Salīdzināt metodes

	Izmaiņu noteikšana	CA-Markov zemes lietojuma pārmaiņu modelis
Nozare	Tālizpēte	Telpiskā analīze
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1989	1997
Autors	Ashbindu Singh	Cellular automata (Clarke) + Markov chain (Muller & Middleton)
Tips	Multitemporal image comparison pipeline	Spatio-temporal land-use change simulation
Pirmavots	Singh, A. (1989). Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10(6), 989–1003.	Clarke, K. C., Hoppen, S., & Gaydos, L. (1997). A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B, 24(2), 247–261.
Citi nosaukumi	Multitemporal Image Analysis, Land-Cover Change Analysis, Bitemporal Change Analysis, Değişim Tespiti	CA-Markov model, cellular automata Markov, land-use change simulation, CA-Markov arazi kullanımı modeli
Saistītās	2	3
Kopsavilkums	Change detection is a remote sensing analysis pipeline that identifies differences in land cover or land use between two or more images acquired at different times over the same geographic area. Systematically reviewed and classified by Ashbindu Singh in 1989, the framework encompasses image differencing, post-classification comparison, vegetation index differencing, and principal component analysis, and remains the canonical reference for evaluating which technique best suits a given application.	CA-Markov is a hybrid spatio-temporal model that projects land-use and land-cover change by combining a Markov chain — which predicts how much of each class will change — with cellular automata, which decide where that change happens. Widely used for urban-growth and land-cover forecasting, it answers both the quantity and the location of change, something neither component does well alone.