Bandingkan kaedah

	Metrik Corak Landskap	Model Perubahan Guna Tanah CA-Markov	Analisis Imej Berasaskan Objek (OBIA)
Bidang	Analisis Reruang	Analisis Reruang	Penderiaan Jauh
Keluarga	Process / pipeline	Process / pipeline	Process / pipeline
Tahun asal	1988	1997	2010
Pengasas	R. V. O'Neill et al.; McGarigal & Marks (FRAGSTATS)	Cellular automata (Clarke) + Markov chain (Muller & Middleton)	Thomas Blaschke
Jenis	Quantitative landscape pattern description	Spatio-temporal land-use change simulation	Image segmentation and classification pipeline
Sumber perintis	O'Neill, R. V., et al. (1988). Indices of landscape pattern. Landscape Ecology, 1(3), 153–162.	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.	Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2–16.
Alias	landscape pattern indices, FRAGSTATS metrics, fragmentation indices, peyzaj metrikleri	CA-Markov model, cellular automata Markov, land-use change simulation, CA-Markov arazi kullanımı modeli	Geographic Object-Based Image Analysis, GEOBIA, Object-Oriented Image Analysis, Nesne Tabanlı Görüntü Analizi
Berkaitan	3	3	3
Ringkasan	Landscape metrics are quantitative indices that describe the composition and spatial configuration of a categorical map — typically land cover — at the patch, class, and whole-landscape levels. Developed in landscape ecology (O'Neill and colleagues, 1988) and made widely usable by the FRAGSTATS software, they turn maps into numbers like patch density, edge density, fragmentation, diversity, and connectivity for ecological, planning, and change analysis.	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.	Object-Based Image Analysis (OBIA) is a remote sensing image processing paradigm that groups pixels into meaningful image objects before classification, rather than analysing each pixel independently. Formally articulated and consolidated by Thomas Blaschke in his landmark 2010 ISPRS review, OBIA draws on multiresolution segmentation algorithms and combines spectral, spatial, contextual, and textural object attributes to produce semantically rich land-cover maps from high-resolution imagery.