自然因子对四川植被NDVI变化的地理探测

doi:10.11821/dlxb201909005

地理学报 ›› 2019, Vol. 74 ›› Issue (9): 1758-1776.doi: 10.11821/dlxb201909005

自然因子对四川植被NDVI变化的地理探测

彭文甫^1,²,张冬梅^1,²,罗艳玫^1,²,陶帅^1,²,徐新良³

^1. 四川师范大学地理与资源科学学院,成都 610068
^2. 西南土地资源评价与监测教育部重点实验室,四川师范大学,成都 610068
^3. 中国科学院资源环境科学数据中心,北京 100101

收稿日期:2018-04-19 修回日期:2019-07-25 出版日期:2019-09-25 发布日期:2019-09-25
作者简介:彭文甫(1964-), 男, 四川乐山人, 博士, 副教授, 主要从事国土资源遥感研究。E-mail: pwfzh@126.com
基金资助:
教育部人文社科基金项目(17YJA850007);国家自然科学基金项目(41371125)

Influence of natural factors on vegetation NDVI using geographical detection in Sichuan Province

PENG Wenfu^1,²,ZHANG Dongmei^1,²,LUO Yanmei^1,²,TAO Shuai^1,²,XU Xinliang³

^1. The Institute of Geography and Resources Science, Sichuan Normal University, Chengdu 610068, China
^2. Key Lab of Land Resources Evaluation and Monitoring in Southwest, Ministry of Education, Sichuan Normal University, Chengdu 610068, China
^3. Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences, Beijing 100101, China

Received:2018-04-19 Revised:2019-07-25 Online:2019-09-25 Published:2019-09-25
Supported by:
Humanities and Social Science Fund of the Ministry of Education of China(17YJA850007);NationalNatural Science Foundation of China(41371125)

摘要/Abstract

摘要：

许多研究已表明基于遥感的植被指数在地表过程和全球变化研究中具有重要作用,对认识植被变化的驱动因素具有重要意义,但自然因子对植被变化影响仍然难以量化。应用地理探测器模型,研究四川地区自然因子变化对植被分布的空间模式和植被变化的交互影响,并确定了促进植被生长的各主要自然因子最适宜特征。结果表明：① 2000-2015年,四川植被覆盖度状况良好,中高、高植被覆盖面积之和均超过94%;归一化植被指数（NDVI）转化表现为NDVI > 0.4以上区域转化明显,中高和高植被覆盖区面积分别呈显著下降和上升趋势;植被覆盖时空变化差异显著,植被覆盖较高区域位于四川盆地东北部、川西北高原地区,植被覆盖较低区域分布于四川盆地中部城市密集区域。② 土壤类型、高程和年均温度变化等因子较好地解释了植被状况的可变性。③ 自然因子对植被NDVI影响存在交互作用,自然因子协同效应呈现相互增强和非线性增强关系,两种因子交互作用增强了单因子的影响。④ 研究揭示的促进植被生长的各主要因子最适宜特征,有助于更好地理解自然因素对植被NDVI变化的影响及其驱动机制。

关键词: 植被NDVI, 自然因子, 地理探测器模型, 四川省

Abstract:

Many studies have shown the importance of using remote sensing to establish a vegetation index for land surface processes and global change research, it is of great significance to understand the driving factors of vegetation change, but the causes for vegetation change and the impact of geographical factors on vegetation change remain elusive. In this study, we examined the geographical factors and spatial patterns of vegetation change and the interactive effects of the geographical factors on vegetation change, and identified the most suitable characteristics of the main geographical factors that promote vegetation growth using the Geographical Detector Model, a new method of spatial counting to detect spatial variability and identify the driving factors. Our results showed that the vegetation cover was in good condition, the coverage area was of medium height, and there was more than 94% of high height vegetation. The spatiotemporal change in vegetation cover was significant from 2000-2015; the transformation of the normalized differential vegetation index (NDVI) was manifested as the transformation of NDVI > 0.4, and the cover area of medium and high height vegetation had a significant decreasing and increasing trend, respectively. The vegetation cover was better in the western and northern Sichuan plateau, while it was poor in the central urban areas of the Sichuan Basin and the Panxi area. Soil type, elevation, and the average annual temperature change could well explain the variability in vegetation condition. The influence of geographical factors on NDVI was interactive; the synergistic effect of the geographical factors on NDVI showed mutual and non-linear enhancement, and the interaction of the two factors enhanced the influence of a single factor on NDVI. This study reveals the most suitable characteristics and the main factors that promote vegetation growth, which is helpful to better understand the influence of natural factors and the driving mechanisms of vegetation NDVI change.

