基于GWR模型的道路网络对森林碳密度干扰的地理变异

doi:10.13249/j.cnki.sgs.2022.06.017

地理科学 ›› 2022, Vol. 42 ›› Issue (6): 1113-1123.doi: 10.13249/j.cnki.sgs.2022.06.017

基于GWR模型的道路网络对森林碳密度干扰的地理变异

林玉英¹^,²^,³(), 李宝银³^,⁴, 邱荣祖⁵, 林金国⁶, 伍世代¹^,⁴^,^*()

1.福建师范大学旅游学院，福建福州 350117
2.智慧旅游福建省高校重点实验室，福建福州 350117
3.福建师范大学生态学博士后科研流动站，福建福州 350117
4.福建师范大学地理科学学院，福建福州 350117
5.福建农林大学交通与土木工程学院，福建福州 350108
6.福建农林大学材料工程学院，福建福州 350108

收稿日期:2021-04-16 修回日期:2021-08-10 出版日期:2022-06-25 发布日期:2022-08-23
通讯作者: 伍世代 E-mail:linyuying2019@fjnu.edu.cn;sdlw8726@sina.com
作者简介:林玉英（1988−），女，福建龙岩人，副教授，博士后，主要从事道路景观生态、旅游地理等研究。E-mail: linyuying2019@fjnu.edu.cn
基金资助:
国家自然科学基金项目（41901221, 41971261）、福建省林业局科技项目（SC-259）、福建省水利厅科技项目（SC-290）资助

Geographic Variation of Road Network Effects on Forest Carbon Density Based on GWR Model: A Case Study of the Upstream District of the Minjiang River

Lin Yuying¹^,²^,³(), Li Baoyin³^,⁴, Qiu Rongzu⁵, Lin Jinguo⁶, Wu Shidai¹^,⁴^,^*()

1. College of Tourism, Fujian Normal University, Fuzhou 350117, Fujian, China
2. The Higher Educational Key Laboratory for Smart Tourism of Fujian Province, Fuzhou 350117, Fujian, China
3. Postdoctoral Research Station of Ecology, Fujian Normal University, Fuzhou 350117, Fujian, China
4. College of Geographical Science, Fujian Normal University, Fuzhou 350117, Fujian, China
5. College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350108, Fujian, China
6. College of Material Engineering, Fujian Agriculture and Forestry University, Fuzhou 350108, Fujian, China

Received:2021-04-16 Revised:2021-08-10 Online:2022-06-25 Published:2022-08-23
Contact: Wu Shidai E-mail:linyuying2019@fjnu.edu.cn;sdlw8726@sina.com
Supported by:
National Natural Science Foundation of China (41901221, 41971261), Science and Technology Project of Fujian Provincial Department of Forestry (SC-259), Science and Technology Project of Fujian Provincial Department of Water Resources (SC-290)

摘要/Abstract

摘要：

以闽江上游地区为例，在分析三明市2007—2016年森林碳密度时空动态的基础上，采用常规的以及改进后的道路网络测度指标，应用缓冲区分析方法和地理加权回归（Geographically Weighted Regression，GWR）模型，从线上和面上分别探讨道路网络对森林碳密度干扰的地理变异规律。结果表明：① 碳密度受到道路网络的较大影响，路网影响域内外碳密度的大小排序为：路网影响域内＜整个研究区＜路网影响域外；多条道路影响域重叠区的碳密度(26.330 Mg/hm²)明显低于单条道路影响域的碳密度(37.406 Mg/hm²)；不同等级道路影响域的碳密度由大到小依次为县道＞高速＞省道＞其它道路＞国道＞乡道。道路网络对2007—2016年碳密度的降低也有明显影响。② GWR模型的分析结果表明，路网对碳密度的影响程度随着样点的变化而变化，具有“空间非平稳性”。碳密度随着路网密度的增加而降低，而随着离道路距离的增加而增加。③ 研究区西北部和中部，GWR的回归系数及相关系数均较大，表明这2个区域道路对碳密度影响大且解释力皆较强。

