Vegetation Mapping in the Permafrost Region: A Case Study on the Central Qinghai-Tibet Plateau

An accurate and detailed vegetation map is of crucial significance for understanding the spatial heterogeneity of subsurfaces, which can help to characterize the thermal state of permafrost. The absence of an alpine swamp meadow (ASM) type, or an insufficient resolution (usually km-level) to capture the spatial distribution of the ASM, greatly limits the availability of existing vegetation maps in permafrost modeling of the Qinghai-Tibet Plateau (QTP). This study generated a map of the vegetation type at a spatial resolution of 30 m on the central QTP. The random forest (RF) classification approach was employed to map the vegetation based on 319 ground-truth samples, combined with a set of input variables derived from the visible, infrared, and thermal Landsat-8 images. Validation using a train-test split (i.e., 70% of the samples were randomly selected to train the RF model, while the remaining 30% were used for validation and a total of 1000 runs) showed that the average overall accuracy and Kappa coefficient of the RF approach were 0.78 (0.68–0.85) and 0.69 (0.64–0.74), respectively. The confusion matrix showed that the overall accuracy and Kappa coefficient of the predicted vegetation map reached 0.848 (0.844–0.852) and 0.790 (0.785–0.796), respectively. The user accuracies for the ASM, alpine meadow, alpine steppe, and alpine desert were 95.0%, 83.3%, 82.4%, and 86.7%, respectively. The most important variables for vegetation type prediction were two vegetation indices, i.e., NDVI and EVI. The surface reflectance of visible and shortwave infrared bands showed a secondary contribution, and the brightness temperature and the surface temperature of the thermal infrared bands showed little contribution. The dominant vegetation in the study area is alpine steppe and alpine desert. The results of this study can provide an accurate and detailed vegetation map, especially for the distribution of the ASM, which can help to improve further permafrost studies.

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Consumption of atmospheric methane by the Qinghai–Tibet Plateau alpine steppe ecosystem

The Cryosphere ◽

10.5194/tc-12-2803-2018 ◽

2018 ◽

Vol 12 (9) ◽

pp. 2803-2819 ◽

Cited By ~ 5

Author(s):

Hanbo Yun ◽

Qingbai Wu ◽

Qianlai Zhuang ◽

Anping Chen ◽

Tong Yu ◽

...

Keyword(s):

Soil Temperature ◽

The Arctic ◽

Tibet Plateau ◽

Active Layer Thickness ◽

Permafrost Region ◽

Freezing And Thawing ◽

Alpine Steppe ◽

Environmental Controls ◽

Steppe Ecosystem ◽

Qinghai Tibet Plateau

Abstract. The methane (CH4) cycle on the Qinghai–Tibet Plateau (QTP), the world's largest high-elevation permafrost region, is sensitive to climate change and subsequent freezing and thawing dynamics. Yet, its magnitudes, patterns, and environmental controls are still poorly understood. Here, we report results from five continuous year-round CH4 observations from a typical alpine steppe ecosystem in the QTP permafrost region. Our results suggest that the QTP permafrost region was a CH4 sink of -0.86±0.23 g CH4-C m−2 yr−1 over 2012–2016, a rate higher than that of many other permafrost areas, such as the Arctic tundra in northern Greenland, Alaska, and western Siberia. Soil temperature and soil water content were dominant factors controlling CH4 fluxes; however, their correlations changed with soil depths due to freezing and thawing dynamics. This region was a net CH4 sink in autumn, but a net source in spring, despite both seasons experiencing similar top soil thawing and freezing dynamics. The opposite CH4 source–sink function in spring versus in autumn was likely caused by the respective seasons' specialized freezing and thawing processes, which modified the vertical distribution of soil layers that are highly mixed in autumn, but not in spring. Furthermore, the traditional definition of four seasons failed to capture the pattern of the annual CH4 cycle. We developed a new seasonal division method based on soil temperature, bacterial activity, and permafrost active layer thickness, which significantly improved the modeling of the annual CH4 cycle. Collectively, our findings highlight the critical role of fine-scale climate freezing and thawing dynamics in driving permafrost CH4 dynamics, which needs to be better monitored and modeled in Earth system models.

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Spatial and Temporal Differences in Alpine Meadow, Alpine Steppe and All Vegetation of the Qinghai-Tibetan Plateau and Their Responses to Climate Change

Remote Sensing ◽

10.3390/rs13040669 ◽

2021 ◽

Vol 13 (4) ◽

pp. 669

Author(s):

Hanchen Duan ◽

Xian Xue ◽

Tao Wang ◽

Wenping Kang ◽

Jie Liao ◽

...

