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F. Peña-Cortés et al. 2010. Spatial and temporal dynamics and future trends of change in La Araucania, Chile
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Numerous investigations have attempted to develop scientists and planners on the dynamics of
change and development of future projections of land occupation (Baker 1989; Cousins2001;
Weng 2002; Luijten 2003; Jackson et al. 2004; Gómez-Mendoza et al. 2006; Fan et al. 2008;
Wang et al. 2010), being widely used scenario and trend analysis (Koomen et al. 2008).
This paper presents an application of a series of indices and metrics related to the structure and
temporary dynamics of landscape in coastal watersheds of La Araucania. The objective is to
identify the direction and magnitude of usage patterns that landscape units have had from 1980
to 2007. It also proposes a scenario to 2017 based on Markov Chain and Cellular Automata in a
GIS environment.
2. Methodology
The study area (figure 1) corresponds to the coastal rim of La Araucania, located between 38°
30’ y 39° 30’ of South Latitude and 72° 50’ y 73° 30’ of West Latitude. Its surface is 221.993
hectares distributed in four coastal watersheds: Moncul (44.747 ha), Budi (48.494 ha), Chelle
(9.267 ha) y Queule river (69.144 ha), which are on a territory formed by mountain ranges,
marine erosion platforms and extensive fluvial and marine plains (Peña-Cortes et al. 2006).
According to Di Castri and Hajek (1976) the weather is oceanic with a Mediterranean influence
and an average annual rainfall from 1200 mm to 1600 mm.
2.1 Mapping process
For the identification and analysis of land use coverages were used 1:60,000 scale aerial
photographs for 1980, 1:20,000 For 1994 and also the regional classification of the cadastre
vegetation resources of Chile (CONAF - CONAMA- BIRF 1999) updated in 2007 in 1:50.000
scale. In all cases we used satellite imagery in support of Landsat and Aster sensor.
On these images we proceeded to classify the various ground covers in a previously established
categorization of 14 classes. For the analysis of spatial patterns and dynamic changes of use
layers were reclassified into eight classes according to the type of coverage. The processing and
data analysis was performed with the software Taiga Idrisi and ArcGis 9.3.
2.2 Analysis of landscape
The analysis of dynamics and patterns of landscape considered calculating magnitude and
direction of changes during the assessed process. Among the first one, rates of change annual
average for each class was obtained (TCC). For the detailed study of its temporal variation
metrics were used at landscape related to the fragments, edges and complex forms described by
Patton (1985) and Henao (1988). The heterogeinity of the landscape mosaic was evaluated by
the diversity index and Shannon evenness. Regarding the direction of change, it was obtained
by an evaluation pixel by pixel to its original state at time 0 to the next state at time 1. The result
is given by the pixel number of class x (1-8) are transformed to the class and (1-8) over a period
of time t (0-1).
2.3 Prospective model
For the development of a future projection for the current dynamics of the landscape, the
method of Markov Chain was used. This produces a transition probability matrix for each
landscape unit simulating a future scenario from the two previous statements. After the
application of Markov chain, a stochastic projection algorithm was applied to evaluate the
probability of each pixel to belong to either category of landscape. Finally, to address the lack of
spatial dependence and topological criteria in evaluating the future scenario, we employed a
Forest Landscapes and Global Change-New Frontiers in Management, Conservation and Restoration. Proceedings of the IUFRO Landscape Ecology
Working Group International Conference, September 21-27, 2010, Bragança, Portugal. J.C. Azevedo, M. Feliciano, J. Castro & M.A. Pinto (eds.)
2010, Instituto Politécnico de Bragança, Bragança, Portugal.