Can vegetation breakpoints in Eastern Mongolia rangeland be detected using Sentinel-1 coherence time series data?
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 liv...
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Taylor & Francis Group
2025-12-01
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| Series: | GIScience & Remote Sensing |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/15481603.2025.2540222 |
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| author | Shuxin Ji Ganzorig Gonchigsumlaa Sugar Damdindorj Tserendavaa Tseren Densmaa Sharavjamts Amartuvshin Otgondemberel Enkh-Amgalan Gurjav Munguntsetseg Puntsagsuren Batnaran Tsabatshir Tumendemberel Gungaa Narantsetseg Batbold Lukas Drees Bayarchimeg Ganbayar Dulamragchaa Orosoo Bayartsetseg Lkhamsuren Badamtsetseg Ganbat Myagmarsuren Damdinsuren Gantogoo Gombosuren Batnyambuu Dashpurev Thanh Noi Phan Nandintsetseg Dejid Thomas Müller Lukas Lehnert |
| author_facet | Shuxin Ji Ganzorig Gonchigsumlaa Sugar Damdindorj Tserendavaa Tseren Densmaa Sharavjamts Amartuvshin Otgondemberel Enkh-Amgalan Gurjav Munguntsetseg Puntsagsuren Batnaran Tsabatshir Tumendemberel Gungaa Narantsetseg Batbold Lukas Drees Bayarchimeg Ganbayar Dulamragchaa Orosoo Bayartsetseg Lkhamsuren Badamtsetseg Ganbat Myagmarsuren Damdinsuren Gantogoo Gombosuren Batnyambuu Dashpurev Thanh Noi Phan Nandintsetseg Dejid Thomas Müller Lukas Lehnert |
| author_sort | Shuxin Ji |
| collection | DOAJ |
| description | 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. However, the detection was challenging especially for winter movements (for summer camps, random forest and BFAST detected 44% and 28% of movements, respectively). (2) Breakpoints in summer pastures mainly occurred from April to June, while on winter pastures, they emerged in October, November, and the following February and March. The breakpoints in October and November can be explained by increasing grazing pressure as the herders moved to the winter camps while those occurring in spring are associated with enhanced vegetation growth after herders left for summer camps. (3) From a spatial perspective, the random forest model predicts summer and winter pastures with homogeneous patterns. In areas with higher productivity and higher grazing pressure, the summer pastures are located along the rivers while the winter pastures are in the surrounding mountainous areas. This is in agreement with the general movement patterns. In drier and less intensively used areas, the predicted pattern agrees less with the known movements. Consequently, there is insufficient evidence to definitively attribute the occurrence of pasture breakpoints solely to herder movements especially in the eastern and southern parts of the eastern Mongolian steppe ecosystem. |
| format | Article |
| id | doaj-art-a128d2210ea74f8e846cd2d4197c450b |
| institution | Kabale University |
| issn | 1548-1603 1943-7226 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj-art-a128d2210ea74f8e846cd2d4197c450b2025-08-20T03:34:57ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262025-12-0162110.1080/15481603.2025.2540222Can vegetation breakpoints in Eastern Mongolia rangeland be detected using Sentinel-1 coherence time series data?Shuxin Ji0Ganzorig Gonchigsumlaa1Sugar Damdindorj2Tserendavaa Tseren3Densmaa Sharavjamts4Amartuvshin Otgondemberel5Enkh-Amgalan Gurjav6Munguntsetseg Puntsagsuren7Batnaran Tsabatshir8Tumendemberel Gungaa9Narantsetseg Batbold10Lukas Drees11Bayarchimeg Ganbayar12Dulamragchaa Orosoo13Bayartsetseg Lkhamsuren14Badamtsetseg Ganbat15Myagmarsuren Damdinsuren16Gantogoo Gombosuren17Batnyambuu Dashpurev18Thanh Noi Phan19Nandintsetseg Dejid20Thomas Müller21Lukas Lehnert22Department of Geography, Ludwig-Maximilian-university of Munich, Munich, GermanyDepartment of Agricultural and Applied Economics, School of Economics and Business, Mongolian University of Life Sciences, Ulaanbaatar, MongoliaDepartment of Agricultural and Applied Economics, School of Economics and Business, Mongolian University of Life Sciences, Ulaanbaatar, MongoliaDepartment of Agricultural and Applied Economics, School of Economics and Business, Mongolian University of Life Sciences, Ulaanbaatar, MongoliaDepartment of Agricultural and Applied Economics, School of Economics and Business, Mongolian University of Life Sciences, Ulaanbaatar, MongoliaDepartment of Cybernetics, School of Economics and Business, Mongolian University of Life Sciences, Ulaanbaatar, MongoliaDepartment of Statistics and Econometrics, School of Economics and Business, Mongolian University of Life Sciences, Ulaanbaatar, MongoliaDepartment of Agricultural and Applied Economics, School of Economics and Business, Mongolian University of Life Sciences, Ulaanbaatar, MongoliaDepartment of