基于地理探测器的科尔沁沙地植被NDVI时空变化特征及其驱动因素

doi:10.17521/cjpe.2022.0020

植物生态学报 ›› 2023, Vol. 47 ›› Issue (8): 1082-1093.DOI: 10.17521/cjpe.2022.0020

所属专题：生态遥感及应用

基于地理探测器的科尔沁沙地植被NDVI时空变化特征及其驱动因素

陈雪萍¹^,³, 赵学勇²^,³^,^*(), 张晶², 王瑞雄¹^,³, 卢建男¹^,³

¹中国科学院大学, 北京 100049
²中国科学院西北生态环境资源研究院, 兰州 730000
³中国科学院西北生态环境资源研究院奈曼沙漠化研究站, 内蒙古通辽 028300

收稿日期:2022-01-13 接受日期:2023-03-13 出版日期:2023-08-20 发布日期:2023-04-06
通讯作者: *赵学勇(zhaoxy@lzb.ac.cn)
基金资助:
国家科技基础资源调查专项(2017FY100200)

Variation of NDVI spatio-temporal characteristics and its driving factors based on geodetector model in Horqin Sandy Land, China

CHEN Xue-Ping¹^,³, ZHAO Xue-Yong²^,³^,^*(), ZHANG Jing², WANG Rui-Xiong¹^,³, LU Jian-Nan¹^,³

¹University of Chinese Academy of Sciences, Beijing 100049, China
²Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
³Naiman Desertification Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Tongliao, Nei Mongol 028300, China

Received:2022-01-13 Accepted:2023-03-13 Online:2023-08-20 Published:2023-04-06
Contact: *ZHAO Xue-Yong(zhaoxy@lzb.ac.cn)
Supported by:
National Science and Technology Basic Resources Survey Special Program of China(2017FY100200)

摘要/Abstract

摘要：

科尔沁沙地是内蒙古地区典型而且高度退化的农牧交错区域。受气候变化和沙地恢复政策的影响, 该区域近20年来植被发生了显著的变化。该研究基于2001-2020年MOD13A2归一化植被指数(NDVI)数据与10个重要的驱动因子(年平均气温、年降水量、坡度、土壤类型、植被类型、地貌类型、人口密度、累积造林面积、牲畜密度和农作物种植面积)的空间关联, 采用地理探测器模型量化了各驱动因子对科尔沁沙地植被NDVI的影响力和相互作用, 确定了有利于植被生长的各驱动因子的适宜范围。结果表明: (1) 20年来, 科尔沁沙地植被覆盖度逐渐增加, 植被明显恢复地区面积超过64.91%, 主要分布在科尔沁沙地北部、中部以及东南缘区域。(2)土壤类型、地貌类型和年平均气温对科尔沁沙地植被NDVI的影响力较大。(3)各驱动因子之间的交互作用为非线性增强和双因子增强, 其中土壤类型与其他因子存在较强的交互作用。(4)有利于科尔沁沙地植被生长的驱动因子适宜范围, 如: 土壤类型为淋溶土, 地貌类型为丘陵及小起伏山地, 年平均气温4.68-5.67 ℃等环境条件, 能够对植被适应变化环境起到缓冲作用。该研究结果有助于更好地理解植被变化的复杂机制, 为后期科尔沁沙地植被恢复的综合治理以及生态工程的合理实施提供科学依据。

关键词: 归一化植被指数, 地理探测器, 驱动因子, 植被恢复, 科尔沁沙地

Abstract:

Aims Horqin Sandy Land is an important, but highly degraded, agro-pastoral region in the northern China region of Nei Mongol. There have been significant changes in vegetation condition over the past two decades, in response to changes in climate as well as restoration policies. In this study, we characterize the spatial and temporal changes in vegetation in the region over the past twenty years, in order to understand the complex mechanism of vegetation change, and provide a scientific basis for comprehensive management and rational implementation of ecological engineering in the future.

Methods We assessed the correlation between a time series of Normalized Difference Vegetation Index (NDVI) (derived from MODIS) from 2001 to 2020 with 10 key driving factors (including mean annual temperature, mean annual precipitation, slope, soil type, vegetation type, geomorphic type, population density, accumulated afforestation area, livestock density, and crops area) in space on random sampling points, which were generated in ArcGIS software. Geodetector model was used to explore the individual relationships as well as their interactions.

