Evaluating the Impact of Distance Measures on Deforestation Simulations in the Fluvial Landscapes of Amazonia

Abstract

Land use and land cover change (LUCC) models frequently employ different accessibility measures as a proxy for human influence on land change processes. Here, we simulate deforestation in Peruvian Amazonia and evaluate different accessibility measures as LUCC model inputs. We demonstrate how the selection, and different combinations, of accessibility measures impact simulation results. Out of the individual measures, time distance to market center catches the essential aspects of accessibility in our study area. The most accurate simulation is achieved when time distance to market center is used in association with distance to transport network and additional landscape variables. Although traditional Euclidean measures result in clearly lower simulation accuracy when used separately, the combination of two complementary Euclidean measures enhances simulation accuracy significantly. Our results highlight the need for site and context sensitive selection of accessibility variables. More sophisticated accessibility measures can potentially improve LUCC models' spatial accuracy, which often remains low.

The effects of armed conflict on forest cover changes across temporal and spatial scales in the Colombian Amazon

“…Third, we delineated a buffer of 50 km around the selected settlements. The buffer distances were selected based on the observed deforestation data with the premise that deforestation usually takes place in proximity to markets and roads (Geist and Lambin 2001;Armenteras et al 2011;Salonen et al 2014;Müller et al 2016). By considering these two levels, we aim to identify whether variables associated with deforestation change when studying areas with different deforestation patterns.…”

Seasonal fluctuation of riverine navigation and accessibility in Western Amazonia: An analysis combining a cost-efficient GPS-based observation system and interviews

“…The results of this study could also provide more accurate data for modelling land use/land cover changes (such as deforestation, see Salonen et al, 2014) and socioeeconomic interactions, both of which are inherently strongly affected by the seasonality of the riverine transport environment (Perz et al, 2013;Soler, Escada, & Verburg, 2009;Verburg, Overmars, & Witte, 2004).…”

“…Limited transportation options, lacking transportation infrastructures and inaccurate schedules can influence considerably livelihood possibilities and development in rural areas. In Peruvian Amazonia, the transportation realities are particularly challenging, and many of the regional differences in development, human livelihoods and land use seem to be linked to accessibility (Salonen et al, 2012;Salonen, Maeda & Toivonen, 2014;Shanley, Luz, & Swingland, 2002). The area lacks comprehensive road infrastructure (Abizaid, 2005;Coomes, Abizaid, & Lapointe, 2009); the great majority of people and cargo are transported by riverboats and larger vessels (Ministerio de Transportes y Comunicaciones, 2010;Rodriguez Achung, 1994).…”

A cost path and network analysis methodology to calculate distances along a complex river network in the Peruvian Amazon

“…Elsewhere, Delamater, Messina, Shortridge, and Grady (2012) compared rasterbased and network-based methods for studying access to health care using road access and found that both methods provided a valid structure for constructing travel time models. Moreover, for complex networks it is clear that the normal Euclidean distance measurement produces biased results on accessibility (Delamater et al, 2012;Salonen, Maeda, & Toivonen, 2014), especially in areas of high topological complexity. When using a manual approach with GE for assessing distances calculated between communities to district capitals and between communities at the sub-basin level, our results indicate no significant difference in measured distances (Fig.…”