1,720,974 research outputs found
Land cover change analysis of a Mediterranean area in Spain using different sources of data: multi-seasonal landsat images, land surface temperature, digital terrain models and texture
No full textThe main objective of this study was to apply a method for the mapping and analysis of land cover changes in a Mediterranean area in southern Spain (Granada Province). The province of Granada is a complex and very heterogeneous area made up of numerous land covers that are difficult to map due to spectral similarities.The inherent difficulty of the mapping of areas with the abovementioned characteristics was addressed by choosing a supervised classification algorithm called random forest and, in addition, by obtaining and incorporating new variables that allowed an improved land cover characterization: multi-seasonal spectral variables corresponding to different stages of land cover phenological development, variables linked to environmental gradients (digital terrain models and land surface temperature) and spatial variability structure textural measures. The same level of accuracy was obtained from the combined use of satellite images with digital terrain models or textural measures. However, the inclusion of the land surface temperature had a more moderate effect and only improved the mapping of some of the land-covers.A post-classification change analysis was conducted by comparing two supervised classifications obtained from the application of the classifier to a selection of different spectral, terrain and textural variables of images from 1998 and 2004. The global accuracy of the change map was 86% (matching percentage). The applied method resulted in a difference in the mapping accuracy of 33% in relation to the method based on the traditional maximum-likelihood classifier and considering only the spectral variables from the Landsat TM-5 satellite sensor
Downscaling Landsat 7 ETM+ thermal imagery using land surface temperature and NDVI images
No full textThermal infrared (TIR) satellite images and derived land surface temperature (LST) are variables of great interest in many remote sensing applications. However, the TIR band has a spatial resolution which is coarser than the other multispectral bands for a given satellite sensor (visible, near and shortwave infrared bands); therefore, the spatial resolution of the retrieved LST from available satellite-borne sensors is not accurate enough to be used in certain applications.The application of a method is shown here for obtaining LST images with enhanced spatial resolution using the LST at a coarser resolution and the Normalized Difference Vegetation Index (NDVI) of the same scene using Downscaling Cokriging (DCK). A LST image with perfect coherence was obtained by applying this method to a Landsat 7 ETM+ image. This implies that, if the downscaled LST image is degraded to its original resolution, the degraded image obtained is identical to the original. Hence high spatial resolution LST images were obtained without altering the original radiometry with the inclusion of artefacts. Moreover, the performance of DCK was compared with global and local TSHARP methods. The RMSE of the sharpened images were 0.85, 0.92 and 1.1 K, respectively
Feature selection approaches for predictive modelling of groundwater nitrate pollution: An evaluation of filters, embedded and wrapper methods
No full textRecognising the various sources of nitrate pollution and understanding system dynamics are fundamental to tackle groundwater quality problems. A comprehensive GIS database of twenty parameters regarding hydrogeological and hydrological features and driving forces were used as inputs for predictive models of nitrate pollution. Additionally, key variables extracted from remotely sensed Normalised Difference Vegetation Index time-series (NDVI) were included in database to provide indications of agroecosystem dynamics. Many approaches can be used to evaluate feature importance related to groundwater pollution caused by nitrates. Filters, wrappers and embedded methods are used to rank feature importance according to the probability of occurrence of nitrates above a threshold value in groundwater. Machine learning algorithms (MLA) such as Classification and Regression Trees (CART), Random Forest (RF) and Support Vector Machines (SVM) are used as wrappers considering four different sequential search approaches: the sequential backward selection (SBS), the sequential forward selection (SFS), the sequential forward floating selection (SFFS) and sequential backward floating selection (SBFS). Feature importance obtained from RF and CART was used as an embedded approach. RF with SFFS had the best performance (mmce = 0.12 and AUC = 0.92) and good interpretability, where three features related to groundwater polluted areas were selected: i) industries and facilities rating according to their production capacity and total nitrogen emissions to water within a 3 km buffer, ii) livestock farms rating by manure production within a 5 km buffer and, iii) cumulated NDVI for the post-maximum month, being used as a proxy of vegetation productivity and crop yield.</p
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
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
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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