Remote Sensing, Vol. 11, Pages 2664: Using Multi-Sensor Satellite Images and Auxiliary Data in Updating and Assessing the Accuracies of Urban Land Products in Different Landscape Patterns (Remote Sensing)

Rapid and accurate updating of urban land areas is of great significance to the study of environmental changes. Although there are many urban land products (ULPs) at present, such as GlobeLand30, Global Urban Footprint (GUF), and Global Human Settlement Layer (GHSL), these products are all static data of a certain year, and are not able to provide high-accuracy updating of urban land areas. In addition, the accuracies of these data and their application value in the update of urban land areas need to be urgently proven. Therefore, we proposed an approach to quickly and accurately update urban land areas in the Kuala Lumpur region of Malaysia, and assessed the accuracies of urban land products in different urban landscape patterns. The approach combined the advantages of multi-source data including existing ULPs, OpenStreetMap (OSM) data, Landsat Operational Land Imager (OLI), and Phased Array type L-band Synthetic Aperture Radar (PALSAR) images. Three main steps make up this approach. First, the urban land training samples were selected in the urban areas consistent with GlobeLand30, GUF, and GHSL, and samples of bare land, vegetation, water bodies, and road auxiliary data were obtained by GlobeLand30 and OSM. Then, the random forest was used to extract urban land areas according to the object’s features in the OLI and PALSAR images. Last, we assessed the accuracies of GlobeLand30, GUF, GHSL, and the results of this study (ULC) by using point and area validation methods. The results showed that the ULC had the highest overall accuracy of 90.18% among the four products and could accurately depict urban land in different urban landscapes. The GHSL was the second most accurate of the four products, and the accuracy in urban areas was much higher than that in rural areas. The GUF had many omission errors in urban land areas and could not delineate a large area of complete spatial information of urban land, but it could effectively extract scattered residential land with small patches. GlobeLand30 had the lowest accuracy and could only express rough, large-scale urban land. The above conclusions provide evidence that ULPs and the approach proposed in this study have a great application potential for high-accuracy updating of urban land areas.