Spatial Heterogeneity and Driving Factors of Ecosystem Service Trade-offs and Synergies in the Changsha-Zhuzhou-Xiangtan Urban Agglomeration

doi:10.16258/j.cnki.1674-5906.2024.06.014

Ecology and Environment ›› 2024, Vol. 33 ›› Issue (6): 969-979.DOI: 10.16258/j.cnki.1674-5906.2024.06.014

• Research Article [Environmental Sciences] • Previous Articles Next Articles

Spatial Heterogeneity and Driving Factors of Ecosystem Service Trade-offs and Synergies in the Changsha-Zhuzhou-Xiangtan Urban Agglomeration

WANG Luying¹(), LI Xiaoma¹^,^*(), GAN Dexin¹, LIU Pengao²^,³^,⁴, GUO Sheng²^,³^,⁴, LI Yi⁵

1. Hunan Provincial Key Laboratory of Landscape Ecology and Planning & Design in Regular Higher Educational Institutions/College of Landscape Architecture and Art Design, Hunan Agricultural University, Changsha 410128, P. R. China
2. Hunan Land Resources Planning Institute, Changsha 410007, P. R. China
3. Hunan Provincial Key Laboratory of Land Resources Evaluation and Utilization, Changsha 410007, P. R. China
4. Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region, Ministry of Natural Resources, Changsha 410007, P. R. China
5. New Rural Development Research Institute of Hunan Agricultural University/Business School of Hunan Agricultural University, Changsha 410128, P. R. China

Received:2024-02-28 Online:2024-06-18 Published:2024-07-30
Contact: LI Xiaoma

长株潭城市群生态系统服务权衡与协同关系的空间异质性及其驱动因素

王鹭莹¹(), 李小马¹^,^*(), 甘德欣¹, 刘鹏翱²^,³^,⁴, 郭胜²^,³^,⁴, 李毅⁵

1.园林生态与规划设计湖南省普通高等学校重点实验室/湖南农业大学风景园林与艺术设计学院，湖南长沙 410128
2.湖南省国土资源规划院，湖南长沙 410007
3.国土资源评价与利用湖南省重点实验室，湖南长沙 410007
4.自然资源部南方丘陵区自然资源监测监管重点实验室，湖南长沙 410007
5.湖南农业大学新农村发展研究院/湖南农业大学商学院，湖南长沙 410128

通讯作者: 李小马
作者简介:王鹭莹（1999年生），女，硕士研究生，主要研究方向为生态系统服务与权衡。E-mail: wangluying1999@163.com
基金资助:
湖南省自然资源厅项目([2024]000416-4);湖南省自然科学基金项目(2021JJ30368);湖南省研究生科研创新项目(QL20220169)

Abstract

Abstract:

Different types of ESs have intricate trade-offs or synergies, and these relationships exhibit strong spatial heterogeneity. Understanding spatial heterogeneity and its driving factors is important for sustainable ecosystem planning and management. Using the Changsha-Zhuzhou-Xiantan Urban Agglomeration (CZTUA) as the study area, we first quantified six important ESs (water conservation (WC), soil conservation (SC), carbon sequestration (CS), habitat quality (HQ), food production (FP), and landscape aesthetics (LA)) in 2020 using the InVEST model and the Intelligent Urban Ecosystem Management System (IUEMS). Then, the correlation coefficients between these ESs were estimated for each 1 km grid to investigate the trade-offs or synergies and their spatial patterns. Finally, logistic regression analysis was used to identify the factors influencing trade-offs or synergies between ESs. The results showed that 1) the spatial patterns of SC, WC, CS, HQ, and LA were high in the southeast and low in the northwest, whereas FP was high in the northwest and low in the southeast in 2020. 2) The trade-offs or synergies between ESs are spatially heterogeneous in the CZTUA. More than 60% of the study area showed synergistic relationships between SC and CS, SC and WC, SC and HQ, SC, and LA, which were mainly distributed in northeastern and southern mountainous areas and western farmlands. Trade-offs between ESs were mainly found in built-up areas and their peripheries. Trade-offs between FP and CS, FP and WC, FP-HQ and FP, and LA covered over 65% of the study area, mainly in the northern plain and southern mountainous areas, and were primarily found in urban and surrounding non-forested areas. 3) The spatial heterogeneity of trade-offs and synergies between ESs is influenced by natural and socioeconomic factors as well as management policies. Synergies between ESs are hindered by steep areas. Synergies between regulating, supporting, and cultural services were promoted by increases in temperature, precipitation, and NDVI. However, synergies between FP and other ESs showed opposite responses to temperature, precipitation, and NDVI increase. Socioeconomic development leads to few synergies or trade-offs between ESs. Ecological protection measures promote synergy between the CS and LA, SC and HQ, SC and LA, SC and CS, and SC and WC.