Key words: NDVI, natural factors, geographical detector model, Sichuan Province

彭文甫, 张冬梅, 罗艳玫, 陶帅, 徐新良. 自然因子对四川植被NDVI变化的地理探测[J]. 地理学报, 2019, 74(9): 1758-1776.

PENG Wenfu, ZHANG Dongmei, LUO Yanmei, TAO Shuai, XU Xinliang. Influence of natural factors on vegetation NDVI using geographical detection in Sichuan Province[J]. Acta Geographica Sinica, 2019, 74(9): 1758-1776.

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影响植被NDVI变化的自然因子之间的交互作用"

两因子交互作用	两因子PD值相加	结果	解释	两因子交互作用	两因子PD值相加	结果	解释
X₁∩X₂=0.294	<0.337=X₁+X₂	C<A+B	相互增强	X₄∩X₈=0.307	<0.375=X₄+X₈	C<A+B	相互增强
X₁∩X₃=0.125	>0.121=X₁+X₃	C>A+B	非线性增强	X₄∩X₉=0.377	<0.541=X₄+X₉	C<A+B	相互增强
X₁∩X₄=0.277	<0.304=X₁+X₄	C<A+B	相互增强	X₄∩X₁₀=0.354	<0.533=X₄+X₁₀	C<A+B	相互增强
X₁∩X₅=0.383	<0.420=X₁+X₅	C<A+B	相互增强	X₄∩X₁₁=0.229	>0.205=X₄+X₁₁	C>A+B	非线性增强
X₁∩X₆=0.211	<0.237=X₁+X₆	C<A+B	相互增强	X₄∩X₁₂=0.218	>0.205=X₄+X₁₂	C>A+B	非线性增强
X₁∩X₇=0.198	<0.199=X₁+X₇	C<A+B	相互增强	X₅∩X₆=0.355	<0.451=X₅+X₆	C<A+B	相互增强
X₁∩X₈=0.300	>0.277=X₁+X₈	C>A+B	非线性增强	X₅∩X₇=0.377	<0.413=X₅+X₇	C<A+B	相互增强
X₁∩X₉=0.398	<0.443=X₁+X₉	C<A+B	相互增强	X₅∩X₈=0.400	<0.491=X₅+X₈	C<A+B	相互增强
X₁∩X₁₀=0.415	<0.435=X₁+X₁₀	C<A+B	相互增强	X₅∩X₉=0.432	<0.675=X₅+X₉	C<A+B	相互增强
X₁∩X₁₁=0.136	>0.107=X₁+X₁₁	C>A+B	非线性增强	X₅∩X₁₀=0.374	<0.649=X₅+X₁₀	C<A+B	相互增强
X₁∩X₁₂=0.123	>0.107=X₁+X₁₂	C>A+B	非线性增强	X₅∩X₁₁=0.346	>0.321=X₅+X₁₁	C>A+B	非线性增强
X₂∩X₃=0.266	>0.242=X₂+X₃	C>A+B	非线性增强	X₅∩X₁₂=0.329	>0.324=X₅+X₁₂	C>A+B	非线性增强
X₂∩X₄=0.246	<0.435=X₂+X₄	C<A+B	相互增强	X₆∩X₇=0.291	>0.230=X₆+X₇	C>A+B	非线性增强
