Evolution of Ecological Spatial Efficiency Pattern and Attribution Analysis of Landscape Pattern in Guizhou Province

doi:10.16258/j.cnki.1674-5906.2025.06.007

Ecology and Environmental Sciences ›› 2025, Vol. 34 ›› Issue (6): 902-913.DOI: 10.16258/j.cnki.1674-5906.2025.06.007

• Research Article [Ecology] • Previous Articles Next Articles

Evolution of Ecological Spatial Efficiency Pattern and Attribution Analysis of Landscape Pattern in Guizhou Province

YANG Haoyu¹(), HUANG Kangjiang¹, CHEN Xiaodong¹, ZHAO Jie¹, XIONG Jun², TIAN Kang¹^,^*()

1. Guizhou Institute of Natural Resources Survey and Planning, Guiyang 550000, P. R. China
2. School of Management, Guizhou University, Guiyang 550000, P. R. China

Received:2025-01-04 Online:2025-06-18 Published:2025-06-11

贵州省生态空间效率演变及景观格局的影响归因

杨昊彧¹(), 黄康江¹, 陈晓东¹, 赵劼¹, 熊军², 田康¹^,^*()

1.贵州省自然资源勘测规划研究院，贵州贵阳 550000
2.贵州大学管理学院，贵州贵阳 550000

通讯作者: * 田康, E-mail: 281846208@qq.com
作者简介:杨昊彧（1994年生），男（布依族），工程师，硕士，国家注册城乡规划师，主要研究方向为国土空间规划。E-mail: yhylobking@163.com
基金资助:
农业农村部-联合国粮食及农业组织：全球环境基金项目(HOLUR-2022-16);2024年度中共贵州省委全面深化改革重大调研课题(GZGGKT2024-039)

Abstract

Abstract:

Understanding the influence of ecological land landscape patterns on ecological space efficiency is crucial for optimizing ecological spatial layouts. This study integrated the Super-Efficiency Slacks-based Measure model, Optimal Parameter-based Geographical Detector and Multi-scale Geographically and Temporally Weighted Regression to analyze the evolution characteristics of ecological space efficiency in Guizhou Province during 2000-2020 and explore the underlying mechanisms of ecological land landscape patterns on ecological space efficiency. The results showed that 1) the comprehensive efficiency of ecological space was jointly determined by pure technical efficiency and scale efficiency; however, the decrease in scale efficiency was the main reason for the decline in comprehensive efficiency and the degradation of its hierarchical structure. 2) From 2000 to 2020, the spatial aggregation characteristics of ecological spatial comprehensive efficiency, pure technical efficiency, and scale efficiency in Guizhou Province continuously improved. The comprehensive efficiency and pure technical efficiency maintained the spatial distribution pattern of higher efficiency in the south from southwest to northeast and lower efficiency in the north. The scale efficiency evolved into a spatial distribution pattern of higher efficiency in the eastern and central regions and lower efficiency in the western and southern regions. 3) Landscape pattern indices mainly influence scale efficiency and subsequently influence comprehensive efficiency. The influences of these indices on scale efficiency were significantly different, and there were different degrees of spatial and temporal heterogeneity in the results of the study. To protect the river system, the layout model of ecological land with a regular shape, multi-blocks, and excellent connectivity was conducive to improving ecological space scale efficiency in Guizhou Province. 4) Ecological space scale efficiency was not independently influenced by each landscape pattern index, but two-factor and nonlinear enhancements were produced by the interaction of two factors. Landscape Shape Index, as the dominant influencing factor, interacted with other independent factors and was the dominant force that caused spatial differentiation of scale efficiency. When optimizing ecological space, precise policies should be implemented according to the efficiency differences of different units, distribution patterns, and influence differences of various indicators to improve the quality and efficiency of the ecological environment.

