秦巴山地山体基面高度的提取及分布

秦巴山地是中国的南北分界线,也是黄河和长江的分水岭,其山体效应的定量化影响秦巴山地山体垂直带的分布格局、非地带性因素的作用强度和机理,以及中国暖温带和北亚热带的具体位置的确定。山体基面高度是影响山体效应最重要和关键的地形因子,其定量化和数字化提取是秦巴山地山体效应定量化研究的重要内容。本研究针对秦巴山地山体效应的定量化研究,使用30 m分辨率的STRM-1数据,分别基于山体特征线和流域分区2种方法提取了秦巴山地的山体基面高度分区,并根据地形起伏度和坡度,确定基面范围,计算了山体基面高度值。结果表明：① 基于山体特征线的方法将秦巴山地分为93个基面高度分区,基于流域分区的方法将秦巴山地分为209个基面高度分区,根据2种分区结果提取的基面高度值相差不大且均体现了秦巴山地地势的特点;② 秦巴山地山体基面高度从东向西呈阶梯状递增的趋势;③ 从南到北,秦巴山地的东段和中段均呈先增高后降低的趋势,即从大巴山向北至汉江谷地降低,再向北至秦岭升高;④ 山地的不同侧翼的山体基面高度不同,秦岭南坡的基面高度（1000~1809 m）明显高于北坡（850~1300 m）。秦巴山地山体基面高度与其植被带分布上限联系密切,实现山体基面高度的数字化提取,为山体效应的定量化研究提供了重要的技术支持。     

山地垂直带谱数字识别的技术实现和图谱构建

山地垂直带谱是地学信息图谱的一个重要组成部分。"带谱数字识别"就是利用现代数字方法和数据,对客观存在的山地垂直自然带谱进行提取,获取比较完备的山地垂直带连续图谱模式。这是从传统山地垂直带研究走向地学信息图谱研究的重要步骤,有助于使我们对山地垂直谱的认识上升到地学信息图谱的高度。本文探索MATLAB语言快速、准确地实现各种识别算法,结合VB.NET构建的用户操作界面,实现带谱的信息提取、分类集成和多样化表达。山地垂直自然带谱的数字识别分为3种不同的模式:山系单侧、山体单峰和山体多峰;依据各自不同的识别算法,可以得到山地连续的带谱模式。本文全面介绍了数字带谱、带谱数字识别的含义,以及数字识别模型的原理、算法和原型系统等实现过程。     

基于DEM的一体化山地特征要素提取

山顶点和山脊线等特征地形要素是构成地表地形及其起伏变化的基本框架,对地形在地表的空间分布具有控制作用。基于DEM研究山顶点、山脊线及其空间组合关系,是DEM地表形态特征研究的重要内容,也是衔接从地形特征分析向山峰等地貌学本源语言的途径之一。本文以四川盆地西南缘与青藏高原过渡地带的川西凉山山原为例,基于山峰-山脊线-控制范围一体化构建的算法策略,识别了山峰和山脊线及其等级、主山脊及其范围。结果表明,研究区内有主峰9座,次峰53座,平均高程2540 m;山脊线230条,其中主山脊9条,平均长度60 km;9大山系,近南北走向,平均控制面积1017 km^2。研究用模糊隶属度方法对算法所提取的主峰、主脉进行精度验证,隶属度介于0.98~1.00和0.37~0.57时提取的主峰、主脉基本吻合算法提取的结果。研究采用一体化山地特征要素提取方法,实现了各山地要素间紧密联系、总体结构与区域地貌特征相对吻合的目标;完成了由栅格单元向地理对象的转变;可以应用于协助地貌类型划分,协同区域地理规划等。     

欧亚大陆山地垂直带数字集成系统的设计与应用

在欧亚大陆山地垂直带880个带谱点数据采集的基础上,设计和开发了山地垂直带数字集成系统。其功能：(1)垂直带谱可视化显示功能：实时生成垂直带地理分布图、垂直带堆积柱状图、上下限高度随经纬度变化曲线;(2)垂直带上下限提取和绝对高度和相对高度转化功能：提取山地垂直自然带分布上限和下限高度,进行垂直带的宽度和垂直带上限海拔...     

