土地利用景观格局核心指数提取:以中国广州市为例

田晶; 邵世维; 黄怡敏; 余梦婷

doi:10.13203/j.whugis20150681

土地利用景观格局核心指数提取:以中国广州市为例

田晶^{1, 2, 3,},
邵世维⁴,
黄怡敏¹,
余梦婷^1, ,

1.
武汉大学资源与环境科学学院, 湖北武汉, 430079
2.
武汉大学地理信息系统教育部重点实验室, 湖北武汉, 430079
3.
武汉大学数字制图与国土信息应用工程国家测绘地理信息局重点实验室, 湖北武汉, 430079
4.
武汉市国土资源和规划信息中心, 湖北武汉, 430014

基金项目:

国家自然科学基金 41701439

详细信息

作者简介:
田晶, 博士, 主要从事地图自动综合和空间数据挖掘研究。tianjing_sres@whu.edu.cn

通讯作者:
余梦婷, 硕士生。astoria-mt@whu.edu.cn

中图分类号: P208;P273
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出版历程
- 收稿日期: 2017-06-29
- 发布日期: 2019-03-04

Towards a Core Set of Landscape Metrics for Land Use: A Case Study from Guangzhou, China

TIAN Jing^{1, 2, 3,},
SHAO Shiwei⁴,
HUANG Yimin¹,
YU Mengting^1, ,

1.
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
2.
Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan 430079, China
3.
Key Laboratory of Digital Mapping and Land Information Application Engineering, NASG, Wuhan University, Wuhan 430079, China
4.
Wuhan Land Resource and Planning Information Center, Wuhan 430014, China

Funds:

The National Natural Science Foundation of China 41701439

More Information

Author Bio:
TIAN Jing, PhD, specializes in automated map generalization and spatial data mining. E-mail: tianjing_sres@whu.edu.cn

Corresponding author:
YU Mengting, postgraduate. E-mail:astoria-mt@whu.edu.cn

摘要

摘要: 景观格局是研究景观功能和动态的基础。景观指数分析在土地利用/土地覆盖、生态系统服务、森林监控、城市蔓生以及生物多样性等方面应用广泛。现有许多景观指数之间存在不同程度的相关性，不仅导致信息冗余，还将影响结果解译的清晰性和准确性。同时，已有研究主要针对栅格数据，针对矢量数据的景观指数分析研究较少。为解决上述问题，以广州市土地利用景观格局为例，在矢量数据格式下计算了44个常用的景观指数，并运用相关分析和因子分析相结合的多元统计分析法，提取了6个核心景观指数，这些指数描述了广州市土地利用景观格局的6个维度：景观破碎度、平均斑块面积、平均形状复杂度、空间分散度、形状复杂度差异和土地类型的相似性。
- 景观指数 /
- 相关分析 /
- 因子分析 /
- 土地利用 /
- 矢量数据
Abstract: The spatial pattern of landscape is fundamental to landscape's function and development. Landscape metric analysis is widely applied in fields such as land use/land cover patterns, ecosystem service, forest monitoring, urban sprawl, regional biodiversity, etc. There are high correlations between many presented landscape metrics, which not only result in redundancy but also influence the clarity and accuracy of interpretation. Meanwhile, most of previous researches on landscape metric analysis use raster data and few use vector data. In order to solve these problems, a case study of Guangzhou land use pattern was performed. Firstly, we calculate 44 common landscape metrics using vector data. Then, multivariate statistical analysis, a combination of Spearman correlation analysis and factor analysis, is applied to solve the problem of redundancy. A core set of six landscape metrics in vector format is extracted, which respectively describe landscape fragmentation, average patch size, average shape complexity, spatial isolation, shape complexity variation and land use similarity.
- landscape metrics /
- correlation analysis /
- factor analysis /
- land use /
- vector data

HTML全文

图 1 核心指数分布图

Figure 1. Distribution of Six Metric Values

下载: 全尺寸图片幻灯片

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