Key words: ecological space efficiency, landscape pattern, influence mechanisms, multi-scale geographically and temporally weighted regression, optimal parameter-based geographical detector

摘要：

探讨生态用地景观格局对生态空间效率的影响，对优化生态空间格局具有重要的指导意义。运用超效率SBM模型、最优参数地理探测器和多尺度时空地理加权回归等方法，分析2000－2020年贵州省生态空间效率演变特征，探究景观格局对生态空间效率的影响机理。结果表明，1）生态空间综合效率受纯技术和规模效率的共同影响，规模效率降低是导致综合效率下降及其等级结构退化的主要原因。2）20年来，三类效率的空间聚集特征不断增强，综合及纯技术效率保持着西南至东北向南一侧效率较高，靠北一侧效率较低的空间分布格局，规模效率演变为贵州中部和东部效率较高、西部和南部效率较低的空间分布格局。3）景观格局指数主要是通过影响规模效率进而作用于综合效率，各景观格局指数在空间上对规模效率的影响存在显著差异且具有不同程度的时空异质性，河流水系的保护配合形态规整的多团块、优连通的生态用地布局模式有利于规模效率的提升。4）规模效率并非受各景观格局指数的独立影响，而是两两交互作用下产生双因子增强和非线性增强的综合结果，作为主导影响因子的LSI与其他指数的交互协同作用是造成规模效率出现空间分异的主要力量。对生态空间优化时，应根据不同单元效率的高低差别、分布格局及各指标的影响差异精准施策，促进生态环境提质增效。

关键词: 生态空间效率, 景观格局, 影响机理, 多尺度时空地理加权回归, 最优参数地理探测器

CLC Number:

YANG Haoyu, HUANG Kangjiang, CHEN Xiaodong, ZHAO Jie, XIONG Jun, TIAN Kang. Evolution of Ecological Spatial Efficiency Pattern and Attribution Analysis of Landscape Pattern in Guizhou Province[J]. Ecology and Environmental Sciences, 2025, 34(6): 902-913.

杨昊彧, 黄康江, 陈晓东, 赵劼, 熊军, 田康. 贵州省生态空间效率演变及景观格局的影响归因[J]. 生态环境学报, 2025, 34(6): 902-913.

Figures/Tables 13

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类型	名称	单位	说明
面积占比	规模占比 PLAND	%	生态空间面积占行政区面积及不同类型用地面积占生态空间面积的比例
面积占比	最大斑块指数 LPI	%	表示最大斑块占生态空间总面积的百分比，值越大说明整体性越好
形状复杂	景观形状指数 LSI	－	表示生态空间整体形状的复杂程度，值越大形状越复杂
形状复杂	面积加权形状指数AWMSI	－	从面积加权的角度考察各斑块对整体形状的综合影响，值越大越复杂
多样均衡	香农均衡度指数SHEI	－	取值0－1，值越大表明各类生态用地越均衡
聚集连通	内聚力指数COHESION	%	表示斑块的自然连通程度，值越大表示斑块连接性越强
	平均最近距离MNN	m	表示斑块的集中程度，值越小说明聚集趋势越强
	边缘密度 ED	m∙hm⁻²	表示生态空间的分割化程度，值越大表明分割程度越高
	聚集度指数 AI	%	表示斑块在空间上的聚集程度，值越大聚集越紧密

类型	名称	单位	说明
面积占比	规模占比 PLAND	%	生态空间面积占行政区面积及不同类型用地面积占生态空间面积的比例
面积占比	最大斑块指数 LPI	%	表示最大斑块占生态空间总面积的百分比，值越大说明整体性越好
形状复杂	景观形状指数 LSI	－	表示生态空间整体形状的复杂程度，值越大形状越复杂
形状复杂	面积加权形状指数AWMSI	－	从面积加权的角度考察各斑块对整体形状的综合影响，值越大越复杂
多样均衡	香农均衡度指数SHEI	－	取值0－1，值越大表明各类生态用地越均衡
聚集连通	内聚力指数COHESION	%	表示斑块的自然连通程度，值越大表示斑块连接性越强
	平均最近距离MNN	m	表示斑块的集中程度，值越小说明聚集趋势越强
	边缘密度 ED	m∙hm⁻²	表示生态空间的分割化程度，值越大表明分割程度越高
	聚集度指数 AI	%	表示斑块在空间上的聚集程度，值越大聚集越紧密