X₂∩X₅=0.331	<0.551=X₂+X₅	C<A+B	相互增强	X₆∩X₈=0.288	<0.308=X₆+X₈	C<A+B	相互增强
X₂∩X₆=0.263	<0.368=X₂+X₆	C<A+B	相互增强	X₆∩X₉=0.373	<0.474=X₆+X₉	C<A+B	相互增强
X₂∩X₇=0.314	<0.330=X₂+X₇	C<A+B	相互增强	X₆∩X₁₀=0.386	<0.466=X₆+X₁₀	C<A+B	相互增强
X₂∩X₈=0.327	<0.408=X₂+X₈	C<A+B	相互增强	X₆∩X₁₁=0.167	>0.138=X₆+X₁₁	C>A+B	非线性增强
X₂∩X₉=0.381	<0.574=X₂+X₉	C<A+B	相互增强	X₆∩X₁₂=0.159	>0.138=X₆+X₁₂	C>A+B	非线性增强
X₂∩X₁₀=0.344	<0.566=X₂+X₁₀	C<A+B	相互增强	X₇∩X₈=0.300	>0.270=X₇+X₈	C<A+B	非线性增强
X₂∩X₁₁=0.265	>0.238=X₂+X₁₁	C>A+B	非线性增强	X₇∩X₉=0.380	<0.436=X₇+X₉	C<A+B	相互增强
X₂∩X₁₂=0.250	>0.238=X₂+X₁₂	C>A+B	非线性增强	X₇∩X₁₀=0.400	<0.428=X₇+X₁₀	C<A+B	相互增强
X₃∩X₄=0.238	>0.209=X₃+X₄	C>A+B	非线性增强	X₇∩X₁₁=0.167	>0.100=X₇+X₁₁	C>A+B	非线性增强
X₃∩X₅=0.360	>0.325=X₃+X₅	C>A+B	非线性增强	X₇∩X₁₂=0.122	>0.100=X₇+X₁₂	C>A+B	非线性增强
X₃∩X₆=0.191	>0.142=X₃+X₆	C>A+B	非线性增强	X₈∩X₉=0.423	<0.541=X₈+X₉	C<A+B	相互增强
X₃∩X₇=0.133	>0.104=X₃+X₇	C>A+B	非线性增强	X₈∩X₁₀=0.426	<0.506=X₈+X₁₀	C<A+B	相互增强
X₃∩X₈=0.208	>0.182=X₃+X₈	C>A+B	非线性增强	X₈∩X₁₁=0.207	>0.178=X₈+X₁₁	C>A+B	非线性增强
X₃∩X₉=0.380	>0.348=X₃+X₉	C>A+B	非线性增强	X₈∩X₁₂=0.189	>0.178=X₈+X₁₂	C>A+B	非线性增强
X₃∩X₁₀=0.366	>0.340=X₃+X₁₀	C>A+B	非线性增强	X₉∩X₁₀=0.446	>0.672=X₉+X₁₀	C>A+B	非线性增强
X₃∩X₁₁=0.034	>0.012=X₃+X₁₁	C>A+B	非线性增强	X₉∩X₁₁=0.378	>0.344=X₉+X₁₁	C>A+B	非线性增强
X₃∩X₁₂=0.017	>0.012=X₃+X₁₂	C>A+B	非线性增强	X₉∩X₁₂=0.342	>0.344=X₉+X₁₂	C>A+B	非线性增强
X₄∩X₅=0.325	<0.518=X₄+X₅	C<A+B	相互增强	X₁₀∩X₁₁=0.372	>0.336=X₁₀+X₁₁	C>A+B	非线性增强
X₄∩X₆=0.239	<0.335=X₄+X₆	C<A+B	相互增强	X₁₀∩X₁₂=0.342	>0.336=X₁₀+X₁₂	C>A+B	非线性增强
X₄∩X₇=0.295	<0.297=X₄+X₇	C<A+B	相互增强	X₁₁∩X₁₂=0.022	<0.044=X₁₁+X₁₂	C<A+B	相互增强

