Remote Sens. 2013, 5(2), 454-472; doi:10.3390/rs5020454
Remote Sensing-Based Fractal Analysis and Scale Dependence Associated with Forest Fragmentation in an Amazon Tri‑National Frontier
Department of Geography, University of Florida, Gainesville, FL 32611, USA
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Received: 10 December 2012 / Revised: 10 January 2013 / Accepted: 14 January 2013 / Published: 24 January 2013
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. View Full-TextKeywords:
deforestation; fractal dimension; MAP region; infrastructure/roads
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
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Sun, J.; Southworth, J. Remote Sensing-Based Fractal Analysis and Scale Dependence Associated with Forest Fragmentation in an Amazon Tri‑National Frontier. Remote Sens. 2013, 5, 454-472.
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