Abstract:
Soil-vegetation-atmosphere transfer (SVAT) is to be predicted for 2050/2100 for a study area
in the southern Ecuadorian Andes. SVAT models require information on land use/ land cover
(LUC) as lower boundary conditions. Since the study area suffers from high deforestation
rates, LUC cannot be assumed as staying constant with time. A spatially explicit land use/land
cover change (LUCC) model is therefore needed for future SVAT prediction.
The numbers of approaches of LUCC modelling are numerous. Difficulties are due to
complex interactions of social and biophysical drivers of change.
In this study a model of LUCC was built using information of past changes derived by
remotely sensed data. Special focus was on forest development patterns. A training period of
14 years between 1987 and 2001 was chosen. Two LUC classifications were accomplished to
Landsat data of the start and end date of this period. A change detection of the training period
provided the basis for predictive LUCC modelling. Potential drivers for LUCC were applied
to the model as GIS layers. The modelling procedure consisted of a combination of Markov
chain analysis (MCA) for quantitative modelling and multi-layer perceptron (MLP) for
revealing potential locations of change. A multi-objective land allocation (MOLA) served as
final integration step. 14 LUC transitions were considered in the modelling procedure.
Unconsidered LUC classes were assumed to stay constant in the future. The model results
were maps of LUC for 2006, 2010 and afterwards for every 10 years up to 2100. An internal
validation was performed with the training data. The results of the prediction were validated
by comparing the model output of 2006 to an ASTER LUC classification of the same time.
The validation methodology comprised crisp and fuzzy map comparison using Kappa
statistics.
The study area featured a deforestation of 13.61% in the training period. The model was able to
explain deforestation in the training period 51% better than just by chance. The location of
predicted deforestation reached a better than chance agreement of 30%. Predicted quantities of
deforestation were 59% conforming with the reference. The validation of the prediction
indicated the difficulty of modelling human impact on the ecosystem. Prospects and limitations
of the model were identified with suggestions for future research tasks. The results of this
study are assumed to present a good groundwork for future SVAT models.
