The present work started from the need to streamline the process of monitoring changes in vegetation in the in urban-rural interface fuel management bands, defined by Portuguese legislation as areas where the existing biomass must be totally or partially removed. The model developed uses a time series of Sentinel 2 satellite images to search for changes in the vegetation cover in a 100 m buffer around built-up areas. The use of satellite data allows analysing large areas and speeds up the task of identifying the places where fuel management took place and the places where there is a need to carry out such management. The objective of the proposed method is to give a script in Python language that can verify the cleanliness of vegetation in the fuel management ranges through multi-temporal analysis of satellite images. • The paper presents a step-by-step procedure for a Sentinel 2 time series vegetation index analysis. • Automated routine to detection of spatiotemporal vegetation changes based on statistical parameters. • Used Python language to do geoprocessing analysis.
Keywords: Python; Sentinel 2; Time Series; Vegetation change; Vegetation index.
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