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参考文献 38

[1]	Gong Z, Zhao S, Gu J . Correlation analysis between vegetation coverage and climate drought conditions in North China during 2001-2013. Journal of Geographical Sciences, 2017,27(2):143-160.
[2]	Zhao Jie, Du Ziqiang, Wu Zhitao , et al. Seasonal variations of day- and nighttime warming and their effects on vegetation dynamics in China's temperate zone. Acta Geographica Sinica, 2018,73(3):395-404.
	[ 赵杰, 杜自强, 武志涛 , 等. 中国温带昼夜增温的季节性变化及其对植被动态的影响. 地理学报, 2018,73(3):395-404.]
[3]	Parmesan C, Yohe G . A globally coherent fingerprint of climate change impacts across natural systems. Nature, 2003,421(6918):37-42.
[4]	He Bin, Chen Aifang, Jiang Weiguo , et al. The response of vegetation growth to shifts in trend of temperature in China. Journal of Geographical Sciences, 2017,27(7):801-816.
[5]	Zhao Lin, Dai Aiguo, Dong Bo . Changes in global vegetation activity and its driving factors during 1982-2013. Agricultural and Forest Meteorology, 2018,249:198-209.
[6]	Fu Gang, Sun Wei, Li Shaowei , et al. Modeling aboveground biomass using MODIS images and climatic data in grasslands on the Tibetan Plateau. Journal of Resources and Ecology, 2017,8(1):42-49.
[7]	Kong Dongdong, Zhang Qiang, Huang Wenlin , et al. Vegetation phenology change in Tibetan Plateau from 1982 to 2013 and its related meteorological factors. Acta Geographica Sinica, 2017,72(1):39-52.
	[ 孔冬冬, 张强, 黄文琳 , 等. 1982-2013年青藏高原植被物候变化及气象因素影响. 地理学报, 2017,72(1):39-52.]
[8]	Luan Jinkai, Liu Dengfeng, Huang Qiang , et al. Analysis of the spatial-temporal change and impact factors of the vegetation index in Yulin, Shaanxi Province, in the last 17 years. Acta Ecologica Sinica, 2018,38(8):2780-2790.
	[ 栾金凯, 刘登峰, 黄强 , 等. 近17年陕西榆林植被指数的时空变化及影响因素. 生态学报, 2018,38(8):2780-2790.]
[9]	Liu Huiyu, Zhang Mingyang, Lin Zhenshan , et al. Spatial heterogeneity of the relationship between vegetation dynamics and climate change and their driving forces at multiple time scales in Southwest China. Agricultural and Forest Meteorology, 2018,256/257:10-21.
[10]	Wang Tao, Bai Hongying . Variation of vegetation NDVI in response to climate changes and human activities in Qinling Mountains. Mountain Research, 2017,35(6):778-789.
	[ 王涛, 白红英 . 秦岭山地植被NDVI对气候变化与人类活动的响应. 山地学报, 2017,35(6):778-789.]
[11]	Leroux L, Bégué, Agnès, Lo Seen D , et al. Driving forces of recent vegetation changes in the Sahel: Lessons learned from regional and local level analyses. Remote Sensing of Environment, 2017,191:38-54.
[12]	Zheng Jie, Feng Wanlan, Niu Xiaojun , et al. Vegetation change and its correlation with meteorogical factors. Bulletin of Soil and Water Conservation, 2016,36(2):99-104.
	[ 郑杰, 冯文兰, 牛晓俊 , 等. 四川省植被变化及其与气象因子的相关性分析. 水土保持通报, 2016,36(2):99-104.]
[13]	Zoungrana J B, Conrad C, Thiel M , et al. MODIS NDVI trends and fractional land cover change for improved assessments of vegetation degradation in Burkina Faso, West Africa. Journal of Arid Environments, 2018,153:66-75.
[14]	ZhangYing, Zhang Chaobin, Wang Zhaoqi , et al. Vegetation dynamics and its driving forces from climate change and human activities in the Three-River Source Region, China from 1982 to 2012. Science of The Total Environment, 2016, 563-564:210-220.
[15]	Qu Sai, Wang Lunche, Lin Aiwen , et al. What drives the vegetation restoration in Yangtze River basin, China: Climate change or anthropogenic factors? Ecological Indicators, 2018,90:438-450.
[16]	Lamchin M, Lee W K, Jeon S W , et al. Long-term trend and correlation between vegetation greenness and climate variables in Asia based on satellite data. Science of The Total Environment, 2018,618:1089-1095.
[17]	Zhang Jianliang, Liu Fangzheng, Cui Guofa . Spatio-temporal variation of vegetation and analysis of its driving factors in Changbai Mountain National Nature Reserve. Acta Ecologica Sinica, 2016,36(12):3525-3536.
	[ 张建亮, 刘方正, 崔国发 . 长白山国家级自然保护区植被时空变化及其驱动因子. 生态学报, 2016,36(12):3525-3536.]
[18]	Whetton R, Zhao Y, Shaddad S , et al. Nonlinear parametric modelling to study how soil properties affect crop yields and NDVI. Computers and Electronics in Agriculture, 2017,138:127-136.
[19]	Wang Zhaoqi, Zhang Yanzhen, Yang Yue , et al. Quantitative assess the driving forces on the grassland degradation in the Qinghai-Tibet Plateau, in China. Ecological Informatics, 2016,33:32-44.