Key words: ecosystem services, trade-offs and synergies, logistic regression, ecological protection policy, urban agglomeration

摘要：

生态系统服务间存在复杂的权衡或协同关系并表现出显著空间异质性，明确生态系统服务权衡与协同关系的空间分异特征并阐明其驱动因素，对生态系统可持续规划与管理具有重要意义。利用InVEST模型和城市生态智慧管理系统（IUEMS）量化2020年长株潭城市群6项关键生态系统服务物质量（水源涵养 (WC)、土壤保持 (SC)、碳固存 (CS)、生境质量 (HQ)、粮食供给 (FP) 和景观美学 (LA)），并运用相关系数法揭示1 km格网尺度的生态系统服务权衡与协同关系及其空间分异特征，最后通过逻辑斯蒂回归分析阐明其驱动因素。结果显示，1）2020年长株潭城市群SC、WC、CS、HQ和LA均呈现东南高西北低的空间分布特征，FP呈现西北高东南低的分布特征。2）长株潭城市群生态系统服务间权衡与协同关系空间差异明显。SC与CS、WC、HQ、LA间协同关系面积占比超过60%，协同关系主要分布在研究区东北部和南部的山区以及西部农田地区，而权衡关系主要位于建成区及其周边地区。FP与CS、WC、HQ、LA间权衡关系面积占比超过65%，权衡关系主要位于北部的平原区域和南部的山区，而协同关系主要位于城市区域及其周边非林地区域。3）自然、社会经济和政策管理因素共同影响生态系统服务间权衡与协同关系的空间异质性。陡坡会抑制生态系统服务之间的协同；温度、降水、NDVI的增加能够促进调节服务、支持服务和文化服务间的协同，但抑制FP与其他生态系统服务间的协同；社会经济发展会促使生态系统服务间趋向低-低协同或权衡关系；绿心保护政策促进SC与其他生态系统服务，以及CS与LA之间的协同。

关键词: 生态系统服务, 权衡与协同, 逻辑斯蒂回归, 生态保护政策, 城市群

CLC Number:

X171.1
X21

WANG Luying, LI Xiaoma, GAN Dexin, LIU Pengao, GUO Sheng, LI Yi. Spatial Heterogeneity and Driving Factors of Ecosystem Service Trade-offs and Synergies in the Changsha-Zhuzhou-Xiangtan Urban Agglomeration[J]. Ecology and Environment, 2024, 33(6): 969-979.

王鹭莹, 李小马, 甘德欣, 刘鹏翱, 郭胜, 李毅. 长株潭城市群生态系统服务权衡与协同关系的空间异质性及其驱动因素[J]. 生态环境学报, 2024, 33(6): 969-979.

Figures/Tables 9

Figure 1 Overview of the studied area

Table 1 Data description

数据类型	数据用途	数据格式	数据来源及处理方法	空间分辨率
土地利用数据	CS, SC, WC, HQ, LA, FP, PD	栅格数据	GLC_FCS30-2020数据集 (Zhang et al., 2021a) (https://essd.copernicus.org/)。通过ArcGIS10.8重分类为农田、林地、草地、水体、建设用地、灌木、湿地7类	30 m
土壤数据	SC	栅格数据	世界土壤数据库 (https://www.fao.org/), 通过ArcGIS 10.8提取土壤砂粒、粘粒、粉粒、有机质含量栅格图层	30 m
降水数据	SC, WC, PD	文本	来源于中国气象局气象数据中心 (http://data.cma.cn) 的 “中国地面气候资料日值数据集 (V3.0)”, 并利用IUEMS平台进行插值处理为30 m×30 m的栅格数据	‒
归一化植被指数（NDVI）	SC, PD	栅格数据	NASA地球数据中心 (https://ladsweb.modaps.eosdis.nasa.gov) 的MODIS产品MOD13A2, 获取月NDVI数据, 并通过ArcGIS 10.8栅格计算器计算年平均NDVI	1 km
DEM数据	SC, PD	栅格数据	美国地质勘探局公开数据集 (https://lpdaac.usgs.gov) 的MODIS产品MODIS SRTMGL1 v003	30 m
蒸散量数据	WC	栅格数据	美国国家航空航天局公开数据集 (https://disc.gsfc.nasa.gov/)	1 km
温度数据	PD	栅格数据	国家地球系统科学数据中心 (http://www. geodata.cn)	1 km
人口密度数据	PD	栅格数据	LandScan人口数据集 (Rose et al., 2021) (https://landscan.ornl.gov/)	1 km
GDP数据	PD	栅格数据	GDP预测数据集 (Zhao et al., 2017) (https://github.com/thestarlab/ChinaGDP)	1 km
道路数据	HQ	矢量图层	开放街道地图 (https://www.openstreetmap.org/), 提取铁路、高速公路、一级道路数据, 并利用ArcGIS 10.8转化为30 m×30 m的道路威胁源栅格图层	‒
粮食供给	FP	文本	湖南省统计局 (http://tjj.hunan.gov.cn) 湖南统计年鉴2021	‒
森林公园名单	LA	文本	湖南省林业局 (https://lyj.hunan.gov.cn/lyj) 湖南省森林公园名录	‒
森林公园经纬度	LA	矢量图层	高德地图API, 利用Python爬取森林公园位置	‒
景观保护区域	PD	矢量图层	湖南省自然资源厅 (https://zrzyt.hunan.gov.cn) 长株潭城市群生态绿心地区总体规划(2010‒2030)	‒

