Land Use Land Cover Change Detection using Remote Sensing and Random Forest Model DOI Open Access

Lingye Tan,

Robert L. K. Tiong, Ziyang Zhang

и другие.

Опубликована: Авг. 30, 2023

Land use and land cover (LULC) datasets for Jinan in 1992, 1998, 2002, 2006, 2011, 2017, 2022 were developed from Landsat images using the Random Forest (RF) classification approach. The relationships between social-economic, political factors time-series LULC data exam-ined periods 1992 2022. results showed effectiveness of RF method with time series images. Combined driving forces analysis, our research can effectively explain detailed change tra-jectories corresponding to different stages give new insights into pat-terns. show a significant increase impervious surface which opposite bare experienced huge decline declined by 95%, due urbanization rapid in-crease population. behind these changes are related population growth, economic development, climate change. Moreover, present employed Principal Components Analysis (PCA) methodology order understand relative significance disparate factors. analysis prove that economy (population, GDP) primary have an obvious impact on use/land surface, land, woodland, farmland, water distinct. Government policies also substantial as well, such Construction Harmonious (COHJ). helpful better understanding mechanisms provide useful knowledge effective resource management planning.

Язык: Английский

Land Use Land Cover Change Detection using Remote Sensing and Random Forest Model DOI Open Access

Lingye Tan,

Robert L. K. Tiong, Ziyang Zhang

и другие.

Опубликована: Авг. 30, 2023

Land use and land cover (LULC) datasets for Jinan in 1992, 1998, 2002, 2006, 2011, 2017, 2022 were developed from Landsat images using the Random Forest (RF) classification approach. The relationships between social-economic, political factors time-series LULC data exam-ined periods 1992 2022. results showed effectiveness of RF method with time series images. Combined driving forces analysis, our research can effectively explain detailed change tra-jectories corresponding to different stages give new insights into pat-terns. show a significant increase impervious surface which opposite bare experienced huge decline declined by 95%, due urbanization rapid in-crease population. behind these changes are related population growth, economic development, climate change. Moreover, present employed Principal Components Analysis (PCA) methodology order understand relative significance disparate factors. analysis prove that economy (population, GDP) primary have an obvious impact on use/land surface, land, woodland, farmland, water distinct. Government policies also substantial as well, such Construction Harmonious (COHJ). helpful better understanding mechanisms provide useful knowledge effective resource management planning.

Язык: Английский

Процитировано

1