Abstract
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.
Highlights
The increased availability of free and analysis-ready remote sensing (RS) data, such as Landsat, supports dynamic analysis in time and space [1,2], since it allows researchers to perform multidimensional classifications of land use/land cover (LULC) based on satellite image time series [3,4,5]
The goal of this study was twofold: (i) access free LULC data to be used as training samples for a long-term satellite image time-series classification; and (ii) identify the LULC changes that occurred from 1995 to 2015 in a rural region characterized by a mixed agro-silvo-pastoral environment in the municipality of Beja, Portugal
This study introduced the use of the Time-Weighted Dynamic Time Warping (TWDTW) method for long-term classification using the Landsat time series
Summary
The increased availability of free and analysis-ready remote sensing (RS) data, such as Landsat, supports dynamic analysis in time and space [1,2], since it allows researchers to perform multidimensional classifications of land use/land cover (LULC) based on satellite image time series [3,4,5]. Likewise, concerning the well-established, advanced, nonparametric classifiers in remote sensing literature [8,13], such as the support vector machine (SVM) and the random forest (RF) [14], it has been recognized that some challenges still remain regarding LULC classifications based on time series [15]. These challenges are: (i) the inexistence/existence and the quality of the samples required to train the algorithm, (ii) the irregular phenological signatures of different LULC types through time, and (iii) the absence of a data temporal continuum [15]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
