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doi: 10.1111/conl.12704
handle: 10261/216214 , 10481/59128
Abstract In the “digital conservation” age, big data from Earth observations and from social media have been increasingly used to tackle conservation challenges. Here, we combined information from those two digital sources in a multimodel inference framework to identify, map, and predict the potential for nature's cultural contributions to people in two contrasting UNESCO biosphere reserves: Doñana and Sierra Nevada (Spain). The content analysis of Flickr pictures revealed different cultural contributions, according to the natural and cultural values of the two reserves. Those contributions relied upon landscape variables computed from Earth observation data: the variety of colors and vegetation functioning that characterize Doñana landscapes, and the leisure facilities, accessibility features, and heterogeneous landscapes that shape Sierra Nevada. Our findings suggest that social media and Earth observations can aid in the cost‐efficient monitoring of nature's contributions to people, which underlie many Sustainable Development Goals and conservation targets in protected areas worldwide.
Cultural values, General. Including nature conservation, geographical distribution, QH1-199.5, Remote sensing, multimodel inference, Multimodel inference, crowdsourced photos, big data, Participatory sensing, cultural values, Ecosystem services, Doñana, ecosystem services, Sierra Nevada, Crowdsourced photos
Cultural values, General. Including nature conservation, geographical distribution, QH1-199.5, Remote sensing, multimodel inference, Multimodel inference, crowdsourced photos, big data, Participatory sensing, cultural values, Ecosystem services, Doñana, ecosystem services, Sierra Nevada, Crowdsourced photos
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 33 | |
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| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
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