Table 1 Data description

数据类型	数据用途	数据格式	数据来源及处理方法	空间分辨率
土地利用数据	CS, SC, WC, HQ, LA, FP, PD	栅格数据	GLC_FCS30-2020数据集 (Zhang et al., 2021a) (https://essd.copernicus.org/)。通过ArcGIS10.8重分类为农田、林地、草地、水体、建设用地、灌木、湿地7类	30 m
土壤数据	SC	栅格数据	世界土壤数据库 (https://www.fao.org/), 通过ArcGIS 10.8提取土壤砂粒、粘粒、粉粒、有机质含量栅格图层	30 m
降水数据	SC, WC, PD	文本	来源于中国气象局气象数据中心 (http://data.cma.cn) 的 “中国地面气候资料日值数据集 (V3.0)”, 并利用IUEMS平台进行插值处理为30 m×30 m的栅格数据	‒
归一化植被指数（NDVI）	SC, PD	栅格数据	NASA地球数据中心 (https://ladsweb.modaps.eosdis.nasa.gov) 的MODIS产品MOD13A2, 获取月NDVI数据, 并通过ArcGIS 10.8栅格计算器计算年平均NDVI	1 km
DEM数据	SC, PD	栅格数据	美国地质勘探局公开数据集 (https://lpdaac.usgs.gov) 的MODIS产品MODIS SRTMGL1 v003	30 m
蒸散量数据	WC	栅格数据	美国国家航空航天局公开数据集 (https://disc.gsfc.nasa.gov/)	1 km
温度数据	PD	栅格数据	国家地球系统科学数据中心 (http://www. geodata.cn)	1 km
人口密度数据	PD	栅格数据	LandScan人口数据集 (Rose et al., 2021) (https://landscan.ornl.gov/)	1 km
GDP数据	PD	栅格数据	GDP预测数据集 (Zhao et al., 2017) (https://github.com/thestarlab/ChinaGDP)	1 km
道路数据	HQ	矢量图层	开放街道地图 (https://www.openstreetmap.org/), 提取铁路、高速公路、一级道路数据, 并利用ArcGIS 10.8转化为30 m×30 m的道路威胁源栅格图层	‒
粮食供给	FP	文本	湖南省统计局 (http://tjj.hunan.gov.cn) 湖南统计年鉴2021	‒
森林公园名单	LA	文本	湖南省林业局 (https://lyj.hunan.gov.cn/lyj) 湖南省森林公园名录	‒
森林公园经纬度	LA	矢量图层	高德地图API, 利用Python爬取森林公园位置	‒
景观保护区域	PD	矢量图层	湖南省自然资源厅 (https://zrzyt.hunan.gov.cn) 长株潭城市群生态绿心地区总体规划(2010‒2030)	‒

