自然资源遥感, 2023, 35(2): 112-121 doi: 10.6046/zrzyyg.2022163

技术方法

基于倾斜摄影测量的三维景观指数构建——以山东田横岛为例

王珏,1,2, 郭振,1,2, 张志卫1,2, 徐文学1,2, 许昊2

1.自然资源部第一海洋研究所海岸带科学与海洋发展战略研究中心,青岛 266061

2.山东科技大学测绘与空间信息学院,青岛 266590

Construction of 3D landscape indices based on tilt photogrammetry: A case study of Tianheng Island in Shandong Province

WANG Jue,1,2, GUO Zhen,1,2, ZHANG Zhiwei1,2, XU Wenxue1,2, XU Hao2

1. Coastal Science and Marine Policy Center, First Institute of Oceanography, MNR, Qingdao 266061, China

2. College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

通讯作者: 郭 振(1983-),男,博士,副研究员,研究方向为海洋空间规划。Email:guozhen@fio.org.cn。

责任编辑: 陈理

收稿日期: 2022-04-22   修回日期: 2022-06-27  

基金资助: 时空演变规律及调控对策研究”(42171292)
外交部亚洲专项资金项目“海洋空间规划产品研发”(WJ0922011)
中国海洋发展基金会国际合作项目“中泰海洋空间规划合作研究”(B19029)

Received: 2022-04-22   Revised: 2022-06-27  

作者简介 About authors

王 珏(1998-),男,硕士研究生,研究方向为3S技术在海洋空间规划中的应用。Email: wangj98sd@outlook.com。

摘要

景观指数是用以反映景观生态结构的组成和空间配置特征的定量指标。当前的景观指数体系普遍建立在对二维空间特性的表征上,评价结果难以准确反映真实三维景观系统的格局与构成,亟需一套描述海岛三维景观特征的指标体系及全过程评价方法。以山东省田横岛为例,基于无人机倾斜摄影测量点云,采用深度学习方法进行点云分类处理,构建了一套涵盖类型及景观尺度的6个三维景观基础指标用以定量化描述海岛三维景观特征,并建立了评价人类建设活动对海岛生态系统影响程度的建筑物景观指数。结果表明: 基于三维景观基础指标分析,田横岛建筑物三维体量较低且空间分布较为密集,高大植被类型具有较高的隔离度、规律性和空间聚集性,低矮的植被类型则多样性、紧凑性和连通性更大; 由于存在维度差异,三维景观指数比二维景观指数包含了更多的空间信息且受地面起伏影响程度较大; 同一景观类型下,形状指数TLSI对于高度变化更为灵敏(灵敏度指数为7.480); 同一景观指数下,建筑物类型较空间特征不规律的植被变化更大(灵敏度指数为5.861),且受建筑物设计特征的影响; 田横岛三维建筑物指数TBI为0.523,其大小随着建筑物的愈加密集、复杂而增加,较建筑物密度指数和空间拥堵指数可更好表达人工构筑物对海岛三维景观格局特征的影响程度。研究旨在为基于现代测绘技术支持下的三维景观指数构建、发展三维空间景观规划与管理评价体系提供方法学支撑和案例研究。

关键词： 三维景观指数; 倾斜摄影测量; 建筑物景观指数; 海岛; 三维景观格局

Abstract

Landscape indices are quantitative indices used to reflect the composition and spatial configuration of a landscape ecological structure. Current landscape index systems are generally constructed based on the characterization of 2D spatial characteristics, thus their evaluation results fail to accurately reflect the pattern and composition of a real 3D landscape system. Accordingly, there is an urgent need to develop an index system used to describe the 3D landscape characteristics of islands and a whole-process evaluation method. With Tianheng Island in Shandong Province as a case study and based on the point clouds of unmanned aerial vehicle (UAV) tilt photogrammetry, as well as the classification and processing of point clouds using the deep learning method, this study constructed six basic 3D landscape indices covering type and landscape scales to quantitatively describe the 3D landscape features of the island. Moreover, this study established the building landscape indices to evaluate the impacts of the construction activities of human beings on the island ecosystem. The results are as follows: ① As revealed by the analysis of basic 3D landscape indices, the buildings on Tianheng Island are characterized by small 3D volumes and dense spatial distribution. Furthermore, tall vegetation exhibits high isolation, regularity, and spatial aggregation, while low vegetation exhibits high diversity, compactness, and connectivity; ② Due to the difference in dimension, 3D landscape indices contain more spatial information than 2D landscape indices and are greatly affected by terrain undulation; ③ In the case of the same landscape type, the landscape shape index (TLSI) is more sensitive to the change in height (sensitivity index: 7.480). In the case of the same landscape index, the building type changes more greatly than vegetation with irregular spatial characteristics (sensitivity index: 5.861) and is influenced by the design characteristics of buildings; ④ Tianheng Island has a 3D building index (TBI) of 0.523, which increases with an increase in the density and complexity of buildings. Compared with building density and spatial congestion indices, TBI can better reflect the influence of artificial structures on the 3D landscape pattern of the island. This study aims to provide methodological support and a case study for the construction of 3D landscape indices based on modern surveying and mapping technology, as well as the planning of 3D spatial landscapes and the development of their management and evaluation system.

