基于湿度空间分布的土壤碳通量采样策略

发布时间：2018-06-03 14:32

本文选题：土壤呼吸 + 全球碳循环　；参考：《浙江农林大学》2015年硕士论文

【摘要】：进入新世纪以来,持续高温、海洋风暴、长期干旱等异常自然气候灾害现象的频繁发生,造成了大量人员伤亡和经济损失。学者们普遍认为当今全球环境气候问题与人类活动有直接的关系。如何应对全球气候变暖与频发自然灾害是全人类共同面对的问题。碳,作为自然界的最基本元素之一,其流动链是研究生态系统中物质循环与能量循环的关键线索。碳元素在地球各个圈层间的迁移转化以及随之发生的能量流动被称为全球碳循环,全球碳循环是全球气候变化研究的核心内容。全球碳循环包括大气、海洋、陆地生态系统等几个主要碳库,其中陆地生态系统受到人类活动的干扰最强烈。土壤碳库是陆地碳库的重要组成部分,因而研究土壤碳库与大气碳库之间的碳循环具有重要意义。土壤呼吸过程是土壤碳库向大气碳库排放CO2的关键生态过程,它主要由土壤微生物呼吸、根系呼吸和异氧动物呼吸三部分组成。作为一个复杂的生态学过程,土壤呼吸不仅受到土壤温湿度、气温、降雨等环境因素影响,同时受到植被、微生物、土地利用方式等生物因素影响,人类活动的作用越来越大。土壤呼吸的空间异质性很强烈,传统的五点法、随机抽样等方法无法对区域土壤碳通量进行准确估算,而大范围的均匀多点采样则需要耗费大量的人力及设备成本,因此确定适当的采样点数量及分布对于区域土壤碳通量的测算非常重要。本文提出一种基于湿度空间分布特征的土壤碳通量采样策略(Soil Carbon Flux Spatial Sampling Strategy Based On Moisture,SMTC):利用无线传感网密集测量区域土壤湿度数据。基于土壤湿度的异质性,将待监测区域划分为若干子区域。通过Hammond McCullagh方程计算各子区域内的最优采样点部署数量,依据最大覆盖原则部署区域内的采样点,最终确定整个监测区域的空间采样点部署策略。试验结果证明本文提出的土壤碳通量部署策略能够获得比均匀部署、随机部署更高的区域土壤碳通量估算准确度。本文提出的方法考虑了各子区域之间土壤碳通量空间分布的异质性,使得采样点的部署位置与土壤碳通量的分布具有较好的相关性。
[Abstract]:Since the new century, the frequent occurrence of abnormal natural climate disasters, such as high temperature, marine storm, long drought and so on, has caused a large number of casualties and economic losses. Scholars generally believe that the global environmental and climate problems are directly related to human activities. How should global warming and frequent natural disasters be all human Carbon, as one of the most basic elements of nature, is one of the most essential elements in nature. Its flow chain is a key clue to the study of the circulation of material and energy in the ecosystem. The migration and transformation of carbon elements between the various layers of the earth and the consequent energy flow are known as the global carbon cycle, and the global carbon cycle is a global climate change study. The global carbon cycle includes several major carbon pools, such as the atmosphere, the ocean and the terrestrial ecosystem, among which the terrestrial ecosystems are most disturbed by human activities. The soil carbon pool is an important part of the terrestrial carbon pool. Therefore, it is of great significance to study the carbon cycle between the soil carbon pool and the atmospheric carbon pool. The soil carbon pool is the key ecological process for the emission of CO2 to the atmospheric carbon pool. It consists mainly of three parts: soil microbial respiration, root respiration and isooxic respiration. As a complex ecological process, soil respiration is affected not only by soil temperature, humidity, temperature, rainfall and other environmental factors, but also by vegetation, microorganism and land use. With the influence of biological factors, the role of human activity is increasing. The spatial heterogeneity of soil respiration is very strong. The traditional five point method and random sampling method can not accurately estimate the soil carbon flux in the region, while the large scale uniform multi point sampling requires a large amount of human force and equipment cost. Therefore, the appropriate sampling points are determined. Measurement and distribution of soil carbon flux in regional soil is very important. In this paper, a soil carbon flux sampling strategy based on spatial distribution of humidity (Soil Carbon Flux Spatial Sampling Strategy Based On Moisture, SMTC) is proposed. The area to be monitored is divided into several sub regions. Through the Hammond McCullagh equation, the number of optimal sampling points in each subregion is calculated, and the sampling points in the region are deployed according to the maximum coverage principle. The deployment strategy of the spatial sampling point in the whole monitoring area is determined. The results of the experiment show that the soil carbon flux deployment strategy proposed in this paper can be shown in this paper. This method considers the heterogeneity of the spatial distribution of soil carbon flux between the subregions, which makes the deployment position of the sampling point well related to the distribution of soil carbon flux.
【学位授予单位】：浙江农林大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：S151.9

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