国外综合贫困研究及对我国贫困地理研究的启示

20世纪60年代以来国外新城市贫困研究的内容和方法逐步多元化,综合贫困指标体系和以地域为基础的研究方法促进了贫困地理研究。本文首先辨析贫困和综合贫困的概念,综合贫困研究的指标体系和研究方法;再评述国外综合贫困研究的主要内容;最后在分析我国城市贫困研究不足的基础上,提出在指标体系、研究内容、研究方法和空间层次等几个方面拓展转型时期我国城市贫困地理的研究视角。     

GOCE+EGM08+RTM组合模型计算高程异常

针对EGM08重力场模型构建过程中存在的不足,提出用GOCE重力场模型替换EGM08模型的中低频部分,用剩余地形模型RTM拓展EGM08模型的甚高频信号。模拟分析表明,GOCE模型能大幅提高高程异常计算的精度,而RTM对高程异常的贡献也不可忽视。实测GPS/水准数据表明,GOCE模型对高程异常的贡献达到43%,而RTM也贡献了1cm的精度。     

协同多源遥感数据的北亚热带森林蓄积量贝叶斯分层估测

精确估算森林蓄积量是国家实现2060年前碳中和目标的迫切需求,而基于遥感的森林蓄积量定量反演是当前遥感应用领域面临的重要挑战和研究热点.光学遥感数据由于无法获取森林高度信息并存在信号饱和问题,反演森林蓄积量的精度较低,而机载Lidar数据能获取高度信息,但成本高、观测范围有限.本研究利用Sentinel-2多光谱、资源...     

土结构性的研究方法及现状

介绍了土结构性的概念和土结构性研究的意义。回顾了土结构性研究的历史。对土结构性的定量化研究方法进行了分类。在分析国内外研究现状的基础上，提出了土力学方法是进行土结构性定量化研究的最为现实和有效的方法。     

气候自然变异在气候变化对水资源影响 评价中的贡献分析——Ⅰ.基准期的模型与方法

基于气候变化影响的水资源评价对水资源规划和管理具有重要意义,随着全球气候变化影响的加剧,这一研究显得越来越紧迫。在目前的气候变化研究中,很少考虑气候自然波动的影响(气候自然变异),常将所有的变化单独归因于气候变化的影响,这在气候变化的影响评价中可能导致错误的理解与判断。气候自然变异分析由于缺乏超长系列的数据资料而长期被人为避开。针对这一问题,本研究提出模型方法体系,通过历史基准期的长系列模拟来分析气候自然变异的影响。选取常用的1961~1990年水文系列作为基准期,提出一种基于拉丁超立方体抽样技术的季节分段抽样模拟方法,实现对气候自然变异的模拟。应用水文模型TOPMODEL对基准期的径流系列进行模拟,基于不确定性分析GLUE方法对基准期内水文模型参数不确定性进行分析,并探讨了气候自然变异的影响。研究结果表明,在气候变化影响评价中,气候自然变异的影响不可忽略,应在气候变化的影响中加以区分和界定。     

基于卫-卫跟踪观测技术利用能量守恒法恢复地球重力场的数值模拟研究

根据卫－卫跟踪观测技术的测量原理,基于能量守恒法建立了一种新的双星相互跟踪和三星相互跟踪的卫星观测方程. 通过数值模拟,采用预处理共轭梯度法恢复120阶地球重力场. 模拟结果表明：第一,双星相互跟踪恢复地球重力场的精度和美国喷气动力实验室公布的EIGEN GRACE02S的结果相符合;第二,三星相互跟踪恢复地球重力场的精度较双星提高约2倍.     

应用Slepian局部谱方法解算中国大陆重力场球谐模型

根据经典的球谐函数方法,为满足正交化要求,观测数据需要覆盖整个球面,而对于地表局部测量数据,则无法应用球谐方法解算重力场模型。针对此问题,采用Slepian局部谱分析方法解算中国大陆范围内的实测重力场变化数据,并以GOCE卫星球谐函数解作为已知模型,评估由于实际陆地重力测点的非均匀分布对球谐函数解的误差影响。通过计算多个阶次中国大陆局部范围的Slepian基函数分布;采用GOCE卫星获得重力场模型的前72阶球谐系数作为已知结果,评价实际测点非均匀分布的解算有效性,并针对中国大陆地区采用Slepian基函数进行解算,通过模型对比选择最优截段项数;针对2005—2008年中国大陆地区流动重力测量获得的重力场变化信号进行解算,获得了72阶重力场变化模型。     

The information system of the French Peatland Observation Service: Service National d'Observation Tourbières – A valuable tool to assess the impact of global changes on the hydrology and biogeochemistry of temperate peatlands through long term monitoring

Mitigating and adapting to global changes requires a better understanding of the response of the Biosphere to these environmental variations. Human disturbances and their effects act in the long term (decades to centuries) and consequently, a similar time frame is needed to fully understand the hydrological and biogeochemical functioning of a natural system. To this end, the ‘Centre National de la Recherche Scientifique’ (CNRS) promotes and certifies long-term monitoring tools called national observation services or ‘Service National d'Observation’ (SNO) in a large range of hydrological and biogeochemical systems (e.g., cryosphere, catchments, aquifers). The SNO investigating peatlands, the SNO ‘Tourbières’, was certified in 2011 ( https://www.sno-tourbieres.cnrs.fr/ ). Peatlands are mostly found in the high latitudes of the northern hemisphere and French peatlands are located in the southern part of this area. Thus, they are located in environmental conditions that will occur in northern peatlands in coming decades or centuries and can be considered as sentinels. The SNO Tourbières is composed of four peatlands: La Guette (lowland central France), Landemarais (lowland oceanic western France), Frasne (upland continental eastern France) and Bernadouze (upland southern France). Thirty target variables are monitored to study the hydrological and biogeochemical functioning of the sites. They are grouped into four datasets: hydrology, fluvial export of organic matter, greenhouse gas fluxes and meteorology/soil physics. The data from all sites follow a common processing chain from the sensors to the public repository. The raw data are stored on an FTP server. After operator or automatic processing, data are stored in a database, from which a web application extracts the data to make them available ( https://data-snot.cnrs.fr/data-access/ ). Each year at least, an archive of each dataset is stored in Zenodo, with a digital object identifier (DOI) attribution ( https://zenodo.org/communities/sno_tourbieres_data/ ).     

东北地区历史时期森林空间格局网格化重建方法研究(英文)

The spatially explicit reconstruction of historical land-cover datasets plays an important role in studying the climatic and ecological effects of land-use and land-cover change(LUCC). Using potential natural vegetation(PNV) and satellite-based land use data, we determined the possible maximum distribution extent of forest cover in the absence of human disturbance. Subsequently, topography and climate factors were selected to assess the suitability of land for cultivation. Finally, a historical forest area allocation model was devised on the basis of the suitability of land for cultivation. As a case study, we used the historical forest area allocation model to reconstruct forest cover for 1780 and 1940 in Northeast China with a 10-km resolution. To validate the model, we compared satellite-based forest cover data with our reconstruction for 2000. A one-sample t-test of absolute bias showed that the two-tailed significance was 0.12, larger than the significant level 0.05, suggesting that the model has strong ability to capture the spatial distribution of forests. In addition, we calculated the relative difference of our reconstruction at the county scale for 1780 in Northeast China. The number of counties whose relative difference ranged from-30% to 30% is 99, accounting for 74.44% of all counties. These findings demonstrated that the provincial forest area could be transformed into forest cover maps well using the model.