Version 1
: Received: 28 August 2023 / Approved: 29 August 2023 / Online: 30 August 2023 (03:33:59 CEST)
How to cite:
Tan, L.; Tiong, R.L.; Zhang, Z.; Najafzadeh, M. Land Use Land Cover Change Detection using Remote Sensing and Random Forest Model. Preprints2023, 2023082000. https://doi.org/10.20944/preprints202308.2000.v1
Tan, L.; Tiong, R.L.; Zhang, Z.; Najafzadeh, M. Land Use Land Cover Change Detection using Remote Sensing and Random Forest Model. Preprints 2023, 2023082000. https://doi.org/10.20944/preprints202308.2000.v1
Tan, L.; Tiong, R.L.; Zhang, Z.; Najafzadeh, M. Land Use Land Cover Change Detection using Remote Sensing and Random Forest Model. Preprints2023, 2023082000. https://doi.org/10.20944/preprints202308.2000.v1
APA Style
Tan, L., Tiong, R.L., Zhang, Z., & Najafzadeh, M. (2023). Land Use Land Cover Change Detection using Remote Sensing and Random Forest Model. Preprints. https://doi.org/10.20944/preprints202308.2000.v1
Chicago/Turabian Style
Tan, L., Ziyang Zhang and Mohammad Najafzadeh. 2023 "Land Use Land Cover Change Detection using Remote Sensing and Random Forest Model" Preprints. https://doi.org/10.20944/preprints202308.2000.v1
Abstract
Land use and land cover (LULC) datasets for Jinan in 1992, 1998, 2002, 2006, 2011, 2017, and 2022 were developed from Landsat images using the Random Forest (RF) classification approach. The relationships between social-economic, political factors and time-series LULC data were exam-ined for the periods between 1992 and 2022. The results showed the effectiveness of using the RF classification method for LULC classification with time series of Landsat images. Combined with driving forces analysis, our research can effectively explain the detailed LULC change tra-jectories corresponding to different stages and give new insights into Jinan LULC change pat-terns. The results show a significant increase in impervious surface which opposite change to bare land which experienced a huge decline declined by 95%, due to urbanization and rapid in-crease of population. The driving forces behind these changes are related to population growth, economic development, and climate change. Moreover, the present research employed Principal Components Analysis (PCA) methodology in order to understand the relative significance of disparate driving factors. The analysis results prove that the economy (population, GDP) and climate change were the primary factors that have an obvious impact on land use/land cover changes and that the driving factors for impervious surface, bare land, woodland, farmland, and water were distinct. Government policies also have a substantial impact on LULC change as well, such as the Construction of Harmonious Jinan (COHJ). The results were helpful for better understanding the mechanisms of LULC change and can provide useful knowledge for effective land resource management and planning.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
