Tracking the historical urban development by classifying Landsat MSS data with training samples migrated across time and space

Autor: Zemin Feng, Yuqing Liu, Yan Shi, Jun Yang
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: International Journal of Digital Earth, Vol 16, Iss 1, Pp 2487-2502 (2023)
Druh dokumentu: article
ISSN: 1753-8947
1753-8955
17538947
DOI: 10.1080/17538947.2023.2230951
Popis: To reveal the historical urban development in large areas using satellite data such as Landsat MSS still need to overcome many challenges. One of them is the need for high-quality training samples. This study tested the feasibility of migrating training samples collected from Landsat MSS data across time and space. We migrated training samples collected for Washington, D. C. in 1979 to classify the city’s land covers in 1982 and 1984. The classifier trained with Washington, D. C.’s samples were used in classifying Boston’s and Tokyo’s land covers. The results showed that the overall accuracies achieved using migrated samples in 1982 (66.67%) and 1984 (65.67%) for Washington, D. C. were comparable to that of 1979 (68.67%) using a random forest classifier. Migration of training samples between cities in the same urban ecoregion, i.e. Washington, D. C., and Boston, achieved higher overall accuracy (59.33%) than cities in the different ecoregions (Tokyo, 50.33%). We concluded that migrating training samples across time and space in the same urban ecoregion are feasible. Our findings can contribute to using Landsat MSS data to reveal the historical urbanization pattern on a global scale.
Databáze: Directory of Open Access Journals
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