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：遥感生态学

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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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2022.0020

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

图/表 10

参考文献 51

[1]	Bai HX, Li DY, Ge Y, Wang JF, Cao F (2022). Spatial rough set-based geographical detectors for nominal target variables. Information Sciences, 586, 525-539. DOI URL
[2]	Chen H, Ren ZY (2013). Response of vegetation coverage to changes of precipitation and temperature in Chinese mainland. Bulletin of Soil and Water Conservation, 33, 78-82.
	[陈欢, 任志远 (2013). 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, 33, 78-82.]
[3]	Cui ZZ, Ma C, Chen DK (2021). Spatiotemporal variation of vegetation and climate response in Horqin Sandy Land from 1982 to 2015. Arid Zone Research, 38, 536-544.
	[崔珍珍, 马超, 陈登魁 (2021). 1982-2015年科尔沁沙地植被时空变化及气候响应. 干旱区研究, 38, 536-544.]
[4]	Ding Y, Zhang M, Qian X, Li C, Chen S, Wang W (2018). Using the geographical detector technique to explore the impact of socioeconomic factors on PM2.5 concentrations in China. Journal of Cleaner Production, 211, 1480-1490. DOI URL
[5]	Du Y (2021). Analysis of Land Cover Changes and Driving Factors in Horqin Sandy Land in the Past 20 Years. Master degree dissertation, Shanghai Normal University, Shanghai.
	[杜云 (2021). 科尔沁沙地近20年土地覆被变化及驱动因素分析. 硕士学位论文, 上海师范大学, 上海.]
[6]	Fan JQ, Xu Y, Ge HY, Wei Y (2020). Vegetation growth variation in relation to topography in Horqin Sandy Land. Ecological Indicators, 113, 106215. DOI: 10.1016/j.ecolind.2020.106215.
[7]	Gu Z, Duan X, Shi Y (2018). Spatiotemporal variation in vegetation coverage and its response to climatic factors in the Red River Basin, China. Ecological Indicators, 93, 54-64. DOI URL
[8]	Guo ZC, Wei W, Shi PJ, Zhou L, Wang XF, Li ZY, Pang SF, Xie BB (2020). Temporal and spatial patterns of land desertification sensitivity in arid regions of Northwest China. Acta Geographica Sinica, 75, 154-171.
	[郭泽呈, 魏伟, 石培基, 周亮, 王旭峰, 李振亚, 庞素菲, 颉斌斌 (2020). 中国西北干旱区土地沙漠化敏感性时空格局. 地理学报, 75, 154-171.]
[9]	Han Z, Wang T, Yan C, Liu Y, Liu L, Li A, Du H (2010). Change trends for desertified lands in the Horqin Sandy Land at the beginning of the twenty-first century. Environmental Earth Sciences, 59, 1749-1757. DOI URL
[10]	He B, Chen A, W Jiang, Chen Z (2017). The response of vegetation growth to shifts in trend of temperature in China. Journal of Geographical Sciences, 27, 801-816. DOI
[11]	Hein L, Ridder N, Hiernaux P, Leemans R (2011). Desertification in the Sahel: towards better accounting for ecosystem dynamics in the interpretation of remote sensing images-Science Direct. Journal of Arid Environments, 75, 164-1172. DOI URL
[12]	Hope A, Boynton W, Stow D, Douglas D (2003). Interannual growth dynamics of vegetation in the Kuparuk River watershed, Alaska based on the Normalized Difference Vegetation Index. International Journal of Remote Sensing, 24, 3413-3425. DOI URL
[13]	Huang F, Wang P, Liu XN (2008). Monitoring vegetation dynamic in Horqin sandy-land from spot vegetation time series imagery. Remote Sensing, 37, 915-920.
[14]	Huang J, Wang J, Bo Y, Xu C, Hu M, Huang D (2014). Identification of health risks of hand, foot and mouth disease in China using the geographical detector technique. International Journal of Environmental Research & Public Health, 11, 3407-3423.
