Knowledge Resource Center for Ecological Environment in Arid Area
| 祁连山典型流域多年冻土区高寒草地对气候变化的响应研究 | |
| 其他题名 | Study on response of alpine grassland in permafrost regions to climate change in typical watersheds in the Qilian Mountain |
| 周兆叶 | |
| 出版年 | 2012 |
| 学位类型 | 博士 |
| 导师 | 叶柏生 ; 宜树华 |
| 学位授予单位 | 中国科学院大学 |
| 中文摘要 | 研究表明，过去几十年来青藏高原的多年冻土发生了退化，主要表现为多年冻土温度上升和活动层厚度增大等。科学家们认为多年冻土退化会导致隔水层减弱甚至消失，引起地下水位下降、表层土壤变干，从而导致高寒草地退化。然而，这些认识是基于空间序列替代时间序列的研究，即在不同退化阶段的多年冻土区域设置样地，开展样方尺度的植被调查，并进行对比，得出多年冻土退化对高寒草地影响的结论。并且，由于交通条件及后勤保障等条件的限制，样地布设的数量往往非常有限，使得研究结果易受到局地因素如地形及人工扰动等的影响。\n本文采用拍摄生长旺季（7月底-8月初）样地多光谱照片、普通照片的方法获取样地植被盖度（Fractional Vegetation Cover, FVC），结合HJ-1A (30m)、MOD13A2 (1000m)遥感数据，采用逐步升尺度的方法获取流域不同尺度FVC，建立FVC与地表温度（MOD11A2，Land Surface Temperature，LST）之间的关系式，从植被生长的水、热限制因子的角度阐明青藏高原东北缘祁连山中部两相邻流域——疏勒河上游流域和大通河源区不同冻土区多年冻土退化对高寒草地的影响机制；预估不同气候变化情景下二者的变化，为国家制定青藏高原生态环境保护和区域可持续发展政策提供科学依据。\n主要结论如下：\n1. 本文通过拍摄样地普通照片和多光谱照片来获取样地FVC，该方法在估测样地盖度方面具有较大优势。逐像元分析多光谱照片NDVI值，确定区分植被和土壤的阈值可通过编程来实现，该方法耗时少、精度高，为研究样地FVC提供了新的、有效的方法。\n2. 建立样地实测FVC值与HJ-1A影像（30m空间分辨率）NDVI值之间的拟合关系式，并用随机样地FVC实测值对该拟合关系式进行验证，结果表明拟合精度较高，通过P<0.05的显著性检验，可用该关系式计算流域FVC。\n3. 两流域不同冻土区FVC在生长旺季的分布特征为：疏勒河上游流域，过渡型冻土区FVC最大，亚稳定型、不稳定型冻土区FVC次之，极稳定型、季节性冻土区FVC最小；大通河源区，从极稳定型、稳定型、亚稳定型、过渡型、不稳定型到季节性冻土区，FVC依次逐渐增大。大通河源区FVC明显高于疏勒河上游流域。\n4. 由于地形复杂程度及气候类型不同，两流域FVC、LST的季节变化存在较大差异。\n生长旺季，疏勒河上游流域季节性冻土区FVC低于极稳定型冻土区FVC；而在生长初、末期，其FVC高于极稳定型冻土区。整个生长季，大通河源区各冻土区FVC从低到高的顺序为：极稳定型、稳定型、亚稳定型、过渡型、不稳定型和季节性冻土区。随着季节的变化，其排序未发生变化。\n由于植被蒸腾作用对地表温度的影响，5月份LST值高于6月份。疏勒河上游流域各冻土区LST均值在5、6月份相差较明显，7-9月份，各冻土区LST值相差较小；在整个生长季，大通河源区各冻土区LST变化趋势几乎是一致的。\n5. 对比两流域不同冻土区生长季（5-9月）FVC、LST，分析植被生长限制因子（热量、水分）的季节变化。在生长茂盛期，对于疏勒河上游流域，FVC-LST之间的斜率由极稳定型、稳定型冻土区的正相关过渡到亚稳定型、过渡型冻土区的微弱的正相关再到不稳定型、季节性冻土区的负相关，植被生长限制因子由热量过渡到水分；而对于大通河源区，从极稳定型到季节性冻土区，FVC-LST之间的斜率由极稳定型、稳定型冻土区的较强的正相关到其它冻土类型区的较弱的正相关，热量是各冻土区植被生长的限制因子。在生长初、末期，热量始终是两个流域大多数冻土区植被生长的制约因子。\n在不同的冻土退化阶段，不同降雨机制影响下的区域，冻土退化对高寒草地的影响不尽相同。换句话说，气温升高引起的冻土退化可能会对干旱-半干旱区的极稳定型、稳定型冻土区以及半湿润区的所有冻土区的高寒草地产生有利影响。现有的研究过分强调了其不利影响方面，在今后的研究中，应客观地看待冻土退化对高寒草地的影响。 |
| 英文摘要 | Permafrost in the Qinghai-Tibetan Plateau (QTP) has degraded over the last few decades. The degradation maily exhibited as the rising of permafrost temperature and the thickening of active layer depth. It was hypothesized that the degradation of permafrost would cause the reduction or even the vanishing of aquifuge, thus lead to the decrease of water table and soil moisture in surface layers, and furthermore leading to the degradation of alpine grassland. This conclusion was based on substituting a space series method for a time course methods, namely, setting up plots in different permafrost zones and comparing the differences among plots. However, due to difficulties of road accessibility and logistic, the number of plots for studying was limited. The results were usually affected by local factors such as topography and disturbances.\n In this study, the FVCs (Fractional Vegetation Cover) of quadrat were estimated with multi-spectral pictures and conventional pictures during peak growing season (between late July and early August). Upscalings of FVCs to 30 m scale and 1 km scale were made by using HJ-1A data (30 m resolution) and MOD13A2 (MODIS NDVI products with 1 km resolution), respectively. Then, the relationship between the FVCs and land surface temperature (LST) was established. Through the analysis of limiting factors of vegetation growth (water and energy) at different types of permafrost zones in two adjacent basins, the Upper Shule River basin and the source regions of Datong River, which are located in the north-east ridge of the QTP, the impacts of permafrost degradation on alpine grassland under the background of global warming were prospected. This study provides some scientific suggestions for the eco-environmental protection and area sustainable development on QTP. The main results are as follows:\n （1）In this study, the FVCs of quadrat were estimated with conventional and multi-spectral pictures. This method has great advantage to evaluate FVC. It is much more efficient to analyze NDVI of every pixel from multi-spectral pictures by programming to determine the threshold value, and can provide a new way to estimate FVC of quadrats.