Keywords： 3D landscape index; tilt photogrammetry; building landscape index; island; 3D landscape pattern

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本文引用格式

王珏, 郭振, 张志卫, 徐文学, 许昊. 基于倾斜摄影测量的三维景观指数构建——以山东田横岛为例[J]. 自然资源遥感, 2023, 35(2): 112-121 doi:10.6046/zrzyyg.2022163

WANG Jue, GUO Zhen, ZHANG Zhiwei, XU Wenxue, XU Hao. Construction of 3D landscape indices based on tilt photogrammetry: A case study of Tianheng Island in Shandong Province[J]. Remote Sensing for Land & Resources, 2023, 35(2): 112-121 doi:10.6046/zrzyyg.2022163

0 引言

景观生态学是研究景观尺度上的空间格局、生态过程及其之间耦合机制的综合性学科,其采用景观指数定量化描述景观格局及变化,以建立格局与过程之间的联系[1]。近年来,遥感测绘、地理信息系统等领域的飞速发展为人们提供了多源且易获取的时空数据和高效的分析平台,使景观生态学取得了长足发展,成为生态学的前沿热点方向之一[2-3]。景观指数作为景观生态学的核心方法,其理论依据与体系构架也日臻成熟,并被应用于研究和探讨全球不同区域、不同生态系统和不同尺度下的景观破碎度和异质性、生境多样性、时空演变、驱动机制和格局优化等问题[4⇓⇓-7]。海岛作为一个相对独立且与外界缺少能流联系的生态系统,对人类活动影响更为敏感,其所提供的生态服务功能也更易受到景观格局效应的影响[8-9]。因此,定量评价海岛景观格局及演变有助于分析生态系统在空间尺度的配置状态与驱动机制,对制定合理的海岛及海岸带规划管理政策具有重要指导意义[10⇓-12]。

当前学术界广泛使用的景观指数体系虽可浓缩景观格局信息,反映其结构的组成和空间配置某些方面特征[13],但其斑块、类型及景观3个层次的众多指数均是建立在二维尺度上。而很多关键的生态学及人类活动强度信息均与其在三维空间的特征高度相关(如地形地貌、流域汇水过程、植被生物量、人工构筑物体量等)。缺失了第三维度的信息,仅从二维平面尺度来研究和评价生态系统的景观完整性及连通性,其所反映的特征在准确性和全面性方面无疑将大打折扣[14]。新一代的测量技术(星载、机载、船载、车载)使地表三维信息的快速提取和高精度数字表面模型(digital surface model,DSM)的构建成为可能,相关学者也开展了一系列研究。Long等[15]基于改进的三维生态足迹(ecological footprint,EF)和城市规模的人类发展指数开发了EWP模型,并评价了2017年中国4个主要岛屿地区的可持续发展,在传统二维EF模型基础上引入了足迹深度和规模; Hu等[16]利用增强回归树模型研究了不同季节的二维和三维因子研究城市热岛,在三维信息的选取方面具有参考价值; Yu等[17]采用回归模型和夏普利加法解释方法,研究了二维和三维景观格局指数及其与地表温度变化的关系; 宋仁波等[18]结合高分辨率影像和全景影像进行三维建模,为大规模城市建筑物建模提供思路; Liu等[19]提出了建筑物体积密度的计算方法,用于量化区域内的建筑物密度; Xu等[20]引用了空间拥挤程度的概念反映了三维空间中建筑物的拥堵情况。特别是无人机倾斜摄影测量,可从多个角度观察被制作建筑物和植被,更加灵活和真实地反映地物的实际情况,极大地弥补了基于卫星正射影像在分辨率、过境频率和立面信息缺失等方面的不足[21]。然而已有的三维景观指数多基于理论层面,缺少可操作性以及从原始数据获取到指数构建再到评估评价一套完整的过程。

本文将在系统梳理和剖析现有景观指数体系的基础上,构建一套描述海岛三维景观特征的指标体系及全过程评价方法,并以山东田横岛为研究区,基于无人机倾斜摄影测量获取激光点云数据,采用支持向量机(support vector machine,SVM)分类器的方法进行点云分类及处理,以此构建海岛DSM,开展三维景观指数案例运用及验证评估。研究旨在为推进现代测绘技术支持下的景观分析由二维向三维的过渡、发展海岛三维空间的景观生态评价方法提供方法学支撑和案例研究。

1 研究区及数据源

1.1 研究区概况

田横岛位于山东省青岛市即墨东部海域的横门湾中,中心位置为N36°25'08",E120°57'32",总面积为1.21 km2,海岸线长8 km,距大陆仅2 km(图1)。田横岛为有居民岛,岛上常住人口约1 200人,主要以养殖、捕捞、旅游业为主,岛内建筑种类繁多,有海神庙等标志性建筑物,岛上南北两坡风格迥异,是我国北方典型的有居民海岛。

