Long-Term Satellite Image Time-Series for Land Use/Land Cover Change Detection Using Refined Open Source Data in a Rural Region

• Published in: Remote sensing (Basel, Switzerland) Vol. 11; no. 9; p. 1104
• Main Authors: Viana, Cláudia M., Girão, Inês, Rocha, Jorge
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-05-2019
• Subjects: Accuracy; Agricultural management; Algorithms; Change detection; Classification; Cluster analysis; Clustering; cropland mapping; Image classification; Image detection; Land cover; Land use; Landsat; Landsat satellites; LULC change; Normalized difference vegetative index; Remote sensing; Rural areas; Rural environments; Rural land use; Satellite imagery; Satellites; Seasonal variations; Spectral signatures; Time series; Training; TWDTW; Vector quantization; Vegetation; Eastern Europe; Portugal
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs11091104

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 affect 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 official 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 difference vegetation index and normalized difference 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 efficient 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.