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 Published:2019-09-25 Online: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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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

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