图1

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图1   田横岛地理位置示意图

Fig.1   Location of Tianheng Island


1.2 数据源及其预处理

基础数据包括无人机飞测数据及遥感影像数据。其中,无人机倾斜摄影数据、激光点云数据为2021年1月于田横岛上由无人机搭载倾斜摄影相机获得,飞行高度为100 m,飞行姿态以单相机倾角45°环绕飞行,获得点云数据密度为27个/m2(图2—3)。基于Terrasolid软件完成点云数据预处理过程: 将原始点云数据按单元进行相关的镶嵌和裁剪工作,获取研究区的对应数据; 对裁剪好的数据进行滤波去噪处理,根据空间点半径范围邻近点数量滤波去除因遮挡造成的离群点和孤立点,设置搜索半径为1 m,相邻点最少为2个,不满足限制条件的点即为噪点[22-23]。

图2

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图2   田横岛三维透视图

Fig.2   3D perspective of Tianheng Island


图3

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图3   田横岛局部三维透视图

Fig.3   3D perspective of local Tianheng Island


多光谱遥感数据为来自美国地质调查局(https://earthexplorer.usgs.gov/)2020年12月的Landsat8 OLI 30 m空间分辨率影像数据,用于计算景观二维指数。本文采用非监督分类结合人工目视解译的方法进行土地利用类型分类,目视解译后于岛内随机生成25个点位,开展现场分类验证[24](图4),Kappa系数达到92%。将生成的矢量文件转为栅格形式导入Fragstats软件中进行二维景观指数计算[25]。

图4

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图4   田横岛土地利用类型

Fig.4   Land use type of Tianheng Island


2 研究方法

本文技术流程如图5所示。

图5

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图5   三维景观指数构建技术流程

Fig.5   3D landscape index construction technology roadmap


数据处理包括3个步骤: 将田横岛点云数据进行分类处理并创建DSM; 根据田横岛点云分类数据提取信息在传统二维景观指数基础上构建景观三维基础指标; 基于建筑物角度创建景观三维建筑物指数并讨论其生态意义。

2.1 点云数据处理

点云数据处理主要分为预处理、点云粗分类、点云精分类和分类结果检查4步。基于预处理后的点云数据,通过反复建立地表三角网模型的方法分离出地表面上的点,创建地面类,在此基础上生成DSM,如图6所示。采用SVM算法进行点云粗分类,预先选取训练样本根据植被和建筑物在表面粗糙度和形态方面存在差异进行训练[26]; SVM训练集、验证集和测试集的比例为6∶2∶2,正确率为94.7%[27]。点云精分类由Terrasolid软件进行人工目视解译校正进行处理。最终基于目视检查的方式,检查以下4项: ①数据是否完整; ②叠加彩色正射影像,判断点云覆盖物类型; ③按点云类别赋予相应颜色值目视进行检查; ④按点云类别和高度赋予相应颜色值目视进行检查。点云分类结果如图7所示,图7(b)和(d)中红色为建筑物,绿色为植被。

图6

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图6   田横岛DSM

Fig.6   DSM of Tianheng Island


图7

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图7   田横岛点云分类

Fig.7   Classification of point cloud on Tianheng Island


2.2 三维景观基础指数构建

传统景观生态格局的概念基于“斑块”构建而来,二维平面中“斑块”指不同于周围背景的、相对均值的非线性区域[28]。本文在三维空间中将“斑块”定义为空间上的独立三维混合体,即不同于周围空间、结构相对独立的几何空间区域,构成海岛三维景观格局的基本组成单元。同样,三维空间中“类型”定义为具有相同共建结构的斑块组成的混合体; “景观”定义为整个景观区域内所有斑块的混合体。基于“斑块-类型-景观”景观格局概念,选取讨论斑块密度(patch density,PD)、形状指数(landscape shape index,LSI)、斑块占比指数(percentage of landscape index,PLAND)、最大斑块指数(largest patch index,LPI)、香浓多样性指数(Shannon diversity index,SHDI)和香农均匀度指数(Shannon evenness index,SHEI)这6种指数在三维尺度下的生态意义: PD作为景观指数的基础指标最直观地反映着维度之间的体量差异; LSI,PLAND和LPI作为“类型”中的典型指数,对于讨论景观破碎度、丰富度和分布起着重要的意义; SHDI和SHEI反映了景观异质性。此基础上将现有二维景观指数增加高度维度信息,引用体积和表面积替代原有公式中的面积信息并从数学角度完善公式几何意义,提出三维景观基础指数以代替传统二维景观指数用以表征对于复杂闭合区域内景观的复杂性、紧凑性和空间排列规律,同时对指数所对应的景观尺度及其表达意义进行了阐述。该指标中体积、表面积信息通过点云处理软件和编程计算获得[29],斑块信息可采用移动窗口法具体实现。

