Local Fractal Connections to Characterize the Spatial Processes of Deforestation in the Ecuadorian Amazon

Abstract

Deforestation by human activities is a common issue in Amazonian countries. This occurs at different spatial and temporal scales causing primary forest loss and land fragmentation issues. During the deforestation process as the forest loses connectivity, the deforested patches create new intricate connections, which in turn create complex networks. In this study, we analyzed the local connected fractal dimension (LCFD) of the deforestation process in the Sumaco Biosphere Reserve (SBR) with two segmentation methods, —CA-wavelet and K-means—to categorize the complexity of deforested patches’ connections and then relate these with the spatial processes. The results showed an agreement with both methods, in which LCFD values below 1 corresponded to isolated patches with simple shapes and those above 1 signified more complex and connected patches. From CA-wavelet a threshold of 1.57 was detected allowing us to identify and discern low and high land transformation, while the threshold for K-means was 1.61. Both values represent the region from which deforestation performs local aggressive expansion networks. The thresholds were used to map the LCFD in which all spatial processes were visually detected. However, the threshold of 1.6 ± 0.03 was more effective in discerning high land transformation. such as shrinkage and attrition, in the deforestation process in the SBR.

Analysis of Forest Fragmentation and Connectivity Using Fractal Dimension and Succolarity

“…The LCFD method has also been used to analyse deforestation processes in the Amazon. In the latter, it revealed differences between isolated patches and more complex connected regions [98].…”

Comprehensive Framework for Analysing the Intensity of Land Use and Land Cover Change in Continental Ecuadorian Biosphere Reserves

“…Anthropic transitions are the result of decisions made by multiple actors and interests [10]; therefore, their control is essential for the integral sustainability of the territory. Understanding transitions in a specific context of global change allows for identifying whether they are due to tendential, constant, or emerging spatial processes [11,12].…”

Unsupervised Deep Learning Approach for Characterizing Fractality in Dried Drop Patterns of Differently Mixed Viscum album Preparations

“…In many studies, DEM images were analyzed exclusively through visual evaluation [16]. To improve this, researchers have introduced the use of fractal descriptors [17][18][19][20], GLCM properties [21], Gabor filters [22], and Fourier transform (FT) [23] for texture feature extraction. Notably, deep learning models, especially convolutional neural networks (CNNs), have shown remarkable capabilities in capturing and learning complex patterns and features from microscopic images, including textures [24].…”