[15]	Huo H, Sun CP (2021). Spatiotemporal variation and influencing factors of vegetation dynamics based on Geodetector: a case study of the northwestern Yunnan Plateau, China. Ecological Indicators, 130, 108005. DOI: 10.1016/j.ecolind.2021.108005.
[16]	Li J, Liu HB, Li CY, Li L (2017). Research on the beginning period of vegetation growth season in northern China based on GIMMS 3g NDVI in the past 30 years. Chinese Journal of Geography, 37, 620-629.
	[李净, 刘红兵, 李彩云, 李龙 (2017). 基于GIMMS 3g NDVI的近30年中国北部植被生长季始期变化研究. 地理科学, 37, 620-629.] DOI
[17]	Li SY, Wang XQ, Li B (2016). Spatio-temporal changes of vegetation coverage in Horqin District based on MODIS data. Journal of Yangtze River Scientific Research Institute, 33(2), 122-126.
	[李生勇, 王晓卿, 李彪 (2016). 基于MODIS数据的科尔沁区植被覆盖时空变化分析. 长江科学院院报, 33(2), 122-126.]
[18]	Liu MZ, Wang YF, Pei HW (2021). Changes in land use and carbon storage in the agro-pastoral ecotone of northern China under the background of returning farmland to forest (grass). Journal of Desert Research, 41(1), 174-182.
	[刘孟竹, 王彦芳, 裴宏伟 (2021). 退耕还林(草)背景下中国北方农牧交错带土地利用及碳储量变化. 中国沙漠, 41(1), 174-182.] DOI
[19]	Liu N, Peng SZ, Chen YM (2021). Temporal effects of climate factors on vegetation growth in the Qingzang Plateau. Chinese Journal of Plant Ecology, 45, 1-9. DOI URL
	[刘宁, 彭守璋, 陈云明 (2021). 气候因子对青藏高原植被生长的时间效应研究. 植物生态学报, 45, 1-9.]
[20]	Liu XF, Zhu XF, Pan YZ, Li YZ, Zhao AZ (2015). Characteristics of temporal and spatial changes of vegetation cover in China from 1982 to 2012. Acta Ecologica Sinica, 35, 5331-5342.
	[刘宪锋, 朱秀芳, 潘耀忠, 李宜展, 赵安周 (2015). 1982-2012年中国植被覆盖时空变化特征. 生态学报, 35, 5331-5342.]
[21]	Liu Z, Liu Y, Li Y (2018). Anthropogenic contributions dominate trends of vegetation cover change over the farming- pastoral ecotone of northern China. Ecological Indicators, 95, 370-378. DOI URL
[22]	Losada N, Alen E, Cotos T (2019). Spatial heterogeneity in Spain for senior travel behavior. Tourism Management, 70, 444-452. DOI
[23]	Lu J, Zhan BL, Xu GC, Ren ZP, Li P (2020). Dynamic change of vegetation and its response to climate and topographic factors in the Xijiang River basin, China. Environmental Science and Pollution Research, 27, 11637-11648. DOI
[24]	Luo XL, Li YG, Yin H, Sui M (2020). The response of vegetation NDVI in Northeast China to SPEI at different time scales. Chinese Journal of Ecology, 39, 412-421.
	[罗新兰, 李英歌, 殷红, 隋明 (2020). 东北地区植被NDVI对不同时间尺度SPEI的响应. 生态学杂志, 39, 412-421.]
[25]	Lv JX, Li XF, Zheng X, Nie Z, Liu SQ (2020). Spatiotemporal changes and driving forces of vegetation in Horqin Sandy Land in the past 40 years. Journal of Ecology, 39, 3-12.
	[吕家欣, 李秀芬, 郑晓, 聂哲, 刘思嫱 (2020). 近40年科尔沁沙地植被时空变化及其驱动力. 生态学杂志, 39, 3-12.]
[26]	Ma L, Wang JR, Liu XT, Huang X, Liu DH, Li HY (2016). Response relationship between vegetation and climate factors in Horgin Sandy Land from 2000 to 2012. Transactions of the Chinese Society for Agricultural Machinery, 47(4), 162-172.
