Colombia has approximately 379,954 hectares of mangrove forests distributed along the Pacific Ocean and the Caribbean Sea coasts. Such forests are experiencing the highest annual rate of loss recorded in South America and, in the last three decades, approximately 40,000 hectares have been greatly affected by natural and, especially, human impacts. This study determined, by the use of Landsat multispectral satellite images, the evolution of three mangrove forests located in the Colombian Caribbean Sea: Malloquín, Totumo, and La Virgen swamps. Mangrove forest at Mallorquín Swamp recorded a loss of 15 ha in the period of 1985–2018, associated with alterations in forest hydrology, illegal logging, urban growth, and coastal erosion. Totumo Swamp lost 301 ha in the period 1985–2018 associated with changes in hydrological conditions, illegal logging, and increased agricultural and livestock uses. La Virgen Swamp presented a loss of 31 ha in the period of 2013–2018 that was linked to the construction of a roadway, alterations of hydrological conditions, illegal logging, and soil urbanization, mainly for tourist purposes. Although Colombian legislation has made efforts to protect mangrove ecosystems, human activities are the main cause of mangrove degradation, and thus it is mandatory for the local population to understand the value of the ecosystem services provided by mangroves.

The use of wireless sensor networks (WSN) for monitoring variables in agricultural environments and natural forests has been increasing in recent years. However, the sizing of these systems is affected by the inaccuracy of the radio wave propagation models used, leading to possible increased costs and measurement errors. This systematic literature review (SLR) aims to identify propagation models widely used in WSN deployments in agricultural or naturally vegetated environments and their effectiveness in estimating signal losses. We also identified today’s wireless technologies most used in precision agriculture (PA) system implementations. In addition, the results of studies focused on the development of new propagation models for different environments are evaluated. Scientific and technical analysis is presented based on articles consulted in different specialized databases, which were selected according to different combinations of criteria. The results show that, in most of the application cases, vegetative models present high error values when estimating attenuation.

This article contains methods, results, and analysis agreed for the development of an application based on the internet of things and making use of machine learning techniques that serves as a support for the identification of the saline wedge in the Magdalena River estuary, Colombia. As a result of this investigation, the process of identifying the most suitable telecommunications architecture to be installed in the estuary is shown, as well as the characteristics of the software developed called SISME (Estuary Monitoring System), and the results obtained after the implementation of prediction techniques based on time series. This implementation supports the maritime security of the port of Barranquilla since it can support decision-making related to the estuary. This research is the result of the project “Implementation of a Wireless System of Temperature, Conductivity and Pressure Sensors to support the identification of the saline wedge and its impact on the maritime safety of the Magdalena River estuary”.