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114 results on '"Tong, Siqin"'

8. Identification of Larch Caterpillar Infestation Severity Based on Unmanned Aerial Vehicle Multispectral and LiDAR Features

Author

He-Ya, Sa, primary, Huang, Xiaojun, additional, Zhou, Debao, additional, Zhang, Junsheng, additional, Bao, Gang, additional, Tong, Siqin, additional, Bao, Yuhai, additional, Ganbat, Dashzebeg, additional, Tsagaantsooj, Nanzad, additional, Altanchimeg, Dorjsuren, additional, Enkhnasan, Davaadorj, additional, Ariunaa, Mungunkhuyag, additional, and Guo, Jiaze, additional

Published
2024
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10. Identifying the drivers of vegetation changes in Inner Mongolia based on residual analysis and Hasse diagram technique.

Author

Mu, Shuangyan, Tong, Siqin, Batunacun, Ah, Rong, Mei, Li, Bao, Gang, Huang, Xiaojun, and Bao, Yuhai

Subjects
VEGETATION dynamics, CITIES & towns, CLIMATE change, REGRESSION analysis, ECONOMIC impact
Abstract

Exploring the effect of climate change and human activities on vegetation is a key requisite for the reconstruction of regional ecological environments. Therefore, based on long‐term vegetation GIMMS Normalized Difference Vegetation Index (NDVI) data, climate data, and statistical data, the present study applied the Hasse diagram technique and combined the multivariate regression residual analysis to quantitatively analyze the impact of human activities and climate change on vegetation in Inner Mongolia from detailed human activities with some innovations. The results show that (1) NDVI shows an overall increasing trend over the last 39 years, with an abrupt change in 2000; moreover, vegetation growth was better before the abrupt change (PI: 1982–2000) than after it (PII: 2001–2020), with significant downward trends in Xilin Gol and Hulunbuir. (2) Human activities can promote as well as inhibit vegetation, and the promotion effect was larger during 1982–2000 than during 2001–2020, whereas the inhibition effect was larger during 2001–2020. In addition, during PI, vegetation in Inner Mongolia generally experienced promotion by human activities and climate change, while during PII, climate‐driven promotion had the strongest effect, followed by human‐driven inhibition mainly distributed in Xilin Gol. (3) The result of the Hasse diagram analysis shows that the dominant pathways of human activities affecting most of the cities were economic factors and urbanization during PI and economization during PII. [ABSTRACT FROM AUTHOR]

Published
2024
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19. Model-Based Identification of Larix sibirica Ledeb. Damage Caused by Erannis jacobsoni Djak. Based on UAV Multispectral Features and Machine Learning

Author

Ma, Lei, primary, Huang, Xiaojun, additional, Hai, Quansheng, additional, Gang, Bao, additional, Tong, Siqin, additional, Bao, Yuhai, additional, Dashzebeg, Ganbat, additional, Nanzad, Tsagaantsooj, additional, Dorjsuren, Altanchimeg, additional, Enkhnasan, Davaadorj, additional, and Ariunaa, Mungunkhuyag, additional

Published
2022
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20. Future changes of dry‐wet climate regions and its contributing climatic factors in China based on CMIP6 models.

Author

Kou, Yu, Tong, Siqin, Mei, Li, and Bao, Yuhai

Subjects
*CLIMATE change models, *WATER management, *CLIMATE change, *CLIMATE change mitigation, *ARID regions, *SOLAR temperature
Abstract

Understanding future dry and wet changes under global climate change is of great regional significance in China. Based on six Global Climate Models (GCM) from the Coupled Model Intercomparison Project phase 6 (CMIP6), we project the changes of dry‐wet climate regions in China in the 21st century under the three SSP‐RCP (shared socioeconomic pathway‐representative concentration pathway) scenarios (SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5), and further identify the key climatic factors that lead to the transition of dry‐wet climate regions. The results show the area of humid and subhumid regions in China will show an upward trend in the future, while the area of semiarid and arid regions will show a downward trend. Especially under the SSP5‐8.5 scenario, the area of the subhumid region has the largest increase trend (0.042% per year), and the area of the arid region has the largest decline trend (−0.048% per year). The three main transfer directions of dry‐wet climate regions are from arid to semiarid regions, from semiarid to subhumid regions, and from subhumid to humid regions in the future. Due to the future extensive wetting trend in the north, the future dry‐wet climate regions will shift to wetter types in Tibet, North China, the Qinling‐Huaihe regions and the northeast. In addition, the key climatic factors for the transition of dry‐wet climate regions are mainly precipitation, maximum temperature and solar radiation. The increase of precipitation plays a dominant role for areas projected to change to wetter types, and the increase of potential evapotranspiration (PET) plays a dominant role for areas projected to change to drier types. The PET changes in changed areas of dry‐wet climate regions are mainly dominated by maximum temperature and solar radiation. Our research has important implications for future water resource management and reconsideration of climate mitigation and adaptation policies. [ABSTRACT FROM AUTHOR]

Published
2023
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