Synthesis of Fractal Surfaces for Remote-Sensing Applications

Riccio, Daniele; Ruello, Giuseppe

doi:10.1109/tgrs.2014.2384595

Cited by 14 publications

(12 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is often the case at microwave frequencies [41]. First studies on application of KA to fBm surfaces (sometimes approximated by Weierstrass-Mandelbrot functions [42]) date back to the last decades of last century [15,41,43,44]. To summarize the obtained results, we have to preliminarily recall that an fBm surface is a 2D random process whose increments over a fixed distance are zeromean Gaussian with variance…”

Section: Ka Model and Fractal Surfacesmentioning

confidence: 96%

Modelling Scattering of Electromagnetic Waves in Layered Media: An Up-to-Date Perspective

Imperatore

Iodice

Pastorino

et al. 2017

International Journal of Antennas and Propagation

View full text Add to dashboard Cite

This paper addresses the subject of electromagnetic wave scattering in layered media, thus covering the recent progress achieved with different approaches. Existing theories and models are analyzed, classified, and summarized on the basis of their characteristics. Emphasis is placed on both theoretical and practical application. Finally, patterns and trends in the current literature are identified and critically discussed.

show abstract

Section: Ka Model and Fractal Surfacesmentioning

confidence: 96%

Modelling Scattering of Electromagnetic Waves in Layered Media: An Up-to-Date Perspective

Imperatore

Iodice

Pastorino

et al. 2017

International Journal of Antennas and Propagation

View full text Add to dashboard Cite

show abstract

“…In quantitative remote sensing research, fractal methods are mostly used in the mapping of surface morphology (spatial structure) such as active radar imagery and snow and ocean imagery [15], but it also has important applications in scale conversion research and is further deepened and expanded. The use of fractals for surface parameter scale conversion modeling usually contains two important research components:…”

Section: Downscaling Of Ndvi Based On Fractal Ifs 21 Review Of Downsmentioning

confidence: 99%

Establishing the Downscaling and Spatiotemporal Scale Conversion Models of NDVI Based on Fractal Methodology

Luan¹

2020

Fractal Analysis - Selected Examples

View full text Add to dashboard Cite

Scale effect is a crucial scientific problem in quantitative remote sensing (RS), and scholars attempt to solve it with scale conversion models, which can characterize the numerical relationship of RS land surface parameters at different resolutions (scales). As a significant land surface parameter, scale conversion of normalized difference vegetation index (NDVI) has been studied for a long time. Therefore, taking NDVI as an example, the development of scaling research is described and analyzed in the paper, and based on fractal theory, the development trends are discussed for land surface parameters in quantitative remote sensing. These are our conclusions: it will be the new trend to establish downscaling models based on fractal theory for land surface parameters in quantitative remote sensing; additionally, it still is the hotspot to establish spatiotemporal scale conversion models for land surface parameters in quantitative remote sensing in the future, and addressed on that, the multi-fractal scaling methodology is proposed, and its availability is analyzed in the paper, which presents significant potential.

show abstract

“…Existing research has shown that fractals can reveal obvious separation of different targets at certain scales [46,53,54]. The fractal feature scales of different targets are related to the sample numbers of the initial curve, the shape or complexity of the curve, and difference between the targets in the study area.…”

Section: Theoretical Assumptionsmentioning

confidence: 99%

Fractal Feature Analysis and Information Extraction of Woodlands Based on MODIS NDVI Time Series

Dong

Sun

2017

Sustainability

View full text Add to dashboard Cite

The quick and accurate extraction of information on woodland resources and distributions using remote sensing technology is a key step in the management, protection, and sustainable use of woodlands. This paper presents a low-cost and high-precision extraction method for large woodland areas based on the fractal features of the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series data for Beijing, China. The blanket method was used for computing the upper and lower fractal signals of each pixel in the NDVI time series images. The fractal signals of woodlands and other land use/land cover types at corresponding scales were analyzed and compared, and the attributes of woodlands were enhanced at the fifth lower fractal signal. The spatial distributions of woodlands were extracted using the Iterative Self-Organizing Data Analysis technique (ISODATA), and an accuracy assessment of the extracted results was conducted using the China Land Use and Land Cover Data Set (CLUCDS) from the same period. The results showed that the overall accuracy, kappa coefficient, and error coefficient were 90.54%, 0.74, and 8.17%, respectively. Compared with the extracted results for woodlands using the MODIS NDVI time series only, the average error coefficient decreased from 30.2 to 7.38% because of these fractal features. The method developed in this study can rapidly and effectively extract information on woodlands from low spatial resolution remote sensing data and provide a robust operational tool for use in further research.Sustainability 2017, 9, 1215 2 of 17 practices. Therefore, it is important to have a rapid and effective information extraction method to support woodland management and sustainable use.The management objectives of woodlands vary at different scales. Global, intercontinental, and national scale studies mainly focus on long-term monitoring and integrated research on the major problems affecting woodland to support decision-making for ecological restoration, environmental protection, and policy needs, for example, the effect of large-scale afforestation on land surface temperature [12] and the response to ecosystem degradation from rapid economic development in China [13]. Regional and provincial scale research mainly concentrates on key ecological engineering and environmental issues related to forestry and the ecological elements or mechanistic studies of woodland systems, for example, the ecological effects of government projects [11]. Studies of specific applications of woodland monitoring methods are considered at city or county scales, and most of these address technical methods [14]. Traditional woodland parameters are usually obtained by field surveys and investigations based on monitoring stations or standard samples, and the process is expensive, time-consuming, labor-intensive, and lacks timeliness. Remote sensing technology with a large coverage, short revisit time, and low cost can rapidly and accurately detect the type, distribution, area, stru...

show abstract

Synthesis of Fractal Surfaces for Remote-Sensing Applications

Cited by 14 publications

References 68 publications

Modelling Scattering of Electromagnetic Waves in Layered Media: An Up-to-Date Perspective

Modelling Scattering of Electromagnetic Waves in Layered Media: An Up-to-Date Perspective

Establishing the Downscaling and Spatiotemporal Scale Conversion Models of NDVI Based on Fractal Methodology

Fractal Feature Analysis and Information Extraction of Woodlands Based on MODIS NDVI Time Series

Contact Info

Product

Resources

About