Environmental changes in biodiversity hotspot ecosystems of
South Ecuador: RESPonse and feedback effECTs (FOR2730)

Abstract:

1. How to simultaneously combat biodiversity loss and maintain ecosystem functioning while improving human welfare remains an open question. Optimization approaches have proven helpful in revealing the trade-offs between multiple functions and goals provided by land-cover configurations. The R package optim- Landuse provides tools for easy and systematic applications of the robust multiobjective land-cover composition optimization approach of Knoke et al. (2016). 2. The package includes tools to determine the land-cover composition that best balances the multiple functions a landscape can provide, and tools for understanding and visualizing the reasoning behind these compromises. A tutorial based on a published dataset guides users through the application and highlights possible use-cases. 3. Illustrating the consequences of alternative ecosystem functions on the theoretically optimal landscape composition provides easily interpretable information for landscape modelling and decision-making. 4. The package opens the approach of Knoke et al. (2016) to the community of landscape modellers and planners and provides opportunities for straightforward systematic or batch applications.

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.