Nowadays, image analysis has a relevant role in most scientific and research areas. This process is used to extract and understand information from images to obtain a model, knowledge, and rules in the decision process. In the case of biological areas, images are acquired to describe the behavior of a biological agent in time such as cells using a mathematical and computational approach to generate a system with automatic control. In this paper, MCF7 cells are used to model their growth and death when they have been injected with a drug. These mammalian cells allow understanding of behavior, gene expression, and drug resistance to breast cancer. For this, an automatic segmentation method called GEMA is presented to analyze the apoptosis and confluence stages of culture by measuring the increase or decrease of the image area occupied by cells in microfluidic devices. In vitro, the biological experiments can be analyzed through a sequence of images taken at specific intervals of time. To automate the image segmentation, the proposed algorithm is based on a Gabor filter, a coefficient of variation (CV), and linear regression. This allows the processing of images in real time during the evolution of biological experiments. Moreover, GEMA has been compared with another three representative methods such as gold standard (manual segmentation), morphological gradient, and a semi-automatic algorithm using FIJI. The experiments show promising results, due to the proposed algorithm achieving an accuracy above 90% and a lower computation time because it requires on average 1 s to process each image. This makes it suitable for image-based real-time automatization of biological lab-on-a-chip experiments.

Coastal regions and surface waters are among the fundamental biological and social development resources worldwide. For this reason, it is essential to thoroughly monitor these regions to determine and characterize their geographical features and environmental health. These geographical regions, however, present several monitoring challenges when using remotely sensed imagery. Small water bodies tend to be surrounded by swamps, marshes, or vegetation, making accurate border detection difficult. Coastal waters, in turn, experience several phenomena due to winds, undercurrents, and waves, which also hamper the detection of environmental hazards like oil spills. In this work, we propose an automated segmentation algorithm that can be applied to these targets in airborne and spaceborne SAR images. The method is based on pointwise detection in fuzzy borders using a parameter estimation of the ${\mathcal{G}}^0$ distribution, which has been successfully used in similar contexts. The underlying assumption is that the sought-for border separates regions with different textures, each having different distribution parameters. Then, stochastic distances can identify the most likely point where this parameter change occurs. A curve interpolation algorithm then estimates the actual contour of the body given the detected points. We assess the adequacy of eight stochastic distances that are mostly applied in the literature. We evaluate the performance of our method in terms of similarity between true and detected boundaries on simulated and actual SAR images, achieving promising results. The performance of our proposal is assessed by Hausdorff distance and Intersection over Union. In the case of synthetic data, the selection of the best stochastic distance depends on the parameters of the ${\mathcal{G}}_I^0$ distribution. In contrast, the harmonic-mean and triangular distances produced the best results in detecting borders in three actual SAR images of lagoons. Finally, we present the results of our proposal applied to an image with oil spills using Bhattacharyya, Hellinger, and Jensen–Shannon distances.

The Pantanal is one of the largest wetlands in the world. This natural region has fundamental importance for water supply and biodiversity conservation. In this paper, we apply a methodology to analyze multi-temporal water and vegetation changes in six different zones of land use/land cover and their relationship with humidity in the Brazilian Pantanal subregion of Cáceres, Mato Grosso. Meteorological data from the INMET Station in Cáceres was used. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) were compared year to year (2019, 2020, 2021). The spatial variations revealed water changes according to the occurrence of wet and dry years. In dry years there was a remarkable increase in mean and maximum values linearly related to a decrease in water availability. Analyzing LULC change dynamics in these areas is crucial for developing new proposals for interventions to monitor the area and thus provide subsidies to goal 15 of the 2030 Sustainable Development Agenda, which aims to stop and restore degradation caused in the environment and to promote reforestation.

Los cambios de uso y cobertura del suelo (LULC) afecta en gran medida a los ecosistemas implicando una modificación de la cobertura natural y provocando consecuencias sobre las características de un suelo, los procesos hídricos y geomorfológicos. Debido a esto, el objetivo consiste en determinar el cambio LULC de la cuenca del arroyo El Negro para los años 1985 y 2023 a partir la clasificación supervisada por medio de imágenes satelitales, analizar los cambios e identificar sectores que evidencian procesos de erosión hídrica. De esta forma, el mayor cambio LULC lo presenta la clase vegetación que se redujo su superficie de 55 km2 (1985) a 13 km2 (2023) y mientras que la cobertura agrícola se incrementó, pasando de 65 Km2 (1985) a casi los 100 km2 (2023) representando casi el 50% de la superficie total. Sumado a este cambio, se pudo identificar sectores donde se desarrollan diferentes procesos de erosión hídrica dentro de campos de producción agrícola (principalmente en zonas de piedemonte) y propias de la evolución de la red de drenaje.

The Pantanal is the largest tropical wetland in the world and covers the countries of Bolivia, Brazil, and Paraguay. Some regions within the Pantanal are part of the Convention on Wetlands or are recognized as Biosphere Reserves and World Heritage Sites by UNESCO. This work's objective is to classify surface water bodies into wetlands using machine learning techniques, through clustering techniques. Initially, the (Normalized Difference Vegetation Index) was used from a mosaic of images covering the study area from the Sentinel 2-A satellite processed on the Google Earth Engine platform. From this index, a threshold is established empirically, and the water bodies are segmented. The clustering technique is then applied to the morphological characteristics of each segmented object. The results obtained show that it is possible to categorize water bodies with unsupervised learning techniques.