1,721,055 research outputs found
Correction: Atzberger, C. Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs. Remote Sens. 2013, 5, 949–981
No full textThe author mistakenly spelt Nadine Brisson as Nadine Gobron in the Acknowledgements of [1]
Estimation of Leaf Area Index Using DEIMOS-1 Data: Application and Transferability of a Semi-Empirical Relationship between two Agricultural Areas
No full textThis work evaluates different procedures for the application of a semi-empirical model to derive time-series of Leaf Area Index (LAI) maps in operation frameworks. For demonstration, multi-temporal observations of DEIMOS-1 satellite sensor data were used. The datasets were acquired during the 2012 growing season over two agricultural regions in Southern Italy and Eastern Austria (eight and five multi-temporal acquisitions, respectively). Contemporaneous field estimates of LAI (74 and 55 measurements, respectively) were collected using an indirect method (LAI-2000) over a range of LAI values and crop types. The atmospherically corrected reflectance in red and near-infrared spectral bands was used to calculate the Weighted Difference Vegetation Index (WDVI) and to establish a relationship between LAI and WDVI based on the CLAIR model. Bootstrapping approaches were used to validate the models and to calculate the Root Mean Square Error (RMSE) and the coefficient of determination (R2) between measured and predicted LAI, as well as corresponding confidence intervals. The most suitable approach, which at the same time had the minimum requirements for fieldwork, resulted in a RMSE of 0.407 and R2 of 0.88 for Italy and a RMSE of 0.86 and R2 of 0.64 for the Austrian test site. Considering this procedure, we also evaluated the transferability of the local CLAIR model parameters between the two test sites observing no significant decrease in estimation accuracies. Additionally, we investigated two other statistical methods to estimate LAI based on: (a) Support Vector Machine (SVM) and (b) Random Forest (RF) regressions. Though the accuracy was comparable to the CLAIR model for each test site, we observed severe limitations in the transferability of these statistical methods between test sites with an increase in RMSE up to 24.5% for RF and 38.9% for SVM
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
Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs
No full textMany remote sensing applications are devoted to the agricultural sector. Representative case studies are presented in the special issue “Advances in Remote Sensing of Agriculture”. To complement the examples published within the special issue, a few main applications with regional to global focus were selected for this review, where remote sensing contributions are traditionally strong. The selected applications are put in the context of the global challenges the agricultural sector is facing: minimizing the environmental impact, while increasing production and productivity. Five different applications have been selected, which are illustrated and described: (1) biomass and yield estimation, (2) vegetation vigor and drought stress monitoring, (3) assessment of crop phenological development, (4) crop acreage estimation and cropland mapping and (5) mapping of disturbances and land use/land cover (LULC) changes. Many other applications exist, such as precision agriculture and irrigation management (see other special issues of this journal), but were not included to keep the paper concise. The paper starts with an overview of the main agricultural challenges. This section is followed by a brief overview of existing operational monitoring systems. Finally, in the main part of the paper, the mentioned applications are described and illustrated. The review concludes with some key recommendations
Inverting the PROSAIL canopy reflectance model using neural nets trained on streamlined databases
No full textThe widely used PROSAIL radiative transfer model was coupled with a simple soil reflectance parameterisation to
estimate the leaf area index (LAI) of winter wheat (Triticum aestivum) from ground-based spectrometer data. To avoid
time-consuming numerical optimisations, a neural net (NN) was used for model inversion. The NN was trained on 3000
spectral patterns generated by the reflectance model. The training database was previously streamlined to provide good
approximation of the response surface while keeping the net compact. Streamlining was achieved by retaining only those
synthetic spectra that belong both to the simulated and actual measurement spaces. The estimated LAI (nobs = 15)
compared well with completely independent reference measurements taken four times during the 2000 growing season in
four commercial winter wheat fields (1.8 ≤ LAI ≤ 8.1). The coefficient of determination (R2) between measured and
estimated LAI was 0.87 with a root mean squared error (RMSE) of 0.89 (m2 m–2). Even for LAIs exceeding 3–4, saturation
effects were low. Three measurement dates yielded RMSE lower than 0.8. Only during stem elongation did RMSE exceed
1. Higher errors for this time period were attributed to abrupt changes in the canopy structure (i.e. average leaf angle) not
taken into account. Compared to the normalised difference vegetation index (NDVI), the inversion of PROSAIL using
hyperspectral reflectances performed well, with errors reduced by more than 50% as compared to the NDVI model
(RMSE: 1.91 m2 m–2).</jats:p
Inverting the PROSAIL canopy reflectance model using neural nets trained on streamlined databases
No full textThe widely used PROSAIL radiative transfer model was coupled with a simple soil reflectance parameterisation to estimate the leaf area index (LAI) of winter wheat (Triticum aestivum) from ground-based spectrometer data. To avoid time-consuming numerical optimisations, a neural net (NN) was used for model inversion. The NN was trained on 3000 spectral patterns generated by the reflectance model. The training database was previously streamlined to provide good approximation of the response surface while keeping the net compact. Streamlining was achieved by retaining only those synthetic spectra that belong both to the simulated and actual measurement spaces. The estimated LAI (nobs = 15) compared well with completely independent reference measurements taken four times during the 2000 growing season in four commercial winter wheat fields (1.8 ≤ LAI ≤ 8.1). The coefficient of determination (R2) between measured and estimated LAI was 0.87 with a root mean squared error (RMSE) of 0.89 (m2 m–2). Even for LAIs exceeding 3–4, saturation effects were low. Three measurement dates yielded RMSE lower than 0.8. Only during stem elongation did RMSE exceed 1. Higher errors for this time period were attributed to abrupt changes in the canopy structure (i.e. average leaf angle) not taken into account. Compared to the normalised difference vegetation index (NDVI), the inversion of PROSAIL using hyperspectral reflectances performed well, with errors reduced by more than 50% as compared to the NDVI model (RMSE: 1.91 m2 m–2)
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
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