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基于RS的藏北草原生物量时空变化及驱动力研究
Alternative TitleResearch on Spatio-temporal Variations and Driving Force of Grassland Aboveground Biomass in Northern Tibet Based on RS
Language中文
史展
Thesis Advisor陶和平
2014
Degree Grantor中国科学院研究生院
Place of Conferral北京
Degree Name博士
Degree Discipline自然地理学
Keyword藏北草原 草地生物量 遥感 模型 驱动力
Abstract草地生态系统是陆地生态系统中最重要、分布最广的生态系统类型之一,对于全球碳循环和气候调节起重要的作用。藏北高原是我国乃至世界上高寒草地分布面积最大的地区,具有生态环境脆弱性和对外力作用响应敏感性的特点。在全球气候变化和人类活动双重作用影响下藏北草原草地沙化、水土流失、毒杂草、鼠害等草地退化问题越来越严重,严重影响了藏北高原草地盖度和生物量,对该区域社会经济发展乃至国家生态安全构成威胁。近年来,随着西藏高原生态安全屏障的建设,藏北高原受到不同方式、不同程度的保护与治理,草地生态系统得到初步修复,对西藏地区社会经济的持续发展和国家生态安全又具有积极的作用。在此背景下,快速、准确获取藏北高原草地生态系统变化信息,掌握草原退化情况和治理措施实施效果,为进一步草地生态系统的保护和管理提供科学依据显得十分迫切。 为此,研究以代表着草原第一性生产力基本水平的生物量指标作为切入点,通过地面实验测量生物量,基于高光谱遥感和多光谱遥感多源数据结合的方法,通过地面采样点到高分辨率高光谱遥感反演生物量再到大尺度中低分辨率的遥感空间的生物量模拟,实现藏北高寒草原生物量的空间制图与变化分析,旨在揭示藏北高寒草原生物量近30a的变化规律,并探讨藏北高寒草原生物量变化的驱动力,为藏北草原退化研究与治理提供依据。 通过对藏北草原的分析,研究得出以下几个重要结论: (1)基于多源遥感数据分析和地面调查、室内试验分析等建立了藏北高原不同尺度的草地生物量遥感反演模型,实现了对藏北高原草地生物量不同空间尺度和长时间序列的遥感估算分析。其中,在草地生物量高光谱遥感模型中证明波段B68、B86和B90的原始波段及组合形式与生物量具有较好的相关性,基于SDr/SDy、DF748和DVI三个光谱特征建立的藏北草原生物量的高光谱遥感模型反演精度高。 (2)藏北高原草地生物量主要分布在200-500kg/hm2之间,平均为406.3 kg/hm2,从东南向西北递减,由低海拔向高海拔递减。高寒草甸地上生物量平均为694.2kg/hm2,主要分布在东部年降水量400mm以上区域以及河流两岸、湖滨等低洼地,高寒草原生物量平均为351.7kg/hm2,主要分布在降水量在300~400mm的区域,高寒荒漠地上生物量平均为277.0kg/hm2,主要分布在藏北高原西北部地区。 (3)随海拔升高,水热梯度分异,草地类型逐渐由高寒草甸过渡到高寒草原,再到高寒荒漠,草地生物量也出现明显的垂直分异现象。4400m以下草地生物量一般高于800kg/hm2,是藏北高原草地生物量的高值区,是高寒草甸的主要分布区;4400m~4600m草地生物量迅速下降到500kg/hm2左右,该区域正是高寒草甸向高寒草原的过渡区;5000m以上草地生物量平均值400kg/hm2左右,且垂直差异不大。 (4)1981~2013年藏北高原草地生物量整体呈上升趋势,反映出藏北高原生态环境趋于好转。从时间序列来看,2000年以后草地生物量增长趋势较为明显,特别是2005年以后,这与近年来降水量增加和采取的一些草地保护措施有关。藏北高原草地生物量增长幅度呈现从东南向西北逐渐降低的趋势,同时高寒草甸区明显高于高寒草原区,显示出水热变化对藏北高原草地影响的空间异质性。在总体好转的同时,人口聚集区、交通线两侧受人类活动强烈影响区,草地生物量则无明显变化,甚至呈现下降趋势,其中以那曲县城附近区域最为严重,说明人类活动对藏北高原草地植被影响非常显著。 (5)藏北草原的生物量变化受全球气候变化和人类活动双重影响。其中,高寒荒漠草原受自然因素影响最为明显,与生长季的降水、气温和可能蒸散量显著相关;高寒草原以自然因素为主导,人类活动的影响为辅;高寒草甸则受人类活动的影响显著,这与“逐水草而居”的传统放牧方式和高寒草甸区人类干扰强烈有关。近期藏北高原的“暖湿化”趋势有利于草地植被改善,但随着全球持续变暖,可能蒸散量将呈显著增加趋势,高寒草原与高寒荒漠区土壤水分状况有可能持续恶化,从而影响草地生物量乃至藏北高原的畜牧业发展。
Other AbstractEcosystem of Grassland is one terrestrial ecosystem of the most important and the most widely distributed ecosystems, which plays an important role for the global carbon cycle and climate regulation. Plateau in Northern Tibet is the area which is distributed the largest alpine grassland in China and even in the world, with characteristics of vulnerability and sensitivity. Under the global climate change and human activities, phenomenon of grassland degradation is becoming more and more common, such as desertification, soil erosion, noxious weed growing and damage caused by rats, etc, seriously posing a threat not only to vegetation cover and biomass, but also to socio-economic development in Tibet and even the ecological security of the whole country. In recent years, with the construction of the security barrier, it has been protected in different ways and degrees in Northern Tibet so that the ecosystem of grassland is preliminarily restored, playing an positive effect to the sustainable socio-economic development in Tibet and the ecological security of the whole