中国北方四类典型下垫面能量分配特征及其环境影响因子研究

中国北方疆域辽阔,多种植被类型交错分布,陆面过程较为复杂.研究该区域地表能量平衡及分配过程,有助于进一步了解其生态系统的生产力及气候变化.本文使用"中国北方协同观测实验"观测资料,选取中国北方四种植被类型——高寒草甸、荒漠草地、玉米农田及半干旱草地,在深入认识地表能量分配特征的基础上,讨论了环境因子对能量分配的影响.结...     

环莫干山民宿的时空分布特征与成因

民宿是中国乡村振兴和旅游转型升级过程中涌现出的新兴业态和空间利用方式,但学术关注滞后。环莫干山地区是目前中国最典型的民宿发展集聚区之一。本研究通过官方统计资料和实地调研获取环莫干山民宿空间发展数据,构建了ArcGIS和地理探测器结合的民宿空间过程分析方法,基于目的地的微观尺度系统地探讨环莫干山民宿时空分布特征和成因。研究发现：① 2011—2017年环莫干山民宿呈现出逐年增长的态势,空间分布范围不断扩大,空间发展方向出现较大变化,逐渐趋向镇域均衡性发展。目前已经在莫干山风景区毗邻村域形成以“后坞-仙潭-燎原-劳岭-兰树坑”为中心的环莫干山面状核心集聚区和边缘村域多点集聚的民宿空间发展结构。不同档次民宿的时空发展趋势具有一定差异。② 环莫干山民宿的空间过程由集聚到强烈集聚,不同档次民宿的集聚程度均呈现出快速提升的趋势。空间发展过程中早期民宿集聚点扩散效应显著。空间集聚过程与地形地势、核心风景区、交通干线和居民点密切相关。③ 风景景观、发展基础、社会因素和区位因素是环莫干山民宿景观格局异质性的重要决定因素维度,但不同等级民宿的空间决定因素维度和指标差异性显著,两因素维度交互作用对于民宿的空间影响力更为显著。研究对于丰富接待业空间选址的类型研究,总结中国典型民宿集聚区空间发展模式具有重要理论意义,同时对于民宿产业选址和民宿集聚区规划实践具有一定现实意义。     

近20年京津唐主体城区地表热场空间特征变化分析

基于Landsat遥感影像获取京津唐主体城区1995~2015年地表温度(Land Surface Temperature, LST)和不透水地表盖度(Impervious Surface Percentage, ISP)数据。采用热点聚集和阈值分割法,依据地表的温度和不透水盖度属性将京津唐主体城区划分成9种地表热场类型,分析并探讨地表热场的发展规律、年际变化状况和区域贡献作用。研究发现,京津唐主体城区地表温度与不透水地表盖度间存在显著的正向相关关系,两者分别呈现“阶梯降”和“两端高、中间低”的变化特征。京津唐主体城区地表热场的发展主轴保持在西北-东南方向,且随时间推移沿主轴呈聚集态势。 京津唐主体城区地表热场的影响范围在空间上持续扩张,对于不同的主体城区,其在整体区域的热场贡献中有差异化表现。     

基于两种无人机航拍影像的林窗和林冠提取分析

森林冠层和林窗的结构及其时空变化是理解森林生态系统格局、动态变化过程的重要基础。在当前生物多样性监测倍受关注的契机下,如何以合适的手段准确描述林窗面积、分布等特征,并与森林固定样地监测数据有效地结合,更好地回答群落构建的理论问题,使森林群落物种多样性维持机制得到更全面的认识,是目前亟待解决的问题。以鼎湖山南亚热带常绿阔叶林20hm2固定监测样地为研究对象,基于不同遥感影像提取方法对其林窗和林冠表层数据进行提取分析。结果表明:基于监督分类的提取方法适合RGB波段航片林窗的提取,在林窗分类中,应首先确定林窗高度、边界木与最小面积,不同分类方法差异主要表现在林冠分类中,林窗分类生产者精度和用户精度表现都较为一致。无人机航拍识别率受地形因素影响较大,在地形复杂林地应按坡度分区域进行飞行以降低误差。相对于地面调查,MD4-1000无人机航片的林窗识别率为98.7%;大疆Phantom4无人机航片的林窗识别率为72.3%,影像后期处理数据量小,同样适用于森林林窗定量研究,符合生态学、林业等从业人员对大型样地林窗长期监测的要求。无人机航拍南亚热带森林物种识别难度较大,基于MD4-1000无人机搭载的高分辨率相机,在地势平缓区域优选的4 hm2样地中可识别林冠表层物种数17种,共2 706个个体。搭载高分辨率无人机在降低飞行高度的基础上可进行部分物种识别。应用无人机近地面遥感对森林固定样地进行林冠监测,可为后期群落构建研究提供数据基础,有望从新的研究角度探讨森林群落物种多样性维持机制。     