[20]	Hein L, de Ridder N, Hiernaux P , et al. Desertification in the Sahel: Towards better accounting for ecosystem dynamics in the interpretation of remote sensing images. Journal of Arid Environments, 2011,75(11):1164-1172.
[21]	Wang J F, Li X H, Christakos G , et al. Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun region, China. International Journal of Geographical Information Science, 2010,24(1):107-127. doi: 10.1080/13658810802443457
[22]	Peng Wenfu, Wang Guangjie, Zhou Jieming , et al. Dynamic monitoring of fractional vegetation cover along Minjiang River from Wenchuan County to Dujiangyan City using multi-temporal landsat 5 and 8 images. Acta Ecologica Sinica, 2016,36(7):1975-1988.
	[ 彭文甫, 王广杰, 徐新良 , 等. 基于多时相 Landsat5/8 影像的岷江汶川—都江堰段植被覆盖动态监测. 生态学报, 2016,36(7):1975-1988.]
[23]	Xu Jiceng, Tang Bin, Lu Tao . Monitoring the riparian vegetation cover after the Wenchuan Earthquake along the Minjiang River Valley based on multi-temporal Landsat TM images: A case study of the Yingxiu-Wenchuan section. Acta Ecologica Sinica, 2013,33(16):4966-4974.
	[ 许积层, 唐斌, 卢涛 . 基于多时相Landsat TM影像的汶川地震灾区河岸带植被覆盖动态监测: 以岷江河谷映秀—汶川段为例. 生态学报, 2013,33(16):4966-4974.]
[24]	Xiao Jianyong, Zhou Dequan, Bai Xiaoyong , et al. Analysis on spatial and temporal change and its future trend of vegetation cover in Sichuan Province. Yangtze River, 2018,49(5):16-21.
	[ 肖建勇, 周德全, 白晓永 , 等. 四川省植被覆盖时空演变及未来变化趋势分析. 人民长江, 2018,49(5):16-21.]
[25]	Zheng Jie, Feng Wenlan, Niu Xiaojun , et al. Vegetation change and its correclation with meteorological factonr in Sichuan Province. Bulletin of Soil and Water Conservation, 2016,36(2):99-104.
	[ 郑杰, 冯文兰, 牛晓俊 , 等. 四川省植被变化及其与气象因子的相关性分析. 水土保持通报, 2016,36(2):99-104.]
[26]	Du Yanxiu . Quantitative study on spatial and temporal variation and driving force of vegetation cover in Sichuan Province[D]. Chengdu: Chengdu Univerisity of Technology.
	[ 杜艳秀 . 四川省植被覆盖时空变化及驱动力定量研究[D]. 成都: 成都理工大学, 2016.]
[27]	Liu Yansui, Li Jintao . Geographic detection and optimizing decision of the differentiation mechanism of rural poverty in China. Acta Geographica Sinica, 2017,72(1):161-173.
	[ 刘彦随, 李进涛 . 中国县域农村贫困化分异机制的地理探测与优化决策. 地理学报, 2017,72(1):161-173.]
[28]	Wang Jinfeng, Xu Chengdong . Geodetector: Principle and prospective. Acta Geographica Sinica, 2017,72(1):116-134.
	[ 王劲峰, 徐成东 . 地理探测器: 原理与展望. 地理学报, 2017,72(1):116-134.]
[29]	Wang J F, Zhang T L, Fu B J . A measure of spatial stratified heterogeneity. Ecological Indicators, 2016,67:250-256.
[30]	Guli Jiapaer, Liang S, Yi Q , et al. Vegetation dynamics and responses to recent climate change in Xinjiang using leaf area index as an indicator. Ecological Indicators, 2015,58:64-76.
[31]	Stanhill G, Cohen S . Solar radiation changes in the United States during the twentieth century: Evidence from sunshine duration measurements. Journal of Climate, 2005,18:1503-1512.
[32]	Nicholson S E, Farrar T J . The influence of soil type on the relationships between NDVI, rainfall, and soil moisture in semiarid Botswana. I. NDVI response to rainfall. Remote Sensing of Environment, 1994,50:107-120.
[33]	Trujillo E, Molotch N P, Goulden M L , et al. Elevation-dependent influence of snow accumulation on forest greening. Nature Geoscience, 2012,5:705-709.
[34]	Sundqvist M K, Liu Z, Giesler R , et al. Plant and microbial responses to nitrogen and phosphorus addition across an elevational gradient in subarctic tundra. Ecology, 2014,95:1819-1835.
[35]	Piao Shilong, Fang Jingyun . Dynamic vegetation cover change over the last 18 years in China. Quaternary Science, 2001,21(4):294-302.
	[ 朴世龙, 方精云 . 最近18年来中国植被覆盖的动态变化. 第四纪研究, 2001,21(4):294-302.]
[36]	Liu Xianfeng, Zhu Xiufang, Pan Yaozhong , et al. Spatiotemporal changes in vegetation coverage in China during 1982-2012. Acta Ecologica Sinica, 2015,35(16):5331-5342.
	[ 刘宪锋, 朱秀芳, 潘耀忠 , 等. 1982-2012年中国植被覆盖时空变化特征. 生态学报, 2015,35(16):5331-5342.]
[37]	Chen Huan, Ren Zhiyuan . Response of vegetation coverage to changes of precipitation and temperature in Chinese Mainland. Bulletin of Soil and Conservation, 2013,33(2):78-82.
	[ 陈欢, 任志远 . 中国大陆植被覆盖对降水与温度变化的响应. 水土保持通报, 2013,33(2):78-82.]
[38]	Wang Zhipeng, Zhangh Xianzhou, He Yongtao , et al. Responses of normalized difference vegetation index (NDVI) to precipitation changes on the grassland of Tibetan Plateau from 2000 to 2015. Chinese Journal of Applied Ecology, 2018,29(1):75-83.
	[ 王志鹏, 张宪洲, 何永涛 , 等. 2000-2015年青藏高原草地归一化植被指数对降水变化的响应. 应用生态学报, 2018,29(1):75-83.]