country. In this context, it is extremely urgent to obtain the variation of the grassland ecosystem rapidly and accurately, master the degree of grassland degradation and the result of protective measures, which is a scientific basis for the next protection and management. So this paper takes biomass as an index to represent the primary productivity of grassland. Based on biomass measured by experiments, this paper estimates biomass from ground sampling point to high resolution and hyperspectral data, and then to the middle-low resolution data, using multiple remote sensing data, with the purpose of mapping biomass of grassland, analyzing its variations, and revealing the rule of variations near thirty years in Northern Tibet. By cause analysis, the driving force can be found out. This research draws the following important conclusions: (1)Based on multiple remote sensing data, ground investigations and laboratory test analysis, this paper established models at different scales. And the result from these models can be used for biomass analysis at different spatial scales and long time series in Northern Tibet. Among such many indexes, it is proved that reflectance of original bands(B68、B86、B90) and combinations has better correlation with biomass and the model builder by Dr/SDy、DF748 and DVI has high precision. (2)Biomass of grassland in Northern Tibet betweens 200-500kg/hm2, the average value is 406.3kg/hm2. It appears that the value is decreasing from the southeast to the northwest, and from low altitude to high altitude. The average aboveground biomass of alpine meadow is 694.2kg/hm2, which is mainly distributed in the area with more than 400mm annual rainfall, both sides of rivers, lakes and other low-lying land. The average aboveground biomass of alpine steppe is 351.7kg/hm2, which is mainly distributed in the area with 300-400mm annual rainfall, whereas the average aboveground biomass of alpine desert grassland is 277.0kg/hm2, distributing in the northwest. (3)With elevation and water thermal gradient differentiation, the type of grassland transforms from alpine meadow, alpine steppe to the alpine desert grassland and also the biomass changes with apparent vertical differentiation. The biomass is usually more than 800kg/hm2 under 4400m, where is the main distributed area of alpine meadow; the biomass declines rapidly to about 500kg/hm2 between 4400-4600m, that is the transition zone from alpine meadow to alpine steppe; the average biomass is 400 kg/hm2 at an altitude of 5000m. (4)It appears that the biomass is increasing from1981 to 2013, reflecting that the ecological environment is getting better. From the time series, the growth trend is obvious after 2000, especially after 2005, this is owed to the increased rainfall and some protected measures. The increased trend is decreasing from southeast to northwest, and the trend is more obvious in alpine meadow tha
Document Type学位论文
Identifierhttp://ir.imde.ac.cn/handle/131551/7856
Collection数字山地与遥感应用中心
Recommended Citation
GB/T 7714
史展. 基于RS的藏北草原生物量时空变化及驱动力研究[D]. 北京. 中国科学院研究生院,2014.
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