Quantitative analysis of deforestation pattern dynamics: Developing forest resilience metrics in an Amazon frontier.

摘要

Deforestation, the process of clearing the Earth's forests, has occurred globally on a massive scale and negatively impacts the resilience of forest landscapes. Aimed to support the understanding of deforestation dynamics, the dissertation first reviews resilience theory, and then demonstrates possible solutions to infer the resilience of deforested landscapes with particular attention to the forest spatial pattern changes via an example in the Amazonian rainforest. The solutions were based on the application of three different methods, and integrating these with remote sensing (RS) and geographic information system (GIS) techniques. Data inputs were fine-grained forest/non-forest classifications of the years 1986, 1991, 1996, 2000, 2005 and 2010 interpreted from Landsat satellite images. The three different methods presented in this dissertation are: first, the combination of a moving window approach and an iterative self-organizing (ISO) unsupervised classification used to describe the 'cross-boundaries' influences of deforestation. These patterns are revealed by forest cover changes, which are initiated from developed areas and then extend into forest areas across multiple spatial scales. Second, the local changes of forest spatial patterns were illustrated, where the dynamics of forest cover were obtained by applying the morphological spatial pattern analysis (MSPA). Two additional MSPA tools, 'hole' and 'outer background', were created to enhance the appreciation of the spatial patterns of developed areas. Third, fractal geometry was introduced to further prompt the understanding of the spatial patterns of developed areas. A fixed-grid scan strategy was adopted to pixelize the entire landscape and to cartographically represent the spatial patterns of developed areas by fractal dimension. Moreover, a configuration framework was proposed to partition the level of deforestation characterized by fractal dimensions and as such can potentially be used to better understand and manage the evolution of forest clearings. These three methods each have their own set of advantages and limitations, and yet work in a complementarily manner and cast particular insight on spatial pattern change during the process of deforestation. Their useful value-added contributions towards resilience theory is discussed at the end, as the ontogenetic stage of resilience theory is moving from the 'consolidating' stage to the 'empirical interactive' stage.