Bonilla Bedoya, Santiago
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Bonilla Bedoya, Santiago
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Quito
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santiagobonilla@uti.edu.ec
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55360745200

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Socioecological system and potential deforestation in Western Amazon forest landscapes

2018 , Bonilla Bedoya, Santiago , Estrella-Bastidas A. , Molina J.R. , Herrera M.Á.

The ecosystem services provided by tropical forests are affected by deforestation. Territorial management strategies aim to prevent and mitigate forest loss. Therefore, modeling potential land use changes is important for forest management, monitoring, and evaluation. This study determined whether there are relationships between forest vulnerability to deforestation (potential deforestation distribution) and the forest management policies applied in the Ecuadorian Amazon. Proxy and underlying variables were used to construct a statistical model, based on the principle of maximum entropy that could predict potential land use changes. Entropy can be seen as a measure of uncertainty for a density function. Receiver operating characteristics (ROC) analysis and the Jackknife Test were used to validate the model. The importance of input variables in the model was determined through: Percent Contribution (PC) and Permutation Importance (PI). The results were compared with prevailing regional forest management strategies. The socioeconomic variables that provided the largest amount of information in the overall model (AUC = 0.81) and that showed most of the information not present in other variables were: “Protected areas-Intangible zone” (PC = 24%, PI = 12.4%), “timber harvesting programs” (PC = 21.7%, PI = 4.7%), “road network” (PC = 18.9%, PI = 7.7%), and “poverty rate” (PC = 3.7%, PI = 6.1%). Also, the biophysical variable “temperature” (PC = 7,9%, PI = 22.3%) provided information in the overall model. The results suggested the need for changes in forest management strategies. Forest policies and management plans should consider integrating and strengthening protected areas and intangible zones, as well as restricting timber harvesting in native forest and establishing forest areas under permanent management. Furthermore, the results also suggested that financial incentive programs to reduce deforestation have to be evaluated because their present distribution is inefficient. In this context, conservation incentive plans need to be revised so that they focus on areas at deforestation risk. © 2018 Elsevier B.V.

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Sentinel satellite data monitoring of air pollutants with interpolation methods in Guayaquil, Ecuador

2023 , Mejía C. D. , Alvarez H. , Zalakeviciute R. , Macancela D. , Sanchez C. , Bonilla Bedoya, Santiago

In Ecuador, there is a limitation on air quality monitoring due to the cost of monitoring networks. Although air quality monitoring stations are instruments for air measurement, they do not cover an entire city due to their scope. Satellite remote sensing is now an effective tool to study atmospheric pollutants and has been applied to continuously assess a region and overcome the limitations of fixed stations. Despite the application of satellite data for air quality monitoring, there are some limitations, such as measurement frequency, cloud cover and wide spatial resolution, which do not allow the assessment of air pollution in cities. Therefore, downscaling, applying interpolation methods, is essential for continuous air quality monitoring at smaller scales. For this research, Nitrogen Dioxide (NO2) data from the Sentinel-5 satellite percussor was used in the city of Guayaquil for January–December 2020, which is considered before, during and after the COVID-19 quarantine. This mid-size port city does not have a permanent monitoring network, which prevents us from knowing the air quality. Due to the limitation of pixel size, this study used satellite data to apply interpolation techniques and reduce pixels to assess air quality. Two categories of interpolation were selected: deterministic and stochastic. The empirical Bayesian kriging (EBK) interpolation obtained a R2 of 0.9546, which was superior to the other methods applied. Therefore, the EBK method had the best accuracy for tropospheric NO2 concentration. Finally, the method used in this research can help monitor air quality in cities lacking continuous monitoring networks, as the reduction of the pixel size gives us a better pattern of pollutants. © 2023 The Authors

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Central parks as air quality oases in the tropical Andean city of Quito

2024 , Zalakeviciute R. , Bonilla Bedoya, Santiago , Mejia Coronel D. , Bastidas M. , Buenano A. , Diaz-Marquez A.

