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Remote Sensing for Natural Resources    2024, Vol. 36 Issue (3) : 206-215     DOI: 10.6046/zrzyyg.2023086
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Ecological vulnerability of highland mountain areas:A case study of Kangding City, Sichuan Province
SU Yue1(), LIU Hong1,2(), YANG Wunian2, OUYANG Yuan1, ZHANG Jinghua1, ZHANG Tengjiao1, HUANG Yong1
1. Chengdu Center of China Geological Survey, Chengdu 610081,China
2. College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
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Abstract  

This study constructed an assessment index system for ecological vulnerability based on the vulnerability scoping diagram (VSD) model. It dynamically assessed the three-phase ecological vulnerability of Kangding City from 2011 to 2019 using the analytic hierarchy process - principal component analysis - entropy weight method (AHP-PCA-EWM). Through the analysis of spatio-temporal variations, spatial correlations, and driving factors, it revealed the spatio-temporal differentiation characteristics and driving mechanism of Kangding’s ecological vulnerability, aiming to provide suggestions for the ecological restoration, conservation, and sustainable development of Kangding. The results of this study are as follows: ① Throughout the study period, Kangding exhibited an overall moderate ecological vulnerability, with increased potentially- and slightly-vulnerable areas and a decreased severely vulnerable area, suggesting a promising ecological evolutionary trend. Moreover, the ecological vulnerability of Kangding manifested spatial distributions characterized by high-high clusters in the western and southeastern portions and low-low clusters in the northeastern and middle portion; ② The spatial distributions of ecological vulnerability in Kangding were subjected to various internal and external factors, with natural driving factors like vegetation cover, biological abundance, soil and water conservation, and meteorology being the dominant ones.
Keywords ecological vulnerability      AHP-PCA-EWM      Moran’s I      geodetector      Kangding City     
ZTFLH:  TP79  
Issue Date: 03 September 2024
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Yue SU
Hong LIU
Wunian YANG
Yuan OUYANG
Jinghua ZHANG
Tengjiao ZHANG
Yong HUANG
Cite this article:   
Yue SU,Hong LIU,Wunian YANG, et al. Ecological vulnerability of highland mountain areas:A case study of Kangding City, Sichuan Province[J]. Remote Sensing for Natural Resources, 2024, 36(3): 206-215.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023086     OR     https://www.gtzyyg.com/EN/Y2024/V36/I3/206
Fig.1  Location of the study area
数据名称 分辨率 内容 数据来源
遥感影像 30 m 轨道号为131/39,131/40数据,并进行遥感影像预处理 地理空间数据云、美国地质调查局
DEM数据 30 m ASTER GDEM数字高程数据 地理空间数据云
气象数据 — 研究区及周边27个气象站点1991—2019年的降水量和温度数据 中国气象数据网
道路及河网矢量数据 — — 地质云
土地利用数据 30 m 土地利用类型主要分为耕地、林地、草地、水域、建设用地和未利用地等6类 全国地理信息资源目录服务系统
