Accounting for spatial issues (spatially explicit simulation, geographical amenities and advantages of land use 10 and cover changes, etc.) to build prospective scenarios is a crucial issue for better assessment of possible impacts on the environment. Such spatialized scenarios and their implications allow societies to reduce the uncertainty of the future by exploring various strategies for land use changes. Despite the wide diversity in existing scenariobuilding techniques, two different approaches can be distinguished (exploratory vs. normative) for their methodological implications. The originality in this study comes from the use of a relevant exploratory 15 (dynamic) approach to map normative scenarios which, in most cases, are represented throughout the combination of narratives and synchronic land use and cover maps. The objective of the article is to apply this dynamic exploratory simulation approach to spatialize normative scenarios within the framework of forest management in southern Chile. In the results, two contrasting images of the future are compared, with the preservation of native forests on one hand and the spread of exotic timber plantations on the other. 20

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Since the s, the northern part of the Amazonian region of Ecuador has been colonized with the support of intensive oil extraction that has opened up roads and supported the settlement of people from Outside Amazonia. These dynamics have caused important forest cuttings but also regular oil leaks and spills, contaminating both soil and water. The PASHAMAMA Model seeks to simulate these dynamics on both environment and population by examining exposure and demography over time thanks to a retro-prospective and spatially explicit agent-based approach. The aim of the present paper is to describe this model, which integrates two dynamics: (a) Oil companies build roads and oil infrastructures and generate spills, inducing leaks and pipeline ruptures a ecting rivers, soils and people. This infrastructure has a probability of leaks, ruptures and other accidents that produce oil pollution a ecting rivers, soils and people. (b) New colonists settled in rural areas mostly as close as possible to roads and producing food and/or cash crops. The innovative aspect of this work is the presentation of a qualitative-quantitative approach explicitly addressed to formalize interdisciplinary modeling when data contexts are almost always incomplete.

International audienceUsually risk assessment falls within the competence of “hard sciences”through environmental and epidemiological measurements, evaluations, and modeling. Even if these approaches bring accurate assessment and evaluation of environmental processes, the perception of local inhabitants is often excluded or at least relegated to second place. Evaluation of human vulnerabilities and capacities to face such hazards requires us to understand the perceptions of the population exposed. Three case studies (Lao, Tunisia, and Ecuador) are presented where we applied a perception-based regional mapping, a mapping tool based on local perceptions, for assessing the connection between land uses and health issues. A selection of the results collected on these three study areas show that the perception of local inhabitants provides a good spatial representation of the different contaminations observed locally, with a good consistency with external data. It also indicates for a certain number of cases that the contamination extends far beyond the simulated radius and impacts peripheral areas. Beyond the analysis of such a method (methodological bias, spatial representation bias, etc.), the objective is to combine our results with epidemiological measurement

a b s t r a c tThis paper examines the suitability of the PBRM, a mapping tool based on the perceptions of local stakeholders, for assessing the connection between land uses and health issues. The area, rural Laos around Luang Phabang city, between the Mekong River valley and mountains, seems to have overcome the formal territorial organization based on exposure risks towards an organization based on access to health and medical facilities. In addition, differential access to safe drinking water has been quite solved by the implementation of private can distribution networks. However, these rapid changes accentuate the social gap between well-connected lowlands and valleys on one hand, and mountain areas on the other hand, increasingly sidelined from this transition. Methodologically, PBRM method explores broader issues at a broader scale but does not give an easy access to non-spatial criteria. Plus, the limits of the SHUs (Spatial Homogeneous Unit) the PBRM establishes are geographically precise regarding topology but not spatiality. These results are action-oriented towards local and development-oriented issues.

The evaluation of deforestation by optical remote sensing remains a challenge in the humid tropical region due to high cloud cover. This paper develops a simple and reproducible method for mapping deforestation of the old-growth forest using open access software. A map of old-growth forest depletion was created using composites from three different dates (2003, 2010, 2016). Four models were tested: the first model using spectral bands (nir, swir1, swir2 and red), the second model was based on the association of spectral bands and spectral indices (NDVI, B54R, NDWI and NBR), the third model was constructed using spectral bands and geomorphological indices (DEM, Slope and Roughness) and the last model combined spectral bands, spectral indices and geomorphological indices. The optimal random forest ntrees and Mtry parameters were determined for each model to optimize the mapping in each model. The out-of-bag error for these four models was 2.15 %, 2.05 %, 1.86 % and 1.85 %, respectively. The fourth model had the lowest error and was hence used to predict deforestation of the old-growth forest. The annual rates of deforestation amounted 0.26 % (69861 ha) and 0.66 % (145768 ha) between 2003 – 2010 and 2010 – 2016, respectively. The area of the old-growth forest in 2016 was 3601607 ha and 215629 ha of forest lost between 2003 and 2016. These results showed that the Random Forest Classification (RFC) model was able to effectively map the reduction of old-growth forests.

The Ituri-Epulu-Aru landscape (IEAL) is experiencing deforestation and forest degradation. This deforestation is at the root of many environmental disturbances in a region characterized by endemism in biodiversity. The importance of this article is to provide useful information for those who wish to discuss a model that can be replicated for other territories affected by deforestation and changes in natural and anthropogenic forest structure. This article focuses on the triangulation of spatialized prospective scenarios in order to identify future trajectories based on the knowledge of historical dynamics through the diachronic analysis of three satellite images (2003–2010–2014–2016). The scenarios were designed in a supervised model implemented in the DINAMICA EGO platform. The three scenarios: business as-usual (BAU), rapid economic growth (REG) and sustainable management of the environment (SME), extrapolating current trends, show that by 2061 this landscape will always be dominated forests (+84%). Old-growth forests occupy 74.2% of the landscape area in the BAU scenario, 81.4% in the SEM scenario and 61.2% in the REG scenario. The SEM scenario gives hope that restoration and preservation of biodiversity priority habitats is still possible if policy makers become aware of it.

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