Urban ecosystem is an intricate agglomeration of human, fauna and flora populations coexisting in natural and artificial environments. As a city develops and expands over time; it may become unbalanced, affecting the quality of ecosystem and urban services and leading to environmental and health problems. Fine particulate matter (particulate matter with aerodynamic diameter ≤2.5 μm - PM2.5) is the air pollutant posing the greatest risk to human health. Quito, the capital city of Ecuador, exhibits a high occurrence of exposure to unhealthy levels of PM2.5 due to a combination of natural and social variables. This study focused on three central parks of this high elevation city, investigating the spatial distribution of PM2.5 concentrations. The particle pollution was then modeled using Normalized Difference Vegetation Index (NDVI). Hazardous instantaneous levels of PM2.5 were consistently found on the edges of the parks along busy avenues, which are also the most frequented areas. This raises concerns about both short- and long-term exposures to toxic traffic pollution in recreational areas within urban dwellings in the global south. The NDVI model successfully predicted the spatial concentrations of PM2.5 in a smaller urban park, suggesting its potential application in other cities. However, further research is required to validate its effectiveness. © 2024 The Authors

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Exploring Wardriving Potential in the Ecuadorian Amazon for Indirect Data Collection

2021 , Santos F. , Pesantes P. , Bonilla Bedoya, Santiago

Digital inclusion in the Ecuadorian amazon is known as a problem, which intensified with the pandemic. Since social distance is now the norm, we constructed a WiFi access point (WAP) scanner to map and analyze its data. We correlated it with ancillary geoinformation to observe its potential and limitations as a method for indirect data collection. Our result indicate that WAP correlate weakly but positively with nightlight, young population, accessibility to economical centres, and negatively with slope. Moreover, we differentiated vulnerability naming patters from Service Set Identifiers (SSDI) and differentiated the number of WAPs according to land cover for differentiate urban from rural areas. This output is now offering increasing applications to get updated rought estimates of internet activity and indirectly correlations to socio-economic conditions, technology practices, and opportunities for natural language processing. Therefore, we conclude that wardriving offer interesting opportunities for mapping social data but also concerns as an indirect data collection method. © Published under licence by IOP Publishing Ltd.

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Modelling the relationship between urban expansion processes and urban forest characteristics: An application to the Metropolitan District of Quito

2020 , Bonilla Bedoya, Santiago , Mora A., Vaca A. , Estrella A. , Herrera M.Á.

The rapid process of global urbanisation engenders changes in urban socio-ecological systems and in the landscape structure. However, the future processes of urban expansion in Latin American cities has been little studied even though the wellbeing of its citizens will depend on territorial management and on planning the provision of ecosystemic benefits and services. This research, considering different socio-ecological dimensions, proposed to determine the causes of potential urban expansion, analysing the dimensions and possible predictors that would explain the expansion of a high Andean city and its influence on peri-urban forest landscapes. To develop a model that integrates the complexity of the system, we used the following five dimensions: biophysics, land cover and management, infrastructure and services, socio-economics, and landscape metrics, and we opted for a binomial analysis through a spatial logistic regression model developed from 33 predictors. Considering the odd radio of the model, we observe that the independent increase in predictors, including building blocks, drinking water, sewerage, waste collection, average land size, the Interspersion and Juxtaposition Index (IJI) and Largest Patch Index (LPI), and the constant behaviour of the others predictors, would increase the probability of a potential urbanisation of the territory. Similarly, the independent increase in predictors, including the presence of protected areas, the presence of protected forests, land cover, unemployment, and the Shannon Diversity Index(SHDI), reduce the probability of the urbanisation process. Our results suggest that the territorial vulnerability from a potential urbanisation process is strongly related to an increase in infrastructure, services, and the average size of properties variables. Moreover, the landscape with the greatest potential for urbanisation presents an adequate intercalation of the different patches that compose it. However, the presence of variables such as protected areas and protective forests, in addition to monitoring indicators such as landscape diversity and mitigation strategies, could be considered to focus the analysis on the current dynamics of urbanisation processes in Latin America. © 2019 Elsevier Ltd