类型	探测因子	指标	单位	类型	探测因子	指标	单位
气候	X₁	年均降水量	mm	植被	X₇	植被类型	-
	X₂	干燥度指数	-	地貌	X₈	地貌	-
	X₃	湿润指数	-	土壤	X₉	土壤类型	-
	X₄	≥ 10 ℃积温	℃	地形	X₁₀	高程	m
	X₅	年均温	℃		X₁₁	坡度	°
	X₆	总辐射	MJ/m²		X₁₂	坡向	°

年份	2000年		2015年		2000-2015年
植被NDVI等级	面积(km²)	比例(%)	面积(km²)	比例(%)	面积变化(km²)	比例(%)
≤ 0.2	2072.104	0.429	1703.75	0.353	-368.354	-0.076
0.2~0.4	5358.020	1.110	4655.25	0.964	-702.770	-0.146
0.4~0.6	19194.216	3.975	17313.5	3.586	-1880.716	-0.390
0.6~0.8	119556.267	24.761	92525.75	19.163	-27030.517	-5.598
> 0.8	336651.193	69.724	366633.5	75.934	29982.307	6.210

植被NDVI等级	≤ 0.2	0.2~0.4	0.4~0.6	0.6~0.8	> 0.8	2015年合计	转入
≤ 0.2	1487.75	182.01	7.50	2.25	0	1703.75	216.00
0.2~0.4	536.75	3059.65	1008.05	50.00	1.00	4655.25	1595.75
0.4~0.6	41.25	1931.10	11377.55	3739.19	225.26	17313.5	10826.75
0.6~0.8	4	154.76	6487.06	67633.64	18250.92	92525.75	24895.50
> 0.8	2.25	30.50	314.02	48131.17	318173.97	366633.5	48475.50
2000年合计	2072	5358.02	19194.17	119556.25	336651.15
转出	584.25	2298.25	7816.25	51920	18476.25
变化量	-368.25	-702.5	-1 879.75	-27 024.5	29 999.25

自然因子	X₁	X₂	X₃	X₄	X₅	X₆	X₇	X₈	X₉	X₁₀	X₁₁	X₁₂
PD	0.1030	0.2337	0.0078	0.2013	0.3171	0.1341	0.0957	0.1741	0.3401	0.3316	0.0042	0.0042
p值	0.000	0.000	1	0.000	0.000	0.000	0.000	0.000	0.000	0.000	1	1

