Multitemporal modeling and simulation of the complex dynamics in urban wetlands: the case of Bogota, Colombia

Abstract

Urban wetlands are essential to the longstanding health and well-being of cities. Acknowledged as rich in biodiversity and highly productive ecosystems, they provide ecosystem services represented in aspects such as air purification, urban climate regulation, physical and mental health, recreation, and contemplation, among a wide variety of other goods and services on which the quality of life of the inhabitants of large cities such as Bogota depends largely. We used cellular automata to model and simulate urban wetland changes in Bogota, Colombia. The study applied the coupled Markov-Future Land Use Simulation (FLUS) model to simulate and analyze land use/land cover (LULC) change over 20 years. First, we used an orthomosaic (1998) and two WorldView-2 satellite images (2004 and 2010), to detect land cover changes. Then, using the artificial neural network FLUS module, we calculated the relationships between land classes and associated drivers and estimated the probability of occurrence of each land class. Finally, we applied Intensity Analysis to examine the observed and projected LULC change (1998–2034). Results indicate that gains in areas of crops and pastures are at the expense of wetlands. In addition, simulation outputs show that wetlands will likely represent less than 2% of the total study area in 2034, representing a 14% decrease in 24 years. The importance of this project lies in its potential contribution to the decision-making process within the city and as an instrument of natural resource management. Additionally, the results of this study could contribute to the United Nations Sustainable Development Goal 6, “Clean water and sanitation," and climate change mitigation.

Assessing and Enhancing Predictive Efficacy of Machine Learning Models in Urban Land Dynamics: A Comparative Study Using Multi-Resolution Satellite Data

“…In another recent publication, Cuellar et al utilized high-resolution WorldView-2 satellite images to model the complex dynamics in urban land change up to 2034 in Bogota using the MC, future land use simulation (FLUS), and ANN methods. Validation confirmed a machine learning accuracy with an average value of 0.85 [36].…”

Comprehensive Framework for Analysing the Intensity of Land Use and Land Cover Change in Continental Ecuadorian Biosphere Reserves

“…However, when performing more complex territorial analysis, it is necessary to design a graphical strategy to communicate the results effectively [24], i.e., featureexpression heat maps [25]. This can bridge the gap between knowledge based on complex models [26,27] and decision making [28,29] at different decision levels of land management and stakeholder engagement events.…”