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Evaluating night-time light sources and correlation with socio-economic development using high-resolution multi-spectral Jilin-1 satellite imagery of Quito, Ecuador

2023 , Watson C.S. , Elliott J.R. , Córdova M. , Menoscal J. , Bonilla Bedoya, Santiago

Artificial light at night (ALAN) has positive and negative effects on social, economic, environmental, and ecological systems, and will increase with urban expansion. In this study, we used a multi-spectral 1.5 m resolution night-time acquisition from a Jilin-1 satellite over the city of Quito, Ecuador, to evaluate spatial lighting patterns in an expanding and topography complex-built environment. We demonstrated a requirement for robust georeferencing and orthorectification due to the complex topography, with errors on the order of 4–6 pixels (5.8–8.4 m CE95). We also quantified differences in observed brightness due to the image acquisition and local geometry. Street light type was distinguishable between high-pressure sodium (HPS) and light emitting diode (LED) sources (F1-score = 0.72–0.83) using a shark random forest decision tree approach. Additionally, street lights could be located within 10 m (F1-score = 0.71) with balanced omissions and commissions. Spatial trends revealed that the road network was the dominant source of illumination, accounting for 45% of illuminated pixels, whereas built-up areas accounted for 23%. Overall, 68% of all illuminated pixels were on or within 10 m of the road. Higher socio-economic development was associated with higher proportions of LED lighting, greater road network lighting and density of street lights, higher overall radiance for built-up areas and the road network, and greater coverage and illumination of designated green spaces. The broad impacts of ALAN mean that addressing the causes and consequences of lighting inequalities is a complex issue. Nonetheless, Jilin-1 night-time imagery offers a low-cost way to map and monitor light sources at high-resolution that will be beneficial to city-planners and progressing Sustainable Development Goals. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

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Urban soil management in the strategies for adaptation to climate change of cities in the Tropical Andes

2022 , Bonilla Bedoya, Santiago , Ángel Herrera, M. , Vaca, A. , Salazar, L. , Zalakeviciute, R. , Mejía, D. , López-Ulloa, M.

The unique characteristics of a city amplify the impacts of climate change; therefore, urban planning in the 21st century is challenged to apply mitigation and adaptation strategies that ensure the collective well-being. Despite advances in monitoring urban environmental change, research on the application of adaptation-oriented criteria remains a challenge in urban planning in the Global South. This study proposes to include urban land management as a criterion and timely strategy for climate change adaptation in the cities of the Tropical Andes. Here, we estimate the distribution of the soil organic carbon stock (OCS) of the city of Quito (2,815 m.a.s.l.; population 2,011,388; 197.09 km2) in the following three methodological moments: i) field/laboratory: city-wide sampling design established to collect 300 soil samples (0–15 cm) and obtain data on organic carbon (OC) concentrations in addition to 30 samples for bulk density (BD); ii) predictors: geographic, spectral and anthropogenic dimensions established from 17 co-variables; and iii) spatial modeling: simple multiple regression (SMRM) and random forest (RFM) models of organic carbon concentrations and density as well as OCS stock estimation. We found that the spatial modeling techniques were complementary; however, SMRM showed a relatively higher fit both (OC: r2 = 20%, BD: r2 = 16%) when compared to RFM (OC: r2 = 8% and BD: r2 = 5%). Thus, soil carbon stock (0–0.15 m) was estimated with a spatial variation that fluctuated between 9.89 and 21.48 kg/m2; whereas, RFM showed fluctuations between 10.38 and 17.67 kg/m2. We found that spatial predictors (topography, relative humidity, precipitation, temperature) and anthropogenic predictors (population density, roads, vehicle traffic, land cover) positively influence the model, while spatial predictors have little influence and show multicollinearity with relative humidity. Our research suggests that urban land management in the 21st century provides key information for adaptation and mitigation strategies aimed at coping with global and local climate variations in the cities of the Tropical Andes. © 2022 Elsevier B.V.

