自然因子对四川植被NDVI变化的地理探测

doi:10.11821/dlxb201909005

Abstract

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

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.

Figures/Tables 23

Fig. 1

Tab. 1

Tab. 2

Fig. 2

Fig. 3

Tab. 3

Tab. 4

Fig. 4

Tab. 5

Tab. 6

Tab. 7

Tab. 8

Tab. 9

Tab. 10

Tab. 11

Interaction between natural factors that influence changes of vegetation NDVI

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

Tab. 11

Tab. 12

Tab. 13

Tab. 14

Tab. 15

Tab. 16

Tab. 17

Tab. 18

Tab. 19

References 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

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

RichHTML

PDF (PC)

Like

Knowledge

Cited

Abstract

Cite this article

share this article

Figures/Tables 23

References 38

Related Articles 15

Metrics

Comments

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]	QI Guizeng, BAI Hongying, ZHAO Ting, MENG Qing, ZHANG Shanhong. Sensitivity and areal differentiation of vegetation responses to hydrothermal dynamics on the southern and northern slopes of the Qinling Mountains in Shaanxi province [J]. Acta Geographica Sinica, 2021, 76(1): 44-56.
[2]	JIN Kai, WANG Fei, HAN Jianqiao, SHI Shangyu, DING Wenbin. Contribution of climatic change and human activities to vegetation NDVI change over China during 1982-2015 [J]. Acta Geographica Sinica, 2020, 75(5): 961-974.
[3]	ZHOU Yuke. Analysis of controlling factors for vegetation productivity in Northeast China [J]. Acta Geographica Sinica, 2020, 75(1): 53-67.
[4]	Jiangbo GAO, Kewei JIAO, Shaohong WU. Revealing the climatic impacts on spatial heterogeneity of NDVI in China during 1982-2013 [J]. Acta Geographica Sinica, 2019, 74(3): 534-543.
[5]	Haidong GAO, Guowei PANG, Zhanbin LI, Shengdong CHENG. Evaluating the potential of vegetation restoration in the Loess Plateau [J]. Acta Geographica Sinica, 2017, 72(5): 863-874.
[6]	Dongdong KONG, Qiang ZHANG, Wenlin HUANG, Xihui GU. Vegetation phenology change in Tibetan Plateau from 1982 to2013 and its related meteorological factors [J]. Acta Geographica Sinica, 2017, 72(1): 39-52.
[7]	Yuehong LONG, Jianxin QIN, Xinguang HE, Zhun YANG. Wavelet multi-resolution analysis of vegetation dynamic change in Dongting Lake Basin [J]. Acta Geographica Sinica, 2015, 70(9): 1491-1502.
[8]	Mingjun DING, Qian CHEN, Liangjie XIN, Lanhui LI, Xiubin LI. Spatial and temporal variations of multiple cropping index in China based on SPOT-NDVI during 1999-2013 [J]. Acta Geographica Sinica, 2015, 70(7): 1080-1090.
[9]	Shiji LI, Xiubin LI, Minghong TAN. Impacts of rural-urban migration on vegetation cover in ecologically fragile areas: Taking Inner Mongolia as a case [J]. Acta Geographica Sinica, 2015, 70(10): 1622-1631.
[10]	YU Bohua, LV Changhe, LV Tingting, YANG Aqiang, LIU Chuang. Datasets of the boundary and area of the Tibetan Plateau [J]. Acta Geographica Sinica, 2014, 69(s1): 65-68.
[11]	ZHANG Xuezhen, DAI Junhu, GE Quansheng. Spatial Differences of Changes in Spring Vegetation activities across Eastern China during 1982-2006 [J]. Acta Geographica Sinica, 2012, 67(1): 53-61.
[12]	LI Zhengguo, TANG Huajun, YANG Peng, ZHOU Qingbo, WUWenbin, ZOU Jinqiu, ZHANG Li, CHANG Hsiaofei. Responses of Cropland Phenophases to Agricultural Thermal Resources Change in Northeast China [J]. Acta Geographica Sinica, 2011, 66(7): 928-939.
[13]	BAI Yan, LIAO Shunbao, SUN Jiulin. Evaluating Methods and Scale Effects of Attribute Information Loss in Rasterization: A Case Study of 1:250 000 Land Cover Data of Sichuan [J]. Acta Geographica Sinica, 2011, 66(5): 709-717.
[14]	WANG Maojun, YANG Xuechun. The Structural Features of Regional Manufacturing Industrial Association Network:A Case Study of Sichuan Province [J]. Acta Geographica Sinica, 2011, 66(2): 212-222.
[15]	ZHANG Geli, XU Xingliang, ZHOU Caiping, ZHANG Hongbin, OUYANG Hua. Responses of Vegetation Changes to Climatic Variations in Hulun Buir Grassland in Past 30 Years [J]. Acta Geographica Sinica, 2011, 66(1): 47-58.