关键词: 森林碳密度, 道路网络, 道路生态, 地理加权回归, 闽江上游

Abstract:

Taking upstream district of the Minjiang River as an example, we firstly analyze the temporal and spatial dynamics of forest carbon density from 2007 to 2016. To reveal the geographical variation of the association between road network and forest carbon density, this work studies the impacts of different road scenarios and the indexes of different road network measurement on forest carbon density using conventional and improved road network measurement indicators as well as the buffer analysis method and Geographic Weighted Regression (GWR) model. The results show that: 1) The carbon density of the study area is obviously affected by the road network and the magnitude of impact is in the order of within the road network effect zone < the entire research area < outside the road network effect zone; The carbon density of the overlapping region of multiple road effect zones (26.330 Mg/hm ²) is significantly lower than the carbon density (37.406 Mg/hm²) of the single road effect zone. The carbon density of the different grade road effect zones is in the order of county road > expressway > provincial road > other road > national road > rural road; Road construction and expansion also have a significant impact on the reduction of carbon density during 2007 to 2016. 2) The degree of influence of roads on carbon density changes with the change of sample points showed with the characteristic of “spatial non-stationarity”. Carbon density decreases with increasing road density and increases with increasing distance from the road. 3) The regression coefficients and correlation coefficients in the northwest and middle regions of the study area are large, indicating that the roads in these two regions have large impacts on carbon density and are well explained by the model. Policy makers should pay special attention when formulating forest carbon sink protection strategies.

Key words: forest carbon density, road network, road ecology, Geographic Weighted Regression (GWR), the upper Minjiang River

中图分类号:

S718.55

林玉英, 李宝银, 邱荣祖, 林金国, 伍世代. 基于GWR模型的道路网络对森林碳密度干扰的地理变异[J]. 地理科学, 2022, 42(6): 1113-1123.

Lin Yuying, Li Baoyin, Qiu Rongzu, Lin Jinguo, Wu Shidai. Geographic Variation of Road Network Effects on Forest Carbon Density Based on GWR Model: A Case Study of the Upstream District of the Minjiang River[J]. SCIENTIA GEOGRAPHICA SINICA, 2022, 42(6): 1113-1123.

