Applying Multi-Sensor Satellite Data to Identify Key Natural Factors in Annual Livestock Change and Winter Livestock Disaster ( Dzud ) in Mongolian Nomadic Pasturelands

Sinkyu Kang (), Nanghyun Cho, Amartuvshin Narantsetseg, Bolor-Erdene Lkhamsuren, Otgon Khongorzul, Tumendemberel Tegshdelger, Bumsuk Seo and Keunchang Jang
Additional contact information
Sinkyu Kang: Department of Environmental Science, Kangwon National University, Chuncheon 24341, Republic of Korea
Nanghyun Cho: Department of Environmental Science, Kangwon National University, Chuncheon 24341, Republic of Korea
Amartuvshin Narantsetseg: Botanic Garden and Research Institute, Mongolian Academy of Sciences, Ulaanbaatar 13330, Mongolia
Bolor-Erdene Lkhamsuren: Department of Environmental Science, Kangwon National University, Chuncheon 24341, Republic of Korea
Otgon Khongorzul: Department of Environmental Science, Kangwon National University, Chuncheon 24341, Republic of Korea
Tumendemberel Tegshdelger: Department of Environmental Science, Kangwon National University, Chuncheon 24341, Republic of Korea
Bumsuk Seo: Department of Environmental Science, Kangwon National University, Chuncheon 24341, Republic of Korea
Keunchang Jang: Forest Environment and Conservation Department, National Institute of Forest Science, Seoul 02455, Republic of Korea

Land, 2024, vol. 13, issue 3, 1-18

Abstract: 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 soum 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 Dzud outbreaks. The countrywide Dzud 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 2 > 0.9 in most soums ). The SMR was less accurate but provided better intuitive insights on the regionality of the key factors and its relationships with local climate and Dzud characteristics. Summer and winter variables appeared to be almost equally important in both models. The primary factors of livestock change and Dzud 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.

Keywords: livestock change; natural factor; multi-sensor satellite data; multivariate analysis; machine learning (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/13/3/391/pdf (application/pdf)
https://www.mdpi.com/2073-445X/13/3/391/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:13:y:2024:i:3:p:391-:d:1359707

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().