<p>This paper aims to present the main features and functionalities of LACUNAE, a software for analyzing lacunarity of digital images, which represents an advance in the application of Fractal Geometry by implementing innovations in relation to other software of this nature already used by the scientific community. A satellite image sample, characterized by the presence of formal and informal areas in the city of Recife, Brazil, was used to validate LACUNAE, using Discriminant Analysis to investigate the software's ability to distinguish spatial patterns between formal and informal urban areas. The lacunarity results obtained showed consistently different canonical means for groups of cells with different urban forms, with the advantage of performing the calculation on a batch of images. The analysis of confidence intervals revealed some limits and complexities in distinguishing urban patterns at certain scales and resolutions. This type of development has the potential to subsidize spatial analyses that consider lacunarity as the main texture metric, and its implementation logic can be replicated for other indicators useful in distinguishing urban morphological patterns in digital images.</p>

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LACUNAE: an image based lacunarity analysis program

  • Mauro Normando Macêdo Barros Filho,
  • Eanes Torres Pereira,
  • Lucas Khalil Azevedo Dantas,
  • Matheus Batista Simões

摘要

This paper aims to present the main features and functionalities of LACUNAE, a software for analyzing lacunarity of digital images, which represents an advance in the application of Fractal Geometry by implementing innovations in relation to other software of this nature already used by the scientific community. A satellite image sample, characterized by the presence of formal and informal areas in the city of Recife, Brazil, was used to validate LACUNAE, using Discriminant Analysis to investigate the software's ability to distinguish spatial patterns between formal and informal urban areas. The lacunarity results obtained showed consistently different canonical means for groups of cells with different urban forms, with the advantage of performing the calculation on a batch of images. The analysis of confidence intervals revealed some limits and complexities in distinguishing urban patterns at certain scales and resolutions. This type of development has the potential to subsidize spatial analyses that consider lacunarity as the main texture metric, and its implementation logic can be replicated for other indicators useful in distinguishing urban morphological patterns in digital images.