Defining Urban Growth Boundary in Semarang City: Integrating Spatial Planning and Predictive Modeling Techniques

IOP Conference Series Earth and Environmental Science, Journal Year: 2025, Volume and Issue: 1443(1), P. 012037 - 012037

Published: Jan. 1, 2025

Abstract Understanding the maximum percentage of urban area within an administrative region, such as Semarang City, necessitates examination spatial planning schemes, development regulations, and local government policies. Concurrently, cellular automata Markov chain approaches can be used to predict how cities will grow in future accurately. This study aims define growth boundary City by integrating with predictive modeling techniques. The Cellular automata-Markov (CA-MC) method predicts developments based on current land use patterns. seeks delineate areas suitable for using data analysis while preserving critical ecological agricultural zones. findings this research contribute formulating informed policies aimed at achieving balanced expansion environmental conservation Semarang, thus fostering resilient inclusive landscapes city.

Language: Английский

Refining Land Cover Classification and Change Detection for Urban Water Management using Comparative Machine Learning Approach.

Environmental Challenges, Journal Year: 2025, Volume and Issue: unknown, P. 101118 - 101118

Published: March 1, 2025

Language: Английский

Advancement and Applications of Forest Remote Sensing in Korea: Past, Present, and Future Perspectives

Korean Journal of Remote Sensing, Journal Year: 2024, Volume and Issue: 40(5-2), P. 783 - 812

Published: Oct. 16, 2024

Kyoung-Min Kim, Joongbin Lim, Sol-E Choi, Nanghyun Cho, Minji Seo, Sunjoo Lee, Hanbyol Woo, Junghee Cheolho Junhee Seunghyun Myoungsoo Won. Korean J. Remote Sens. -0001;0:. https://doi.org/10.7780/kjrs.2024.40.5.2.8

Language: Английский