Santos, Fabián
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Preferred name
Santos, Fabián
Main Affiliation
Quito
Email
ernestosantos@uti.edu.ec
ORCID
Scopus Author ID
57193085558

Research Output
Now showing 1 - 3 of 3
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Impact of heavy metals on community farming activities in the central peruvian andes

2019 , Quispe-Zuniga M.R. , Santos, Fabián , Callo-Concha D. , Greve K.

The high mining potential of the Peruvian Andes has promoted booming foreign investments. The mining activity takes place on campesino community lands and headwaters. Once the government awards a mining concession, mining companies must regularly negotiate land rent with communities over the whole duration of the mining operation, often leading to disagreements. Our research objective is to identify the mining impacts on the farming activities of campesino communities in the Junin region, central Peruvian Andes. Using a mixed-methods approach involving in-depth interviews, water and soil analysis, land-cover classification and participatory mapping, we analyzed the mining-community agreements and the mining impacts on the farming lands. We arrived at two primary conclusions. First, mining activities in terms of heavy metal concentrations impact on farming lands, although the contribution of previous and concurrent activities cannot be distinguished. Second, the diverging and short-termed interests of the involved parties which circumscribe the agreements may potentially lead to conflicts. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.

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A geographically weighted random forest approach for evaluate forest change drivers in the Northern Ecuadorian Amazon

2019 , Santos, Fabián , Graw V. , Bonilla S.

The Tropical Andes region includes biodiversity hotspots of high conservation priority whose management strategies depend on the analysis of forest dynamics drivers (FDDs). These depend on complex social and ecological interactions that manifest on different space–time scales and are commonly evaluated through regression analysis of multivariate datasets. However, processing such datasets is challenging, especially when time series are used and inconsistencies in data collection complicate their integration. Moreover, regression analysis in FDD characterization has been criticized for failing to capture spatial variability; therefore, alternatives such as geographically weighted regression (GWR) have been proposed, but their sensitivity to multicollinearity has not yet been solved. In this scenario, we present an innovative methodology that combines techniques to: 1) derive remote sensing time series products; 2) improve census processing with dasymetric mapping; 3) combine GWR and random forest (RF) to derive local variables importance; and 4) report results based in a clustering and hypothesis testing. We applied this methodology in the northwestern Ecuadorian Amazon, a highly heterogeneous region characterized by different active fronts of deforestation and reforestation, within the time period 2000–2010. Our objective was to identify linkages between these processes and validate the potential of the proposed methodology. Our findings indicate that land-use intensity proxies can be extracted from remote sensing time series, while intercensal analysis can be facilitated by calculating population density maps. Moreover, our implementation of GWR with RF achieved accurate predictions above the 74% using the out-of-bag samples, demonstrating that derived RF features can be used to construct hypothesis and discuss forest change drivers with more detailed information. In the other hand, our analysis revealed contrasting effects between deforestation and reforestation for variables related to suitability to agriculture and accessibility to its facilities, which is also reflected according patch size, land cover and population dynamics patterns. This approach demonstrates the benefits of integrating remote sensing–derived products and socioeconomic data to understand coupled socioecological systems more from a local than a global scale. © 2019 Santos et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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An Approach Based on Web Scraping and Denoising Encoders to Curate Food Security Datasets

2023 , Santos, Fabián , Acosta N.

Ensuring food security requires the publication of data in a timely manner, but often this information is not properly documented and evaluated. Therefore, the combination of databases from multiple sources is a common practice to curate the data and corroborate the results; however, this also results in incomplete cases. These tasks are often labor-intensive since they require a case-wise review to obtain the requested and completed information. To address these problems, an approach based on Selenium web-scraping software and the multiple imputation denoising autoencoders (MIDAS) algorithm is presented for a case study in Ecuador. The objective was to produce a multidimensional database, free of data gaps, with 72 species of food crops based on the data from 3 different open data web databases. This methodology resulted in an analysis-ready dataset with 43 parameters describing plant traits, nutritional composition, and planted areas of food crops, whose imputed data obtained an R-square of 0.84 for a control numerical parameter selected for validation. This enriched dataset was later clustered with K-means to report unprecedented insights into food crops cultivated in Ecuador. The methodology is useful for users who need to collect and curate data from different sources in a semi-automatic fashion. © 2023 by the authors.

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