Table 2 Methods for quantifying ecosystem services

类别	生态系统服务类型	计算方法	计算公式
调节服务	土壤保持 (SC)	IUEMS模型减少泥沙淤积模块 (韩宝龙等, 2021)	该模块采用修正后的通用水土流失方程 (RUSLE) 来计算土壤保持量, 计算公式如下: Q_sc=R×K×L×S×(1−C×P) 式中: Q_sc——土壤保持量; R——降雨侵蚀力因子, 基于月降水量计算得到; K——土壤可蚀性因子, 通过土壤砂粒、粘粒、粉粒、有机质含量计算得到 (Renard et al., 1997); L——坡长因子; S——坡度因子, 通过DEM数据计算得到; C——植被覆盖因子, 通过NDVI数据换算得到; P——水土保持措施因子。水土保持措施因子参考《生态产品总值核算规范》(国家发展和改革委员会等, 2022), 其中耕地、林地、草地和灌丛的水土保持因子为1, 水体、湿地和建设用地的水土保持因子为0
	水源涵养 (WC)	IUEMS模型水源涵养模块 (韩宝龙等, 2021)	本研究参考Ouyang et al. (2016) 在中国生态系统评估框架中所使用的水源涵养模型, 基于降水量、暴雨径流量、蒸散量和土地利用类型来计算水源涵养量。计算公式如下: ${{Q}_{wc}}\mathrm{=}\sum\nolimits_{i=1}^{n}{{{A}_{i}}}\times ({{P}_{i}}-{{R}_{i}}-{{E}_{i}})\times {{10}^{-3}}$ 式中: Q_wc ——为涵养水源量; P_i——降雨量; R_i ——地表径流量; E_i ——为蒸散发量; A_i——i类土地利用类型。R_i ——降水量和径流系数的乘积，其中径流系数参考Ouyang et al. (2016)和《生态产品总值核算规范》(国家发展和改革委员会等, 2022), 耕地、林地、草地、水体、建设用地、灌丛和湿地的径流系数分别为24.78%、2.00%、9.37%、0.00%、100%、4.20%和0.00%
	碳固存 (CS)	IUEMS模型固碳释氧模块 (韩宝龙等, 2021)	该模块将植被吸收二氧化碳的量作为碳固存的指标, 可通过NPP估算, 计算公式如下: Q_t_,CO2=M_CO2/M_C×C_NEP C_NEP=aC_NPPM_C6/M_C6H10O5 式中: Q_t_,CO2——生态系统二氧化碳固定量; M_CO2/M_C——C转化为CO₂的系数, 44/12; C_NEP——净生态系统生产力; α——NEP和NPP的转换系数; C_NPP——净初级生产力; M_C6/M_C6H10O5——干物质转化为C的系数, 72/162。NEP和NPP的转换系数参考《生态产品总值核算规范》(国家发展和改革委员会等，2022), 其中耕地、林地、草地、水体、建设用地、灌丛和湿地的NPP与NEP转换系数分别为0.0700、0.0970、0.084、0.0000、0.0000、0.0700和0.0840
支持服务	生境质量 (HQ)	InVEST模型生境质量模块 (Sharp et al., 2014)	该模块通过计算威胁因子对生境的负面影响来获得生境退化程度, 并根据生境退化程度和生境适宜性计算生境质量 (Sharp et al., 2014), 计算公式如下: ${{Q}_{xj}}\mathrm{=}{{H}_{j}}\times \left( 1-\frac{D_{xj}^{z}}{D_{xj}^{z}+{{k}^{z}}} \right)$ 式中: Q_xj ——土地利用类型j中栅格x的生境质量; H_j ——土地利用类型j的生境适宜度 (表4), 值域为0‒1; k——半饱和常数, 设定为0.05; z——归一化常量, 设定为2.5; D_xj ——土地利用类型j中栅格x的生境胁迫水平 ${{D}_{xj}}\mathrm{=}\sum\nolimits_{r=1}^{R}{\sum\nolimits_{y=1}^{{{Y}_{r}}}{\left( \frac{{{w}_{r}}}{\sum\nolimits_{r=1}^{R}{{{w}_{r}}}} \right){{r}_{y}}{{i}_{rxy}}}}{{\beta }_{x}}{{S}_{jr}}$ ${{i}_{rxy}}\mathrm{=1}-\left( \frac{{{d}_{xy}}}{{{d}_{r,\max }}} \right)$(线性衰减) ${{i}_{rxy}}\mathrm{=exp}\left( -\left( \frac{2.99}{{{d}_{r,\max }}} \right){{d}_{xy}} \right)$(指数型衰减) R——威胁因子; Y_r——威胁因子r的栅格个数; w_r——威胁因子权重 (表3); r_y——栅格y的威胁因子值; i_rxy——栅格y的威胁因子值r_y对于栅格x的胁迫程度; β_x——栅格x的可达性, 设定为1; S_jr——土地利用类型j对于威胁因子r的敏感度 (表4); d_xy——生境栅格x与威胁因子栅格y的距离; d_r_,_max——威胁因子r的最大威胁距离 (表3)
供给服务	粮食供给 (FP)	基于农作物产量与NDVI线性关系的评估 (Groten et al., 1993)	将各区县粮食产量根据NDVI占比分配至农田栅格 (Peng et al., 2019; Yang et al., 2021), 计算公式如下: ${{F}_{ij}}\mathrm{=}\frac{{{N}_{ij}}}{{{N}_{j}}}\times {{F}_{j}}$ 式中: F_ij ——j县的栅格 $i$ 的粮食供给; F_j ——j县粮食总产量，通过地区统计年鉴获取区县尺度上的粮食产量; N_ij ——j县的栅格i的NDVI; N_j ——是第j县农田的NDVI之和
文化服务	景观美学 (LA)	基于景观指标的评估 (Xia et al., 2023)	基于景观度的方法来评估景观美学服务 (Frank et al., 2013; Xia et al., 2023), 计算公式如下: ${{Q}_{\mathrm{L}{{\mathrm{A}}_{i}}}}\mathrm{=}\frac{1}{3}\times ({{Q}_{\mathrm{N}{{\mathrm{T}}_{i}}}}+{{Q}_{\mathrm{N}{{\mathrm{S}}_{i}}}}+{{Q}_{\mathrm{L}{{\mathrm{D}}_{i}}}})$ 式中: Q_LA_i——栅格i的景观美学量; Q_NT_i——栅格i的自然度，利用hemeroby指数来表征 (Xia et al., 2023), 耕地、林地、草地、水体、建设用地、灌丛和湿地的自然度标准化后的值分别为33.33、100、66.67、83.33、0、0、83.33; Q_NS_i——为栅格i的自然景观可达性，通过到森林公园的欧氏距离来反映，距离越近景观可达性越高; Q_LD_i——栅格i的基于土地利用类型的香农多样性指数。3个值均标准化到1‒100范围

