Details zur Publikation

Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.3390/rs14071673
Lizenz creative commons licence
Titel (primär) Mapping impervious surface using phenology-integrated and Fisher transformed linear spectral mixture analysis
Autor Ouyang, L.; Wu, C.; Li, J.; Liu, Y.; Wang, M.; Han, J.; Song, C.; Yu, Q.; Haase, D.
Quelle Remote Sensing
Erscheinungsjahr 2022
Department CLE
Band/Volume 14
Heft 7
Seite von art. 1673
Sprache englisch
Topic T5 Future Landscapes
Keywords impervious surface area; phenology information; Fisher transformation; linear spectral mixture analysis; endmember variability; Google Earth Engine; seasonally exposed soil; VIS model; Shanghai; Landsat
Abstract The impervious surface area (ISA) is a key indicator of urbanization, which brings out serious adverse environmental and ecological consequences. The ISA is often estimated from remotely sensed data via spectral mixture analysis (SMA). However, accurate extraction of ISA using SMA is compromised by two major factors, endmember spectral variability and plant phenology. This study developed a novel approach that incorporates phenology with Fisher transformation into a conventional linear spectral mixture analysis (PF-LSMA) to address these challenges. Four endmembers, high albedo, low albedo, evergreen vegetation, and seasonally exposed soil (H-L-EV-SS) were identified for PF-LSMA, considering the phenological characteristic of Shanghai. Our study demonstrated that the PF-LSMA effectively reduced the within-endmember spectral signature variation and accounted for the endmember phenology effects, and thus well-discriminated impervious surface from seasonally exposed soil, enhancing the accuracy of ISA extraction. The ISA fraction map produced by PF-LSMA (RMSE = 0.1112) outperforms the single-date image Fisher transformed unmixing method (F-LSMA) (RMSE = 0.1327) and the other existing major global ISA products. The PF-LSMA was implemented on the Google Earth Engine platform and thus can be easily adapted to extract ISA in other places with similar climate conditions.
dauerhafte UFZ-Verlinkung
Ouyang, L., Wu, C., Li, J., Liu, Y., Wang, M., Han, J., Song, C., Yu, Q., Haase, D. (2022):
Mapping impervious surface using phenology-integrated and Fisher transformed linear spectral mixture analysis
Remote Sens. 14 (7), art. 1673 10.3390/rs14071673