# Uncovering Vegetation Changes in the Urban-Rural Interface through Semi-Automatic Methods
###### Abstract
The detection of image-to-image changes consists of comparing the spectral signals between satellite images acquired on two different dates to verify the changes that have occurred. According to [15] these analyses can be divided into: (i) differences between ratings, which compares images classification of different dates [16]; (ii) spectral mixture analysis (SMA) similar to (i), but allows accessing subpixel information [17; 18]; and (iii) spectral or indices, e.g., the normalised difference vegetation index (NDVI) [19] or the normalised burn ratio (NBR) [20], differences that compare these values in images of different dates looking for changes. Time series-based change detection methods use data acquired across several observation dates [21]. These analyses employ the variation of pixel spectral signal over a given period to create metrics that assist in mapping or monitoring a particular location [22]. The temporal patterns of spectral indices are used to determine qualitatively and quantitatively any changes that may exist [23]. The time series consists of three main components [24]: (i) long-term directional trend; (ii) seasonality and systematic movements; and (iii) short-term fluctuation, also known as noise, is the component resulting from the extraction of trend and seasonality of the time series, irregularities, and non-systematic movements. The authors also emphasise that the analyses in remote sensing can use these components together or separately, according to the type of research developed [14] stressed that the residual component is of great interest in detecting degradations and disturbances in forest areas. We can also divide analyses based on time series according to the general characteristics of the methods employed. There are limit-based models (thresholds) to identify certain types of land cover and a deviation from this limit value compared to the previous date is identified as a change. The vegetation change tracker (VCT) algorithm [25] uses temporal-spectral information to detect disturbances that result in a significant or complete reduction of the canopy of woody vegetation and biomass and the work of [26] that established a set of limits for all bands of the Landsat in order to detect burnt areas fall into this category. We also found models based on segmentation of the spectral signal. They check long-term trends in the spectral response of a given target. The general process is to segment the time series into a set of line segments capable of identifying the general characteristics present in the trajectory. The abrupt or gradual divergences found in this trajectory are identified as changes [4,5]. The LandTrendr algorithm, developed by [27] is one example of this kind of models. Finally, there are models based on statistical boundaries that use statistical information from a set of satellite images. Here, changes are identified when statistically significant deviations from a standard limit are found. The grasslands moving index--GMI [28] was designed to identify cuts of the undergrowth in grassland areas through spectral differences of the NDVI. [PERSON] [15] points out that the studies developed in this category usually do not use local spatial information to assist in the detection of changes. However, [29] considered this question and used the spatially normalised NDVI value'sNDVI' based on the mean of neighbouring pixels in order to reduce phenological differences. Thus [30,31] also considered the local context when applying the statistical test Welch \(t\)-test to search for changes in vegetation. Considering all advances reported in the literature we proposed a new hybrid semi-automatic method that conjugates time series NDVI images and a neighbourhood moving window to detect statistically significant changes in vegetation. Furthermore, our method uses free available data (Sentinel) and open access code (Python) that makes it easy to implement.