植被数据 — 依靠完成预处理的遥感影像提取各期归一化植被指数NDVI和植被覆盖度数据 —
植被含水量及土壤含水量数据 — 通过已采集的川西高原各区域的植被样本以及土壤样本建立生态水反演模型提取 野外实测和完成预处理的遥感影像提取
土壤类型数据 1 km 对康定市区域范围内1∶100万土壤类型数据进行重采样处理 中国科学院资源环境科学数据中心
GDP数据 — 查阅康定市及其周边7个县市的经济统计数据,利用ArcGIS进行要素转点以及空间插值 《康定市统计年鉴》及市统计公报
人口密度数据 1 km 对康定市区域范围内1 km×1 km栅格数据进行重采样 WorldPop
Tab.1  Data source and preprocessin
准则层 要素层 指标层 单位 相关性
暴露度(A1) 自然暴露(B1) 地震危险度(C1) — 正相关
暴雨致灾因子(C2) — 正相关
降水距平百分率(C3) — 正相关
年均气温(C4) ℃ 负相关
距水系距离(C5) km 负相关
人为暴露(B2) 土地综合利用程度(C6) — 正相关
耕地占比率(C7) % 正相关
敏感性(A2) 地貌条件(B3) 地形敏感性(C8) — 正相关
土壤侵蚀(B4) 土壤侵蚀程度(C9) t/(km2·a) 正相关
生态活力(B5) 植被覆盖度(C10) — 负相关
植被含水量(C11) mg/hm2 负相关
土壤含水量(C12) g/cm2 负相关
生物丰度指数(C13) — 负相关
适应性(A3) 经济发展(B6) 人均GDP(C14) 万元 负相关
社会发展(B7) 人口密度(C15) 人/km2 负相关
道路密度指数(C16) km/km2 负相关
Tab.2  Assessment index system of ecological vulnerability
指标 W1j 2011年 2015年 2019年
W2j Wj W2j Wj W2j Wj
地震危险度 0.023 0.123 98 0.066 0.120 66 0.065 0.110 01 0.061
暴雨致灾因子强度 0.103 0.040 11 0.080 0.042 81 0.082 0.042 34 0.080
降水距平百分率 0.059 0.077 37 0.084 0.126 18 0.106 0.109 01 0.097
年均气温 0.035 0.013 41 0.027 0.013 18 0.027 0.012 70 0.026
距水系距离 0.071 0.127 12 0.118 0.129 69 0.118 0.126 08 0.115
土地利用程度综合指数 0.031 0.002 57 0.011 0.003 04 0.012 0.002 80 0.011
耕地占比率 0.010 0.001 25 0.004 0.001 18 0.004 0.000 93 0.004
土壤侵蚀程度 0.065 0.000 20 0.004 0.000 17 0.004 0.000 56 0.007
植被覆盖度 0.046 0.140 20 0.100 0.139 28 0.099 0.138 64 0.097
植被含水量 0.145 0.141 37 0.178 0.085 39 0.137 0.138 62 0.172
土壤含水量 0.081 0.141 72 0.133 0.138 26 0.130 0.136 24 0.127
生物丰度指数 0.021 0.101 37 0.058 0.109 75 0.060 0.106 02 0.058
人均GDP 0.093 0.079 69 0.083 0.081 44 0.107 0.067 36 0.096
人口密度 0.031 0.000 10 0.002 0.000 01 0.001 0.000 01 0.001
道路密度指数 0.015 0.000 09 0.002 0.000 08 0.001 0.000 15 0.002
Tab.3  Weights of each evaluation index in 2011, 2015 and 2019
依据 交互作用
q ( x 1 ? x 2 ) < m i n ( q ( x 1 ) , q ( x 2 ) 非线性减弱
m i n ( q ( x 1 ) , q ( x 2 ) ) < q ( x 1 ? x 2 ) < m a x ( q ( x 1 ) , q ( x 2 ) ) 单因子非线性减弱
q ( x 1 ? x 2 ) > m a x ( q ( x 1 ) , q ( x 2 ) 双因子增强
q ( x 1 ? x 2 ) = q ( x 1 ) + q ( x 2 ) 独立
q ( x 1 ? x 2 ) > q ( x 1 ) + q ( x 2 ) 非线性增强
Tab.4  Interaction relationship
Fig.2  Grading results of ecological vulnerability assessment in Kangding City
脆弱性分级 2011年 2015年 2019年
面积/km2 占比/% 面积/km2 占比/% 面积/km2 占比/%
潜在脆弱 18.04 0.16 18.29 0.16 84.42 0.73
微度脆弱 556.11 4.83 995.21 8.63 1 291.37 11.24
轻度脆弱 4 563.38 39.58 5 944.77 51.57 5 202.89 45.15
中度脆弱 6 035.03 52.35 4 297.23 37.28 4 802.10 41.67
重度脆弱 354.71 3.09 271.77 2.36 139.01 1.21
脆弱性指数 0.596 0.544 0.533
Tab.5  Area statistics for the 3 phases of vulnerability region in Kangding
年份 Moran’s I 标准差 Z值 P值
2011年 0.765 8 0.004 5 171.798 4 0.001
2015年 0.832 1 0.004 7 178.792 5 0.001
2019年 0.808 9 0.004 4 182.745 9 0.001
Tab.6  Global Moran’s index
Fig.3  LISA clustering maps of the 3 phases ecological vulnerability in Kangding City
Fig.4  Statistics of q-values of ecological vulnerability driving factors in Kangding
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