Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data

Urban and peri-urban environments are composed of a wide variety of materials, making land cover classification challenging. The objective of this research is to determine how effectively multi-season Landsat Enhanced Thematic Mapper Plus (ETM+) and single-season Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) data can be combined to map 17 land cover categories in the Greater Boston area of, Massachusetts, USA. The key goal of this work is to test the integration of radar and optical data. The contribution of different dimensions of input data to a random forest classifier was evaluated with map accuracy statistics. PALSAR data produced a 30.99% overall classification accuracy. Higher classification accuracy (72.24%) was achieved by adding texture variables derived from the PALSAR data. A September Landsat image produced a map accuracy of 77.96%. The inclusion of Landsat images from other three seasons increased map accuracy to 86.86% and Landsat derived texture variables further increased the map accuracy to 92.69%. The highest map accuracy (93.82%) was achieved by combining Landsat and PALSAR. Though combining PALSAR and Landsat only increased the overall accuracy by 1.1%, it was a statistically significant increase, whose magnitude was limited by the high accuracy already achieved with Landsat data. Moreover, confusion matrices and land cover maps indicated that most of this increase was from three urban land cover types (low density residential, high density residential, and commercial/industrial). The results demonstrate the value of combining multitemporal Landsat imagery, ALOS PALSAR data, and texture variables for land cover classification in urban and peri-urban environments.

► Spectral, polarimetric, temporal, and spatial dimensions contribute to increased accuracies.
► Land cover map derived from the dual polarization PALSAR data has low accuracy.
► The temporal and spatial dimensions of Landsat data are important inputs.
► The best classification result is derived from the combination of Landsat and SAR.
► Inclusion SAR improved accuracy by 1.1%, mostly contributed from urban land cover.