Applying Multi-Sensor Satellite Data to Identify Key Natural Factors in Annual Livestock Change and Winter Livestock Disaster (<i>Dzud</i>) in Mongolian Nomadic Pasturelands
In the present study, we tested the applicability of multi-sensor satellite data to account for key natural factors of annual livestock number changes in county-level <i>soum</i> districts of Mongolia. A schematic model of nomadic landscapes was developed and used to select potential dri...
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2024-03-01
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| author | Sinkyu Kang Nanghyun Cho Amartuvshin Narantsetseg Bolor-Erdene Lkhamsuren Otgon Khongorzul Tumendemberel Tegshdelger Bumsuk Seo Keunchang Jang |
| author_facet | Sinkyu Kang Nanghyun Cho Amartuvshin Narantsetseg Bolor-Erdene Lkhamsuren Otgon Khongorzul Tumendemberel Tegshdelger Bumsuk Seo Keunchang Jang |
| author_sort | Sinkyu Kang |
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| description | In the present study, we tested the applicability of multi-sensor satellite data to account for key natural factors of annual livestock number changes in county-level <i>soum</i> districts of Mongolia. A schematic model of nomadic landscapes was developed and used to select potential drivers retrievable from multi-sensor satellite data. Three alternative methods (principal component analysis, PCA; stepwise multiple regression, SMR; and random forest machine learning model, RF) were used to determine the key drivers for livestock changes and <i>Dzud</i> outbreaks. The countrywide <i>Dzud</i> in 2010 was well-characterized by the PCA as cold with a snowy winter and low summer foraging biomass. The RF estimated the annual livestock change with high accuracy (R<sup>2</sup> > 0.9 in most <i>soums</i>). The SMR was less accurate but provided better intuitive insights on the regionality of the key factors and its relationships with local climate and <i>Dzud</i> characteristics. Summer and winter variables appeared to be almost equally important in both models. The primary factors of livestock change and <i>Dzud</i> showed regional patterns: dryness in the south, temperature in the north, and foraging resource in the central and western regions. This study demonstrates a synergistic potential of models and satellite data to understand climate–vegetation–livestock interactions in Mongolian nomadic pastures. |
| format | Article |
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| language | English |
| publishDate | 2024-03-01 |
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| spelling | doaj-art-a149d82bd2f14cbda213bfcb88e621b92025-08-20T02:05:21ZengMDPI AGLand2073-445X2024-03-0113339110.3390/land13030391Applying Multi-Sensor Satellite Data to Identify Key Natural Factors in Annual Livestock Change and Winter Livestock Disaster (<i>Dzud</i>) in Mongolian Nomadic PasturelandsSinkyu Kang0Nanghyun Cho1Amartuvshin Narantsetseg2Bolor-Erdene Lkhamsuren3Otgon Khongorzul4Tumendemberel Tegshdelger5Bumsuk Seo6Keunchang Jang7Department of Environmental Science, Kangwon National University, Chuncheon 24341, Republic of KoreaDepartment of Environmental Science, Kangwon National University, Chuncheon 24341, Republic of KoreaBotanic Garden and Research Institute, Mongolian Academy of Sciences, Ulaanbaatar 13330, MongoliaDepartment of Environmental Science, Kangwon National University, Chuncheon 24341, Republic of KoreaDepartment of Environmental Science, Kangwon National University, Chuncheon 24341, Republic of KoreaDepartment of Environmental Science, Kangwon National University, Chuncheon 24341, Republic of KoreaDepartment of Environmental Science, Kangwon National University, Chuncheon 24341, Republic of KoreaForest Environment and Conservation Department, National Institute of Forest Science, Seoul 02455, Republic of KoreaIn the present study, we tested the applicability of multi-sensor satellite data to account for key natural factors of annual livestock number changes in county-level <i>soum</i> districts of Mongolia. A schematic model of nomadic landscapes was developed and used to select potential drivers retrievable from multi-sensor satellite data. Three alternative methods (principal component analysis, PCA; stepwise multiple regression, SMR; and random forest machine learning model, RF) were used to determine the key drivers for livestock changes and <i>Dzud</i> outbreaks. The countrywide <i>Dzud</i> in 2010 was well-characterized by the PCA as cold with a snowy winter and low summer foraging biomass. The RF estimated the annual livestock change with high accuracy (R<sup>2</sup> > 0.9 in most <i>soums</i>). The SMR was less accurate but provided better intuitive insights on the regionality of the key factors and its relationships with local climate and <i>Dzud</i> characteristics. Summer and winter variables appeared to be almost equally important in both models. The primary factors of livestock change and <i>Dzud</i> showed regional patterns: dryness in the south, temperature in the north, and foraging resource in the central and western regions. This study demonstrates a synergistic potential of models and satellite data to understand climate–vegetation–livestock interactions in Mongolian nomadic pastures.https://www.mdpi.com/2073-445X/13/3/391livestock changenatural factormulti-sensor satellite datamultivariate analysismachine learning |
| spellingShingle | Sinkyu Kang Nanghyun Cho Amartuvshin Narantsetseg Bolor-Erdene Lkhamsuren Otgon Khongorzul Tumendemberel Tegshdelger Bumsuk Seo Keunchang Jang Applying Multi-Sensor Satellite Data to Identify Key Natural Factors in Annual Livestock Change and Winter Livestock Disaster (<i>Dzud</i>) in Mongolian Nomadic Pasturelands Land livestock change natural factor multi-sensor satellite data multivariate analysis machine learning |
| title | Applying Multi-Sensor Satellite Data to Identify Key Natural Factors in Annual Livestock Change and Winter Livestock Disaster (<i>Dzud</i>) in Mongolian Nomadic Pasturelands |
| title_full | Applying Multi-Sensor Satellite Data to Identify Key Natural Factors in Annual Livestock Change and Winter Livestock Disaster (<i>Dzud</i>) in Mongolian Nomadic Pasturelands |
| title_fullStr | Applying Multi-Sensor Satellite Data to Identify Key Natural Factors in Annual Livestock Change and Winter Livestock Disaster (<i>Dzud</i>) in Mongolian Nomadic Pasturelands |
| title_full_unstemmed | Applying Multi-Sensor Satellite Data to Identify Key Natural Factors in Annual Livestock Change and Winter Livestock Disaster (<i>Dzud</i>) in Mongolian Nomadic Pasturelands |
| title_short | Applying Multi-Sensor Satellite Data to Identify Key Natural Factors in Annual Livestock Change and Winter Livestock Disaster (<i>Dzud</i>) in Mongolian Nomadic Pasturelands |
| title_sort | applying multi sensor satellite data to identify key natural factors in annual livestock change and winter livestock disaster i dzud i in mongolian nomadic pasturelands |
| topic | livestock change natural factor multi-sensor satellite data multivariate analysis machine learning |
| url | https://www.mdpi.com/2073-445X/13/3/391 |
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