我国有居民海岛普遍以中低层建筑物为主,建筑物结构较为单一。本文提出三维建筑物影响指数(three-dimensional building impact index,TBI),旨在探究景观区域内建筑物空间格局对自然环境的影响能力,进而分析人类活动对景观干扰强度。相比传统二维景观指数注重平面的延展度,构建三维指数增加高度数据的同时,提高了建筑物离散性,进一步加深了对斑块离散程度的研究。本文通过引用建筑密实比(building coverage ratio,BCR)表示建筑物表面积和建筑物体积之间的量度,平均建筑物结构指数(mean building structure index,MBSI)表示建筑物面积和建筑物高度比值,加以统计容积率(plot ratio,PR)指标进行指数构建,将其复合累加反映人类活动影响下建造建筑物对原有景观生态格局的影响能力[30-31]。并引用Liu等[19]和Xu等[20]的指数方法,添加建筑物密度指数(building volume density,BVD)和空间拥挤指数(space crowding density,SCD),用于后续讨论分析。上述指数计算汇总如表1所示。表1计算公式中: Ni为i类景观的斑块数量; Vi为i类景观的总体积; L为i类景观类型中所有斑块轮廓线的总长度; Si为i类景观的总表面积; E为岛屿的总表面积; Vmax为某一斑块类型中最大的斑块体积; V为海岛地面以上的体积; m为景观种类的个数; Eb为建筑物总表面积; Vb为建筑物总体积; Sb为建筑物占地面积; h为建筑物平均高度; Sp为建筑物p的占地面积; Hp为建筑物p的高度; Vp为建筑物p的体积; A为研究区面积; max{Hp}为区内最大建筑物高度; S为岛屿的总面积; k为建筑物数量。

表1   三维景观指数公式及说明

Tab.1  3D landscape index formula and explanation

景观尺度指数名称计算公式公式说明
类型三维斑块密度TPDTPD=NiVi　描述单位体积上的斑块数,是描述景观破碎化的重要基础指标,表示三维景观内的空间异质性和均匀性
三维形状指数TLSITLSI=0.25SiLViVi　描述斑块形状与相同面积的规则圆形或正方形之间的偏差,测量其形状复杂程度
三维斑块占比指数TPLANDTPLAND=SiE×100%描述各景观类别在海岛区域景观格局中的比重,量化了各斑块类型在景观中的比例丰度
三维最大斑块指数TLPITLPI=VmaxV×100%描述各类景观中最大斑块所占该类景观的体积之比,有助于确定景观的优势类型,其大小决定着景观的丰富度或地物占比情况
景观斑块占比PiPi=ViVi类斑块所占体积比,反映斑块类型(类)占景观的比例,是景观多样性统计中的基础
三维香农多样性指数TSHDITSHDI=-∑i=1m(PilnPi)减去所有斑块类型中各斑块类型的丰度比例乘以该比例的总和,用以表示海岛区域内不同斑块类型的多少,即丰富度问题
三维香农均匀度指数TSHEITSHEI=-lnm∑i=1m(PilnPi)香农多样性指数除以给定景观丰度下的最大可能多样性,TSHEI=0表明景观仅由一种斑块组成,无多样性; TSHEI=1表明各斑块类型均匀分布,有最大多样性
其他BCRBCR=EbVb建筑物表面积和建筑物体积之间的量度
MBSIMBSI=Sbh建筑物面积和建筑物高度比值
PRPR=VbV区域内的地上建筑物总面积与净用地面积的比率
TBITBI=BCR+103/MBSI+PR描述景观尺度内建筑物空间分布格局对原有空间的影响程度
BVDBVD=∑p=1kSp-HpS用于量化建筑物密度程度的指数,其值高度依赖于岛屿的总面积
SCDSCD=∑p=1kVpAmax{Hp}所有建筑物的体积累加值占研究区体积的百分比,反映了三维空间中建筑物的拥堵

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2.3 二维、三维景观指数灵敏度评价方法

在环境质量评价中,指数的灵敏度涉及对环境质量的分辨问题,本文提出一种二维、三维景观指数的灵敏度评价体系,从地物类型和指数类型2个角度出发,探讨本文中三维指数同现有二维景观指数体系的增幅趋势。灵敏度物理意义非常明确,它表示某一自变量每变化单位相对量时所引起因变量的相对变化量。采用地物灵敏度系数(ground object sensitivity coefficient,GSC)表示某一地物类型的指数受维度变化的影响幅度; 指数灵敏度系数(exponential sensitivity coefficient,ESC)表示单一指数受维度变化的影响幅度。其具体公式分别为:

GSC=1n∑i=0nI3DiI2Di
ESC=1M∑j=0MI3DjI2Dj

式中: I2Di为二维景观指数; I3Di为在某一二维景观指数基础上采用体积、表面积信息代替原有公式中面积信息的三维景观指数; i为第i类地物类型; j为第j个景观指数; n和M分别为地物类型和三维指数的数量。

