Science & Technology Review >

2016 , Vol. 34 >Issue 2: 247 - 254

DOI: https://doi.org/10.3981/j.issn.1000-7857.2016.2.042

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Analysis on influence factors of soil organic carbon density using a geographically weighted regression model

  • LI Long ,
  • YAO Yunfeng ,
  • QIN Fucang ,
  • ZHANG Meili ,
  • GAO Yuhan ,
  • CHANG Weidong
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  • 1. College of Ecology and Environmental Science, Inner Mongolia Agricultural University, Hohhot 010018, China;
    2. Forest Bureau in Aohan Banner, Chifeng 024300, China

Received date: 2015-03-11

  Revised date: 2015-08-04

  Online published: 2016-02-04

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Abstract

This research was conducted in Huanghuadianzi watershed in Aohan Chifeng, Inner Mongolia. The influence factors of soil organic carbon density were mainly divided into human factors and natural factors; altitude, slop, normalized differential vegetation index (NDVI) and the shortest distance from path or the village (DIST) were selected as the influence factors. Based on field data samples of the study area, both remote sensing and geographic information system were applied. A geographically weighted regression model was used to study the spatial variations of soil organic carbon density and the different environmental factors. The results showed that the soil organic carbon density changed in the study area from 1.91 to 16.63 kg/m2, with an average density 7.42 kg/m2. The influence degrees of soil organic carbon density in different driving factors ranked as altitude >slop >NDVI >DIST. The influence of each factor on the soil organic carbon changed with spatial difference. Altitude and slope respectively showed a positive and negative correlation with soil organic carbon density. In general soil organic carbon density decreased with the increasing of altitude and slope in most of the study area and the correlation coefficients were -0.436 and -0.223, while positive effect were only in a few areas. On the other hand, the NDVI and DIST showed a positive correlation with soil organic carbon density, with the correlation coefficients of NDVI being from 1.37 to 1.45 and DIST being from 0.15 to 0.47. In order to analyze the spatial variation of each influence factor, a map of the regression coefficient distribution of the environmental factors and soil organic carbon density in the study area was provided, which provided a scientific basis for the efficient utilization of soil and the development of precision agriculture according to the local conditions.

Key words： soil organic carbon content; environmental factors; geographically weighted regression model; samll watershed; spatial variation

Cite this article

LI Long , YAO Yunfeng , QIN Fucang , ZHANG Meili , GAO Yuhan , CHANG Weidong . Analysis on influence factors of soil organic carbon density using a geographically weighted regression model[J]. Science & Technology Review, 2016 , 34(2) : 247 -254 . DOI: 10.3981/j.issn.1000-7857.2016.2.042

