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Projecting towards the future: The Dinamica EGO software

Dinamica EGO [1] is free software developed for environmental analysis and landscape dynamics, which allows the creation of future trends from a self-training procedure using previous situations at different times. The platform construct models through a graphical interface in which the calculation functions (functors), the initial data (maps and tables) and the results (maps, tables and weights) are incorporated through the connectors of its ports (Figure 1). Dynamica EGO has implemented calculation engines that allow maximizing hardware resources (parallel processing, memory, ...).
Its main use is based on the application of various calculation functions related to changes in land cover and land use structures, as well as a part of the analysis of landscapes available to the platform. In this way, a model with great flexibility is generated using the variables that affect the change according to the territory analyzed. The resulting model, once calibrated, will allow the analysis and the proposal of a future scenario of the changes that have occurred over time in land uses or landscapes.
Figure 1. Graphic Design with functions and connectors for Land Use/Cover Changes (LUCC) Modeling


The results of future trends analysis also allow their application to evaluate their possible environmental incidents. Some examples of environmental analyzes using the pre-established result with Dynamica EGO are linked  to the study of different scenarios such as deforestation [2, 3], recent changes in land use and climate change [4, 5, 6] and also the evaluation of processes related to water resources {7,8,9].


More Info: 

1] Ferreira, B.M, Soares Filho, B.S, Pereira, F.M.Q. 2019. The Dinamica EGO Virtual Machine. Science of Computer Programming. 

[2] Ghilardi, A., Bailis, R., Mas, J.-F., Skutsch, M., Elvir, J.A., Quevedo, A., Masera, O., Dwivedi, P., Drigo, R., Vega, E., 2016. Spatiotemporal modeling of fuel wood environmental impacts: Towards improved accounting for non-renewable biomass. Environ. Model. Softw. 82, 241–254.

[3] Jaramillo-Giraldo, C., Soares Filho, B., Carvalho Ribeiro, S.M., Gonçalves, R.C., 2017. Is It Possible to Make Rubber Extraction Ecologically and Economically Viable in the Amazon? The Southern Acre and Chico Mendes Reserve Case Study. Ecol. Econ. 134, 186–197.

[4] Ahmed, S., Bramley, G., 2015. How will Dhaka grow spatially in future?-Modelling its urban growth with a near-future planning scenario perspective. Int. J. Sustain. Built Environ. 4, 359–377.

[5] Maeda, E., Pellikka, P., Siljander, M., J.F. Clark, B., 2010. Potential impacts of agricultural expansion and climate change on soil erosion in the Eastern Arc Mountains of Kenya.

[6] Troupin, D., Carmel, Y., 2016. Landscape patterns of development under two alternative scenarios: Implications for conservation. Land Use Policy 54, 221–234.

[7] Lima, L.S., Coe, M.T., Filho, B.S.S., Cuadra, S.V., Dias, L.C.P., Costa, M.H., Lima, L.S., Rodrigues, H.O., 2014. Feedbacks between deforestation, climate, and hydrology in the Southwestern Amazon: implications for the provision of ecosystem services. Landsc. Ecol. 29, 261–274.

[8] Veerbeek, W., Pathirana, A., Ashley, R., Zevenbergen, C., 2015. Enhancing the calibration of an urban growth model using a memetic algorithm. Comput. Environ. Urban Syst. 50, 53–65.

[9] Huong, H.T.L., Pathirana, A., 2013. Urbanization and climate change impacts on future urban flooding in Can Tho city, Vietnam. Hydrol Earth Syst Sci 17, 379–394.