Important findings The results demonstrated that: (1) over the past 20 years, the vegetation coverage of Horqin Sandy Land has been gradually recovering, primarily in the northern, central and southeastern marginal areas of the study area, recovery area accounted for more than 64.91%. (2) Changes in NDVI were primarily explained in Horqin Sandy Land by variation in three factors, soil type, geomorphic type, and mean annual temperature. (3) The interactions between explanatory factors were nonlinearly and mutually enhanced, of these, there was a strong interaction between soil type and other factors. (4) Increases in vegetation cover in Horqin Sandy Land was primarily observed in association with alfisol, hills or small undulating mountains, and annual average temperature ranges 4.68-5.67 °C and so on. Future restoration programs may want to prioritize sites with these conditions.

Key words: Normalized Difference Vegetation Index (NDVI), geodetector model, driving factor, vegetation restoration, Horqin Sandy Land

陈雪萍, 赵学勇, 张晶, 王瑞雄, 卢建男. 基于地理探测器的科尔沁沙地植被NDVI时空变化特征及其驱动因素. 植物生态学报, 2023, 47(8): 1082-1093. DOI: 10.17521/cjpe.2022.0020

CHEN Xue-Ping, ZHAO Xue-Yong, ZHANG Jing, WANG Rui-Xiong, LU Jian-Nan. Variation of NDVI spatio-temporal characteristics and its driving factors based on geodetector model in Horqin Sandy Land, China. Chinese Journal of Plant Ecology, 2023, 47(8): 1082-1093. DOI: 10.17521/cjpe.2022.0020

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https://www.plant-ecology.com/CN/Y2023/V47/I8/1082

图/表 10

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类型 Type	驱动因子 Driving factor	指标 Index	单位 Unit	数据来源 Data resource
自然因素 Natural factor	X₁	年平均气温 Mean annual temperature	℃	中国气象数据共享网 China Meteorological Data Service Center http://www.nmic.cn/
	X₂	年降水量 Mean annual precipitation	mm	中国气象数据共享网 China Meteorological Data Service Center http://www.nmic.cn/
	X₃	坡度 Slope	。	美国NASA获取的SRTM 30 m DEM数据 SRTM 30 m DEM data acquired by NASA https://nasasearch.nasa.gov/
	X₄	土壤类型 Soil type	-	中国科学院资源环境科学与数据中心 Resource and Environment Science and Data Center, Chinese Academy of Sciences (CAS) https://www.resdc.cn/
	X₅	植被类型 Vegetation type	-	中国科学院资源环境科学与数据中心 Resource and Environment Science and Data Center, CAS https://www.resdc.cn/
	X₆	地貌类型 Geomorphic type	-	中国科学院资源环境科学与数据中心 Resource and Environment Science and Data Center, CAS https://www.resdc.cn/
人为因素 Anthropogenic factor	X₇	人口密度 Population density	人·km^-2 person·km^-2	内蒙古自治区统计年鉴 Inner Mongolia Statistical Yearbook http://tj.nmg.gov.cn/tjyw/jpsj/
	X₈	累积造林面积 Accumulated afforestation area	hm²·km^-2	中国林业统计年鉴 China Forestry Statistical Yearbook https://www.tongjinianjian.com/111453.html
	X₉	牲畜密度 Livestock density	头·km^-2 head·km^-2	内蒙古自治区统计年鉴 Inner Mongolia Statistical Yearbook http://tj.nmg.gov.cn/tjyw/jpsj/
	X₁₀	农作物种植面积 Crops area	hm²·km^-2	内蒙古自治区统计年鉴 Inner Mongolia Statistical Yearbook http://tj.nmg.gov.cn/tjyw/jpsj/

类型 Type	驱动因子 Driving factor	指标 Index	单位 Unit	数据来源 Data resource
自然因素 Natural factor	X₁	年平均气温 Mean annual temperature	℃	中国气象数据共享网 China Meteorological Data Service Center http://www.nmic.cn/
	X₂	年降水量 Mean annual precipitation	mm	中国气象数据共享网 China Meteorological Data Service Center http://www.nmic.cn/
	X₃	坡度 Slope	。	美国NASA获取的SRTM 30 m DEM数据 SRTM 30 m DEM data acquired by NASA https://nasasearch.nasa.gov/
	X₄	土壤类型 Soil type	-	中国科学院资源环境科学与数据中心 Resource and Environment Science and Data Center, Chinese Academy of Sciences (CAS) https://www.resdc.cn/
	X₅	植被类型 Vegetation type	-	中国科学院资源环境科学与数据中心 Resource and Environment Science and Data Center, CAS https://www.resdc.cn/
	X₆	地貌类型 Geomorphic type	-	中国科学院资源环境科学与数据中心 Resource and Environment Science and Data Center, CAS https://www.resdc.cn/
人为因素 Anthropogenic factor	X₇	人口密度 Population density	人·km^-2 person·km^-2	内蒙古自治区统计年鉴 Inner Mongolia Statistical Yearbook http://tj.nmg.gov.cn/tjyw/jpsj/
	X₈	累积造林面积 Accumulated afforestation area	hm²·km^-2	中国林业统计年鉴 China Forestry Statistical Yearbook https://www.tongjinianjian.com/111453.html
	X₉	牲畜密度 Livestock density	头·km^-2 head·km^-2	内蒙古自治区统计年鉴 Inner Mongolia Statistical Yearbook http://tj.nmg.gov.cn/tjyw/jpsj/
	X₁₀	农作物种植面积 Crops area	hm²·km^-2	内蒙古自治区统计年鉴 Inner Mongolia Statistical Yearbook http://tj.nmg.gov.cn/tjyw/jpsj/