3 结果与分析

3.1 田横岛三维景观指数计算与分析

田横岛岛屿面积适中,总占地面积为1.21 km2,无人机测得地面以上总点云体积为1 483 242 m3,约为0.001 5 km3,总表面积为2 712 460 m2。岛内整体地势西高东低,西侧多为山地,最大高度为80.83 m; 岛内建筑物多集中于东、中、西3个村落,建筑物平均高度为3.81 m,多为单层或2层建筑物; 建筑物总占地面积为52 384 m2,总表面积为65 141 m2,总体积为199 442 m3,建筑物空间分布较为集中。基于本文所提出的三维景观指数,其计算结果如表2所示。

表2   不同尺度指数计算结果

Tab.2  Index calculation results of different scales

景观
尺度		景观指数低等高
度植被		中等高
度植被		高等高
度植被		建筑物
类型TPD9.3346.12113.7961.345
TLSI21.40925.06154.88737.764
TLPI8.3603.2961.0740.016
TPLAND0.3550.2750.1160.037
景观尺度景观指数海岛
景观TSHDI0.732
TSHEI0.614
TBI0.523

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高等高度植被TPD为13.796,说明高度越高的植物不仅在平面上分布广泛,在空间上破碎程度也越高; TLSI值为54.887,符合高植被地区高程起伏变化大的客观规律; TLPI和TPLAND同属于类型类中统计斑块的指数类型,由景观斑块组成的每个斑块的面积或体积可能是景观中包含的唯一最重要和有用的信息,这些信息不仅是许多斑块、类别和景观指数的基础,而且斑块体积本身在三维尺度具有很大的生态效用。比较TPLAND的值即可确定海岛中的优势景观类型为低等高度植被(0.355),并且其TLPI为8.360确定低等高度植被中的斑块在全海岛景观类型所含斑块中体积最大; TSHDI为0.732,表示斑块类型增加或各斑块类型在景观中呈均衡化趋势; TSHEI等于香农多样性指数除以给定景观丰度下的最大可能多样性(各斑块类型均等分布)。

田横岛区域TBI为0.523,TBI的数值大小反映着人类活动对于原有海岛生态环境的变化程度。海岛受人类活动的影响较小,建筑物景观结构较为单一。当岛内建筑物由低矮平房变为高层居民楼后,即建筑物结构变复杂后,平均高度和总体积将增加,TBI指数增大; 当岛内建筑物数量增多,即空间分布更为广泛后,除高度外其余指标均将增加,TBI指数亦增大。

3.2 二维、三维景观基础指标对比分析

景观格局分析中传统的二维景观格局指数是基于平面信息所得,当需要考虑地形因素时,此指数可能受到局限,而地形是影响景观格局的重要自然因子。不同于基于遥感影像数字高程模型(digital elevation model,DEM)数据提取三维表面积和周长的传统三维指数创建方法,基于无人机激光点云数据不仅可以提取计算出不同斑块、地类和景观在空间上的体积量和分布位置,同时在数据精度方面具有DEM无法比较的优势。

选取现有二维景观生态指数PD,LSI,PLAND,LPI,SHDI和SHEI,同本文构建的景观三维指标进行对比分析,其中PD,LSI,PLAND和LPI为类型级,SHDI和SHEI为景观级。基于Fragstats软件计算二维景观生态指数,二维和三维指标计算结果如表3所示,同时计算GSC和ESC。

表3   二维、三维指数计算结果对比

Tab.3  Comparison of 2D and 3D index calculation results

景观类型/指数维度PD/TPDLSI/TLSILPI/TLPIPLAND/TPLANDSHDI/TSHDISHEI/TSHEIGSC
低等高度植被二维5.4677.3426.0220.237——1.877
三维9.33421.4098.3600.355——
中等高度植被二维7.8706.5213.1150.178——1.806
三维6.12125.0613.2960.275——
高等高度植被二维4.2838.6390.8400.384——2.789
三维13.79654.8871.0740.116——
建筑物二维0.2472.2430.1120.123——5.681
三维1.34537.7640.0160.037——
海岛尺度二维————0.5680.663—
三维————0.7320.614—
ESC—2.7887.4870.9670.9111.2890.926—

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构建GSC描述同一景观类型对应的多种景观指数增幅比例累加后均值,低等高度植被和中等高度植被分别为1.877和1.806,增幅程度较小,表明高程较低的景观类型三维指数变化较小,同时所构建的三维指数基本符合二维指数变化趋势,没有明显异常的指数; 高等高度植被GSC为2.789,建筑物GSC为5.681,增幅程度远远大于低矮植被,其值充分反映地形起伏越大的景观类型,二维景观指数所忽视的高程值影响生态评价的程度越大。

结果可知TLSI对应的ESC值最大,为7.487,由公式可知TLSI表示地形的起伏复杂程度,植被的高度同TLSI成正相关,因此三维指数比二维指数更适用于描述景观类型在空间上的变化。多样性指数中TSHDI和TSHEI受维度变化影响不显著,ESC分别为1.289和0.926,增幅较小,结果表明二维景观多样性指数仍然具有典型性。