地貌类型	X₁	X₂	X₃	X₄	X₆	X₇	X₈	X₉	X₁₀	X₁₁	X₁₂
平原	0.1133	0.0357	0.0781	0.0333	0.0495	0.0628	0.0125	0.0748	0.0557	0.0121	0.0288
台地	0.0503	0.0690	0.0203	0.0922	0.0815	0.1222	0.0115	0.0782	0.1080	0.0067	0.0061
小起伏山地	0.1998	0.1715	0.0103	0.1861	0.1953	0.3339	0.0829	0.2367	0.2903	0.0162	0.0210
中起伏山地	0.1810	0.2927	0.0200	0.2554	0.3004	0.3351	0.0898	0.3246	0.3715	0.0383	0.0087
大起伏山地	0.1379	0.2905	0.0048	0.2340	0.4532	0.0893	0.2011	0.3617	0.4620	0.0384	0.0096
极大起伏山地	0.2429	0.0489	0.1104	0.0080	0.2084	0.0942	0.4068	0.5262	0.2625	0.1055	0.0617

自然因子对四川植被NDVI变化的地理探测

Influence of natural factors on vegetation NDVI using geographical detection in Sichuan Province

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土壤类型	X₁	X₂	X₃	X₄	X₆	X₇	X₈	X₉	X₁₀	X₁₁	X₁₂
淋溶土	0.0059	0.0276	0.0201	0.0618	0.0546	0.0602	0.0454	0.0876	0.0648	0.0645	0.0126
半淋溶土	0.1914	0.1081	0.0435	0.2581	0.3155	0.0607	0.0500	0.2898	0.4194	0.1006	0.0752
初育土	0.0104	0.0185	0.0238	0.0501	0.0270	0.1072	0.0177	0.0357	0.0432	0.0182	0.0082
半水成土	0.4057	0.0143	0.0512	0.0487	0.0252	0.0207	0.0234	0.0298	0.0137	0.0070	0.1748
水成土	0.1098	0.1619	0.1231	0.0063	0.1338	0.2520	0.0063	0.3365	0.1579	0.0877	0.4586
人为土	0.1351	0.0003	0.1245	0.0151	0.0011	0.0084	0.0133	0.1026	0.0000	0.0362	0.0118
高山土	0.0392	0.0930	0.0441	0.0321	0.2360	0.0207	0.0454	0.1097	0.2729	0.0257	0.0188
铁铝土	0.0303	0.0048	0.0292	0.0624	0.0410	0.1712	0.0206	0.0449	0.0171	0.0487	0.0149

一级气候区	二级名称	X₁	X₂	X₃	X₄	X₅	X₆	X₇	X₈	X₉	X₁₀	X₁₁	X₁₂
北亚热带	秦巴区	0.0735	0.1949	0.0624	0.2074	0.3250	0.0559	0.0291	0.0676	0.3193	0.4073	0.0563	0.0715
中亚热带	四川区	0.0678	0.1162	0.0403	0.1084	0.1805	0.0509	0.0145	0.2444	0.2129	0.2034	0.0407	0.0058
	贵州区	0.0172	0.0022	0.9004	0.0509	0.0022	0.9175	0.8952	0.0038	0.9014	0.0009	0.0540	0.0460
	滇北区	0.0047	0.0165	0.1901	0.1260	0.0645	0.2739	0.1082	0.1213	0.2928	0.4317	0.4048	0.3876
高原气候区	昌都区	0.0739	0.1438	0.0414	0.0887	0.2085	0.0048	0.1408	0.1243	0.1878	0.3188	0.1186	0.1354
高原气候区	青南区	0.1819	0.1419	0.0166	0.1012	0.3245	0.0428	0.2432	0.1330	0.3749	0.3339	0.0312	0.0146

自然因子	植被NDVI适宜类型或范围	植被NDVI均值
年均降水量(mm)	1394~1739	0.906
干燥度指数	0~0.2	0.908
湿润指数	96~186	0.906
≥ 10 °C积温(℃)	3638~4709	0.907
年均温(℃)	11.48~16.40	0.911
总辐射(MJ/m²)	3779.82~4215.35	0.912
高程(m)	1885~2852	0.902
坡度(°)	4.64~9.63	0.881
坡向(°)	0~22.5、157.5~202.5	0.881
植被类型	针叶林、阔叶林	0.907
地貌类型	丘陵、小起伏山地和中起伏山地等	0.902
土壤类型	红壤、黄壤、暗棕壤、草甸土、褐土、棕壤	0.903

