2001—2020年间河南省郑州市生态环境质量时空变化及驱动因素分析

doi:10.6046/zrzyyg.2023203

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摘要

生态环境质量是地区发展水平的重要组成部分,客观定量地对其进行长时序动态监测与分析,可为城市可持续发展和生态环境建设提供科学依据。基于Landsat遥感影像,通过主成分分析法构建遥感生态指数(remote sensing ecological index,RSEI)作为评价指标,运用Sen+Mann-Kendall趋势分析、Hurst指数、地理探测器探究河南省郑州市2001—2020年生态环境质量时空变化特征以及各驱动因子影响程度。研究表明: ①2001—2020年间郑州市整体生态环境质量处于中等水平,生态环境指数呈先下降再上升再下降趋势,空间分布表现为东部平原低、西南山地丘陵高。除2010年受高温影响生态环境质量恶化面积大幅增加外,区域总体生态环境质量以不变和轻微改善为主。②2001—2020年间郑州市生态环境质量变化趋势显著,上升趋势区域占比56.34%,下降趋势区域占比42.26%。结合Hurst指数,东部生态环境质量下降趋势区域未来变化以持续性特征为主,西南部生态环境质量上升趋势区域未来变化以反持续性特征为主。③驱动力分析表明,2001—2020年间土地利用类型和人口密度是影响郑州市生态环境质量变化的主要因子,其解释力强度明显高于其他因素。高程和年均降水等自然因素影响力逐渐减弱,而反映城市化水平的夜间灯光指数影响力逐渐增强。研究可为郑州市生态环境评价与保护工作提供科学依据。

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	奥勇
	汪雅
	王晓峰
	吴京盛
	张亦恒
	李雪娇

关键词 ：生态环境质量, 遥感生态指数, Sen+Mann-Kendall, Hurst指数, 地理探测器

Abstract：

Ecological quality is an important indicator of a regional development level. Objective, quantitative dynamic monitoring and analysis of long-term ecological quality can provide a scientific basis for urban sustainable development and ecological construction. Based on Landsat remote sensing images, this study constructed the remote sensing ecological index (RSEI) as an evaluation index using principal component analysis. Accordingly, this study explored the spatiotemporal change characteristics of ecological quality in Zhengzhou from 2001 to 2020, as well as the extent of influence of various driving factors, using the Sen+Mann-Kendall trend analysis, the Hurst index, and geographical detectors. The results indicate that from 2001 to 2020, Zhengzhou maintained moderate ecological quality overall. The RSEI showed downward, upward, and then downward trends sequentially. Spatially, the eastern plains showed lower ecological quality, whereas the southwestern mountainous and hilly areas exhibited higher ecological quality. The regional ecological quality remained unchanged predominantly or saw slight improvements over these years except for 2010, when the area of zones with ecological quality deteriorating significantly increased due to high temperature. From 2001 to 2020, the ecological quality in Zhengzhou exhibited significant trends, with 56.34% of areas showing an upward trend and 42.26% exhibiting a downward trend. These results, along with the Hurst index, reveal that the downward trend in ecological quality in the eastern part is primarily characterized by sustainable changes in the future, while the upward trend in ecological quality in the southwestern partition is primarily characterized by anti-sustainable changes in the future. Driving force analysis indicates that over the 20 years, primary factors influencing changes in ecological quality in Zhengzhou included land use type and population density, whose explanatory power is significantly stronger than other factors. The impact of natural factors, such as elevation and average annual precipitation, has gradually diminished, while the influence of the night light index, which reflects the urbanization level, has progressively increased. The results of this study will provide a scientific basis for the evaluation and preservation of ecosystems in Zhengzhou.

Key words： ecological quality remote sensing ecological index Sen-Mann-Kendall Hurst index geodetector

收稿日期: 2023-07-11 出版日期: 2025-02-17

ZTFLH:	X821
	TP79

基金资助:陕西省土地整治重点实验室开放基金项目“污损土地遥感调查与评估研究”(2018-JC08);自然资源部“退化及未利用土地整治工程重点实验室”开放基金项目“土壤重金属污染快速识别技术”(SXDJ2019-8)

作者简介: 奥勇(1965- ),男,博士,副教授,主要从事生态遥感方面研究。Email: aoyong@chd.edu.cn。

引用本文:

奥勇, 汪雅, 王晓峰, 吴京盛, 张亦恒, 李雪娇. 2001—2020年间河南省郑州市生态环境质量时空变化及驱动因素分析[J]. 自然资源遥感, 2025, 37(1): 102-112.
AO Yong, WANG Ya, WANG Xiaofeng, WU Jingsheng, ZHANG Yiheng, LI Xuejiao. Spatiotemporal changes of ecological quality and their driving factors in Zhengzhou City over the last 20 years. Remote Sensing for Natural Resources, 2025, 37(1): 102-112.