3.3 三维建筑物景观指数验证

基于Liu和Xu的指数方法[19-20]验证本文提出的TBI合理性,并采用情景分析法探讨海岛区域内不同建筑物空间特征所对应的指数变化。

1)情景1: 假设将岛内建筑物点云数据复制1倍并堆积在其建筑物最高点的平面上(即建筑物平均高度由3.8 m增加至7.6 m,体积和表面积增加2倍,占地面积不变,其目的在于改变建筑物空间结构特征),如图8(a)所示,同理当点云数据复制2倍、3倍后,将增加后的信息带入计算TBI,BVD和SCD。

图8

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图8   情景模型概念图

Fig.8   Scene model display diagram


2)情景2: 假设在岛内非建筑物的地面上随机分布增加1 000个正方体建筑物点云数据,每个立方体边长为3.8 m(3.8 m约等于建筑物原有平均高度,其目的在于增加正方体后不改变原有建筑物平均高度和最大高度,改变建筑物的空间分布特征),如图8(b)所示,同理当增加2 000个和3 000个正方体建筑物点云数据后,将增加后的信息带入计算TBI,BVD和SCD。

2种不同情景模型的结果分析如图9所示。当岛内空间结构特征发生变化时,TBI,BVD和SCD随着建筑物空间结构复杂程度增加而增加,当在建筑物顶端复制1倍建筑物时,增幅比例分别为1.307,2.001和2.087; 当在建筑物顶端复制2倍建筑物时,增幅比例分别为1.590,3.002和1.391; 当在建筑物顶端复制3倍建筑物时,增幅比例分别为1.848,4.002和1.043。由增幅比例结果可知,BVD呈线性增长模式,TBI为增幅程度小于BVD,TBI和SCD随着建筑物空间结构的愈加复杂,增幅比例有所减小,但SCD最终增幅程度趋于1。结果表明,TBI比起BVD和SCD更趋于理论逻辑,对于人工构筑物空间变化具有较高的灵敏度。当岛内建筑物空间分布特征发生变化时,TBI,BVD和SCD随着建筑物空间分布密集程度增加而增加,当增加1 000个正方体后,增幅比例分别为1.458,1.276和1.277; 当增加2 000个正方体后,增幅比例分别为1.756,1.551和1.553; 当增加3 000个正方体后,增幅比例分别为1.978,1.827和1.829。由增幅比例结果可知,相比较而言TBI对于建筑物分布程度变量更敏感,且随着正方体个数的增加,其增幅比例逐渐减小。

图9

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图9   情景分析结果

Fig.9   Scenario analysis results


4 结论

针对当前景观生态学普遍基于二维空间构造景观指数所带来的三维信息度缺失,评价结果难以准确反映真实三维景观系统的格局与特征问题,本文基于无人机倾斜摄影测量点云构建了一套包括类型及景观尺度共6个三维景观基础指标用来定量化描述海岛生态景观的三维格局与构成,并构建了建筑物景观指数用以评价人类建设活动对海岛生态系统的影响程度。主要结论如下:

1)基于三维景观基础指标分析,田横岛总体积为0.001 5 km3,总表面积为2.71 km2; 高大植被类型具有较高的隔离度、规律性和空间聚集性,低矮的植被类型则多样性、紧凑性和连通性更大; 建筑物类型的计算结果反映了岛内多为低矮建筑物且空间分布较为密集。由于存在维度差异,三维景观指数比二维指数存在着明显的增幅程度: 不同指数对比中TLSI增幅最为明显,ESC为7.487,结果反映涉及地类地形起伏变化的指数,三维指数包含更丰富的空间信息,其结果可更精确地表达三维景观信息; 不同地类对比中建筑物指数变化最大,GSC为5.681,比起空间特征不规律的植被,三维指数更受建筑物设计特征的影响。

2)海岛三维建筑物指数TBI基于岛陆多为低矮建筑物的现状特点反映其空间上的景观格局,TBI大小决定建筑物空间结构和分布程度,田横岛TBI为0.523,当岛内建筑物分布愈加密集,其值愈大。该指数可应用范围于包括海岛的闭合系统,用以反映人类活动强度程度。基于相关情景模拟分析,当建筑物空间结构复杂程度增加时,TBI增长率为1.307,1.590和1.848,BVD呈线性增加,SCD增幅程度逐渐趋于1; 当建筑物分布密集程度增加时,TBI增长率分别为1.458,1.756和1.978,灵敏度大于BVD和SCD。其结果表明TBI相较BVD和SCD,对于人工构筑物空间变化具有较高的灵敏度,更好地反映了三维空间内建筑物的拥挤程度。

3)本研究重点在于三维景观体系的构建以及从建筑物角度出发探讨海岛上的相关景观影响因子,下一步研究重点将结合植被、道路电线铺设、气候等相关影响因子,在当前指数基础上进一步研发三维植被指数以及其他针对不同专题的三维景观指数以期为更精准地表达三维空间景观的格局与动态演变过程,为助力海岛海岸带空间规划管理评价体系提供方法支撑。