从珠三角到大珠三角再到粤港澳大湾区：改革开放以来中国的国家尺度重组

尺度重组是解释当前国家、城市等地域组织参与全球化竞争的工具媒介。文章以中国华南地区改革开放以来广东、香港、澳门三地跨界合作为例,借鉴行动者网络理论的研究方法深入剖析珠三角、大珠三角及粤港澳大湾区3个“新国家空间”的尺度重组过程,揭示中国尺度重组的政治经济逻辑。研究发现,行动者拥有的资源决定了其在尺度网络中的地位,而中国的“新国家空间”形成主要依托中央政府推动,其发展目标的设定具有决定性作用,港澳特区政府、广东省地方政府、乡镇企业和外资企业等其他行动者在协同合作中将自身的发展目标与之融合形成了“强制通过点”。纵观三阶段的演变过程,制度因素的重要性越来越得到凸显。     

基于LOG算子的侧扫声呐图像水下小目标检测算法研究

针对侧扫声呐图像噪声干扰严重、分辨率低、目标轮廓模糊等特点,提出了一种基于LOG算子的侧扫声呐图像水下小目标检测算法。首先,根据侧扫声呐图像中水下小目标成像特点,对声呐图像进行滤波及聚类分割,大幅降低图像中噪声;然后,采用斑点检测思想,提取侧扫声呐图像中疑似目标区域;最后,基于自动阈值分割算法对声呐图像进行分割,获取目标区域二值图像,使用二阶矩估计目标尺度,剔除虚假目标,最终实现水下小目标准确检测。实验结果表明：该方法计算速度快、检测成功率高,对侧扫声呐图像中的水下小目标具有良好的检测效果。     

Retrieving volume FeO and TiO_2 abundances of lunar regolith with CE-2 CELMS data using BPNN method

The volume FeO and TiO_2 abundances(FTAs) of lunar regolith can be more important for understanding the geological evolution of the Moon compared to the optical and gamma-ray results. In this paper, the volume FTAs are retrieved with microwave sounder(CELMS) data from the Chang'E-2 satellite using the back propagation neural network(BPNN) method. Firstly, a three-layered BPNN network with five-dimensional input is constructed by taking nonlinearity into account. Then, the brightness temperature(TB) and surface slope are set as the inputs and the volume FTAs are set as the outputs of the BPNN network.Thereafter, the BPNN network is trained with the corresponding parameters collected from Apollo, Luna,and Surveyor missions. Finally, the volume FTAs are retrieved with the trained BPNN network using the four-channel TBderived from the CELMS data and the surface slope estimated from Lunar Orbiter Laser Altimeter(LOLA) data. The rationality of the retrieved FTAs is verified by comparing with the Clementine UV-VIS results and Lunar Prospector(LP) GRS results. The retrieved volume FTAs enable us to re-evaluate the geological features of the lunar surface. Several important results are as follows. Firstly, very-low-Ti(<1.5 wt.%) basalts are the most spatially abundant, and the surfaces with TiO_2> 5 wt.% constitute less than 10% of the maria. Also, two linear relationships occur between the FeO abundance(FA) and the TiO_2 abundance before and after the threshold, 16 wt.% for FA. Secondly, a new perspective on mare volcanism is derived with the volume FTAs in several important mare basins, although this conclusion should be verified with more sources of data. Thirdly, FTAs in the lunar regolith change with depth to the uppermost surface,and the change is complex over the lunar surface. Finally, the distribution of volume FTAs hints that the highlands crust is probably homogeneous, at least in terms of the microwave thermophysical parameters.     

The effect of terrain factors on rice production: A case study in Hunan Province

Journal of Geographical Sciences - Rice (Oryza sativa L.) is the most important staple crop of China, and its production is related to both natural condition and human activities. It is fundamental...     

Multiscale geographically and temporally weighted regression: exploring the spatiotemporal determinants of housing prices

Understanding scale effects is important and indispensable for geography studies. However, spatial and spatiotemporal statistical tools for measuring the operational scales of different processes are rather limited. This article extends the popular geographically and temporally weighted regression (GTWR) model to consider operational scale effects by proposing multiscale GTWR (MGTWR), which offers a flexible and scalable framework for identifying and analysing multiscale processes by specifying flexible bandwidths for various covariates. Then, MGTWR is employed to explore spatiotemporal variations and how influential factors are associated with housing prices in Shenzhen. This article attempts to extend GTWR to MGTWR in consideration of scale effects, thereby highlighting the importance of different levels of spatiotemporal heterogeneity. Furthermore, the empirical results of this study can provide valuable policy implications for real estate development in areas where urban planning should address multiscale effects in both temporal and spatial dimensions.     

A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements

Local place names are frequently used by residents living in a geographic region. Such place names may not be recorded in existing gazetteers, due to their vernacular nature, relative insignificance to a gazetteer covering a large area (e.g. the entire world), recent establishment (e.g. the name of a newly-opened shopping center) or other reasons. While not always recorded, local place names play important roles in many applications, from supporting public participation in urban planning to locating victims in disaster response. In this paper, we propose a computational framework for harvesting local place names from geotagged housing advertisements. We make use of those advertisements posted on local-oriented websites, such as Craigslist, where local place names are often mentioned. The proposed framework consists of two stages: natural language processing (NLP) and geospatial clustering. The NLP stage examines the textual content of housing advertisements and extracts place name candidates. The geospatial stage focuses on the coordinates associated with the extracted place name candidates and performs multiscale geospatial clustering to filter out the non-place names. We evaluate our framework by comparing its performance with those of six baselines. We also compare our result with four existing gazetteers to demonstrate the not-yet-recorded local place names discovered by our framework.