References

[1] 陈泮勤. 地球系统碳循环[M]. 北京: 科学出版社, 2004. Chen Panqin. Carbon cycle of the earth's system[M]. Beijing: Science Press, 2004.
[2] 解宪丽, 孙波, 周慧珍, 等. 不同植被下中国土壤有机碳的储量与影响因子[J]. 土壤学报, 2004, 41(5): 687-699. Xie Xianli, Sun Bo, Zhou Huizhen, et al. Soil carbon stocks and their influencing factors under native vegetations in China[J]. Acta Pedologica Sinica, 2004, 41(5): 687-699.
[3] Tu J, Xia Z G. Examining spatially varying relationships between land use and water quality using geographically weighted regression I: Model design and evaluation[J]. Science of the Total Environment, 2008, 407: 358-378
[4] 吴建国, 张小全, 徐德应. 土地利用变化对土壤有机碳贮量的影响[J]. 应用生态学报, 2004, 15(4): 593-599 Wu Jianguo, Zhang Xiaoquan, Xu Deying. Impact of land-use change on soil carbon storage[J]. Chinese Journal of Applied Ecology, 2004, 15 (4): 593-599.
[5] 许信旺, 潘根兴, 曹志红, 等. 安徽省土壤有机碳空间差异及影响因素 [J]. 地理研究, 2007, 26(6): 1078-1086. Xu Xinwang, Pan Genxing, Cao Zhihong, et al. A study on the influence of soil organic carbon density and its spatial distribution in Anhui Province of China[J]. Geographical research, 2007, 26(6): 1078-1086.
[6] 赵明松, 张甘霖, 王德彩, 等. 徐淮黄泛平原土壤有机质空间变异特征及主控因素分析[J]. 土壤学报, 2013, 50(1): 1-11. Zhao Mingsong, Zhang Ganlin, Wang Decai, et al. Spatial variability of soil organic matter and its dominating factors in XU-Huai alluvial plain [J]. Acta Pedologica Sinica, 2013, 50(1): 1-11.
[7] 覃文忠, 王建梅, 刘妙龙. 地理加权回归分析空间数据的空间非平稳性[J]. 辽宁师范大学学报:自然科学版, 2005, 28(4): 476-479. Qin Wenzhong, Wang Jianmei, Liu Miaolong. Spatial nonstationarity of geographically weighted regression analysis of spatial data[J]. Journal of Liaoning Normal University: Natural Science Edition, 2005, 28(4): 476-479.
[8] Brunsdon C, Fotheringham A S, Charlton M. Geographically weighted regression: A method for exploring spatial nonstationarity[J]. Geographical Analysis, 1996, 28(4): 281-298.
[9] 邵一希, 李满春, 陈振杰, 等. 地理加权回归在区域土地利用格局模拟中的应用——以常州市孟河镇为例[J]. 地理科学, 2010, 30(1): 92-97. Shao Yixi, Li Manchun, Chen Zhenjie, et al. Simulation on regional spatial land use patterns using geographically weighted regression:a case study of Menghe town, Changzhou[J]. Scientia Geographica Sinica, 2010, 30(1): 92-97.
[10] 覃文忠. 地理加权回归基本理论与应用研究[D]. 上海: 同济大学图书馆, 2007. Qin Wenzhong. The basic theoretics and application research on geogra phically weighted regression[D]. Shanghai: Tongji University, 2007.
[11] 曹天邦, 黄克龙, 李剑波, 等. 基于GWR的南京市住宅地价空间分异及演变[J]. 地理研究, 2013, 32(12): 2324-2333. Cao Tianbang, Huang Kelong, Li Jianbo, et al. Research on spatial variation and evolution of residential land price in Nanjing based on GWR model[J]. Geographical Research, 2013, 32(12): 2324-2333.
[12] 颜峻. 经济发展对工伤事故水平影响的空间异质性研究[J]. 中国安全科学学报, 2013, 23(5): 145-150. Yan Jun. Study on Spatial heterogeneity of economic development's impact on work-related accidents level[J]. China Safety Science Journal, 2013, 23(5): 145-150.
[13] 孙文义, 郭胜利. 黄土丘陵沟壑区小流域土壤有机碳空间分布及其影响因素[J]. 生态学报, 2011, 31(6): 1604-1616. Sun Wenyi, Guo Shengli. The spatial distribution of soil organic carbon and it's influencing factors in hilly region of the Loess Plateau [J]. Acta Ecologica Sinica, 2011, 31(6): 1604-1616.
[14] Brunsdon C, Fotheringham A S, Charlton M, et al. Geographically weighted regression-modelling spatial non-stationarity[J]. Society, 2010, 47(3): 431-443.