Table 2 Methods for quantifying ecosystem services

类别	生态系统服务类型	计算方法	计算公式
调节服务	土壤保持 (SC)	IUEMS模型减少泥沙淤积模块 (韩宝龙等, 2021)	该模块采用修正后的通用水土流失方程 (RUSLE) 来计算土壤保持量, 计算公式如下: Q_sc=R×K×L×S×(1−C×P) 式中: Q_sc——土壤保持量; R——降雨侵蚀力因子, 基于月降水量计算得到; K——土壤可蚀性因子, 通过土壤砂粒、粘粒、粉粒、有机质含量计算得到 (Renard et al., 1997); L——坡长因子; S——坡度因子, 通过DEM数据计算得到; C——植被覆盖因子, 通过NDVI数据换算得到; P——水土保持措施因子。水土保持措施因子参考《生态产品总值核算规范》(国家发展和改革委员会等, 2022), 其中耕地、林地、草地和灌丛的水土保持因子为1, 水体、湿地和建设用地的水土保持因子为0
	水源涵养 (WC)	IUEMS模型水源涵养模块 (韩宝龙等, 2021)	本研究参考Ouyang et al. (2016) 在中国生态系统评估框架中所使用的水源涵养模型, 基于降水量、暴雨径流量、蒸散量和土地利用类型来计算水源涵养量。计算公式如下: ${{Q}_{wc}}\mathrm{=}\sum\nolimits_{i=1}^{n}{{{A}_{i}}}\times ({{P}_{i}}-{{R}_{i}}-{{E}_{i}})\times {{10}^{-3}}$ 式中: Q_wc ——为涵养水源量; P_i——降雨量; R_i ——地表径流量; E_i ——为蒸散发量; A_i——i类土地利用类型。R_i ——降水量和径流系数的乘积，其中径流系数参考Ouyang et al. (2016)和《生态产品总值核算规范》(国家发展和改革委员会等, 2022), 耕地、林地、草地、水体、建设用地、灌丛和湿地的径流系数分别为24.78%、2.00%、9.37%、0.00%、100%、4.20%和0.00%
	碳固存 (CS)	IUEMS模型固碳释氧模块 (韩宝龙等, 2021)	该模块将植被吸收二氧化碳的量作为碳固存的指标, 可通过NPP估算, 计算公式如下: Q_t_,CO2=M_CO2/M_C×C_NEP C_NEP=aC_NPPM_C6/M_C6H10O5 式中: Q_t_,CO2——生态系统二氧化碳固定量; M_CO2/M_C——C转化为CO₂的系数, 44/12; C_NEP——净生态系统生产力; α——NEP和NPP的转换系数; C_NPP——净初级生产力; M_C6/M_C6H10O5——干物质转化为C的系数, 72/162。NEP和NPP的转换系数参考《生态产品总值核算规范》(国家发展和改革委员会等，2022), 其中耕地、林地、草地、水体、建设用地、灌丛和湿地的NPP与NEP转换系数分别为0.0700、0.0970、0.084、0.0000、0.0000、0.0700和0.0840
支持服务	生境质量 (HQ)	InVEST模型生境质量模块 (Sharp et al., 2014)	该模块通过计算威胁因子对生境的负面影响来获得生境退化程度, 并根据生境退化程度和生境适宜性计算生境质量 (Sharp et al., 2014), 计算公式如下: ${{Q}_{xj}}\mathrm{=}{{H}_{j}}\times \left( 1-\frac{D_{xj}^{z}}{D_{xj}^{z}+{{k}^{z}}} \right)$ 式中: Q_xj ——土地利用类型j中栅格x的生境质量; H_j ——土地利用类型j的生境适宜度 (表4), 值域为0‒1; k——半饱和常数, 设定为0.05; z——归一化常量, 设定为2.5; D_xj ——土地利用类型j中栅格x的生境胁迫水平 ${{D}_{xj}}\mathrm{=}\sum\nolimits_{r=1}^{R}{\sum\nolimits_{y=1}^{{{Y}_{r}}}{\left( \frac{{{w}_{r}}}{\sum\nolimits_{r=1}^{R}{{{w}_{r}}}} \right){{r}_{y}}{{i}_{rxy}}}}{{\beta }_{x}}{{S}_{jr}}$ ${{i}_{rxy}}\mathrm{=1}-\left( \frac{{{d}_{xy}}}{{{d}_{r,\max }}} \right)$(线性衰减) ${{i}_{rxy}}\mathrm{=exp}\left( -\left( \frac{2.99}{{{d}_{r,\max }}} \right){{d}_{xy}} \right)$(指数型衰减) R——威胁因子; Y_r——威胁因子r的栅格个数; w_r——威胁因子权重 (表3); r_y——栅格y的威胁因子值; i_rxy——栅格y的威胁因子值r_y对于栅格x的胁迫程度; β_x——栅格x的可达性, 设定为1; S_jr——土地利用类型j对于威胁因子r的敏感度 (表4); d_xy——生境栅格x与威胁因子栅格y的距离; d_r_,_max——威胁因子r的最大威胁距离 (表3)
供给服务	粮食供给 (FP)	基于农作物产量与NDVI线性关系的评估 (Groten et al., 1993)	将各区县粮食产量根据NDVI占比分配至农田栅格 (Peng et al., 2019; Yang et al., 2021), 计算公式如下: ${{F}_{ij}}\mathrm{=}\frac{{{N}_{ij}}}{{{N}_{j}}}\times {{F}_{j}}$ 式中: F_ij ——j县的栅格 $i$ 的粮食供给; F_j ——j县粮食总产量，通过地区统计年鉴获取区县尺度上的粮食产量; N_ij ——j县的栅格i的NDVI; N_j ——是第j县农田的NDVI之和
文化服务	景观美学 (LA)	基于景观指标的评估 (Xia et al., 2023)	基于景观度的方法来评估景观美学服务 (Frank et al., 2013; Xia et al., 2023), 计算公式如下: ${{Q}_{\mathrm{L}{{\mathrm{A}}_{i}}}}\mathrm{=}\frac{1}{3}\times ({{Q}_{\mathrm{N}{{\mathrm{T}}_{i}}}}+{{Q}_{\mathrm{N}{{\mathrm{S}}_{i}}}}+{{Q}_{\mathrm{L}{{\mathrm{D}}_{i}}}})$ 式中: Q_LA_i——栅格i的景观美学量; Q_NT_i——栅格i的自然度，利用hemeroby指数来表征 (Xia et al., 2023), 耕地、林地、草地、水体、建设用地、灌丛和湿地的自然度标准化后的值分别为33.33、100、66.67、83.33、0、0、83.33; Q_NS_i——为栅格i的自然景观可达性，通过到森林公园的欧氏距离来反映，距离越近景观可达性越高; Q_LD_i——栅格i的基于土地利用类型的香农多样性指数。3个值均标准化到1‒100范围

Table 3 Influence distance and weight of threat factors of habitat quality in Changsha-Zhuzhou-Xiangtan urban agglomeration

威胁因子	最大影响距离/km	权重	衰减类型
耕地	8	0.7	指数型
建设用地	12	1	指数型
铁路	10	1	线性
一级公路	5	0.3	线性

Table 4 Habitat suitability and sensitivity of various land use types to threat factors in Changsha-Zhuzhou-Xiangtan urban agglomeration

序号	土地利用类型	生境适宜度	敏感度
序号	土地利用类型	生境适宜度	耕地	建设用地	铁路	一级公路
1	耕地	0.4	0.3	0.5	0.1	0.15
2	林地	1	0.8	0.9	0.55	0.65
3	草地	0.8	0.45	0.8	0.25	0.3
4	水体	1	0.7	0.7	0.9	0.4
5	建设用地	0	0	0	0	0
6	灌丛	0.9	0.4	0.4	0.2	0.25
7	湿地	1	0.85	0.9	0.35	0.65

