From land cover to land use: applying random forest classifier to Landsat imagery for urban land-use change mapping

Abstract

The extensive record of Landsat imagery is commonly used to map urban land-cover and land-use change. Random forest (RF) classification was applied for mapping more detailed urban land-use and change categories than is typically attempted with Landsat data. Two dates of Landsat imagery (1990 and 2015) were utilized with surface reflectance, Vegetation-Impervious-Soil (V-I-S) fractions, grey-level cooccurrence matrix (GLCM) of V-I-S, and temporal variation of V-I-S inputs. GLCM V-I-S and temporal variation of Vegetation as input features of RF classifiers slightly improved accuracies of land use maps. A change map derived from an overlay analysis between the 2015 map and a Landsat-derived urban expansion map was more accurate than one from post-classification comparison of 1990 and 2015 maps. For the Taiwan study area, Transportation Corridor land use tended to lead conversion to Residential and Employment types in relatively undeveloped districts, and extensive urban land-use change occurred in peri-urban areas.

Keywords:

• Landsat
• land cover
• land use
• random forest
• urban expansion

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was partially supported by Yin Chin Foundation of U.S.A., STUF United Fund Inc., the Long Jen-Yi Travel fund, William & Vivian Finch Scholarship, and a doctoral stipend through San Diego State University.