Remote Sensing-Based Fractal Analysis and Scale Dependence Associated with Forest Fragmentation in an Amazon Tri‑National Frontier

Abstract

Abstract:In the Amazon, the development and paving of roads connects regions and peoples, and over time can form dense and recursive networks, which often serve as nodes for continued development. These developed areas exhibit robust fractal structures that could potentially link their spatial patterns with deforestation processes. Fractal dimension is commonly used to describe the growth trajectory of such fractal structures and their spatial-filling capacities. Focusing on a tri-national frontier region, we applied a box-counting method to calculate the fractal dimension of the developed areas in the Peruvian state of Madre de Dios, Acre in Brazil, and the department of Pando in Bolivia, from 1986 through 2010. The results indicate that development has expanded in all three regions with declining forest cover over time, but with different patterns and rates in each country. Such differences were summarized within a proposed framework to indicate deforestation progress/level, which can be used to understand and regulate deforestation and its evolution in time. In addition, the role and influence of scale was also assessed, and we found local fractal dimensions are not invariant at different spatial scales and thus concluded such scale-dependent features of fragmentation patterns are here mainly shaped by the road paving.

Land Cover Change in the Andes of Southern Ecuador—Patterns and Drivers

“…As in other studies in montane forest areas [95] and in the Amazon lowlands [88,[96][97][98][99], we identified roads as the most significant attractor of landscape change and proximate driver of deforestation (Figures 10a and 11).…”

Using Fractal Analysis in Modeling the Dynamics of Forest Areas and Economic Impact Assessment: Maramureș County, Romania, as a Case Study

“…This is caused by an increasing exploitation through legal and illegal logging, reflected by the economic changes over this period (Figure 7a-e) to a large extent governed by changes in the legislation [39]. The box-counting method is generally used for the fractal analysis of the forested areas, to describe spatial patterns [26,34,40] and their dynamics in the deforestation process [27], or to describe root morphology [28]. Here we used fractal analyses to study the evolution of fragmentation related to the space occupied by forested areas and affected by deforestation, by means of the Fractal Fragmentation Index [34], using the Pyramid Dimension method, as well as the degree of heterogeneity and the dispersion of the deforested areas by Fixed Grid 2D Lacunarity [35].…”

Examining the Satellite-Detected Urban Land Use Spatial Patterns Using Multidimensional Fractal Dimension Indices

“…Although the information entropy index is regarded as a good measure of spatial distribution, it is difficult to compare across different types of land use because of severe spatial scale effects. To solve this problem, Chen and Liu used the box counting method [64] to extend the information entropy to the information entropy dimension by the relation [55] …”

“…Based on the land use information derived from eight satellite images of Indianapolis, USA taken by five remote sensors, Liang et al evaluated the efficiency of three fractal algorithms (isarithm, triangular prism, and variogram) for describing urban landscapes [54]. The aforementioned studies show that fractal dimensions can describe the non-linear characteristics of urban land use patterns and have the potential to examine the inherent regularity, hierarchy and scale invariance of urban land use spatial patterns [55,56]. Despite these significant contributions of fractal dimensions to land use spatial pattern analysis, no substantial research has yet attempted to simultaneously use multiple fractal dimension indices to thoroughly examine land use spatial patterns.…”