Figure 2 Spatial distribution of ecosystem services in the Changsha-Zhuzhou-Xiangtan urban agglomeration

Figure 3 Area proportion of ecosystem services trade off and synergies in the Changsha-Zhuzhou-Xiangtan urban agglomeration

Figure 4 Spatial distribution of ecosystem services trade off and synergies in Changsha-Zhuzhou-Xiangtan urban agglomeration

Table 5 Driving factors of ecosystem services trade off and synergies

生态系统服务	指标	驱动因素
生态系统服务	指标	SLO	PRE	TEM	NDVI	GDP	POP	CLP	EPP
SC-HQ	回归系数	−0.120* ³⁾	−0.123***	0.616***	0.931***	−0.00900	0.119***	−0.678***	0.366*
SC-HQ	Wald统计值	4.820	13.761	352.932	262.312	0.227	21.530	350.358	5.317
SC-CS	回归系数	−0.481***¹⁾	−0.106***	0.329***	0.609***	−0.0340	0.107***	−0.816***	0.342**
SC-CS	Wald统计值	147.872	20.750	162.913	171.843	2.193	15.247	498.439	6.951
SC-WC	回归系数	−0.0890	−0.183***	0.578***	0.974***	0.00800	0.0890***	−0.632***	0.400**
SC-WC	Wald统计值	3.257	38.410	362.366	321.619	0.141	11.358	311.639	6.831
SC-LA	回归系数	−0.795***	−0.117***	0.409***	0.640***	0.020	0.0990***	−0.742***	0.363*
SC-LA	Wald统计值	320.194	19.301	220.192	145.947	1.105	15.162	460.464	5.161
SC-FP	回归系数	−0.389***	0.0260	−0.356***	−0.267*	−0.0330	0.0750*	0.126*	0.00500
SC-FP	Wald统计值	14.338	0.183	38.129	6.617	1.021	4.479	4.756	0.000
CS-HQ	回归系数	−0.969***	−0.0600	0.476***	1.640***	0.0390	−0.187	2.535***	13.707
CS-HQ	Wald统计值	118.281	1.186	101.138	192.042	0.0370	2.277	60.108	0.00300
CS-WC	回归系数	−0.782***	−0.0210	0.249***	0.615***	1.880	0.225	8.637***	−0.428
CS-WC	Wald统计值	101.332	0.250	44.074	24.875	0.977	0.621	48.321	0.676
CS-LA	回归系数	−0.932***	−0.0650	0.314***	0.868***	0.011	−0.176**	1.076***	1.260*
CS-LA	Wald统计值	212.495	3.091	86.596	94.205	0.023	7.681	53.695	4.670
CS-FP	回归系数	−1.872***	−0.0330	−0.229**	−1.349***	−0.194***	−0.0600	3.564***	0.0150
CS-FP	Wald统计值	359.116	0.707	10.539	277.810	44.096	1.685	1154.520	0.00800
FP-HQ	回归系数	−7.159***	−0.357***	−0.539***	−0.461***	−0.0980***	−0.161***	3.188***	−0.291
FP-HQ	Wald统计值	972.797	38.800	24.332	23.413	10.998	11.863	1085.881	1.746
FP-WC	回归系数	−6.105***	−0.290***	−0.393***	−1.117***	−0.432***	−0.153*	7.033***	−0.172
FP-WC	Wald统计值	907.020	29.529	15.469	101.194	110.532	4.950	1428.355	0.605
FP-LA	回归系数	−5.007***	−0.169** ²⁾	−0.733***	−0.544***	−0.0750**	−0.060	2.379***	−0.301
FP-LA	Wald统计值	703.614	9.942	52.164	40.258	6.953	1.763	959.568	2.169
LA-HQ	回归系数	−1.445***	0.360***	0.544***	−0.636*	−0.132	19.371***	−2.262***	10.448
LA-HQ	Wald统计值	126.688	25.227	111.804	5.391	0.0120	20.529	30.932	0.001
LA-WC	回归系数	−1.350***	0.225***	0.373***	−1.530***	0.158	−0.419***	−0.563	11.029
LA-WC	Wald统计值	158.104	15.756	70.232	47.915	2.435	11.075	2.231	0.00200
HQ-WC	回归系数	−1.029***	0.100	0.437***	−0.947	0.224	7.635	−2.058***	11.203
	Wald统计值	19.219	0.513	20.975	2.675	0.00500	2.420	17.301	0.000