基于生成式对抗网络的地形特征线提取方法

地形特征线反映了地形表面的起伏形态,其提取对地理分析和研究具有重大意义.近年来,深度学习为图像处理技术提供了巨大的发展潜力.研究提出了基于深度学习模型——pix2pix的地形特征线提取方法,在山地和高原两种地形下进行了实验,并且将该方法应用于多比例尺底图的地形等高线提取研究.实验结果表明,pix2pix模型不仅在地形特征线提取方面有良好的效果,并且在特定的山地地形下可以进行山脊线和山谷线的语义区分.     

基于“DEM-NDVI-土地覆盖分类”的天山博格达自然遗产地山地垂直带提取与变化分析

本文基于Landsat影像数据获取天山博格达自然遗产地土地覆盖分类,结合归一化植被指数（NDVI）和数字高程模型（DEM）构建“DEM-NDVI-土地覆盖分类”散点图分析研究区植被受海拔和坡向的水热空间变化影响的分布特征,通过概率统计分析提取博格达遗产地山地垂直带,并结合研究区的气温、降水数据和NDVI变化特征分析垂直带变化的原因。研究结果表明：① 本文利用“DEM-NDVI-土地覆盖分类”散点图,揭示了研究区1989年和2016年的NDVI值和分类类别随着海拔上升的变化特征,其中NDVI值随着海拔上升呈现“倒U形”变化,而不同分类类别在一定的海拔区间内呈现出聚集效应,且不同分类类别有明显的高程界限。② 1989年和2016年博格达遗产地山地垂直带分带上限分别为：1278 m和1185 m（温带荒漠草原带）、1784 m和1759 m（山地草原带）、2706 m和2730 m（山地针叶林带）、3272 m和3293 m（高山草甸带）、3636 m和3690 m（高山垫状植被带）。③ 博格达遗产地1989年和2016年山地垂直带受区域气温升高和降雨增加的影响有较为明显的改变,其中温带荒漠草原带最为敏感,其上限变化最大,向下收缩93 m;山地针叶林带的分布范围则向两侧扩张49 m;山地草甸带带宽基本保持不变,但整体上移了约20 m;冰雪带则受到全球气候变暖的影响向上退缩54 m。     

基于“DEM-NDVI-土地覆盖分类”的天山博格达自然遗产地植被垂直带提取与变化分析

本文基于Landsat影像数据获取天山博格达自然遗产地土地覆盖分类,结合归一化植被指数(NDVI)和数字高程模型(DEM)构建"DEM-NDVI-土地覆盖分类"散点图分析研究区植被受海拔和坡向的水热空间变化影响的分布特征,通过概率统计分析提取博格达遗产地山地垂直带,并结合研究区的气温、降水数据和NDVI变化特征分析垂直带变化的原因。研究结果表明:(1)本文利用"DEM-NDVI-土地覆盖分类"散点图,揭示了研究区1989年和2016年的NDVI值和分类类别随着海拔上升的变化特征,其中NDVI值随着海拔上升呈现"倒U形"变化,而不同分类类别在一定的海拔区间内呈现出聚集效应,且不同分类类别有明显的高程界限。(2) 1989年和2016年博格达遗产地山地垂直带分带上限分别为:1278 m和1185 m(温带荒漠草原带)、1784 m和1759 m(山地草原带)、2706 m和2730 m(山地针叶林带)、3272 m和3293 m(高山草甸带)、3636 m和3690 m(高山垫状植被带)。(3)博格达遗产地1989年和2016年山地垂直带受区域气温升高和降雨增加的影响有较为明显的改变,其中温带荒漠草原带最为敏感,其上限变化最大,向下收缩93 m;山地针叶林带的分布范围则向两侧扩张49 m;山地草甸带带宽基本保持不变,但整体上移了约20 m;冰雪带则受到全球气候变暖的影响向上退缩54 m。     