	[马龙, 王静茹, 刘廷玺, 黄星, 刘丹辉, 李虹雨 (2016). 2000-2012年科尔沁沙地植被与气候因子间的响应关系. 农业机械学报, 47(4), 162-172.]
[27]	Mu SJ, Zhu C, Zhou KX, Li JL (2017). Research on grassland degradation control countermeasures and ways of increasing carbon sinks in Inner Mongolia. Acta Agrestia Sinica, 25, 217-225.
	[穆少杰, 朱超, 周可新, 李建龙 (2017). 内蒙古草地退化防治对策及碳增汇途径研究. 草地学报, 25, 217-225.] DOI
[28]	Parmesan C, Yohe G (2003). A globally coherent fingerprint of climate change impacts across natural systems. Nature, 421, 37-42. DOI
[29]	Peng W, Kuang T, Tao S (2019). Quantifying influences of natural factors on vegetation NDVI changes based on geographical detector in Sichuan, western China. Journal of Cleaner Production, 56, 233-245.
[30]	Peng WF, Wang GJ, Zhou JM, Xu XL, Luo HL, Zhao JF, Yang JC (2016). Dynamic monitoring of vegetation coverage in the Wenchuan-Dujiang Weir section of the Minjiang River based on multi-temporal Landsat 5/8 images. Acta Ecologica Sinica, 36, 1975-1988.
	[彭文甫, 王广杰, 周介铭, 徐新良, 罗怀良, 赵景峰, 杨存建 (2016). 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, 36, 1975-1988.]
[31]	Song Y, Wang J, Ge Y, Xu C (2020). An optimal parameters- based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: cases with different types of spatial data. GIScience & Remote Sensing, 57, 593-610.
[32]	Wang J, Wang K, Zhang M, Zhang C (2015). Impacts of climate change and human activities on vegetation cover in hilly southern China. Ecological Engineering, 81, 451-461. DOI URL
[33]	Wang JF, Xu CD (2017). Geodetector: principles and prospects. Acta Geographica Sinica, 72, 116-134.
	[王劲峰, 徐成东 (2017). 地理探测器: 原理与展望. 地理学报, 72, 116-134.] DOI
[34]	Wang JF, Zhang TL, Fu BJ (2016). A measure of spatial stratified heterogeneity. Ecological Indicators, 67, 250-256. DOI URL
[35]	Wang T (2003). Desert and Desertification in China. Hebei Science and Technology Publishing House, Shijiazhuang.
	[王涛 (2003). 中国沙漠与沙漠化. 河北科技出版社, 石家庄.]
[36]	Wang WH, Ma XQ, Zou XH, Tian C, Jin QF, Guo FT (2017). Spatiotemporal evolution characteristics of vegetation coverage in Fujian Province from 2000 to 2010. Research of Soil and Water Conservation, 33, 23-35.
	[王文辉, 马祥庆, 邹显花, 田超, 靳全峰, 郭福涛 (2017). 2000-2010年福建省植被覆盖度的时空演变特征. 水土保持研究, 33, 23-35.]
[37]	Wang X, Chen F, Hasi E, Li J (2008). Desertification in China: an assessment. Earth-Science Reviews, 88, 188-206. DOI URL
[38]	Wang X, Li Y, Wang XY (2021). Temporal and spatial variations in NDVI and analysis of the driving factors in the desertified areas of northern China from 1998 to 2015. Frontiers in Environmental Science, 9, 633020. DOI: 10.3389/fenvs.2021.633020.
[39]	Wang YY (2016). Research on the Temporal and Spatial Dynamic Changes of Vegetation in Northeast China and Its Response to Climate. PhD dissertation, Northeast Normal University, Changchun.
	[王彦颖 (2016). 中国东北植被时空动态变化及其对气候响应研究. 博士学位论文, 东北师范大学, 长春.]
[40]	Wei FL, Liu TX, Zhang SW, Ding L, Cui DX, Zhao ZF (2012). Vegetation cover change in Horqin Sandy Land and its relationship with climate factors. Research of Soil and Water Conservation, 19, 254-258.