Table 5 Driving factors of ecosystem services trade off and synergies

生态系统服务	指标	驱动因素
生态系统服务	指标	SLO	PRE	TEM	NDVI	GDP	POP	CLP	EPP
SC-HQ	回归系数	−0.120* ³⁾	−0.123***	0.616***	0.931***	−0.00900	0.119***	−0.678***	0.366*
SC-HQ	Wald统计值	4.820	13.761	352.932	262.312	0.227	21.530	350.358	5.317
SC-CS	回归系数	−0.481***¹⁾	−0.106***	0.329***	0.609***	−0.0340	0.107***	−0.816***	0.342**
SC-CS	Wald统计值	147.872	20.750	162.913	171.843	2.193	15.247	498.439	6.951
SC-WC	回归系数	−0.0890	−0.183***	0.578***	0.974***	0.00800	0.0890***	−0.632***	0.400**
SC-WC	Wald统计值	3.257	38.410	362.366	321.619	0.141	11.358	311.639	6.831
SC-LA	回归系数	−0.795***	−0.117***	0.409***	0.640***	0.020	0.0990***	−0.742***	0.363*
SC-LA	Wald统计值	320.194	19.301	220.192	145.947	1.105	15.162	460.464	5.161
SC-FP	回归系数	−0.389***	0.0260	−0.356***	−0.267*	−0.0330	0.0750*	0.126*	0.00500
SC-FP	Wald统计值	14.338	0.183	38.129	6.617	1.021	4.479	4.756	0.000
CS-HQ	回归系数	−0.969***	−0.0600	0.476***	1.640***	0.0390	−0.187	2.535***	13.707
CS-HQ	Wald统计值	118.281	1.186	101.138	192.042	0.0370	2.277	60.108	0.00300
CS-WC	回归系数	−0.782***	−0.0210	0.249***	0.615***	1.880	0.225	8.637***	−0.428
CS-WC	Wald统计值	101.332	0.250	44.074	24.875	0.977	0.621	48.321	0.676
CS-LA	回归系数	−0.932***	−0.0650	0.314***	0.868***	0.011	−0.176**	1.076***	1.260*
CS-LA	Wald统计值	212.495	3.091	86.596	94.205	0.023	7.681	53.695	4.670
CS-FP	回归系数	−1.872***	−0.0330	−0.229**	−1.349***	−0.194***	−0.0600	3.564***	0.0150
CS-FP	Wald统计值	359.116	0.707	10.539	277.810	44.096	1.685	1154.520	0.00800
FP-HQ	回归系数	−7.159***	−0.357***	−0.539***	−0.461***	−0.0980***	−0.161***	3.188***	−0.291
FP-HQ	Wald统计值	972.797	38.800	24.332	23.413	10.998	11.863	1085.881	1.746
FP-WC	回归系数	−6.105***	−0.290***	−0.393***	−1.117***	−0.432***	−0.153*	7.033***	−0.172
FP-WC	Wald统计值	907.020	29.529	15.469	101.194	110.532	4.950	1428.355	0.605
FP-LA	回归系数	−5.007***	−0.169** ²⁾	−0.733***	−0.544***	−0.0750**	−0.060	2.379***	−0.301
FP-LA	Wald统计值	703.614	9.942	52.164	40.258	6.953	1.763	959.568	2.169
LA-HQ	回归系数	−1.445***	0.360***	0.544***	−0.636*	−0.132	19.371***	−2.262***	10.448
LA-HQ	Wald统计值	126.688	25.227	111.804	5.391	0.0120	20.529	30.932	0.001
LA-WC	回归系数	−1.350***	0.225***	0.373***	−1.530***	0.158	−0.419***	−0.563	11.029
LA-WC	Wald统计值	158.104	15.756	70.232	47.915	2.435	11.075	2.231	0.00200
HQ-WC	回归系数	−1.029***	0.100	0.437***	−0.947	0.224	7.635	−2.058***	11.203
	Wald统计值	19.219	0.513	20.975	2.675	0.00500	2.420	17.301	0.000

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	ZHANG Z Z, ZHANG L, SUN G Y, et al., 2023. Spatial and temporal effect and driving factors of ecosystem service trade-off in the Qingjiang River Basin, China[J]. Chinese Journal of Applied Ecology, 34(4): 1051-1062. DOI

Spatial Heterogeneity and Driving Factors of Ecosystem Service Trade-offs and Synergies in the Changsha-Zhuzhou-Xiangtan Urban Agglomeration

长株潭城市群生态系统服务权衡与协同关系的空间异质性及其驱动因素

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