	X₁	X₂	X₃	X₄	X₅	X₆	X₇	X₈	X₉	X₁₀	X₁₁	X₁₂
X₁	0.103
X₂	0.294	0.234
X₃	0.125	0.266	0.008
X₄	0.277	0.246	0.238	0.201
X₅	0.383	0.331	0.360	0.325	0.317
X₆	0.211	0.263	0.191	0.239	0.355	0.134
X₇	0.198	0.314	0.133	0.295	0.377	0.291	0.096
X₈	0.300	0.327	0.208	0.307	0.400	0.288	0.300	0.174
X₉	0.398	0.381	0.380	0.377	0.432	0.373	0.380	0.423	0.340
X₁₀	0.415	0.344	0.366	0.354	0.374	0.386	0.400	0.426	0.446	0.332
X₁₁	0.136	0.265	0.034	0.229	0.346	0.167	0.167	0.207	0.378	0.372	0.004
X₁₂	0.123	0.250	0.017	0.218	0.329	0.159	0.122	0.189	0.372	0.342	0.022	0.004

分区	1	2	3	4	5	6	7	8	9	10	11	12	13
1
2	Y
3	N	Y
4	Y	N	Y
5	Y	Y	Y	Y
6	Y	N	Y	Y	Y
7	Y	Y	Y	Y	N	Y
8	Y	Y	N	Y	Y	Y	Y
9	Y	Y	Y	Y	Y	Y	Y	Y
10	N	N	N	N	N	N	N	N	N
11	N	Y	N	Y	Y	Y	Y	N	Y	N
12	Y	N	Y	N	Y	Y	Y	Y	Y	N	Y
13	Y	Y	Y	Y	Y	Y	Y	Y	Y	N	Y	Y
NDVI均值	0.898	0.824	0.902	0.845	0.829	0.869	0.746	0.903	0.651	0.648	0.897	0.866	0.167

分区	1	2	3	4	5	6
1
2	Y
3	Y	N
4	N	Y	Y
5	Y	Y	Y	Y
6	Y	Y	Y	Y	Y
NDVI均值	0.890	0.903	0.892	0.873	0.843	0.648

分区	1	2	3	4	5	6
1
2	Y
3	Y	Y
4	Y	Y	N
5	Y	Y	Y	Y
6	Y	Y	N	Y	Y
NDVI均值	0.640	0.821	0.879	0.895	0.912	0.887

分区	1	2	3	4	5	6
1
2	Y
3	Y	Y
4	Y	Y	N
5	Y	Y	Y	Y
6	N	Y	Y	Y	Y
NDVI均值	0.769	0.892	0.906	0.907	0.886	0.811

分区	1	2	3	4	5	6
1
2	Y
3	Y	Y
4	Y	Y	Y
5	Y	Y	N	Y
6	Y	Y	Y	Y	Y
NDVI均值	0.841	0.882	0.902	0.889	0.888	0.848

分区	1	2	3	4	5	6
1
2	Y
3	Y	Y
4	Y	Y	Y
5	Y	Y	N	Y
6	Y	Y	Y	Y	Y
NDVI均值	0.648	0.686	0.623	0.573	0.610	0.547

分区	1	2	3	4	5	6
1
2	Y
3	Y	Y
4	Y	Y	Y
5	Y	Y	Y	Y
6	Y	Y	Y	N	Y
NDVI均值	0.792	0.784	0.859	0.850	0.888	0.906

[1]	白燕, 廖顺宝, 孙九林. 栅格化属性精度损失的评估方法及其尺度效应分析——以四川省1:25 万土地覆被数据为例[J]. 地理学报, 2011, 66(5): 709-717.
[2]	王茂军, 杨雪春. 四川省制造产业关联网络的结构特征分析[J]. 地理学报, 2011, 66(2): 212-222.
[3]	史春云, 张捷, 尤海梅, 李东和, 王艳. 四川省旅游区域核心—边缘空间格局演变[J]. 地理学报, 2007, 62(6): 631-639.
[4]	杨国良,张捷,艾南山,刘波. 旅游流齐夫结构及空间差异化特征——以四川省为例[J]. 地理学报, 2006, 61(12): 1281-1289.