中国山地垂直带信息系统的设计与开发

本文收集了大量翔实的山地垂直带资料,建立了我国山地垂直带数据库,创建了山地垂直带谱"数字引擎".使垂直带谱数字化成为可能。采用ArcObject组件开发技术及Visual Basic,自主开发中国山地垂直带信息系统(1.0)。该系统是一个面向对象(山地垂直带谱)的地理信息系统;以山地垂直带"数字引擎"为系统内核,在功能上实现了山地垂直带分布与地理空间区域的联接,同时实现了大范围内选择多个区域进行数据分析的功能。系统界面友好,易操作,显示直观、明了,为自然地理和生态学家者提供了一个比较丰富的山地垂直带谱数据集和有效的分析工具。标志着山地垂直带谱研究进入了数字时代。     

基于GIS的高海拔山区风电场智能微观选址研究

风电场微观选址是风电场建设的关键问题,关系到整个风电场建设的成败。目前常用的风电场选址设计软件在处理高海拔山区复杂地形时,由于地形对各处的风速影响较大,风能资源评估结果不够准确,导致选址效果并不理想。本文提出一种基于GIS空间分析的风电场智能微观选址方法,首先在GIS软件中对研究区的风能资源进行评估,得到风能资源的分布结果;通过山脊线、山顶点的自动提取,再对风能资源分布、山脊线、山顶点的结果进行叠置分析,在风能资源丰富的区域,选取山脊线上的山顶点位置布置风电机组,提供了初步的微观选址方案。本文通过云南省姚安县三道箐地区的风电场项目实例,对该方法的有效性进行了验证。结果表明,运用本文方法得到的风电场微观选址结果与实际项目的结果很接近,可以作为高海拔山区风电场微观选址。     

基于垂直带谱的太白山区山地植被遥感信息提取

遥感数据因其全覆盖的优势被广泛应用于山地植被信息的调查和研究。为了实现山区植被类型的高精度提取,本文以太白山区为实验区,结合山地植被的垂直地带性分布规律,利用太白山植被垂直带谱、高分辨率遥感影像（GF1/GF2/ZY3）和1:1万的数字表面模型（Digital Surface Model, DSM）数据,进行了多层次、多尺度的影像分割,构建了具有植被垂直带谱信息的地形约束因子,并据此进行样本选择和面向对象的分类,分类总精度达92.9%,kappa系数达到0.9160。该方法相比于未辅以垂直带谱信息的分类,总精度提高了10%。研究结果表明,分类过程中加入具有垂直带谱信息的地形约束因子,能显著地提高样本选择的效率和准确率,为后续的植被分类提供了精度的保证。通过人机交互的方式,将垂直带谱知识应用到分类中,可以有效地提高山地植被分类的精度。     

喜马拉雅山脉与我国东部中低山区冰蚀地貌对比研究

我国东部中低山区第四纪冰川的存在与否一直存在争议,但冰川必然对山体进行冰蚀作用形成冰蚀地貌。为了分析我国东部中低山区的山体地貌是否具有冰蚀特征,该文选取了现代冰川集中发育区-喜马拉雅山脉作为冰蚀地貌的研究对象,通过宏观分析,除了由缩口、三角脊、残弧组成的冰斗系统外,还发现冰蚀作用过程存在避谷、吞脊、切壁、穿梁等特殊习性。分析认为冰蚀过程主要受制于雪线高程、积雪高程与坡向坡角三方面因素的控制。以此为基础,对我国东部的江西庐山、山东蒙山、大兴安岭主峰黄岗梁三个地区的山体地貌进行对比分析,发现这些山区的山体地貌与喜马拉雅山脉的冰蚀地貌具有一致性。文章从冰蚀过程的角度确认了我国东部中低山区存在第四纪冰川的事实。     

喜马拉雅山与我国东部中低山区冰蚀地貌对比研究

我国东部中低山区第四纪冰川的存在与否一直存在争议,但冰川必然对山体进行冰蚀作用形成冰蚀地貌。为了分析我国东部中低山区的山体地貌是否具有冰蚀特征,该文选取了现代冰川集中发育区—喜马拉雅山脉作为冰蚀地貌的研究对象,通过宏观分析,除了由缩口、三角脊、残弧组成的冰斗系统外,还发现冰蚀作用过程存在避谷、吞脊、切壁、穿梁等特殊习性。分析认为冰蚀过程主要受制于雪线高程、积雪高程与坡向坡角三方面因素的控制。以此为基础,对我国东部的江西庐山、山东蒙山、大兴安岭主峰黄岗梁三个地区的山体地貌进行对比分析,发现这些山区的山体地貌与喜马拉雅山脉的冰蚀地貌具有一致性。文章从冰蚀过程的角度确认了我国东部中低山区存在第四纪冰川的事实。     

