Multiscale local connections of deforestation process in the Ecuadorian Amazon

The deforestation process is nowadays recognized as a global concern due to a variety of environmental issues associated with forest loss. Exacerbated deforestation rates in basins such as the Amazon river basin is contributing tremendously to environmental global degradation and climate change. Previous studies regarding the deforestation process suggest that soils and forest loss are correlated exhibiting non-linear and multi-scale behaviors. Based on this, we conduct a novel approach based on image analysis of the deforestation process to improve the understanding of local connections of this process in a Biosphere reserve in the Ecuadorian Amazon. Understanding the connections between deforested patches and how they are strengthening the deforestation process could provide new features for understanding forest loss associated with agricultural expansion. Thus, these map features can be used for modeling purposes of agricultural expansion and forest loss impact. This work is based on the assessment of cumulative images of deforestation in the Sumaco biosphere reserve in the Ecuadorian Amazon from 1985 to 2018. For this, we rigorously sampling every deforested pixel of the images through the moving window technique to calculate the fractal dimension of the connected pixels at different scales. Once fractal dimensions are calculated, we classify these values to mapping the existing relations. The mapping results show different complexity levels in local connections of the deforestation process. These spatial relations can improve the understanding of deforestation patterns and provide relevant information for decision-making to conservation programs.

Acknowledgements: The authors acknowledge the support of Project No. PGC2018-093854-B-
I00 of the Ministerio de Ciencia, Innovación y Universidades of Spain and the financial support from Boosting Agricultural Insurance based on Earth Observation data - BEACON project under agreement Nº 821964,funded under H2020EU, DT-SPACE-01-EO-2018-2020.