Agricultural and Applied Economics, School of Economics and Business, Mongolian University of Life Sciences, Ulaanbaatar, MongoliaDepartment of Statistics and Econometrics, School of Economics and Business, Mongolian University of Life Sciences, Ulaanbaatar, MongoliaInstitute of Natural Resource and Agricultural Economics, Ulaanbaatar, MongoliaInstitute for Social-Ecological Research, Frankfurt am Main, GermanyDepartment of Management, School of Economics and Business, Mongolian University of Life Sciences, Ulaanbaatar, MongoliaDepartment of Accounting, School of Economics and Business, Mongolian University of Life Sciences, Ulaanbaatar, MongoliaDepartment of Agricultural and Applied Economics, School of Economics and Business, Mongolian University of Life Sciences, Ulaanbaatar, MongoliaDepartment of Finance, School of Economics and Business, Mongolian University of Life Sciences, Ulaanbaatar, MongoliaDepartment of Management, School of Economics and Business, Mongolian University of Life Sciences, Ulaanbaatar, MongoliaDepartment of Cybernetics, School of Economics and Business, Mongolian University of Life Sciences, Ulaanbaatar, MongoliaInstitute of Meteorology and Climate Research Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology, Garmisch-Partenkirchen, GermanyDepartment of Geography, Ludwig-Maximilian-university of Munich, Munich, GermanySenckenberg Biodiversity and Climate Research Centre, Senckenberg Gesellschaft für Naturforschung, Frankfurt (Main), GermanySenckenberg Biodiversity and Climate Research Centre, Senckenberg Gesellschaft für Naturforschung, Frankfurt (Main), GermanyDepartment of Geography, Ludwig-Maximilian-university of Munich, Munich, GermanyMongolian 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. However, the detection was challenging especially for winter movements (for summer camps, random forest and BFAST detected 44% and 28% of movements, respectively). (2) Breakpoints in summer pastures mainly occurred from April to June, while on winter pastures, they emerged in October, November, and the following February and March. The breakpoints in October and November can be explained by increasing grazing pressure as the herders moved to the winter camps while those occurring in spring are associated with enhanced vegetation growth after herders left for summer camps. (3) From a spatial perspective, the random forest model predicts summer and winter pastures with homogeneous patterns. In areas with higher productivity and higher grazing pressure, the summer pastures are located along the rivers while the winter pastures are in the surrounding mountainous areas. This is in agreement with the general movement patterns. In drier and less intensively used areas, the predicted pattern agrees less with the known movements. Consequently, there is insufficient evidence to definitively attribute the occurrence of pasture breakpoints solely to herder movements especially in the eastern and southern parts of the eastern Mongolian steppe ecosystem.https://www.tandfonline.com/doi/10.1080/15481603.2025.2540222BreakpointsInSAR coherenceBFASTrandom forestgrazing |
| spellingShingle | Shuxin Ji Ganzorig Gonchigsumlaa Sugar Damdindorj Tserendavaa Tseren Densmaa Sharavjamts Amartuvshin Otgondemberel Enkh-Amgalan Gurjav Munguntsetseg Puntsagsuren Batnaran Tsabatshir Tumendemberel Gungaa Narantsetseg Batbold Lukas Drees Bayarchimeg Ganbayar Dulamragchaa Orosoo Bayartsetseg Lkhamsuren Badamtsetseg Ganbat Myagmarsuren Damdinsuren Gantogoo Gombosuren Batnyambuu Dashpurev Thanh Noi Phan Nandintsetseg Dejid Thomas Müller Lukas Lehnert Can vegetation breakpoints in Eastern Mongolia rangeland be detected using Sentinel-1 coherence time series data? GIScience & Remote Sensing Breakpoints InSAR coherence BFAST random forest grazing |
| title | Can vegetation breakpoints in Eastern Mongolia rangeland be detected using Sentinel-1 coherence time series data? |
| title_full | Can vegetation breakpoints in Eastern Mongolia rangeland be detected using Sentinel-1 coherence time series data? |
| title_fullStr | Can vegetation breakpoints in Eastern Mongolia rangeland be detected using Sentinel-1 coherence time series data? |
| title_full_unstemmed | Can vegetation breakpoints in Eastern Mongolia rangeland be detected using Sentinel-1 coherence time series data? |
| title_short | Can vegetation breakpoints in Eastern Mongolia rangeland be detected using Sentinel-1 coherence time series data? |
| title_sort | can vegetation breakpoints in eastern mongolia rangeland be detected using sentinel 1 coherence time series data |
| topic | Breakpoints InSAR coherence BFAST random forest grazing |
| url | https://www.tandfonline.com/doi/10.1080/15481603.2025.2540222 |
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