图/表 8

图1

图2

Table 1

Table 2

Table 3

图3

图4

图5

参考文献 33

[1]	孙清芳, 刘延坤, 李云红, 等. 森林碳汇功能的研究进展[J]. 环境科学与管理, 2013, 38 (3): 47-50 doi: 10.3969/j.issn.1673-1212.2013.03.011
	Sun Qingfang, Liu Yankun, Li Yunhong et al. Research progress on functions of forest carbon sink. Environmental Science and Management, 2013, 38 (3): 47-50 doi: 10.3969/j.issn.1673-1212.2013.03.011
[2]	徐伟义, 金晓斌, 杨绪红, 等. 中国森林植被生物量空间网格化估计[J]. 自然资源学报, 2018, 33 (10): 1725-1741 doi: 10.31497/zrzyxb.20170802
	Xu Weiyi, Jin Xiaobin, Yang Xuhong et al. The estimation of forest vegetation biomass in China in spatial grid. Journal of Natural Resources, 2018, 33 (10): 1725-1741 doi: 10.31497/zrzyxb.20170802
[3]	Trumbore S E, Chadwick O A, Amundson R. Rapid exchange between soil carbon and atmospheric carbon dioxide driven by temperature change[J]. Science, 1996, 272 (272): 393-396
[4]	Schmidt C, Domaratzki M, Kinnunen R P et al. Continent-wide effects of urbanization on bird and mammal genetic diversity[J]. Proceedings of the Royal Society B: Biological Sciences, 2020, 287 (1920): 20192497 doi: 10.1098/rspb.2019.2497
[5]	潘萍, 孙玉军, 欧阳勋志, 等. 江西省马尾松林生态系统碳密度空间变异特征[J]. 应用生态学报, 2019, 30 (6): 1885-1892
	Pan Ping, Sun Yujun, Ouyang Xunzhi et al. Spatial variation of carbon density in Pinus massoniana forest in Jiangxi Province, China [J]. Chinese Journal of Applied Ecology, 2019, 30 (6): 1885-1892
[6]	黄超, 贺红士, 梁宇, 等. 气候变化、林火和采伐对大兴安岭森林碳储量的影响[J]. 应用生态学报, 2018, 29 (7): 2088-2100
	Huang Chao, He Hongshi, Liang Yu et al. Effects of climate change, fire and harvest on carbon storage of boreal forests in the Great Xing’an Mountains, China. Chinese Journal of Applied Ecology, 2018, 29 (7): 2088-2100
[7]	任继勤, 夏景阳. 基于碳密度−林龄关系的黑龙江省森林碳汇潜力预测[J]. 环境科学研究, 2017, 30 (4): 552-558
	Ren Jiqin, Xia Jingyang. Prediction of forest carbon sink potential in Heilongjiang Province: The carbon density-age relationship-based approach. Research of Environmental Sciences, 2017, 30 (4): 552-558
[8]	吴胜男, 李岩泉, 于大炮, 等. 基于VAR模型的森林植被碳储量影响因素分析——以陕西省为例[J]. 生态学报, 2015, 35 (1): 196-203
	Wu Shengnan, Li Yanquan, Yu Dapao et al. Analysis of factors that influence forest vegetation carbon storage by using the VAR model: A case study in Shaanxi Province. Acta Ecologica Sinica, 2015, 35 (1): 196-203
[9]	Forman R T T, Alexander L E. Roads and their major ecological effects[J]. Ecology, Evolution, and Systematics, 1998, 29 (29): 207
[10]	Valipour M. Future of agricultural water management in Africa[J]. Archives of Agronomy & Soil Science, 2015, 61 (7): 907-927
[11]	Jaeger J A G, Schwarzvon Raumer H G, Esswein H et al. Time series of landscape fragmentation caused by transportation infrastructure and urban development: A case study from Baden-Württemberg, Germany[J]. Ecology & Society, 2007, 12 (1): 181-194
[12]	Selva N, Kreft S, Kati V et al. Roadless and low-traffic areas as conservation targets in Europe[J]. Environmental Management, 2011, 48 (5): 865-877 doi: 10.1007/s00267-011-9751-z
[13]	Asner G P, Powell G V N, Mascaro J et al. High-resolution forest carbon stocks and emissions in the Amazon[J]. Proceedings of the National Academy of Sciences of the United States of America, 2010, 107 (38): 16738-16742 doi: 10.1073/pnas.1004875107
[14]	Hu X, Zhang L, Ye L et al. Locating spatial variation in the association between road network and forest biomass carbon accumulation[J]. Ecological Indicators, 2017, 73: 214-223 doi: 10.1016/j.ecolind.2016.09.042
[15]	刘世梁, 温敏霞, 崔保山, 等. 道路影响域的界定及其空间分异规律——以纵向岭谷区为例[J]. 地理科学进展, 2008, 27 (4): 122-128 doi: 10.11820/dlkxjz.2008.04.018
	Liu Shiliang, Wen Minxia, Cui Baoshan et al. Definition and spatial differentiation of road-effect zone: A case study in Longitudinal Range-Gorge Region. Progress in Geography, 2008, 27 (4): 122-128 doi: 10.11820/dlkxjz.2008.04.018
[16]	Coffin A W. From roadkill to road ecology: A review of the ecological effects of roads[J]. Journal of Transport Geography, 2007, 15 (5): 396-406 doi: 10.1016/j.jtrangeo.2006.11.006
[17]	Hu X, Wu Z, Wu C et al. Effects of road network on diversiform forest cover changes in the highest coverage region in China: An analysis of sampling strategies[J]. Science of the Total Environment, 2016, 565 (565): 28-39
[18]	Lin Y, Hu X, Zheng X et al. Spatial variations in the relationships between road network and landscape ecological risks in the highest forest coverage region of China[J]. Ecological Indicators, 2019, 96: 392-403 doi: 10.1016/j.ecolind.2018.09.016
