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Can vegetation breakpoints in Eastern Mongolia rangeland be detected using Sentinel-1 coherence time series data?

Ji, Shuxin ; Gonchigsumlaa, Ganzorig; Damdindorj, Sugar; Tseren, Tserendavaa; Sharavjamts, Densmaa; Otgondemberel, Amartuvshin; Gurjav, Enkh-Amgalan; Puntsagsuren, Munguntsetseg; Tsabatshir, Batnaran; Gungaa, Tumendemberel; Batbold, Narantsetseg; Drees, Lukas; Ganbayar, Bayarchimeg; Orosoo, Dulamragchaa; Lkhamsuren, Bayartsetseg; Ganbat, Badamtsetseg; Damdinsuren, Myagmarsuren; Gombosuren, Gantogoo; Dashpurev, Batnyambuu 1,2; ... mehr

Abstract:

Mongolian society and food production depend heavily on livestock farming, which is usually practiced through nomadic systems. Consequently, movement patterns of herders are crucial in respect of finding sufficient forage and sustainable use of pastures. Since vegetation presumably changes after livestock pasture use, this study hypothesizes that changes in Interferometric Synthetic Aperture Radar (InSAR) data over time are linked to herder and livestock mobility. In this study, a combination of InSAR, optical, and weather time series data has been explored as a tool for spatio-temporal grazing monitoring. To detect movement patterns, a new random forest-based method to detect breakpoints in vegetation condition has been developed and compared to the widely used Breaks For Additive Season and Trend (BFAST) algorithm. In contrast to BFAST, the new method accounts for vegetation changes caused by weather events such as snow and rainfall. The results have been validated using test sites spread across the entire eastern Mongolian steppe ecosystem, covering different rangeland use intensities. The results indicate that (1) random forest performed better than BFAST, indicating that random forest is able to separate vegetation changes caused by grazing from those caused by natural events. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000184841
Veröffentlicht am 29.09.2025
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung (IMK)
Institut für Meteorologie und Klimaforschung Atmosphärische Umweltforschung (IMKIFU)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 31.12.2025
Sprache Englisch
Identifikator ISSN: 1548-1603, 0749-3878, 1943-7226, 2331-1762
KITopen-ID: 1000184841
Erschienen in GIScience and Remote Sensing
Verlag Taylor and Francis
Band 62
Heft 1
Seiten Art.-Nr.:
Vorab online veröffentlicht am 28.07.2025
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