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Urban soils as a spatial indicator of quality for urban socio-ecological systems

2021 , Bonilla Bedoya, Santiago , López-Ulloa M. , Mora-Garcés A. , Macedo-Pezzopane J.E. , Salazar L. , Herrera M.Á.

The development of criteria and indicators to quantify the transition to sustainability of the urban socio-ecological systems quality is determinant for planning policies and the 21st century urban agenda. This study models the spatial variation in the concentration and distribution of some macronutrients, micronutrients, and trace nutrients in the soil of a high-altitude city in the Andes. Meanwhile, machine learning methods were employed to study some interactions between the different dimensions that constitute an urban socio-ecosystem that caused these variations. We proposed a methodology that considered two phases: a) field work to collect data on 300 soil samples; laboratory analysis to measure the concentrations of 24 macronutrients, micronutrients, and trace nutrients; and the design of geophysical, spectral, and urban co-variables; b) statistical and geo-informatics analysis, where multivariate analysis grouped the elements into factors; and, machine learning integrated with co-variables was applied to derive the intensity of each factor across the city. Multivariate statistics described the variation in soil co-concentrations with a moderate percentage (42%). Four factors were determined that grouped some of the analyzed elements, as follows: F1 (Zn, S, Cu, Pb, Ni, and Cr), F2 (Ba, Ag, K, In, and Mg), F3 (B, V, Li, and Sr), and F4 (Si and Mn). The percentage R2 out-of-bag of the spatial model were: F1 = 20%, F2 = 8%, F3 = 14%, and F4 = 10%. Our outputs show that the enrichment and contamination by anthropogenic factors, such as the increase in population density, land use, road network, and traffic generated by fossil fuel vehicles, should be prioritized in urban planning decisions. © 2021

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Urban socio-ecological dynamics: applying the urban-rural gradient approach in a high Andean city

2020 , Bonilla Bedoya, Santiago , Estrella A. , Vaca Yánez A. , Herrera M.Á.

The urban-rural dichotomy and the simple cause-effect relationship do not allow establishing specific criteria for territorial management from a socio-ecological perspective. The gradient approach could be a powerful tool to understand urban socio-ecological dynamics. This research applied a methodological protocol to obtain urban-rural gradients while considering the specific characteristics of a mid-size Andean city. To achieve this goal, a mixed classification process was applied to a Landsat 8 image. Subsequently, a factor analysis (FA) grouped 25 urbanisation variables. Finally, we applied agglomerative hierarchical clustering. FA established four factors that explained (72%) of the urbanisation metrics’ variation. From this information, we obtained factor maps and a gradient map. The resulting map differentiated six gradients that contrast with the city’s territorial planning based on the urban-rural dichotomy. This study is a starting point to apply the gradient approach in land-use management and urban ecology planning for Andean cities. © 2019, © 2019 Landscape Research Group Ltd.

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Gradient boosting machine to assess the public protest impact on urban air quality

2021 , Zalakeviciute R. , Rybarczyk Y. , Alexandrino K. , Bonilla Bedoya, Santiago , Mejia D. , Bastidas M. , Diaz V.

Political and economic protests build-up due to the financial uncertainty and inequality spreading throughout the world. In 2019, Latin America took the main stage in a wave of protests. While the social side of protests is widely explored, the focus of this study is the evolution of gaseous urban air pollutants during and after one of these events. Changes in concentrations of NO2, CO, O3 and SO2 during and after the strike, were studied in Quito, Ecuador using two approaches: (i) inter-period observational analysis; and (ii) machine learning (ML) gradient boosting machine (GBM) developed business-as-usual (BAU) comparison to the observations. During the strike, both methods showed a large reduction in the concentrations of NO2 (31.5–32.36%) and CO (15.55–19.85%) and a slight reduction for O3 and SO2. The GBM approach showed an exclusive potential, especially for a lengthier period of predictions, to estimate strike impact on air quality even after the strike was over. This advocates for the use of machine learning techniques to estimate an extended effect of changes in human activities on urban gaseous pollution. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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