[19]	Yuying L, Xisheng H, Mingshui L et al. Is forest landscape pattern more affected in road overlap zone: Evidence from an upstream area of the Minjiang River of Fujian Province in the subtropical region of China[J]. Applied Ecology and Environmental Research, 2020, 18 (2): 3693-3711 doi: 10.15666/aeer/1802_36933711
[20]	耿甜伟, 陈海, 张行, 等. 基于GWR的陕西省生态系统服务价值时空演变特征及影响因素分析[J]. 自然资源学报, 2020, 35 (7): 1714-1727 doi: 10.31497/zrzyxb.20200715
	Geng Tianwei, Chen Hai, Zhang Xing et al. Spatiotemporal evolution of land ecosystem service value and its influencing factors in Shaanxi Province based on GWR. Journal of Natural Resources, 2020, 35 (7): 1714-1727 doi: 10.31497/zrzyxb.20200715
[21]	武鹏, 李同昇, 李卫民. 县域农村贫困化空间分异及其影响因素——以陕西山阳县为例[J]. 地理研究, 2018, 37 (3): 593-606
	Wu Peng, Li Tongsheng, Li Weimin. Spatial differentiation and influencing factors analysis of rural poverty at county scale: A case study of Shanyang County in Shaanxi Province, China. Geographical Research, 2018, 37 (3): 593-606
[22]	王宇航, 赵鸣飞, 康慕谊, 等. 黄土高原地区NDVI与气候因子空间尺度依存性及非平稳性研究[J]. 地理研究, 2016, 35 (3): 493-503 doi: 10.11821/dlyj201603008
	Wang Yuhang, Zhao Mingfei, Kang Muyi et al. Spatial scale-dependent and non-stationarity relationships between NDVI and climatic factors in the Loess Plateau. Geographical Research, 2016, 35 (3): 493-503 doi: 10.11821/dlyj201603008
[23]	孙克, 徐中民. 基于地理加权回归的中国灰水足迹人文驱动因素分析[J]. 地理研究, 2016, 35 (1): 37-48 doi: 10.11821/dlyj201601004
	Sun Ke, Xu Zhongmin. The impacts of human driving factors on grey water footprint in China using a GWR model. Geographical Research, 2016, 35 (1): 37-48 doi: 10.11821/dlyj201601004
[24]	杨文越, 李涛, 曹小曙. 广州市社区出行低碳指数格局及其影响因素的空间异质性[J]. 地理研究, 2015, 34 (8): 1471-1480 doi: 10.11821/dlyj201508006
	Yang Wenyue, Li Tao, Cao Xiaoshu. The spatial pattern of Community Travel Low Carbon Index (CTLCI) and spatial heterogeneity of the relationship between CTLCI and influencing factors in Guangzhou. Geographical Research, 2015, 34 (8): 1471-1480 doi: 10.11821/dlyj201508006
[25]	Fang J, Ci L. Changes in forest biomass carbon storage in China between 1949 and 1998[J]. Science, 2001, 292 (5525): 2320-2322 doi: 10.1126/science.1058629
[26]	Guo Z, Hu H, Li P et al. Spatio-temporal changes in biomass carbon sinks in China’s forests from 1977 to 2008[J]. Science China Life Sciences, 2013, 56 (7): 661-671 doi: 10.1007/s11427-013-4492-2
[27]	林玉英, 李宝银, 邱荣祖, 等. 道路网络生态干扰测度指数的改进及其空间分异特征——以闽江上游为例[J]. 地理科学, 2021, 41 (6): 951-959
	Lin Yuying, Li Baoyin, Qiu Rongzu et al. Improvement of road network measurement indexes and their spatial differentiation characteristics: Taking the upper Minjiang River as example. Scientia Geographica Sinica, 2021, 41 (6): 951-959
[28]	石民丰, 黄玮, 郭巧梅, 等. 基于GIS的武汉市道路密度空间分异特征[J]. 地理空间信息, 2013 (3): 103-105 doi: 10.11709/j.issn.1672-4623.2013.03.038
	Shi Minfeng, Huang Wei, Guo Qiaomei et al. Spatial distribution characteristics of road density in Wuhan based on GIS. Geospatial Information, 2013 (3): 103-105 doi: 10.11709/j.issn.1672-4623.2013.03.038
[29]	蔡雪娇, 吴志峰, 程炯. 基于核密度估算的路网格局与景观破碎化分析[J]. 生态学杂志, 2012, 31 (1): 158-164
	Cai Xuejiao, Wu Zhifeng, Cheng Jiong. Analysis of road network pattern and landscape fragmentation based on kernel density estimation. Chinese Journal of Ecology, 2012, 31 (1): 158-164
[30]	Lin Y, Hu X, Lin M et al. Spatial paradigms in road networks and their delimitation of urban boundaries based on KDE[J]. Isprs International Journal of Geo-Information, 2020, 9 (4): 204-221 doi: 10.3390/ijgi9040204
[31]	范科红, 李阳兵, 冯永丽. 基于GIS的重庆市道路密度的空间分异[J]. 地理科学, 2011, 31 (3): 365-371
	Fan Kehong, Li Yangbin, Feng Yongli. Spatial distribution of road density in Chongqing based on GIS. Scientia Geographica Sinica, 2011, 31 (3): 365-371
[32]	叶丽敏, 邱荣祖, 林玉英, 等. 福建省道路网密度空间分异特征[J]. 公路, 2018, 63 (5): 206-210
	Ye Limin, Qiu Rongzu, Lin Yuying et al. Patial differentiation characteristics of road network density in Fujian Province. Highway, 2018, 63 (5): 206-210
[33]	王佳, 钱雨果, 韩立建, 等. 基于GWR模型的土地覆盖与地表温度的关系——以京津唐城市群为例[J]. 应用生态学报, 2016, 27 (7): 2128-2136
	Wang Jia, Qian Yuguo, Han Lijian et al. Relationship between land surface temperature and land cover types based on GWR model: A case of Beijing-Tianjin-Tangshan urban agglomeration, China. Chinese Journal of Applied Ecology, 2016, 27 (7): 2128-2136

