Hugo Carlos

Urbanisation is a major driver of changes in the configuration and composition of the landscapes, threatening biodiversity by reducing connectivity and simplifying ecosystems. This study examines the impact of variation in the structure and composition of urban landscapes on bird species richness and functional composition in Chetumal, Mexico. Through passive acoustic monitoring, we assessed bird species richness and, using Sentinel-2 satellite imagery, we calculated vegetation indices, spectral heterogeneity, texture, and landscape metrics to characterise habitat composition and configuration. Our results show that species richness decreased with increasing urbanization, with the forest landscape supporting more species than urban sites, particularly insectivores and shrub-foraging birds. The forest site was more aggregated and contiguous and had a higher effective mesh size, indicating greater structural connectivity. In contrast, urban sites were patchier and had more heterogeneous spectral signatures, reflecting landscape disaggregation. Functional composition of the bird communities also shifted, with urban sites harbouring more generalist species. Our findings highlight the importance of maintaining vegetated corridors and structural complexity to support ecologically diverse bird communities. The integration of acoustic biodiversity monitoring with spatial landscape metrics offers a powerful approach for mitigating biodiversity loss in rapidly expanding tropical cities.

Este estudio analiza los patrones espaciales de riesgo relacionados con ideación suicida, depresión, ansiedad y estrés en la población que radica en la ciudad de Mérida Yucatán. Los valores correspondientes a los niveles de depresión, ansiedad y estrés en la población fueron recabados a partir de tamizajes por medio de la plataforma APPSI. Los elementos de infraestructura como servicios de salud mental y puntos de venta de alcohol fueron tomados del Directorio Nacional de Unidades Económicas (DENUE). Se utilizó la unidad espacial H3 en resolución 7, ya que permite un análisis detallado a escalas más pequeñas que las de un Área Geoestadística Básica (AGEB), identificando zonas críticas para intervenciones focalizadas. Los resultados indican una mayor concentración de riesgo en el centro y norte de la ciudad, particularmente en áreas con mayor densidad de puntos de venta de alcohol y menor accesibilidad a servicios de salud mental. Estos hallazgos destacan la importancia de considerar factores geoespaciales al evaluar la vulnerabilidad de la población y sugieren que las intervenciones deben priorizar las áreas con mayor concentración de riesgo para una respuesta más efectiva.

By considering two differential interferometric SAR signals, recovered from synthetic aperture radar (SAR) images, it has been possible to estimate the glacier velocity vector, from a method proposed by the authors Joughin, Kwok, and Fahnestock (JKF) in 1998. Although the JKF method normally works well under certain SAR observation conditions, we found a reformulated version of the main equation of this technique that may improve this interesting methodology. Thus, we present a mathematical review of this method, and a validation of our result in terms of accuracy, with some computer simulations. The innovation proposed is a simplified way to implement JKF’s work in the Sentinel Application Platform (SNAP) software, exemplified with some images from the Canadian Arctic. Generally, a north–east–up displacement estimation is considered, by using reference orthogonal coordinates, independent of the SAR image coordinates. However, we propose a methodology to estimate this velocity vector in terms of ascending or descending image coordinates. Given the importance of the JKF work, we believe that this investigation could contribute to the improvement of this technique, beyond the existence of other modern and independent methodologies.

Mental health problems are one of the various ills that afflict the world’s population. Early diagnosis and medical care are public health problems addressed from various perspectives. Among the mental illnesses that most afflict the population is depression; its early diagnosis is vitally important, as it can trigger more severe illnesses, such as suicidal ideation. Due to the lack of homogeneity in current diagnostic tools, the community has focused on using AI tools for opportune diagnosis. Unfortunately, there is a lack of data that allows the use of IA tools for the Spanish language. Our work has a cross-lingual scheme to address this issue, allowing us to identify Spanish and English texts. The experiments demonstrated the methodology’s effectiveness with an F1-score of 0.95. With this methodology, we propose a method to solve a classification problem for depression tweets (or short texts) by reusing English language databases with insufficient data to generate a classification model, such as in the Spanish language. We also validated the information obtained with public data to analyze the behavior of depression in Mexico during the COVID-19 pandemic. Our results show that the use of these methodologies can serve as support, not only in the diagnosis of depression, but also in the construction of different language databases that allow the creation of more efficient diagnostic tools.

Registration of remote sensing images has been approached using different strategies; one of the most popular is based on similarity measures. There are different measures of similarity in the literature: Normalized Cross-Correlation (NCC), Mutual Information (MI), etc. Normalized Mutual Information (NMI) has received the most attention in image processing; among the most important limitations are its high computational cost and lack of robustness to strong radiometric changes. For this reason, in this work, we introduce a co-registration approach based on the Histogram Kernel Predictability (HKP). This formulation reduces numerical errors and requires less computing time in comparison to NMI. To the best of our knowledge, this is the first work for registering any remote sensing images by using HKP. Additionally, we propose to use an algorithm based on meta-heuristics called Evolutionary Centers Algorithm (ECA), which allows having fewer iterations to solve the registration problem. In addition, we incorporate a parallelization scheme that permits reducing processing times. The results show that our proposal can solve co-registration problems that the NMI cannot solve while obtaining competitive computational times and registration errors comparable with other existing works in the literature. The HKP approach solves most of all the transformations of a set of simulated registration problems, while the NMI, in some cases, only solves half of the registration problems. Moreover, we compare our approach with feature-based methods in real datasets. This research presents an alternative to remote sensing problems where MI has traditionally been used.

Fishing is an ancient practice that dates back to at least the beginning of the Upper Paleolithic period about 40,000 years ago. Nowadays, Fishing is one of the most important activities, as it provides a source of food and economic income worldwide. A key challenge in ecology and conservation is to decrease the Illegal, Unreported and Unregulated fishing (IUU). IUU fishing depletes fish stocks, destroys marine habitats, distorts competition, puts honest fishers at an unfair disadvantage, and weakens coastal communities, particularly in developing countries. One strategy to decrease the IUU fishing is monitoring and detecting the fishing vessel behaviors. Satellite–based Automatic Information Systems (S– AIS) are now commonly installed on most ocean–going vessels and have been proposed as a novel tool to explore the movements of fishing fleets in near real time. In this article, we present a dictionary–based method to classify, by using AIS data, between two fishing gear types: trawl and purse seine. The data was obtained from Global Fishing Watch. Our experiments show that our proposal has a good performance in classifying fishing behaviors, which could help to prevent overexploit and improve the strategies of the fisheries management.

We present the Regression-based Linear Quadratic Regulator (R-LQR), a new approach for determining locally-optimal control feedback policies for robots with non-linear dynamics and non-quadratic cost functions. Our proposal uses a free-derivative algorithm based on local quadratic regressions to obtain the robot motion policy. In addition, our methodology allows to define a notion of scale that translates into the definition of neighborhoods of valid policy and into the exploration of larger areas of the search space to find the optimal policies. The results show that our formulation allows to reach policies with lower costs than existing algorithms and to avoid problems when the behavior of the cost function makes the optimization difficult.