The increasing availability and volume of remote sensing data, such as Landsat satellite
images, have allowed the multidimensional analysis of land use/land cover (LULC) changes. However,
the performance of image classification is highly dependent on the quality and quantity of the training
set and its temporal continuity, which may a ect the accuracy of the classification and bias the analysis
of the LULC changes. In this study, we intended to apply a long-term LULC analysis in a rural region
based on a Landsat time series of 21 years (1995 to 2015). Here, we investigated the use of open LULC
source data to provide training samples and the application of the K-means clustering technique to
refine the broad range of spectral signatures for each LULC class. Experiments were conducted on a
predominantly rural region characterized by a mixed agro-silvo-pastoral environment. The open
source data of the o cial Portuguese LULC map (Carta de Uso e Ocupação do Solo, COS) from 1995,
2007, 2010, and 2015 were integrated to generate the training samples for the entire period of analysis.
The time series was computed from Landsat data based on the normalized di erence vegetation index
and normalized di erence water index, using 221 Landsat images. The Time-Weighted Dynamic Time
Warping (TWDTW) classifier was used, since it accounts for LULC-type seasonality and has already
achieved promising overall accuracy values for classifications based on time series. The results
revealed that the proposed method was e cient in classifying a long-term satellite time-series with an
overall accuracy of 76%, providing insights into the main LULC changes that occurred over 21 years.info:eu-repo/semantics/publishedVersio