变化等级	路域内		路域外		三明市
变化等级	面积/hm²	比例/%	面积/hm²	比例/%	面积/hm²	比例/%
降低	14832.44	32.51	16149.37	23.46	30950.77	27.04
不变	11634.24	25.50	17803.48	25.86	29426.13	25.71
提高	19163.90	42.00	34891.35	50.68	54097.88	47.26
合计	45630.58	100.00	68844.20	100.00	114474.78	100.00

模型	道路指标	残差平方和	Sigma无偏估计	修正AIC	拟合优度R²
注：LDE为常规的道路网络线密度指标、WLDE为加权的LDE指标、KDE为基于核密度估算方法计算道路网络线密度指标、WKDE为加权的KDE指标、REZD为道路网络影响域面密度指标、MREZD为考虑坡度的道路网络影响域面密度指标、DNR为离道路距离指标。
GWR	LDE	2468.68	1.49	4197.54	0.151
	WLDE	2458.05	1.49	4197.12	0.154
	KDE	2373.78	1.47	4160.88	0.183
	WKDE	2388.69	1.47	4165.70	0.178
	REZD	2610.00	1.54	4271.79	0.102
	MREZD	2608.26	1.54	4270.90	0.102
	DNR	2386.47	1.47	4159.28	0.179
OLS	LDE	2612.39	1.51	4207.84	0.101
	WLDE	2620.48	1.51	4211.39	0.098
	KDE	2544.65	1.49	4177.68	0.124
	WKDE	2561.85	1.50	4185.41	0.118
	REZD	2832.40	1.57	4300.67	0.025
	MREZD	2833.01	1.57	4300.91	0.025
	DNR	2617.75	1.51	4210.19	0.099