Spatial distribution modeling of temperature increase for the uplifted mountain terrains and its characteristics in Southwest China

Local temperature changes in mountain areas are significantly affected by the uplifted mountain terrains. Understanding how temperature increase with mountain terrains is an important component in accurately modeling the spatial distribution of temperature. The study, after minimizing the effect of elevation and latitude, quantitatively simulated the temperature increase in the uplifted mountain terrains, described the characteristics in the spatial distribution of warming areas with different magnitudes, and identified the correlated indices of mountain bodies for warming. Selecting Yunnan Province in southwest China as the study area, we simulated the warming field on a baseline surface at the average elevation of 2000 m and average latitude of 24.96°. The results indicated that the warming magnitudes in different local areas varied with the change in the spatial locations, and the warming process concentrated in the mountainous regions. Throughout the entire study area, the warming field presented a general pattern of three terraces from the regions of high mountains to middle mountains and then low mountains. The areasof high warming magnitude mainly surrounded large mountain bodies and were distributed on the upper part. The areas of low warming magnitude clustered in the valleys and basins of the middle mountain region, mostly on the lower part of the large mountain bodies and its branches. The areas with zero warming magnitude occurred in the low mountains and broad valleys, which were distributed largely on the lower parts of the middle mountains and in most of the valleys. Quantified sampling analysis demonstrated good positive correlation between the warming magnitudes in uplifted mountain terrains and the volume index of the mountain body, as well as elevation difference, with the coefficients corresponding to 0.82 and 0.91, respectively.     

基于DEM的福建省小流域划分研究

以1∶10万的数字线划图为工作底图,在ARC/INFO软件环境下,采用空间内插法生成福建省的DEM。其在ARC/INFO软件的水文模块环境下,经过数据预处理、流向分析、汇流分析和流域识别等过程,最终自动提取1435个小流域。研究结果表明:在山区,流域界线基本与分水岭吻合,但在平坦地区由于等高线稀疏,加上福建海岸线非常曲折,海湾支离破碎,流域提取的结果不尽人意,流域界线需要人工进一步修正。基于DEM的流域的快速提取,大大节省了的人力、物力,从提取的效率和精度来看都是切实可行的。随着DEM的精度将不断提高,这种方法将为在全省范围内实现流域各自然要素空间组合特征的快速分析与站点观测数据的综合管理,以及流域信息的空间可视化浏览、查询、统计和流域水文模型的应用分析奠定了坚实的基础,从而推动了生态环境保护、生态环境建设等生态环境管理工作的实施。     

Automatic recognition of loess landforms using Random Forest method

The automatic recognition of landforms is regarded as one of the most important procedures to classify landforms and deepen the understanding on the morphology of the earth. However, landform types are rather complex and gradual changes often occur in these landforms, thus increasing the difficulty in automatically recognizing and classifying landforms. In this study, small-scale watersheds, which are regarded as natural geomorphological elements, were extracted and selected as basic analysis and recognition units based on the data of SRTM DEM. In addition, datasets integrated with terrain derivatives(e.g., average slope gradient, and elevation range) and texture derivatives(e.g., slope gradient contrast and elevation variance) were constructed to quantify the topographical characteristics of watersheds. Finally, Random Forest(RF) method was employed to automatically select features and classify landforms based on their topographical characteristics. The proposed method was applied and validated in seven case areas in the Northern Shaanxi Loess Plateau for its complex andgradual changed landforms. Experimental results show that the highest recognition accuracy based on the selected derivations is 92.06%. During the recognition procedure, the contributions of terrain derivations were higher than that of texture derivations within selected derivative datasets. Loess terrace and loess mid-mountain obtained the highest accuracy among the seven typical loess landforms. However, the recognition precision of loess hill, loess hill–ridge, and loess sloping ridge is relatively low. The experiment also shows that watershed-based strategy could achieve better results than object-based strategy, and the method of RF could effectively extract and recognize the feature of landforms.     

