热带地理

热带地理 ›› 2019, Vol. 39 ›› Issue (4): 531-537.doi: 10.13284/j.cnki.rddl.003152

• 专刊：无人机在生态学和地理学中的应用 • 上一篇    下一篇

基于无人机高分辨率航空影像的榆树疏林空间分布格局及其地形效应

吴 隐1, 2，韩 东1，姚雪玲1，张 静2，王 锋1   

  1. （1. 中国林业科学研究院 荒漠化研究所 北京 100091；2. 首都师范大学 资源环境与旅游管理学院 北京市资源环境与地理信息系统重点实验室 北京 100048）
  • 出版日期:2019-07-10 发布日期:2019-07-10
  • 通讯作者: 王锋（1981—），男，安徽合肥人，博士，研究员，主要研究方向生态遥感和模型，（E-mail）wangfeng@caf.ac.cn。
  • 作者简介:吴隐（1994—），男，湖南娄底人，硕士研究生，主要研究方向生态遥感，（E-mail）wuyin.0719@gmail.com；
  • 基金资助:

    十三五重点研发计划（2016YFC0500801、2017YFC0503804）；国家自然科学基金（31570710）

Spatial Pattern and Landforms Effects of Elm (Ulmus pumila) Sparse Forest Based on High Spatial-Resolution Aerial Images from Unmanned Aerial Vehicle (UAV)

Wu Yin 1,2, Han Dong 1, Yao Xueling1, Zhang Jing 2 and Wang Feng1   

  1. (1. Institute of Desertification Studies, Chinese Academy of Forestry, Beijing 100091, China; 2 Beijing Key Laboratory of Resource Environment and Geographic Information System, Capital Normal University, Beijing 100048, China)
  • Online:2019-07-10 Published:2019-07-10

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摘要：

依托位于内蒙古自治区正蓝旗浑善达克沙地榆树疏林草原长期生态定位观测大样地（42°57′53″ N、115°57′30″ E），利用无人机获取的高精度数字高程模型数据和样地内3 768棵榆树空间位置和胸径、树高和冠幅的地面调查数据。综合坡度、坡向、坡向变率和小尺度的海拔高差，基于决策树分类的方法对样地地形进行分类，分析榆树疏林在不同地形上的数量、密度和榆树结构的空间特征。主要结果为：1）样地地形分为沙平地、沙甸、阳坡、阴坡和沙脊5种类型，各类型面积分别占样地总面积的52.89%、17.25%、12.47%、10.05%和7.35%。2）在沙平地、沙甸、阳坡、阴坡和沙脊5种地形下的榆树密度分别为28.9、17.0、41.2、141.7和65.2棵/hm2。3）位于沙地阳坡的榆树胸径、冠幅和树高最大，分别为18.9±7.52cm、5.19±2.33m和4.89±2.33 m。4）榆树在沙丘阴坡的分布密度最高，阳坡的榆树胸径、冠幅和树高显著大于其他地形部位。研究结果表明：基于综合地形因子的沙地微地形分类可更好地表征榆树疏林的空间分布规律，同时也证明了无人机可成为分析植物空间分布格局的有效工具。

关键词: 无人机, 植被格局, 微地形, 榆树疏林, 浑善达克沙地

Abstract:

The purpose of this study is to explore the spatial characteristics of quantity, density, and structure of the elm (Ulmus pumila) on different micro-landforms based on the high spatial-resolution Digital Elevation Model (DEM) through Unmanned Aerial Vehicles (UAVs) and detailed ground investigation. The study sites are located at the long-term monitoring plot in Elm Sparse Forest Grassland Ecosystem (ESFOGE-Plot), Otindag sandy land, Inner Mongolia, China. The micro-landforms of ESFOGE-Plot were classified into plain, lowland, sunny slope, shady slope, and ridge by decision tree method considering five factors: slope, aspect, slope of aspect, height, and elevation. The results showed that: 1) the areas covered by plain, lowland, sunny slope, shady slope, and ridge on the ESFOGE-Plot were 52.89%, 17.25%, 12.47%, 10.05%, and 7.35%, respectively. 2) The number of elm trees per hectare on the five corresponding landforms were 28.9, 17.0, 41.2, 141.7, and 65.2, respectively. 3) The Diameter at Breast Height (DBH), canopy diameter, and tree height of elm trees on the sunny slope were 18.9 ± 7.52 cm, 5.19 ± 2.33 m, and 4.89 ± 2.33 m, respectively. 4) The density of elm trees on the shading slope was highest while the average size of elm trees on the sunny slope was largest. The micro-landforms based on comprehensive terrain factors better represent the spatial pattern of elm trees. It is demonstrated that UAVs are an efficient tool to study the spatial pattern of vegetation.

Key words: unmanned aerial vehicle, vegetation pattern, micro-landforms, elm (Ulmus pumila) sparse forest, Otingdag sandy land