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The objective of this work is to develop a dynamic geographical information system (DGIS) applicable for the simulation of spatio-temporal phenomena. DGIS is designed and implemented as a dynamic raster GIS in a fully integrated systems strategy using theory of modelling and simulation, discrete event systems specifications, and knowledge-based simulation methodologies. The approach accommodates the representations and operators of current GIS in a broader systems representation scheme that includes the constructs for handling continuous time, model base, multiple resolution, hierarchy, taxonomy, variable structure, and intelligent agents. DGIS is defined as an endomorphic system that can be transformed by the user into different, application-specific, spatial dynamic simulation environments depending on the models selected to fit the various aspects of its structure. In this paper we describe the theory, methods, and technologies upon which the system is designed and implemented and discuss how it can support complex spatial simulations. To probe operational applicability, an instance of DGIS is generated: a fire spread simulation environment, which is tested in the simulation of 15 real fire events. [ABSTRACT FROM AUTHOR]

One of the main applications of machine learning (ML) in remote sensing (RS) is the pixel-level classification of satellite images into land cover types. Although classes with different spectral signatures can be easily separated, e.g. aquatic and terrestrial land cover types, others have similar spectral signatures and are hard to separate using only the information within a single pixel. This work focused on the separation of two cover types with similar spectral signatures, cocoa agroforest and forest, over an area in Pará, Brazil. For this, we study the training and application of several ML algorithms on datasets obtained from a single composite image, a time-series (TS) composite obtained from the same location and by preprocessing the TS composite using simple TS preprocessing techniques. As expected, when ML algorithms are applied to a dataset obtained from a composite image, the median producer's accuracy (PA) and user's accuracy (UA) in those two classes are significantly lower than the median overall accuracy (OA) for all classes. The second dataset allows the ML models to learn the evolution of the spectral signatures over 5 months. Compared to the first dataset, the results indicate that ML models generalize better using TS data, even if the series are short and without any preprocessing. This generalization is further improved in the last dataset. The ML models are subsequently applied to an area with different geographical bounds. These last results indicate that, out of seven classifiers, the popular random forest (RF) algorithm ranked fourth, while XGBoost (×GB) obtained the best results. The best OA, as well as the best PA/UA balance, were obtained by performing feature construction using the M3GP algorithm and then applying XGB to the new extended dataset. [ABSTRACT FROM AUTHOR]