Magnitude and forming factors of mass elevation effect on Qinghai-Tibet Plateau

Mass elevation effect(MEE) refers to the thermal effect of huge mountains or plateaus, which causes the tendency for temperature-related montane landscape limits to occur at higher elevations in the inner massifs than on their outer margins. MEE has been widely identified in all large mountains, but how it could be measured and what its main forming-factors are still remain open. This paper, supposing that the local mountain base elevation(MBE) is the main factor of MEE, takes the Qinghai-Tibet Plateau(QTP) as the study area, defines MEE as the temperature difference(ΔT) between the inner and outer parts of mountain massifs, identifies the main forming factors, and analyzes their contributions to MEE. A total of 73 mountain bases were identified, ranging from 708 m to 5081 m and increasing from the edges to the central parts of the plateau. Climate data(1981–2010) from 134 meteorological stations were used to acquire ΔT by comparing near-surface air temperature on the main plateau with the free-air temperature at the same altitude and similar latitude outside of the plateau. The ΔT for the warmest month is averagely 6.15℃, over 12℃ at Lhatse and Baxoi. A multivariate linear regression model was developed to simulate MEE based on three variables(latitude, annual mean precipitation and MBE), which are all significantly correlated to ΔT. The model could explain 67.3% of MEE variation, and the contribution rates of three independent variables to MEE are 35.29%, 22.69% and 42.02%, respectively. This confirms that MBE is the main factor of MEE. The intensive MEE of the QTP pushes the 10℃ isotherm of the warmest month mean temperature 1300–2000 m higher in the main plateau than in the outer regions, leading the occurrence of the highest timberline(4900 m) and the highest snowline(6200 m) of the Northern Hemisphere in the southeast and southwest of the plateau, respectively.     

山东中更新世以来冰碛年龄、雪线高程与气候演化——以蒙山、崂山为例

古雪线直接反映了冰期时的气候特征,因此雪线是古冰川研究最终要解决的问题。2017年笔者等基于蒙山雪线的初步研究,发现东亚地区存在的雪线低洼区,采用了东亚冷槽的概念来表述该槽状雪线低洼区,并初步绘制了东亚冷槽的雪线高程。本文主要介绍了山东段(蒙山-崂山)的研究情况。根据山东蒙山、崂山34个光释光、宇生核素等方法获得的冰碛年龄数据及对应冰期雪线高程研究,表明崂山的雪线比蒙山要低,且在MIS6之前的冰期,崂山东侧冰碛多被现代海面淹没。研究表明,我国东部的气候敏感度要明显强于西部高原区,冰期时强劲的北路寒潮是我国东部地区冰川形成的核心气候因素。     

LAST GLACIATION AND MAXIMUM GLACIATION IN THE QINGHAI-XIZANG (TIBET) PLATEAU: A CONTROVERSY TO M. KUHLE,S ICE SHEET HYPOTHESIS

Since the late 1950's, many Chinese scientists have explored the remains of the Quaternary glaciation in the Qinghai-Xizang (Tibet) Plateau and its surrounding mountains. In the main, 3-4 glaciations have been recognized. The largest one occurred in the Late Middle Pleistocene with piedmont glaciers, ice caps and trellis valley glaciers in many high peak regions. But here is no evidence of a unified ice sheet covering the whole plateau as described by M. Kuhle. Due to the further uplifting of the Himalayas and Qinghai-Xizang Plateau the climate became progressively drier, diminishing the extension of glaciers during the Late Pleistocene. The elevation of the snow line during the Last Glaciation was about 4,000 m on the south, east and northeast edges of the plateau and ascended to 5500 m on the hinder northwest of the plateau. The thermal effect of the big plateau massif, the sharp increase of aridity from the southeast rim to the northwest inland area and the abrupt decrease of precipitation during the