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001-es BibID:	BIBFORM085030
035-os BibID:	(cikkazonosító)1314 (WOS)000534628800087 (Scopus)85084862983
Első szerző:	Varga Orsolya Gyöngyi (geográfus)
Cím:	Effects of Category Aggregation on Land Change Simulation Based on Corine Land Cover Data / Varga Orsolya Gyöngyi, Robert Gilmore Pontius, Szabó Zsuzsanna, Szabó Szilárd
Dátum:	2020
ISSN:	2072-4292
Megjegyzések:	Several factors influence the performance of land change simulation models. One potentially important factor is land category aggregation, which reduces the number of categories while having the potential to reduce also the size of apparent land change in the data. Our article compares how four methods to aggregate Corine Land Cover categories influence the size of land changes in various spatial extents and consequently influence the performance of 114 Cellular Automata-Markov simulation model runs. We calculated the reference change during the calibration interval, the reference change during the validation interval and the simulation change during the validation interval, along with five metrics of simulation performance, Figure of Merit and its four components: Misses, Hits, Wrong Hits and False Alarms. The Corine Standard Level 1 category aggregation reduced change more than any of the other aggregation methods. The model runs that used the Corine Standard Level 1 aggregation method tended to return lower sizes of changing areas and lower values of Misses, Hits, Wrong Hits and False Alarms, where Hits are correctly simulated changes. The behavior-based aggregation method maintained the most change while using fewer categories compared to the other aggregation methods. We recommend an aggregation method that maintains the size of the reference change during the calibration and validation intervals while reducing the number of categories, so the model uses the largest size of change while using fewer than the original number of categories.
Tárgyszavak:	Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk aggregation land change modelling CA-Markov model validation
Megjelenés:	Remote Sensing. - 12 (2020), p. 1-16. -
További szerzők:	Pontius, Robert Gilmore Szabó Zsuzsanna (1985-) (környezetgazdálkodási és vidékfejlesztési agrármérnök) Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:	TNN123457 Egyéb
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM082563
Első szerző:	Varga Orsolya Gyöngyi (geográfus)
Cím:	Intensity Analysis and the Figure of Merit's components for assessment of a Cellular Automata-Markov simulation model / Orsolya Gyöngyi Varga, Robert Gilmore Pontius, Sudhir Kumar Singh, Szilárd Szabó
Dátum:	2019
ISSN:	1470-160X
Megjegyzések:	Some popular metrics to evaluate land change simulation models are misleading. Therefore, land change scientists have called for the development of methods to evaluate various aspects of modelling applications. This article answers the call by giving novel methods to compare three types of land change: 1) reference change during the calibration time interval, 2) simulation change during the validation time interval, and 3) reference change during the validation time interval. We compare these changes by using Intensity Analysis' three levels and the Figure of Merit's four components: Misses, Hits, Wrong Hits and False Alarms. We illustrate the concepts by applying a Cellular Automata - Markov land change model to a case study in northeast Hungary. We used reference maps of five land categories to calibrate the model during 2000-2006, then to validate the simulation during 2006-2012. Intensity Analysis' time interval level shows that the simulation change and the reference change decelerated from 2000-2006 to 2006-2012. Intensity Analysis' category level shows that the simulation losses were less than what a pure Markov chain would have dictated. Intensity Analysis' transition level shows that the model's Markov algorithm simulated correctly that the gain of Forest targeted Agriculture and Wetland. The Figure of Merit's components reveals more allocation error than quantity error. Our collection of metrics show that more error derived from the Cellular Automata algorithm than from the Markov algorithm. We recommend that scientists use Intensity Analysis and the Figure of Merit's components to reveal various fundamental aspects of modelling applications.
Tárgyszavak:	Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk cellular automata CA-Markov Figure of Merit Intensity analysis land change validation
Megjelenés:	Ecological Indicators. - 101 (2019), p. 933-942. -
További szerzők:	Pontius, Robert Gilmore Singh, Sudhir Kumar (1970-) (geográfus) Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:	EFOP-3.6.1-16-2016-00022 EFOP TNN 123457 Egyéb
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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Saját polcon: