Mapping Rural Settlements from Landsat and Sentinel Time Series by Integrating Pixel- and Object-Based Methods

Ru Xu
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Ru Xu: State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

Land, 2021, vol. 10, issue 3, 1-18

Abstract: Rural settlements account for 45% of the world’s population and are targeted places for poverty eradication. However, compared to urban footprints, the distribution of rural settlements is not well characterized in most existing land use and land cover maps because of their patchy and scattered organization and relative stability over time. In this study, we proposed a pixel- and object-based method to map rural settlements by employing spectral-texture-temporal information from Landsat and Sentinel time series. Spectral indices (maximum normalized difference vegetation index (NDVI) and minimum normalized difference built-up index (NDBI composite) and texture indices (vertical transmit and vertical receive (VV) polarization of mean synthetic aperture radar (SAR) composite) were calculated from all available Landsat and Sentinel-1A data from 1 January 2016 to 31 December 2018. These features were then stacked for segmentation to extract potential rural settlement objects. To better differentiate settlements from bare soil, the gradient of annual NDVI maximum (namely, gradient of change, use gradient for simplicity) from 1 January 1987 to 31 December 2018 was used. The rural training samples were selected from global urban footprint (GUF) products with a post filtering process to remove sample noise. Scatter plots between pixel- and object-based values per feature were delineated by t-distribution ellipses to determine the thresholds. Finally, pixel- and object-based thresholds were applied to four features (NDVI, NDBI, VV, gradient) in Google Earth Engine (GEE) to obtain the distribution of rural settlements in eight selected Asian regions. The derived maps of rural settlements showed consistent accuracy, with a producer’s accuracy (PA) of 0.87, user’s accuracy (UA) of 0.93 and overall accuracy (OA) reaching 90% in different landscape conditions, which are better than existing land cover products.

Keywords: rural settlement; spectral-texture-temporal; pixel- and object-based classification; time series; Sentinel; Landsat (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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