Keyword(s):

Climate Change ◽

Alpine Meadow ◽

Global Climate ◽

Growing Season ◽

Tibet Plateau ◽

Vegetation Types ◽

Analysis Method ◽

Alpine Steppe ◽

Qinghai Tibet Plateau ◽

Variation Trends

Alpine meadow and alpine steppe are the two most widely distributed nonzonal vegetation types in the Qinghai-Tibet Plateau. In the context of global climate change, the differences in spatial-temporal variation trends and their responses to climate change are discussed. It is of great significance to reveal the response of the Qinghai-Tibet Plateau to global climate change and the construction of ecological security barriers. This study takes alpine meadow, alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau as the research objects. The normalized difference vegetation index (NDVI) data and meteorological data were used as the data sources between 2000 and 2018. By using the mean value method, threshold method, trend analysis method and correlation analysis method, the spatial and temporal variation trends in the alpine meadow, alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau were compared and analyzed, and their differences in the responses to climate change were discussed. The results showed the following: (1) The growing season length of alpine meadow was 145~289 d, while that of alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau was 161~273 d, and their growing season lengths were significantly shorter than that of alpine meadow. (2) The annual variation trends of the growing season NDVI for the alpine meadow, alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau increased obviously, but their fluctuation range and change rate were significantly different. (3) The overall vegetation improvement in the Qinghai-Tibet Plateau was primarily dominated by alpine steppe and alpine meadow, while the degradation was primarily dominated by alpine meadow. (4) The responses between the growing season NDVI and climatic factors in the alpine meadow, alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau had great spatial heterogeneity in the Qinghai-Tibet Plateau. These findings provide evidence towards understanding the characteristics of the different vegetation types in the Qinghai-Tibet Plateau and their spatial differences in response to climate change.

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Non-growing season soil CO2 flux and its contribution to annual soil CO2 emissions in two typical grasslands in the permafrost region of the Qinghai-Tibet Plateau

European Journal of Soil Biology ◽

10.1016/j.ejsobi.2015.10.004 ◽

2015 ◽

Vol 71 ◽

pp. 45-52 ◽

Cited By ~ 17

Author(s):

Tao Zhang ◽

Genxu Wang ◽

Yan Yang ◽

Tianxu Mao ◽

Xiaopeng Chen

Keyword(s):

Co2 Emissions ◽

Co2 Flux ◽

Growing Season ◽

Tibet Plateau ◽

Permafrost Region ◽

Soil Co2 ◽

Soil Co2 Flux ◽

Soil Co2 Emissions ◽

Qinghai Tibet Plateau

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Water regime shifts in the active soil layer of the Qinghai–Tibet Plateau permafrost region, under different levels of vegetation

Geoderma ◽

10.1016/j.geoderma.2008.12.008 ◽

2009 ◽

Vol 149 (3-4) ◽

pp. 280-289 ◽

Cited By ~ 18

Author(s):

Wang Genxu ◽

Li Shengnan ◽

Hu Hongchang ◽

Li Yuanshou

Keyword(s):

Water Regime ◽

Soil Layer ◽

Regime Shifts ◽

Tibet Plateau ◽

Permafrost Region ◽

Qinghai Tibet Plateau ◽

Different Levels

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Diversity and Functions of Endophytic Fungi Associated with Roots and Leaves of Stipa purpurea in an Alpine Steppe at Qinghai-Tibet Plateau

Journal of Microbiology and Biotechnology ◽

10.4014/jmb.2002.02056 ◽

2020 ◽

Vol 30 (7) ◽

pp. 1027-1036

Author(s):

Xiaoyan Yang ◽

Hui Jin ◽

Lihong Xu ◽

Haiyan Cui ◽

Aiyi Xin ◽

...

Keyword(s):

Endophytic Fungi ◽

Tibet Plateau ◽

Alpine Steppe ◽

Stipa Purpurea ◽

Qinghai Tibet Plateau

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Evaluation of Landslide Susceptibility of the Ya'an-Linzhi Section of the Sichuan-Tibet Railway based on Deep Learning

10.21203/rs.3.rs-714294/v2 ◽

2021 ◽

Author(s):

Shibao Wang ◽

Jianqi Zhuang ◽

Jiaqi Mu ◽

Jia Zheng ◽

Jiewei Zhan ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Influencing Factors ◽

Landslide Susceptibility ◽

Pearson Correlation ◽

Confusion Matrix ◽

Tibet Plateau ◽

Validation Data ◽

Landslide Hazards ◽

Qinghai Tibet Plateau

Abstract The Qinghai-Tibet Plateau is one area with the most frequent landslide hazards due to its unique geology, topography, and climate conditions, posing severe threats to engineering construction and human settlements. The Sichuan-Tibet Railway that is currently under construction crosses the Qinghai-Tibet Plateau; there are frequent landslide disasters along the line, which seriously threaten the construction of the railway. This paper applied two deep learning (DL) algorithms, the convolutional neural network (CNN) and deep neural network (DNN), to landslide susceptibility mapping of the Ya’an-Linzhi section of the Sichuan-Tibet Railway. A geospatial database was generated based on 587 landslide hazards determined by Interferometric Synthetic Aperture Radar (InSAR) Stacking technology, field geological hazard surveys, and 18 landslide influencing factors were selected. The landslides were randomly divided into training data (70%) and validation data (30%) for the modeling training and testing. The Pearson correlation coefficient and information gain method were used to perform the correlation analysis and feature selection of 18 influencing factors. Both models were evaluated and compared using the receiver operating characteristic (ROC) curve and confusion matrix. The results show that better performance in both the training and testing phases was provided by the CNN algorithm (AUC = 0.88) compared to the DNN algorithm (AUC = 0.84). Slope, elevation, and rainfall are the main factors affecting the occurrence of landslides, and the high and very high landslide susceptibilities were primarily distributed in the Jinsha, Lancang, and Nujiang River Basins along the railway. The research results provide a scientific basis for the construction of the Ya'an-Linzhi section of the Sichuan-Tibet Railway within the region, as well as the disaster prevention and mitigation work during future safe operations.

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