Land cover monitoring by fractal analysis of digital images

Velázquez-García, J. Jesús; Oleschko, Klaudia; Muñoz-Villalobos, J. Arcadio; Velásquez-Valle, Miguel Agustín; Menes, Mario Martínez; Parrot, Jean-François; Korvin, Gábor; Cerca, Mariano

doi:10.1016/j.geoderma.2009.11.014

Cited by 7 publications

(4 citation statements)

References 36 publications

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“…Because field-taken digital RGB images with high spatial resolution potentially provide more accurate estimation of vegetation cover than visual methods by reducing the impact of human subjectivity [1,14]. They have been widely used for estimating forest canopy cover i.e., gap fraction analysis, [17,18], forest understory cover [16], crop cover [4,[19][20][21][22], crop residual cover [23] and grassland vegetation cover [5,24].…”

Section: Introductionmentioning

confidence: 99%

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A Semi-Automated Method to Extract Green and Non-Photosynthetic Vegetation Cover from RGB Images in Mixed Grasslands

Guo

2020

Sensors

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Green (GV) and non-photosynthetic vegetation (NPV) cover are both important biophysical parameters for grassland research. The current methodology for cover estimation, including subjective visual estimation and digital image analysis, requires human intervention, lacks automation, batch processing capabilities and extraction accuracy. Therefore, this study proposed to develop a method to quantify both GV and standing dead matter (SDM) fraction cover from field-taken digital RGB images with semi-automated batch processing capabilities (i.e., written as a python script) for mixed grasslands with more complex background information including litter, moss, lichen, rocks and soil. The results show that the GV cover extracted by the method developed in this study is superior to that by subjective visual estimation based on the linear relation with normalized vegetation index (NDVI) calculated from field measured hyper-spectra (R2 = 0.846, p < 0.001 for GV cover estimated from RGB images; R2 = 0.711, p < 0.001 for subjective visual estimated GV cover). The results also show that the developed method has great potential to estimate SDM cover with limited effects of light colored understory components including litter, soil crust and bare soil. In addition, the results of this study indicate that subjective visual estimation tends to estimate higher cover for both GV and SDM compared to that estimated from RGB images.

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Section: Introductionmentioning

confidence: 99%

“…Many studies have demonstrated the potential of field-taken digital RGB images to extract the green vegetation (GV) coverage [10,[24][25][26] or crop residuals from soil background [23]. However, there has been less success separating GV and non-photosynthetic vegetation (NPV) using the RGB images.…”

Section: Introductionmentioning

confidence: 99%

A Semi-Automated Method to Extract Green and Non-Photosynthetic Vegetation Cover from RGB Images in Mixed Grasslands

Guo

2020

Sensors

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“…There have been several fractal-based methods proposed for the analysis of complexity in images, including the analysis of image textures [1][2][3][4][5][6][7]. Fractal geometry in image analysis has also found practical applications in a number of areas [8][9][10]44,[11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Texture descriptors by a fractal analysis of three-dimensional local coarseness

Florindo

Landini

Bruno

2015

Digital Signal Processing

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This work proposes a new method of extracting texture descriptors from digital images based on local scaling properties of the greyscale function using constraints to define connected local sets. The texture is first mapped onto a three-dimensional cloud of points and the local coarseness under different scales is assigned to each point p. This measure is obtained from the size of the largest "connected" set of points within a cube centred at p. Here, the "connected set" is defined as the set of points such that for each point in the local domain there is at least one other point at a distance smaller than a threshold t. Finally, the Bouligand-Minkowski fractal descriptors of the local coarseness of each pixel are computed. The classificatory power of the descriptors on the Brodatz, Vistex, UIUC and UMD databases showed an improvement over the results obtained with other well-known texture descriptors reported in the literature. The performance achieved also suggests possible applications to real-world problems where the images are best analysed as textures.

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“…Neste livro Mandelbrot destacou a possibilidade destas imagens representarem objetos naturais. Estudos têm utilizado os conceitos relacionados a fractais para a descrição de fenômenos físicos e químicos reais ou para a representação de objetos naturais[28,29,30]. Assim, a utilização dos conceitos dos fractais no corregistro contribuiria para o fornecimento de novas informações, nesse trabalho o alvo seria o comportamento da variação da dimensão fractal no processo de corregistro.A medida de dimensão fractal pode ser obtida através de métodos como o "Box Counting".…”

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Estudo avaliativo da informação mútua generalizada e de métricas clássicas como medidas de similaridade para corregistro em imagens fractais e cerebrais

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Land cover monitoring by fractal analysis of digital images

Cited by 7 publications

References 36 publications

A Semi-Automated Method to Extract Green and Non-Photosynthetic Vegetation Cover from RGB Images in Mixed Grasslands

A Semi-Automated Method to Extract Green and Non-Photosynthetic Vegetation Cover from RGB Images in Mixed Grasslands

Texture descriptors by a fractal analysis of three-dimensional local coarseness

Estudo avaliativo da informação mútua generalizada e de métricas clássicas como medidas de similaridade para corregistro em imagens fractais e cerebrais

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