	[魏丰良, 刘廷玺, 张圣微, 丁磊, 崔德新, 赵泽锋 (2012). 科尔沁沙地植被覆盖变化及其与气候因子的关系研究. 水土保持研究, 19, 254-258.]
[41]	Wei FY (2007). The Modern Climate Statistical Diagnosis and Prediction Technology. Beijing Metrology Press, Beijing.
	[魏凤英 (2007). 现代气候统计诊断预测技术. 北京气象出版社, 北京.]
[42]	Xiao Y, Ouyang ZY, Wang LY, Rao EM, Jiang L, Zhang L (2016). Ecosystem quality spatial characteristics and driving forces in Inner Mongolia. Acta Ecologica Sinica, 36, 6019-6030.
	[肖洋, 欧阳志云, 王莉雁, 饶恩明, 江凌, 张路 (2016). 内蒙古生态系统质量空间特征及其驱动力. 生态学报, 36, 6019-6030.]
[43]	Zhang C (2013). Vegetation Cover Change in Inner Mongolia and Its Relationship with Regional Climate. Master degree dissertation, Nanjing University of Information Science and Technology, Nanjing.
	[张超 (2013). 内蒙古植被覆盖变化及其与区域气候相互关系. 硕士学位论文, 南京信息工程大学, 南京.]
[44]	Zhang G, Dong J, Xiao X, Hu Z, Sheldon S (2012). Effectiveness of ecological restoration projects in Horqin Sandy Land, China, based on SPOT-VGT NDVI data. Ecological Engineering, 38, 20-29. DOI URL
[45]	Zhang H, Li M, Song JY, Han HW (2021). Analysis of driving factors of vegetation NDVI changes in Qilian Mountain National Park based on geographic detectors. Chinese Journal of Ecology, 40, 2530-2540.
	[张华, 李明, 宋金岳, 韩宏武 (2021). 基于地理探测器的祁连山国家公园植被NDVI变化驱动因素分析. 生态学杂志, 40, 2530-2540.]
[46]	Zhang SY, Nie Y, Zhang HY, Li YL, Han YD, Liu XH, Wang B (2020). Analysis of spatial and temporal changes and driving forces of vegetation NDVI in Inner Mongolia based on geographic detectors. Acta Agrestia Sinica, 28, 1460-1472. DOI
	[张思源, 聂莹, 张海燕, 李永利, 韩燕东, 刘晓煌, 王兵 (2020). 基于地理探测器的内蒙古植被NDVI时空变化与驱动力分析. 草地学报, 28, 1460-1472.] DOI
[47]	Zhang X, Wang J, Gao Y, Wang LX (2021). Variations and controlling factors of vegetation dynamics on the Qingzang Plateau of China over the recent 20 years. Geography and Sustainability, 2, 74-95. DOI URL
[48]	Zhao HL, Dahei JZ, Zhou RL, Li YL, Zuo XA, Huang G (2008). The impact of human activities and climate change on the vegetation of sandy grassland in Horqin. Advances in Earth Science, 4, 408-414.
	[赵哈林, 大黑俊哉, 周瑞莲, 李玉霖, 左小安, 黄刚 (2008). 人类活动与气候变化对科尔沁沙质草地植被的影响. 地球科学进展, 4, 408-414.]
[49]	Zhou D, Zhao X, Hu H, Shen H, Fang J (2015). Long-term vegetation changes in the four mega-sandy lands in Inner Mongolia, China. Landscape Ecology, 30, 1613-1626. DOI URL
[50]	Zhou X, Yamaguchi Y, Arjasakusuma S (2018). Distinguishing the vegetation dynamics induced by anthropogenic factors using vegetation optical depth and AVHRR NDVI: a cross-border study on the Mongolian Plateau. Science of the Total Environment, 616, 730-743.
[51]	Zhu L, Meng J, Zhu L (2020). Applying geodetector to disentangle the contributions of natural and anthropogenic factors to NDVI variations in the middle reaches of the Heihe River Basin. Ecological Indicators, 117, 106545. DOI: 10.1016/j.ecolind.2020.106545.

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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