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遥感技术与应用  2021, Vol. 36 Issue (2): 420-430    DOI: 10.11873/j.issn.1004-0323.2021.2.0420
数据与图像处理     
数字表面模型(DSM)参数指示林窗特征的有效性研究
赵亮1(),刘宇1,2(),罗勇1,3
1.中国科学院地理科学与资源研究所,生态系统网络观测与模拟重点实验室,北京 100101
2.中国科学院大学,北京 100049
3.成都理工大学地球科学学院,四川 成都 610059
The Efficiency of Parameters Derived from the Digital Surface Model (DSM) in Indicating the Forest Gap Features
Liang Zhao1(),Yu Liu1,2(),Yong Luo1,3
1.Key Laboratory of Ecosystem Observation and Modelling, Institute of Geographical Sciences and Natural Resource Research, Chinese Academy of Sciences, Beijing 100101, China
2.University of Chinese Academy of Sciences,Beijing 100049,China
3.College of Earth Sciences College,Chengdu University of Technology,Chengdu 610059,China
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摘要:

林窗的空间格局不仅对林下物种多样性有重要作用,而且也是量化不同林型结构特征的重要指标。近年来,灵活便捷的小型无人机航拍技术的快速发展为获取高分辨率的森林冠层三维结构信息提供了可低成本获取的途径。利用航拍影像提取林窗斑块来计算景观指数是描述林窗格局有效的传统方法,但提取细小林窗往往难度大,尤其是在需要处理大量航拍数据时会大大增加时间成本。基于无人机航拍的RGB影像,在三维建模获取的高精度数字表面模型(DSM)的基础上建立森林高差模型(DSMr),进而提出基于高差模型的信息熵(H)、标准差(STD)、偏度系数(SK)、峰度系数(EK)、纹理参数(GLCM,GLDV)来快速反映林窗空间分布格局的方法,并以黄土高原中部恢复较好的天然林和北部人工刺槐林窗数据检验其有效性。研究结果显示:SK、EK、GLCM、GLDV与林窗格局指数有一定的相关性,两类样方DSMr的SK和EK均与边界密度(ED)显著负相关,SK与斑块密度指数(PD)呈现出显著负相关关系,纹理参数均与景观形状指数(LSI)显著正相关。因此,DSMr参数能有效指示林窗的结构和分布格局,为林窗的生态过程与效应研究提供了更加快捷的变量。

关键词: 无人机三维建模高精度数字表面模型林窗格局指数相对数字高差模型    
Abstract:

Spatial pattern of forest gap mitigates the diversity of understory species, and is also an effective structural indicator to quantify the characteristics of forest types. Recently, the rapid development of photography technology combined with flexible and convenient small Unmanned Aerial Vehicle(UAV) provides a low-cost way to obtain high-resolution and 3D structural information of forest canopies. Traditionally, the pattern metrics describing the pattern of the forest gaps was calculated based on the gap mapping by using aerial imagery . However, it is difficult to extract small forest gaps, especially when it is necessary to process a large amount of aerial data, which will greatly increase the time cost. Based on the RGB images acquired from UAV aerial photography, the relative height model (DSMr) was established on the basis of high-resolution Digital Surface Model (DSM) constructed by 3D modeling. We put forward a method that can quickly reflect the spatial distribution pattern of forest gaps based on the parameters including information entropy (H), standard deviation (STD), skewness coefficient (SK), kurtosis coefficient (EK) and texture parameter (GLCM,GLDV) of the DSMr. Then the validity of metrics are tested by using the forest gap data of a well restored natural forest site a in the central Loess Plateau and a forest site shaped by plantations (Robinia pseudoacacia) in the north Loess Plateau. The results showed that the SK, EK, GLCM and GLDV have a positive correlation with the traditional pattern indices of forest gap. Both the SK and EK are significantly negatively correlated with the edge density index (ED); the SK is negatively correlated with the patch density (PD). The texture parameters are positively correlated with landscape shape index (LSI). In summary, DSMr parameters can effectively indicate the structure and distribution pattern of forest gaps, and the quantification of three-dimensional structure of forest gaps provides more readily available variables for researches of forest gaps.

Key words: UAV    3D modeling    High-precision digital surface model    Forest gap    pattern metrics    Relative height model
收稿日期: 2019-12-04 出版日期: 2021-05-24
ZTFLH:  TP79  
基金资助: 中国科学院大科学培育专项项目“全球干旱区生态系统大科学计划”(121311KYSB20170004)
通讯作者: 刘宇     E-mail: zhaoliangcdut@163.com;liuyu@igsnrr.ac.cn
作者简介: 赵亮(1991-),男,四川南充人,博士研究生,主要从事生态环境遥感研究。Email: zhaoliangcdut@163.com
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引用本文:

赵亮,刘宇,罗勇. 数字表面模型(DSM)参数指示林窗特征的有效性研究[J]. 遥感技术与应用, 2021, 36(2): 420-430.

Liang Zhao,Yu Liu,Yong Luo. The Efficiency of Parameters Derived from the Digital Surface Model (DSM) in Indicating the Forest Gap Features. Remote Sensing Technology and Application, 2021, 36(2): 420-430.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.2.0420        http://www.rsta.ac.cn/CN/Y2021/V36/I2/420

图1  任家台各样方正射影像和数字表面模型
图2  大南沟各样方正射影像和数字表面模型
指数类型指数名称计算公式

边界/斑块密度指数

Edge/patch density

斑块面积比(PLAND)

PLAND=Ai/A

Ai为景观类型i的总面积,A为景观面积(m2 )

边界密度(ED)

ED=(E/A)×100

E为景观中或景观类型边界总长度(m);A 为整个景观面积(m2 )

斑块密度(PD)

PD=N/A

N为景观类型的斑块数量,A为景观总面积

形状指数

Shape indices

邻近指数(CONTIG)

CONTIG=[(r=1nCijr)aij-1]/(V-1)

Cijr为景观类型i中斑块j的像元r的邻接值;aij为斑块ij的面积;V是3×3窗口内全部栅格邻接值之和;窗口中心和对角线上邻接栅格赋值1,与其上下左右相邻的栅格赋值为2

景观形状指数(LSI)

LSI=0.25E/A

E为景观中或景观类型边界总长度(m);A 为整个景观面积(m2 )

聚集度指数

Aggregation indices

欧式最近邻距离(ENN)

ENN=i=1mj=1nhijN

hij为从斑块ij到它最近同类斑块之间的距离,N为斑块数量

聚集度指数(AI)

AI=2Inn+i=1nj=1nPijIn(Pij)

N为斑块数量,pij为斑块类型ij个斑块的周长(m)

表1  选择景观指数及其计算公式
图3  任家台天然林样地(1~12)、大南沟人工林样各样方(13~24)林窗
图4  林窗面积、数量频率分布图
图5  H、STD与边界/斑块密度指数的相关关系
图6  SK、EK与边界/斑块密度指数的相关关系
图7  纹理参数与形状指数相关关系
图8  纹理参数与聚集度指数相关关系
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