Сравнение на методи

	Метрики за ландшафтни модели	CA-Markov Модел за промяна на земеползването	Обектно-базиран анализ на изображения (OBIA)
Област	Пространствен анализ	Пространствен анализ	Дистанционно сондиране
Семейство	Process / pipeline	Process / pipeline	Process / pipeline
Година на възникване	1988	1997	2010
Създател	R. V. O'Neill et al.; McGarigal & Marks (FRAGSTATS)	Cellular automata (Clarke) + Markov chain (Muller & Middleton)	Thomas Blaschke
Тип	Quantitative landscape pattern description	Spatio-temporal land-use change simulation	Image segmentation and classification pipeline
Основополагащ източник	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.
Други названия	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
Свързани	3	3	3
Резюме	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.