指标	最小值	最大值	下四分位数	中位数	上四分位数	平均值	标准差
注：指标含义见表2。
LDE	−1.430	0.018	−0.747	−0.552	−0.377	−0.571	0.296
WLDE	−14.038	1.866	−5.527	−4.052	−2.335	−4.340	2.866
KDE	−1.264	0.021	−0.761	−0.512	−0.338	−0.542	0.303
WKDE	−11.756	0.612	−5.792	−3.855	−2.180	−4.228	2.677
REZD	−1.841	1.230	−0.868	−0.420	0.030	−0.374	0.663
MREZD	−1.869	1.216	−0.865	−0.406	0.039	−0.371	0.666
DNR	0.031	0.722	0.183	0.354	0.431	0.328	0.164

基于GWR模型的道路网络对森林碳密度干扰的地理变异

Geographic Variation of Road Network Effects on Forest Carbon Density Based on GWR Model: A Case Study of the Upstream District of the Minjiang River

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 33

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	盛科荣, 陈欢欢, 张嘉慧, 高勇. 航空网络嵌入对中国城市知识生产的影响研究[J]. 地理科学, 2022, 42(6): 963-974.
[2]	卢新海, 李佳, 刘超, 匡兵, 蔡大伟, 侯娇. 中国城市土地绿色利用效率驱动因素及空间分异[J]. 地理科学, 2022, 42(4): 611-621.
[3]	王波, 雷雅钦, 汪成刚, 汪磊. 建成环境对城市活力影响的时空异质性研究：基于大数据的分析[J]. 地理科学, 2022, 42(2): 274-283.
[4]	宋飏, 王婷婷, 张瑜, 钱思彤, 王士君. 东北三省企业空间格局演化与区位选择因素[J]. 地理科学, 2021, 41(7): 1199-1209.
[5]	林玉英, 李宝银, 邱荣祖, 林金国, 伍世代. 道路网络生态干扰测度指数的改进及其空间分异特征——以闽江上游为例[J]. 地理科学, 2021, 41(6): 951-959.
[6]	化祥雨, 金祥荣, 吕海萍, 叶娅芬, 邵元海. 高质量发展耦合协调时空格局演化及影响因素——以浙江省县域为例[J]. 地理科学, 2021, 41(2): 223-231.
[7]	辛晓华, 吕拉昌. 中国主要城市技术创新影响环境污染的空间分异与机理[J]. 地理科学, 2021, 41(1): 129-139.
[8]	魏宗财, 甄峰, 莫海彤, 刘晨瑜, 彭丹丽. 基于地理加权回归的中心城区共享单车出行特征及影响因素研究[J]. 地理科学, 2020, 40(7): 1082-1091.
[9]	曾冰. 基于NPP/VIIRS夜间灯光数据的湘鄂赣省际交界区县域经济空间格局及影响因素[J]. 地理科学, 2020, 40(6): 900-907.
[10]	王彬燕, 田俊峰, 施响, 王士君. 基于HLM和GWR的汪清县农村贫困成因探究[J]. 地理科学, 2020, 40(3): 409-418.
[11]	于洪雁, 王群勇, 张博, 刘继生. 中国旅游供需耦合协调发展的空间分异及驱动机制研究[J]. 地理科学, 2020, 40(11): 1889-1898.
[12]	徐维祥, 李露, 黄明均, 刘程军. 浙江县域“四化同步”与居民幸福协调发展的时空分异特征及其形成机制[J]. 地理科学, 2019, 39(10): 1631-1641.
[13]	吴连霞, 赵媛, 吴开亚, 郝丽莎, 王玉娟. 中国人口老龄化区域差异及驱动机制研究[J]. 地理科学, 2018, 38(6): 877-884.
[14]	关伟, 郝金连. 东北地区旅游经济影响因素时空特征研究[J]. 地理科学, 2018, 38(6): 935-943.
[15]	姜磊, 周海峰, 柏玲. 外商直接投资对空气污染影响的空间异质性分析——以中国150个城市空气质量指数（AQI）为例[J]. 地理科学, 2018, 38(3): 351-360.