类型	指标	说明
投入	生态空间用地面积	包含林地、草地、水域、湿地和荒漠五类；单位：km²
投入	能源消耗量	单位：万吨标准煤
产出	平均植被净初生产力	反映生态系统的固碳能力；单位：kg∙m⁻²∙a⁻¹
	生态环境质量指数均值	表征区域环境污染及环境质量状况
	生态系统服务价值	从经济视角核算生态系统为社会提供的产品及服务价值，通过单位面积价值当量因子法分类计算并求和（谢高地等，2015）；单位：亿元

类型	指标	说明
投入	生态空间用地面积	包含林地、草地、水域、湿地和荒漠五类；单位：km²
投入	能源消耗量	单位：万吨标准煤
产出	平均植被净初生产力	反映生态系统的固碳能力；单位：kg∙m⁻²∙a⁻¹
	生态环境质量指数均值	表征区域环境污染及环境质量状况
	生态系统服务价值	从经济视角核算生态系统为社会提供的产品及服务价值，通过单位面积价值当量因子法分类计算并求和（谢高地等，2015）；单位：亿元

交互类型	依据
独立	q(X₁∩X₂)=q(X₁)+q(X₂)
非线性减弱	q(X₁∩X₂)<Min[q(X₁), q(X₂)]
单因子非线性减弱	Min[q(X₁), q(X₂)]<q(X₁∩X₂)<Max[q(X₁), q(X₂)]
非线性增强	q(X₁∩X₂)>q(X₁)+q(X₂)
双因子增强	q(X₁∩X₂)>Max[q(X₁), q(X₂)]

Evolution of Ecological Spatial Efficiency Pattern and Attribution Analysis of Landscape Pattern in Guizhou Province

贵州省生态空间效率演变及景观格局的影响归因

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指数	2000年	2005年	2010年	2015年	2020年
生态PLAND /%	70.370	70.261	70.271	70.271	69.707
林地PLAND/%	74.194	75.373	75.797	75.720	74.101
草地PLAND/%	25.370	24.196	23.567	23.569	24.770
水域PLAND/%	0.400	0.403	0.610	0.682	1.102
湿地PLAND/%	0.005	0.005	0.005	0.006	0.005
荒漠PLAND/%	0.031	0.023	0.021	0.023	0.021
LPI/%	87.308	86.654	86.059	86.445	86.491
LSI	44.228	44.171	43.568	42.787	44.172
AWMSI	34.483	34.284	33.632	33.029	34.195
SHEI	0.369	0.361	0.363	0.4365	0.385
COHESION/%	99.958	99.957	99.955	99.956	99.950
MNN/m	131.199	128.663	130.454	131.992	128.514
ED/(m∙hm⁻²)	49.725	50.249	49.650	49.145	50.521
AI/%	96.363	96.325	96.368	96.406	96.303

变量及检验指标	GTWR		MGTWR
变量及检验指标	时间带宽/期	空间带宽个数	时间带宽/期	空间带宽个数
常数项	2.805	62.377	0.315	4.283
生态PLAND	2.805	62.377	0.400	5.233
水域PLAND	2.805	62.377	3.886	41.601
LPI	2.805	62.377	1.172	15.952
LSI	2.805	62.377	1.891	25.749
SHEI	2.805	62.377	0.727	9.897
COHESION	2.805	62.377	0.400	5.233
ED	2.805	62.377	0.727	9.897
R_adj²	0.804		0.915
RSS	80.991		26.902
AICc	559.151		354.841

类型	自变量	p值	q值	排序	分类数	离散方法
面积占比	生态PLAND	0.001	0.156^{** 1)}	4	9	等间隔分类
	水域PLAND	0.000	0.072^**	6	8	分位数分类
	LPI	0.000	0.177^**	3	10	自然断点分类
形状复杂度	LSI	0.000	0.393^**	1	10	等间隔分类
多样均衡性	SHEI	0.000	0.121^**	5	9	几何间隔分类
聚集连通性	COHESION	0.005	0.208^**	2	7	自然断点分类
聚集连通性	ED	0.000	0.069^**	7	9	分位数分类

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