链接本文:

https://www.gtzyyg.com/CN/10.6046/zrzyyg.2023203 或 https://www.gtzyyg.com/CN/Y2025/V37/I1/102

Fig.1 研究区域概况示意图

指标	计算公式	说明
绿度(NDVI)	$N D V I = (ρ N I R - ρ R) / (ρ N I R + ρ R)$	$ρ N I R$ 为近红外波段反射率; $ρ R$ 为红光波段反射率
湿度(Wet)	$W e t T M = 0.031 ? 5 ρ B + 0.202 ? 1 ρ G + 0.310 ? 2 ρ R + 0.159 ? 4 ρ N I R - 0.680 ? 6 ρ S W I R 1 - 0.610 ? 9 ρ S W I R 2$ $W e t O L I = 0.151 ? 1 ρ B + 0.197 ? 3 ρ G + 0.328 ? 3 ρ R + 0.340 ? 7 ρ N I R - 0.711 ? 7 ρ S W I R 1 - 0.455 ? 9 ρ S W I R 2$	$ρ B$ , $ρ G$ , $ρ R$ , $ρ N I R$ , $ρ S W I R 1$ 和 $ρ S W I R 2$ 分别为TM与OLI数据的蓝、绿、红、近红外、短波红外1和短波红外2波段的反射率数据
热度(LST)	$L S T = K 2 / l n (K 1 / T + 1)$ $L λ = [ε T + (1 - ε) L ↓] τ + L ↑$ $T = [L λ - L ↑ - τ (1 - ε) L ↓] / τ ε$	$L λ$ 为热红外辐射亮度值; $T$ 为黑体热辐射亮度; $ε$ 为地表比辐射率; $τ$ 为大气透过率; $L ↑$ 为大气上行辐射亮度; $L ↓$ 为大气下行辐射亮度; $K 1$ , $K 2$ 为传感器定标参数
干度(NDBSI)	$N D B S I = (S I + I B I) / 2$ $S I = [(ρ S W I R 1 + ρ R) - (ρ N I R + ρ B)] / [(ρ S W I R 1 + ρ R) + (ρ N I R + ρ B)]$ $I B I = 2 ρ S W I R 1 ρ S W I R 1 + ρ R - ρ N I R ρ N I R + ρ R + ρ G ρ G + ρ S W I R 1 /$ $2 ρ S W I R 1 ρ S W I R 1 + ρ R + ρ N I R ρ N I R + ρ R + ρ G ρ G + ρ S W I R 1$	$S I$ 为裸土指数; $I B I$ 为建筑指数; $ρ B$ , $ρ G$ , $ρ R$ , $ρ N I R$ , $ρ S W I R 1$ 分别为TM与OLI数据的蓝、绿、红、近红外和短波红外1波段的反射率数据

Tab.1 指标计算公式

$q$ 值范围	交互作用类型
$q (X 1 ? X 2) m i n [q (X 1), q (X 2)]$	非线性减弱
$m i n [q (X 1), q (X 2)] q (X 1 ? X 2) m a x [q (X 1), q (X 2)]$	单线性减弱
$m a x [q (X 1), q (X 2)] q (X 1 ? X 2) q (X 1) + q (X 2)$	双因子增强
$q (X 1 ? X 2) = q (X 1) + q (X 2)$	相互独立
$q (X 1 ? X 2) q (X 1) + q (X 2)$	非线性增强

Tab.2 因子交互作用类型

Tab.3 不同年份第一主成分载荷与贡献率

Fig.2 2001—2020年郑州市RSEI等级分布

Tab.4 2001—2020年郑州市生态环境质量等级面积与占比

Tab.5 2001—2020年郑州市生态环境质量等级变化情况

Fig.3 2001—2020年郑州市生态环境质量等级变化空间分布

Fig.4 2001—2020年郑州市RSEI变化趋势

Fig.5 2001—2020年郑州市RSEI变化Hurst指数

因子类型	2001年		2006年		2010年		2015年		2020年		2000—2020年
因子类型	$q$ 值	排序	$q$ 值	排序	$q$ 值	排序	$q$ 值	排序	$q$ 值	排序	$q$ 值	排序
年均降水	0.134	5	0.132	7	0.112	6	0.103	7	0.141	4	0.124	7
年均气温	0.232	3	0.164	5	0.107	7	0.149	6	0.123	6	0.155	5
高程	0.264	1	0.181	3	0.187	3	0.164	3	0.137	5	0.186	3
坡度	0.093	8	0.073	8	0.096	8	0.033	8	0.031	8	0.065	8
土地利用类型	0.171	4	0.251	2	0.362	1	0.295	1	0.207	2	0.257	1
人口密度	0.255	2	0.256	1	0.262	2	0.217	2	0.239	1	0.246	2
GDP	0.111	7	0.176	4	0.156	5	0.126	5	0.111	7	0.136	6
夜间灯光数据	0.129	6	0.153	6	0.181	4	0.163	4	0.177	3	0.161	4

Tab.6 因子探测结果

Fig.6 2001—2020郑州市RSEI驱动因子交互探测结果
X1为年平均降水; X2为年平均气温; X3为高程; X4为坡度; X5为土地利用类型; X6为人口密度; X7为GDP; X8为夜间灯光数据

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