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Local Parameter Estimation of Topographic Normalization for Forest Type Classification
No full textRadiometric distortions caused by rugged terrain make the classification of forest types from satellite imagery a challenge. Various band-specific topographic normalization models are expected to eliminate or reduce these effects. The quality of these models also depends on the approach to estimate empirical parameters. Generally, a global estimation of these parameters from a whole satellite image is simple, but it may tend to overcorrection, particularly for larger areas. A land-cover-specific method usually performs better, but it requires obtaining a priori land classification, which presents another challenge in many cases. Empirical parameters can be directly estimated from local pixels in a given window. In this letter, we propose and evaluate a central-pixel-based parameter estimation method for topographic normalization using local window pixels. We tested the method with Landsat 8 imagery and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) in very rough terrain with diverse forest types. Visual comparison and statistical analyses showed that the proposed method performed better at a range of window sizes compared with an uncorrected image or with a global parameter estimation approach. The intraclass spectral variability of each forest type has been reduced significantly, and it can yield higher accuracy of forest type classification. The proposed method does not require the a priori knowledge of land covers. Its simplicity and robustness suggest that this method has the potential to be a standard preprocessing approach for optical satellite imagery, particularly for rough terrain
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
Further developing processing techniques of optical satellite images in the context of forest monitoring
No full textFurther Developing Processing Techniques of Optical Satellite Images in the Context of Forest Monitoring
Full text linkSummary
The efficient monitoring of forests is essential in the fields of forest resource assessment, management, and scientific decision-making. Integrated remote sensing imagery and field observations are widely used and are considered highly efficient in the field of forest monitoring (such as the monitoring of forest stocks, biomass, forest carbon, and forest type maps) on a local, regional and even global scale.
In the field of forest monitoring by means of remote sensing, optical satellite imagery plays a critical role. However, for optical satellite image processing techniques applied to high-precision forest monitoring to be truly effective, a number of challenges still exist, such as the variability of the atmosphere, topography, illumination conditions and scale issues. To mitigate or eliminate the impact of these challenges, under the technical framework of the Lin4Carbon project, several refined image processing techniques have been proposed and have also been proven to be quite advanced techniques. This thesis consists of five manuscripts. Each manuscript is inspired by problems encountered in the implementation of the Lin4Carbon project. It is worth noting that in order to demonstrate the universality and advancement of these techniques, the locations and data are not entirely limited to the Lin4carbon project.
Manuscript I proposes a simple and straightforward, moving window-based, rotation-correction topographic normalization model as the means to achieve improved forest mapping. An underlying assumption is that the same forest type has stable spectral characteristics, which are essential if forest monitoring is to be accurate. Our proposed model yields less intra-class heterogeneity and without the “overcorrection” phenomenon in vision when compared to uncorrected data or global correction methods.
Manuscript II further discusses the shortcomings of various topographic normalization methods based on global parameter estimations. In general, the band-specific methods lead to overcorrection, while land-cover-specific methods have difficulties in obtaining prior data on land types, and a variety of newly-advanced topographic normalization methods have difficulty achieving satisfactory results, especially when these methods are applied to sites with large areas and significant differences in landscape. Thus, this manuscript aims to establish a simple, generalized, standard, topographic normalization model for large-area optical satellite images. We assume that the relationship between the spectrum signature and the illumination conditions is specific to the site. Also, the empirical parameters of each pixel can be computed directly from a given window size. Compared with Manuscript I, this manuscript was given the same conclusions in the case of the use of new Landsat 8 images and new illumination conditions. In addition, we further tested our proposed model’s performance on the classification of forest types. We conclude that the proposed method achieves higher classification accuracy and requires fewer training samples.
Manuscript III aims to help understand the mechanism of the dynamic effects of illumination conditions on the normalized difference vegetation index (NDVI), within one year, in mountain forests. The NDVI is widely used to assess forest cover, forest biomass, forest carbon, etc. As such, the quality of an NDVI is directly related to output precision. In steep mountain forests, the issue of the variability of NDVI caused by topography appears not to have attracted much attention. In these regions of high topographic variability, the intra-annual illumination conditions vary significantly from the dynamics of the sun-terrain-sensor geometry. These variances, in turn, affect the spectral response and the derived vegetation indices. The seasonal variations of forest NDVIs in rough terrain are studied in this manuscript. We conducted a statistical analysis of random samplings from May 2013 to October 2014, of all available cloud-free NDVI images of Landsat 8 OLI. We studied how illumination conditions (IL) affect intra-annual NDVI on deciduous and coniferous forests. The findings indicate that IL and NDVI have significant positive linear correlations, and the slope coefficients of linear functions are U-shaped over the course of a year. Meanwhile, we found a positive linear correlation between IL heterogeneity and NDVI variability. Thus, the effects of illumination conditions on NDVI or NDVI-related estimations should be taken into account in both forest monitoring and the quantitative analysis of mountainous areas.
Manuscript IV aims to develop a robust and straightforward haze removal method for optical satellite images. In addition to cloud contamination, multispectral remotely-sensed images are often degraded by haze, which in turn reduces visual interpretability and affects further image analysis. Thus, haze detection and removal techniques are essential to multispectral image preprocessing. We successfully removed the haze from the Landsat 8 OLI data in the project Lin4Carbon area. We also found an improved method to achieve better performance in very high spatial resolution images, which we describe in this manuscript. Unlike the existing haze thickness map-based (HTM-based ) method, the proposed method estimates the HTM from the blue band’s mean vector L2-norm for each pixel, using a given window size. Also, we improved the compensation strategies for both haze and haze-free pixels. The proposed method has been successfully applied to a variety of very high-resolution optical satellite imagery with complex haze coverage in densely built-up areas.
Manuscript V aims to reconstruct a higher spatial resolution multispectral image from a so-called “pan-weighted multispectral reconstruction” approach. To some extent, multispectral data is one of the more critical data sources for various index and index-based forest variable estimation, forest type classification, etc. compared to panchromatic images. Interpolation and pansharpening methods are commonly used to improve the spatial resolution of multispectral data. However, the disadvantages of these methods are also evident, in that traditional interpolation methods result in loss of spatial detail, and pansharpening methods tend to suffer from spectrum loss of fidelity. Thus, we attempt to reconstruct higher spatial resolution multispectral images through a method involving joint panchromatic and multispectral images. The proposed method is characterized by the fact that the reconstructed multispectral image pixels inherit the spatial details of the neighboring pixels of the panchromatic image. Through quantitative analysis and visual comparison, it shows that the proposed method produces better performance than traditional interpolation methods. Manuscript V also demonstrates two potential applications, namely refined pansharpening and NDVI calculation. This method would be an alternative or improved upsampled interpolation method, which may become a widely accepted technique used to refine and preprocess satellite images.
Overall, this thesis attempts to solve several of the technical problems encountered in the implementation of the Lin4Carbon project on optical satellite image processing. The data availability and application potential of optical satellite images are often limited by bottlenecks, including their susceptibility to variations from the atmosphere, terrain and illumination conditions. Once these bottlenecks are overcome, optical remote sensing will be more likely to be widely used in forest monitoring. All of the methods presented in this thesis are concerned with the refinement of image pre-processing techniques, which are in turn expected to have a wide range of applications and are applicable to various optical satellite images
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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