Author
Listed:
- Ge Li
(State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
School of Civil and Architectural Engineering, Shandong University of Technology, Zibo 255049, China)
- Juanle Wang
(State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China)
- Yanjie Wang
(State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
College of Geoscience and Surveying Engineering, China University of Mining & Technology (Beijing), Beijing 100083, China)
- Haishuo Wei
(State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
School of Civil and Architectural Engineering, Shandong University of Technology, Zibo 255049, China)
- Altansukh Ochir
(School of Engineering and Applied Sciences, National University of Mongolia, Ulaanbaatar 210646, Mongolia)
- Davaadorj Davaasuren
(School of the Art & Sciences, National University of Mongolia, Ulaanbaatar 14201, Mongolia)
- Sonomdagva Chonokhuu
(School of Engineering and Applied Sciences, National University of Mongolia, Ulaanbaatar 210646, Mongolia)
- Elbegjargal Nasanbat
(Information and Research Institute of Meteorology, Hydrology and Environment, Ulaanbaatar 15160, Mongolia)
Abstract
Grassland biomass is the embodiment of grassland productivity, and the material basis for the maintenance of the grassland ecosystem. Grassland is the main vegetation type in the Mongolian Plateau. Grassland changes in the core region of the China–Mongolia–Russia Economic Corridor of the Belt and Road Initiative have an important impact on regional ecology, environmental conservation, and sustainable development. This study established three types of models for estimating grassland production through statistical analysis methods using NDVI, EVI, MSAVI, and PsnNet remote sensing indices retrieved from a Moderate Resolution Imaging Spectroradiometer (MODIS) dataset. This was combined with ground-measured grassland data and meteorological data. Based on model evaluation, the spatial and temporal distribution and variation characteristics of grassland along the Mongolia part of the China–Mongolia Railway were obtained through inversion for the period from 2006 to 2015. The results showed that all the models had good simulation effects. The optimal model was an exponential model based on MSAVI—with its simulation accuracy reaching 78%. Grassland production in the study area has increased slightly in the past ten years, with little change in the first five years and a fluctuating increase in the next five years. The average grassland production (per unit production) in the past ten years was 3400.39 kg/ha and the average total production was 9707.88 × 10 4 t. Grassland production increased slightly in most areas along the railway, and in some areas it continued to decline. The regional spatial distribution of increased and decreased grassland production was significantly different. With better grassland resources in the northeastern part of the study area—the area around Chinggis City and the capital of Hentiy Province—had the most significant growth. However, the southern Gobi area—with its trend towards land degradation in the area where the southern Gobi and desert steppe transitions to steppe and dry steppe—had a significant decrease. This meant that the risk of grassland degradation still existed. There were also quantitative and spatial differences in the areas where grassland production decreased on both sides of the railway. The decrease in grassland production on the western side of the railway was more obvious than on the eastern side, and the reduction area was dispersed on the western side and relatively concentrated on the eastern side. In future research, the identification of key areas of grassland degradation along the China–Mongolia Railway as well as its driving forces should be investigated further.
Suggested Citation
Ge Li & Juanle Wang & Yanjie Wang & Haishuo Wei & Altansukh Ochir & Davaadorj Davaasuren & Sonomdagva Chonokhuu & Elbegjargal Nasanbat, 2019.
"Spatial and Temporal Variations in Grassland Production from 2006 to 2015 in Mongolia Along the China–Mongolia Railway,"
Sustainability, MDPI, vol. 11(7), pages 1-14, April.
Handle:
RePEc:gam:jsusta:v:11:y:2019:i:7:p:2177-:d:221959
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Citations
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Cited by:
- Jiahao Zhai & Chiwei Xiao & Zhiming Feng & Ying Liu, 2022.
"Spatio-Temporal Patterns of Land-Use Changes and Conflicts between Cropland and Forest in the Mekong River Basin during 1990–2020,"
Land, MDPI, vol. 11(6), pages 1-17, June.
- Xiya Liang & Pengfei Li & Juanle Wang & Faith Ka Shun Chan & Chuluun Togtokh & Altansukh Ochir & Davaadorj Davaasuren, 2021.
"Research Progress of Desertification and Its Prevention in Mongolia,"
Sustainability, MDPI, vol. 13(12), pages 1-17, June.
- Edith Olmos-Trujillo & Julián González-Trinidad & Hugo Júnez-Ferreira & Anuard Pacheco-Guerrero & Carlos Bautista-Capetillo & Claudia Avila-Sandoval & Eric Galván-Tejada, 2020.
"Spatio-Temporal Response of Vegetation Indices to Rainfall and Temperature in A Semiarid Region,"
Sustainability, MDPI, vol. 12(5), pages 1-18, March.
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