No abstract

Agent Based Models (ABMs) are powerful tools for population-environment research but are subject to trade-offs between model complexity and abstraction. This study strikes a compromise between abstract and highly specified ABMs by designing a spatially explicit, stylized ABM and using it to explore policy scenarios in a setting that is facing substantial conservation and development challenges. Specifically, we present an ABM that reflects key Land Use / Land Cover (LULC) dynamics and livelihood decisions on Isabela Island in the Galápagos Archipelago of Ecuador. We implement the model using the NetLogo software platform, a free program that requires relatively little programming experience. The landscape is composed of a satellite-derived distribution of a problematic invasive species (common guava) and a stylized representation of the Galápagos National Park, the community of Puerto Villamil, the agricultural zone, and the marine area. The agent module is based on publicly available data and household interviews, and represents the primary livelihoods of the population in the Galápagos Islands -tourism, fisheries, and agriculture. We use the model to enact hypothetical agricultural subsidy scenarios aimed at controlling invasive (1) sharpening conceptualizations of population-environment systems, (2) testing alternative scenarios, and (3) uncovering critical data gaps.

Este estudio investiga la comunidad de Puerto Villamil ubicada en la Isla Isabela del archipiélago de los Galápagos. Se examina el desarrollo urbano y las interacciones humanas-ambientales a través de una perspectiva de la ciencia de información geográfica que incluye: (1) la construcción de una serie de tiempo de fotografías aéreas e imagenes satélites, (2) el mapeo de la infraestructura de agua y alcantarillado actual dentro el contexto del crecimiento comunal, con especial referencia a las medidas de la calidad del agua dentro y adyacente a la comunidad; el uso de grupos de discusión y entrevistas a oficiales de salud y residentes sobre las enfermedades trasmitidas por el agua y un análisis de la proximidad de la comunidad y sus infraestructuras al ambiente circundante.

No abstract

Seasonal dynamics in the north-eastern Peruvian Amazon were assessed within a multitemporal LULC framework informed by Landsat 7 ETM + imagery of the study area for 2001 that coincided with seasonal flooding dynamics. Three images (12 March, 31 May, and 20 September 2001) were classified separately using a hybrid classification method that combined unsupervised and supervised techniques, and attributed with a classification scheme consisting of 18 LULC classes. A multitemporal classification that included 25 LULC classes was created from the three single-date classifications using a panel analysis technique. While some of the classes describe LULC 'changes' (land-use and land-cover change (LULCC)) that were stable over time (e.g. low sediment water during March, May, and September 2001), others were complex and included multiple trajectories of change. Panel analysis extracts pixel histories of change over three or more observations as a trend or trajectory, rather than segmenting those changes into piecemeal periods as normally done with from-to change detection. This technique was then assessed by testing hypothesized forest trajectories of LULCC. Traditional quantitative accuracy assessment techniques are less appropriate for panel analysis, and so a qualitative accuracy assessment was performed to evaluate the validity of the multitemporal classification. This study suggests that there may not be one typical year-round behaviour for seasonal environments, and that population-environment interaction studies would benefit from incorporating this knowledge into future research. This analysis further demonstrates the effectiveness of a multitemporal remote-sensing approach for gauging landscape fluctuations in seasonal environments.