2.5D Morphogenesis: modeling landuse and landcover dynamics in the Ecuadorian Amazon

Abstract

The Ecuadorian Amazon, lying in the headwaters of the Napo and Aguarico River valleys, is experiencing rapid change in Land Use and Land Cover (LULC) conditions and regional landscape diversity uniquely tied to the spontaneous agricultural colonization of the Oriente region of northeastern Ecuador beginning in the mid to late 1970s. Spontaneous colonization occurred on squattered lands located adjacent to oil company roads and in government development sectors composed of multiple 50 ha land parcels organized into `piano key' shaped family farms or fincas. Portions of these fincas were deforested for agricultural extensification depending upon the age of the finca and several site and situation factors. Because fincas are managed at the household level as spatially discrete, temporally independent units, land conversion at the finca-level is recognized as the chief proximate cause of deforestation within the region.

Focusing on the spatial and temporal dynamics of deforestation, agricultural extensification, and plant succession at the finca-level, and urbanization at the community-level, a cell-based morphogenetic model of Land Use and Land Cover Change (LULCC) was developed as the foundation for a predictive model of regional LULCC dynamics and landscape diversity. Here, LULC characteristics are determined using a time-series of remotely sensed data (i.e., Landsat Thematic Mapper (TM) and Multispectral Scanner (MSS)) using an experimental [semi-traditional] (hybrid unsupervised-supervised) classification scheme resulting in a time-series data set including LULC images for 1973, 1986, 1989, 1996, and 1999. Pixel histories of LULC type across the time-series were integrated into LULC trajectories and converted into seed or input data sets for LULC modeling to alternate time periods and for model validation. LULC simulations, achieved through cellular automata (CA) methodologies, were run on an annual basis to the year 2010 using 1973 as the initial conditions and the satellite time-series as the `check points' in the simulations. The model was developed using the Imagine Spatial Modeler of the ERDAS image processing software, and enhanced using the Spatial Modeler Language (SML). The model works by (a) simulating the present by extrapolating from the past using the image time-series, (b) validating the simulations via the remotely sensed time-series of past conditions and through field observations of current conditions, (c) allowing the model to iterate to the year 2010, and (d) comparing model outputs to an autoregressive time-series approach for annual conditions that are compared via paired t-tests of pattern metrics run at the landscape-level to define compositional and structural differences between successive model outputs.