Browsing by Author "Lounela, Anu"

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    Dwelling in Political Landscapes: Contemporary Anthropological Perspectives
    (2019-05-27) Department of Design; Lounela, Anu; Berglund, Eeva; Kallinen, Timo; NODUS
    People all over the globe are experiencing unprecedented and often hazardous situations as environments change at speeds never before experienced. This edited collection proposes that anthropological perspectives on landscape have great potential to address the resulting conundrums. The contributions build on broadly phenomenological, structuralist and multi-species approaches to environmental perception and experience, but they also argue for incorporating political power into analysis alongside dwelling, cosmology and everyday practice. The book’s 13 ethnographically rich chapters explore how the material and the conceptual are entangled in and as landscapes, but it also looks at how these processes unfold at many scales in time and space, involving different actors with different powers. Thus it reaches towards new methodologies and new ways of using anthropology to engage with the sense of crisis concerning environment, movements of people, climate change and other planetary transformations. Dwelling in political landscapes: contemporary anthropological perspectives builds substantially upon anthropological work by Tim Ingold, Anna Tsing and Philippe Descola and on related work beyond, which emphasises the ongoing and open-ended, yet historically conditioned ways in which humans and nonhumans produce the environments they inhabit. In such work, landscapes are understood as the medium and outcome of meaningful life activities, where humans, like other animals, dwell. This means that landscapes are neither social/cultural nor natural, but socio-natural. Protesting against and moving on from the proverbial dualisms of modern, Western and maybe capitalist thought, is only the first step in renewing anthropology’s methodology for the current epoch, however. The contributions ask how seemingly disconnected temporal, representational, economic and other systemic dynamics fold back on lived experience that are materialised in landscapes.
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    Identifying Key Drivers of Peatland Fires Across Kalimantan's Ex-Mega Rice Project Using Machine Learning
    (AMERICAN GEOPHYSICAL UNION, 2021-12) Horton, Alexander J.; Virkki, Vili; Lounela, Anu; Miettinen, Jukka; Alibakhshi, Sara; Kummu, Matti; Department of Built Environment; Water and Environmental Eng.; University of Helsinki; VTT Technical Research Centre of Finland
    Throughout Indonesia ecological degradation, agricultural expansion, and the digging of drainage canals has compromised the integrity and functioning of peatland forests. Fragmented landscapes of scrubland, cultivation, degraded forest, and newly established plantations are then susceptible to extensive fires that recur each year. However, a comprehensive understanding of all the drivers of fire distribution and the conditions of initiation is still absent. Here we show the first analysis in the region that encompasses a wide range of driving factors within a single model that captures the inter-annual variation, as well as the spatial distribution of peatland fires. We developed a fire susceptibility model using machine learning (XGBoost random forest) that characterizes the relationships between key predictor variables and the distribution of historic fire locations. We then determined the relative importance of each predictor variable in controlling the initiation and spread of fires. The model included land-cover classifications, a forest clearance index, vegetation indices, drought indices, distances to infrastructure, topography, and peat depth, as well as the Oceanic Niño Index (ONI). The model performance consistently scores highly in both accuracy and precision across all years (>75% and >67.5% respectively), though recall metrics are much lower (>25%). Our results confirm the anthropogenic dependence of extreme fires in the region, with distance to settlements and distance to canals consistently weighted the most important driving factors within the model structure. Our results may help target the root causes of fire initiation and propagation to better construct regulation and rehabilitation efforts to mitigate future fires.