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在建设海洋强国的关键时期,海岛的生态保护和开发利用均至关重要。城镇化对海岛生态系统带来了深刻的影响,开展海岛资源环境承载力评估对管控人类开发行为、维护海岛生态平衡具有重要意义。论文以我国北方典型海岛—庙岛群岛为研究区,构建了一套综合开发强度和生态状况的海岛资源环境承载力评估方法并开展评估,结果显示：1）群岛尺度上,海岛资源环境承载力处于临界超载状态;2）单岛尺度上,南长山岛处于轻度超载状态,北长山岛、庙岛和大钦岛处于临界超载状态,小黑山岛、大黑山岛、砣矶岛、小钦岛、南隍城岛和北隍城岛处于可载状态,不同海岛应实施不同的保护与利用模式;3）区块尺度上,海岛资源环境承载力表现出了明显的空间分异性特征,可载区占41.7%,临界超载区占30.0%,轻度、中度和重度超载区分别占15.9%、8.1%和4.3%,超载区主要分布在各岛的城镇建设区域。

Chi Y, Shi H H, Sun J K, et al.

Evaluation on island resources and environment carrying capacity under the background of urbanization

[J]. Journal of Natural Resources, 2017, 32(8):1374-1384.

DOI:10.11849/zrzyxb.20160753      [本文引用: 1]

Island ecosystem is vulnerable due to the special position, limited area and isolated space of islands, and the conservation and exploitation of islands are both essential to the construction of strong marine country. Urbanization has profound impact on island ecosystem, which threatened biodiversity and ecosystem productivity, and changed the landscape pattern, thus the evaluation on island resources and environment carrying capacity is of great significance for controlling human activities and maintaining ecological balance. Miaodao Archipelago, typical islands in North China which locate in Changdao County of Shandong Province, was used as the study area. The evaluation model of island resources and environment carrying capacity which integrated the exploitation intensity and ecological status was established. The methods of remote sensing (RS), geographic information system (GIS) and field investigation were adopted, and the resources and environment carrying capacities at archipelago scale, island scale and grid scale were analyzed, respectively. The results indicated: at archipelago scale, the resources and environment carrying capacity was in status of critical overloading; at island scale, Nanchangshan Island was in status of mild overloading, whereas Beichangshan Island, Miao Island and Daqin Island were in status of critical overloading, and the other six islands were in status of no overloading, which suggested that different modes of conservation and exploitation should be implemented in different islands; at grid scale, the island resources and environment carrying capacity had significant spatial heterogeneity with no overloading zones (41.7%), critical overloading zones (30.0%), mild overloading zones (15.9%), moderate overloading zones (8.1%) and severe overloading zones (4.3%) in descending order, where no overloading zones distributed in the non-urban construction areas, critical overloading zones located in suburb areas and parts of non-urban construction areas, and overloading zones concentrated in urban construction areas. Urban construction inevitably decreased the island ecological function. Construction scale control, spatial allocation optimization, environmental impact mitigation, and ecological restoration and construction were important measures to enhance the island resources and environment carrying capacity. The evaluation model comprehensively reflected the features and spatial heterogeneity of island resources and environment carrying capacity, thus provided a basis for allocations of island conservation and exploitation, and it can be widely applied to the evaluation of island resources and environment carrying capacity in different regions and different types of islands.

田义超, 黄鹄, 张强, 等.

基于蜂巢格网的北部湾典型海岛景观生态风险空间异质性研究

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[J]. Marine Environmental Science, 2021, 40(4):527-534,41.

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Jiang N, Chen C, Han H F.

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张志明, 罗亲普, 王文礼, 等.

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Hu Y, Dai Z, Guldmann J M.

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Yu S, Chen Z, Yu B, et al.

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[J]. Science of the Total Environment, 2020, 725:138429.

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宋仁波, 朱瑜馨, 郭仁杰, 等.

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[J]. 自然资源遥感, 2022, 34(1):93-105.doi:10.6046/zrzyyg.2021039.

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[J]. Environmental Science and Pollution Research International, 2021, 28(47):66804-66818.

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Urban morphology is a crucial contributor to urban heat island (UHI) effects. However, few studies have explored the complex effect of 2D/3D urban morphology on UHIs from a multiscale perspective. In this study, we chose the central area of Jinan city, which is commonly known as the "furnace," as the case study area. The 2D/3D urban morphology indexes-building coverage ratio (BCR) (for assessing the 2D building density), building volume density (BVD) (for assessing the 3D building density), and frontal area index (FAI) (for assessing 3D ventilation conditions) were calculated and derived to investigate the complexity of the relationship between 2D/3D urban morphology and the land surface temperature (LST) at different scales using the maximum information coefficient (MIC) and geographically weighted regression (GWR). The results indicated that (1) these 2D/3D urban morphology indexes are essential factors that are responsible for LST variation, and BCR is the most important urban morphology index affecting LST, followed by BVD and FAI. Importantly, the relationship between the BCR, BVD, FAI, and LST was an inverse U-shaped curve. (2) The relationship between 2D/3D urban morphology and LST variation showed a significant scale effect. With increased grid size, the correlation between the BCR, BVD, and FAI and the LST strengthened, "inflection point" of inverse U-shaped curve significantly declined, and their explanation rate of the LST first increased and then decreased, with a maximum value at the 700 m scale. Additionally, the FAI exerted a stronger negative effect, while the BCR and BVD generally had stronger positive effects on the LST as the grid size increased. This study extends our scientific understanding of the complex effect of urban morphology on the LST and is of great practical significance for multiscale urban thermal environment regulation.