\n (2) Relationship between observed FVC and NDVI from HJ-1A remote sensing data with 30m spatial resolution was built and the relations were validated by random plots. The results of accuracy test among them had high precision (P<0.05), so the fitted formulas can be used to calculate FVC at basin scale.\n (3) The mean FVC value of different types of permafrost during peak growing season was described as follows, for the Upper Shule River basin, the FVC in transition permafrost zone was the greatest, and its average was significantly different from those of unstable and sub-stable permafrost, while vegetation cover minimized on the extreme stable zones, and its average was obviously different from those of stable and seasonal frost. Meanwhile, the FVC of the source regions of Datong River increased gradually from extreme stable permafrost to seasonal frost zones.(4) For the two basins, due to the great differences of terrain and climate type, the seasonal variation of FVC and LST were significantly different. \n (4) For the Upper Shule River basin, during peak growing season, the FVC in seasonal frost zone was the lowest, while vegetation cover minimized on the extreme stable zones at the beginning and end of growing season. Meanwhile, during the whole growing season, the FVC of the source regions of Datong River increased gradually from extreme stable permafrost to seasonal frost zones. \n Due to the influence of vegetation transpiration, the value of LST was higher in May than in June. LST of the Upper Shule River basin in different types of permafrost was different, significantly, in May and in June, however, during July to September, the difference between them was minimal. At the same time, for the source regions of Datong River, the variation trend of FVC in different types of permafrost zones was almost the same during the whole growing season.\n (5) Scatter diagram between FVC and LST of different types of permafrost zones during the whole growing season were built, and the limiting factor (i.e. water and energy) of vegetation growth were inferred. For basin A, during peak growing season, the gradients between FVC and LST at basin A changed from positive in the extreme stable and stable permafrost, to relatively weak positive in the sub-stable and transition permafrost, and to negative in the unstable and seasonal frost zones. From extreme stable to seasonal frost, the limiting factor of vegetation growth was transitioned from energy to water. And for basin B, from extreme stable permafrost to seasonal frost zones, the gradients altered from positive in the extreme stable and stable permafrost, to weekly positive in the other types of permafrost. Energy was the limiting factor for vegetation growth for the all types of permafrost of basin B.\n \n \n \n\n\n(6) The effects of permafrost degradation were different at different seasons, at different stages of degradation, and in different precipitation regions. In other words, warming of permafrost might benefit the growth of alpine grassland in extreme stable and stable permafrost zones of the semi-arid basin and all types of permafrost zones of semi-humid basin. Based on our results and the spatial distributions of temperature and precipitation on the QTP, we speculated that permafrost degradation would also benefit alpine grassland at the plateau scale. Existing studies focused too much on the adverse effects of permafrost degradation. We should objectively consider both adverse and beneficial effects of permafrost degradation in a warming climate in future studies. |
| 中文关键词 | 青藏高原 ; 多光谱相机 ; 盖度 ; 冻土类型 ; FVC-LST ; 限制因子 |
| 英文关键词 | Qinghai-Tibet Plateau (QTP) Multi-spectral camera Fractional of Vegetation Cover (FVC) Permafrost types FVC-LST Limiting factor |
| 语种 | 中文 |
| 国家 | 中国 |
| 来源学科分类 | 地图学与地理信息系统 |
| 来源机构 | 中国科学院西北生态环境资源研究院 |
| 资源类型 | 学位论文 |
| 条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/287105 |
| 推荐引用方式 GB/T 7714 | 周兆叶. 祁连山典型流域多年冻土区高寒草地对气候变化的响应研究[D]. 中国科学院大学,2012. |
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