment made to the guidelines for FUG formation that sets a higher threshold for women representation in the EC, to evaluate the impact of women on firewood extraction. The results show that higher female participation in the ECs of FUGs leads to a significant decrease in firewood extraction. These results suggest that in countries with common property resources, the effectiveness of collective action institutions depends also on their gender composition
Short-and long-term impact of violence on education: The case of timor leste
No full textThis paper analyzes the impact of the wave of violence that occurred in Timor Leste in 1999 on education outcomes. We examine the short-term impact of the violence on school attendance in 2001 and its longer-term impact on primary school completion of the same cohorts of children observed again in 2007. We compare the educational impact of the 1999 violence with the impact of other periods of high-intensity violence during the 25 years of Indonesian occupation. The short-term effects of the conflict are mixed. In the longer term, we find evidence of a substantial loss of human capital among boys in Timor Leste who were exposed to peaks of violence during the 25-year long conflict. The evidence suggests that this result may be due to household trade offs between education and economic welfare. © The Author 2013. Published by Oxford University Press on behalf of the International Bank for Reconstruction and Development / THE WORLD BANK. All rights reserved
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
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