Xu Y, Liu M, Hu Y, et al.

Analysis of three-dimensional space expansion characteristics in old industrial area renewal using GIS and barista:A case study of Tiexi District,Shenyang,China

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With rapid urban development in China in the last two decades, 3D characteristics have been the main feature of urban morphology. Nevertheless, the vast majority of urban growth research has only focused on area expansion horizontally, with few studies conducted in a 3D perspective. In this paper, the characteristics of 3D expansion that occurred in Tiexi from 1997 to 2011 were evaluated based on geographic information system (GIS) tools, remote-sensing images, and Barista software. Landscape index, the spatiotemporal distribution of changes in buildings’ renewal modes and variations in city skylines as well as the relationship between number and size of high-rise buildings are the specific phenomena and data utilized to quantify the 3D urban expansion. The results showed that the average height of Tiexi increased by 0.69 m annually, the average urban capacity increased by 490.15 m3 annually, and space congestion degree increased by 0.11% annually. The average annual increase of the building evenness index was 36.43. The renewal area occupied up to 75.38% of the total area. The change of the skyline was more consistent with the east–west direction. The change in the south direction was significant, while in the north direction it was relatively slow. The overall shape of the city was that of a weak pyramid, with the angle of the top of the pyramid gradually becoming larger. The methods proposed in this paper laid a foundation for a wide range of study of 3D urban morphology changes.

孙宏战. 基于体素模型和点云数据的精细三维景观格局分析[D]. 长春: 吉林大学, 2021.

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[J]. Geomatics and Spatial Information Technology, 2017, 40(6):181-183.

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魏金龙, 李明阳, 赵邑晨, 等.

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[J]. 西北林学院学报, 2021, 36(2):164-171.

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[J]. Journal of Northwest Forestry University, 2021, 36(2):164-171.

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闫钧华, 苏恺, 苏荣华, 等.

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[J]. 宇航计测技术, 2021, 41(4):38-44.

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吴未, 范诗薇, 许丽萍, 等.

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[J]. 自然资源学报, 2016, 31(3):413-424.

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Wu W, Fan S W, Xu L P, et al.

Grain size effect of landscape metrics in Wuxi City

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DOI:10.11849/zrzyxb.20150303      [本文引用: 1]

Wuxi city in the Yangtze River Delta Area is taken as a representative case of urbanizing regions of China with great pressure of biodiversity conservation and environmental protection. The multi-weight factors model based on natural and artificial factors was applied in the process of identifying local ecological contribution pattern patches. Five main factors, i.e., slope, height, land use type, distances to settlements and traffic network, and their relative weights were obtained from previous results. The basic spatial cell unit is 30 m. By the scale the landscape pattern was converted to ArcGrid formats with grain sizes of 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90 and 100 cells. Above two steps were computed in ArcGIS 10.0 environment based on the dataset interpreted from TM images. Landscape metrics were used to detect the grain size effects of ecological pattern. Five landscape connectivity indicators, including number of links (<em>NL</em>), number of components (<em>NC</em>), integral index of connectivity (<em>IIC</em>), probability of connectivity (<em>PC</em>) and importance value of <em>PC</em> (<em>dPC</em>), were computed in ConeforSensinode 2.2 environment. Landscape metrics at class and landscape levels, including total class area (<em>CA</em>), number of patches (<em>NP</em>), patch density (<em>PD</em>), largest patch index (<em>LPI</em>), landscape shape index (<em>LSI</em>), perimeter area fractal dimension (<em>PAFRAC</em>), aggregation index (<em>AI</em>), splitting index (<em>SPLIT</em>), mean shape index (<em>MSI</em>), area-weighted patch fractal dimension (<em>AWMPFD</em>), cohesion index (<em>COHESION</em>), division index (<em>DIVISION</em>), Shannon's diversity index (<em>SHDI</em>), Shannon's evenness index (<em>SHEI</em>), were computed in Fragstats 4.0 environment. The results showed that with the increase of grain size, these metrics changed dramatically, and there existed scale domains of landscape metrics. The scale domains of landscape metrics at class and landscape levels were 2-30 cells and 2-10 cells respectively, and that of landscape connectivity indicators was 2-7 cells. The scale domain of 2-7 cells, i.e. 60-210 m, was recommended. The scale domain of landscape connectivity indicators was more precise compared with those of other landscape metrics. Landscape connectivity indicators were suitable for the research of grain size effect. However, it should be noticed that the response degrees of different landscape connectivity indicators were different.

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