[15] Brunsdon C, Fotheringham A S, Charlton M. Spatial nonstationarity and autoregressive models[J]. Environment and Planning A, 1998, 30 (6): 957-973.
[16] Fotheringham A S, Brunsdon C, Charlton M. Geographically weighted regression[M]. West Sussex: John Wiley & Sons Ltd, 2002.
[17] 张玉铭, 毛任钊, 胡春胜, 等. 华北太行山前平原农田土壤养分的空间变异性研究[J]. 应用生态学报, 2004, 15(11): 2049-2054. Zhang Yuming, Mao Renzhao, Hu Chunsheng, et al. Spatial variability of farmland soil nutrients at Taihang piedmont[J]. Chinese Journal of Applied Ecology, 2004, 15(11): 2049-2054.
[18] 杨扬, 杨建宇, 李绍明, 等. 玉米倒伏胁迫影响因子的空间回归分析 [J]. 农业工程学报, 2011, 27(6): 244-249. Yang Yang, Yang Jianyu, Li Shaoming, et al. Spatial regression analysis on influence factors of maize lodging stress[J]. Transactions of the CSAE, 2011, 27(6): 244-249.
[19] 牟娟. 基于GWR 模型的经济空间分析[D]. 青岛: 山东科技大学, 2010. Mu Juan. Spatial analysis of the economic based on GWR mode[D]. Qingdao: Shandong University of Science and Technology, 2010.
[20] 马瑛. 基于地理加权回归模型的农用地地价格空间结构研究[M]. 武汉: 华中农业大学, 2012. Ma Ying. The spatial structure of agricultural land price based on GWR model[M]. Wuhan: Huazhong Agricultural University, 2012.
[21] 王库. 地理权重回归在土壤pH空间预测中的应用[J]. 湖南农业大学学报: 自然科学版, 2013, 39(1): 73-79. Wang Ku. Application of geographically weighted regression on the spatial prediction of soil pH[J]. Journal of Hunan Agricultural University: Natural Sciences Edition, 2013, 39(1): 73-79.
[22] 李龙, 吴丽芝, 姚云峰, 等. 小流域土壤有机碳含量的空间变异特征研究——以内蒙古赤峰市黄花甸子流域为例[J]. 水土保持研究, 2013, 20(5): 18-23. Li Long, Wu Lizhi, Yao Yunfeng, et al. Spatial variations of organic carbon in small watershed-Taking Huanghuadianzi watershed as an example[J]. Research of Soil and Water Conservation, 2013, 20(5): 18-23.
[23] 王库. 基于地理权重回归模型的土壤有机质空间预测[J]. 土壤通报, 2013, 44(1): 21-28. Wang Ku. Spatial estimation of soil organic matter by using geographically weighted regression mode[J]. Chinese Journal of Soil Science, 2013, 44(1): 21-28.
[24] 唐国勇, 黄道友, 黄敏, 等. 红壤丘陵景观表层土壤有机碳空间变异特点及其影响因子[J]. 土壤学报, 2010, 47(4): 753-759. Tang Guoyong, Huang Daoyou, Huang Min, et al. Spatial variations of organic carbon in surface soils in a hilly landscape of the red-earth region and their affecting factors[J]. Acta Pedologica Sinica, 2010, 47 (4): 753-759.
[25] 杜有新, 吴从建, 周赛霞, 等. 庐山不同海拔森林土壤有机碳密度及分布特征[J]. 应用生态学报, 2011, 22(7): 1675-1681. Du Youxin, Wu Congjian, Zhou Saixia, et al. Forest soil organic carbon density and its distribution characteristics along an altitudinal gradient in Lushan Mountains of China[J]. Chinese Journal of Applied Ecology, 2011,22(7): 1675-1681.
[26] 陈海滨, 陈志彪, 陈志强. 南方红壤侵蚀区地形对土壤有机质空间分布的影响——以长汀县河田地区为例[J]. 福建农业学报, 2010, 25 (3): 369-373. Chen Haibin, Chen Zhibiao, Chen Zhiqiang. Impact of topography on spatial distribution of organic matters in red eroded soil in south China: A case study at hetian in Changting county[J]. Fujian Journal of Agricultural Sciences, 2010, 25(3): 369-373.
[27] 贾松伟. 黄土丘陵区不同坡度下土壤有机碳流失规律研究[J]. 水土保持研究2009, 16(2): 30-33. Jia Songwei. Soil organic carbon loss under different slope gradients in loess hilly region[J]. Research of Soil and Water Conservation, 2009, 16(2): 30-33.
[28] 吕萍, 甄辉. 基于GWR 模型的北京市住宅用地价格影响因素及其空间规律研究[J]. 经济地理, 2010, 30(3): 472-478. Lü Ping, Zhen Hui. Affecting factors research of Beijing residential land price based on GWR model[J]. Economic Geography, 2010, 30 (3): 472-478.
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