交互作用类型 Interaction type	q值关系 q value relationship
非线性减弱 Non-linear reduction	q(X₁∩X₂) < min(q(X₁), q(X₂))
单因子非线性减弱 Single factor non-linear reduction	min(q(X₁), q(X₂)) < q(X₁∩X₂) < max(q(X₁), q(X₂))
双因子增强 Bi-factor enhancement	q(X₁∩X₂) > max(q(X₁), q(X₂))
独立 Independent	q(X₁∩X₂) = q(X₁) + q(X₂)
非线性增强 Non-linear enhancement	q(X₁∩X₂) > q(X₁) + q(X₂)

交互作用类型 Interaction type	q值关系 q value relationship
非线性减弱 Non-linear reduction	q(X₁∩X₂) < min(q(X₁), q(X₂))
单因子非线性减弱 Single factor non-linear reduction	min(q(X₁), q(X₂)) < q(X₁∩X₂) < max(q(X₁), q(X₂))
双因子增强 Bi-factor enhancement	q(X₁∩X₂) > max(q(X₁), q(X₂))
独立 Independent	q(X₁∩X₂) = q(X₁) + q(X₂)
非线性增强 Non-linear enhancement	q(X₁∩X₂) > q(X₁) + q(X₂)

分级 Class	X₁(℃)	X₂(mm)	X₃(°)	X₄	X₅	X₆	X₇(person·km^-2)	X₈(hm²·km^-2)	X₉(head·km^-2)	X₁₀(hm²·km^-2)
1	4.68-5.67	462.77-492.69	0-2	淋溶土 Alfisol	针叶林 Needle leaf forest	平原 Plain	17.67-41.26	1.69-2.94	8.99-70.65	13.30-13.40
2	5.67-6.45	492.69-511.24	2-3	钙层土 Pedocal	阔叶林 Broadleaf forest	台地 Platform	41.26-49.39	2.94-4.41	70.65-96.30	13.40-16.09
3	6.45-7.07	511.24-529.69	3-5	初育土 Primary	灌丛 Shrub	丘陵 Hill	49.39-56.81	4.41-4.89	96.30-110.69	16.09-18.02
4	7.07-7.66	529.69-547.57	5-8	半水成土 Semi- hydromorph	草原 Steppe	小起伏山地 Small undulating mountain	56.81-67.65	4.89-5.14	110.69-118.50	18.02-18.89
5	7.66-8.22	547.57-566.55	8-10	水成土 Hydromorph	草甸 Meadow	中起伏山地 Middle undulating mountain	67.65-86.71	5.14-5.29	118.50-128.85	18.89-20.18
6	8.22-8.72	566.55-587.78	10-13	盐碱土 Saline-alkali	人工林Artificial forest		86.71-132.62	5.29-5.56	128.85-141.48	20.18-21.56
7	8.72-9.32	587.78-628.31	13-15	湖泊水库 Lakes and reservoirs			132.62-260.95	5.56-7.89	141.48-224.26	21.56-33.17
8			15-17	河流 Rivers
9			17-30	河内沙洲 Sandbank

基于地理探测器的科尔沁沙地植被NDVI时空变化特征及其驱动因素

Variation of NDVI spatio-temporal characteristics and its driving factors based on geodetector model in Horqin Sandy Land, China

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NDVI分级 NDVI class	2001		2010		2020
NDVI分级 NDVI class	面积 Area (km²)	比例 Proportion (%)	面积 Area (km²)	比例 Proportion (%)	面积 Area (km²)	比例 Proportion (%)
<0.2	4 696	2.79	6 905	4.10	7 265	4.31
0.2-0.4	114 900	68.20	116 634	69.22	85 884	50.97
0.4-0.6	48 028	28.50	44 176	26.22	70 181	41.66
≥0.6	866	0.51	775	0.46	5 160	3.06

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