Natural Driving Forces of Vegetation Cover Characteristics and Change Trends in the Guangdong-Hong Kong-Macao Greater Bay Area

doi:10.16258/j.cnki.1674-5906.2022.09.001

Ecology and Environment ›› 2022, Vol. 31 ›› Issue (9): 1713-1724.DOI: 10.16258/j.cnki.1674-5906.2022.09.001

• Research Articles • Next Articles

Natural Driving Forces of Vegetation Cover Characteristics and Change Trends in the Guangdong-Hong Kong-Macao Greater Bay Area

FENG Xianhui¹^,²^,^*(), ZENG Zhilin³

1. The school of Architecture, South China University of Technology, Guangzhou 510641, P. R. China
2. State key laboratory of subtropical architecture science, Guangzhou 510641, P. R. China
3. Yangtze University Health Science Center, Jingzhou 434023, P. R. China

Received:2022-04-11 Online:2022-09-18 Published:2022-11-07
Contact: FENG Xianhui

粤港澳大湾区植被覆盖特征与变化趋势的自然驱动力研究

冯娴慧¹^,²^,^*(), 曾芝琳³

1.华南理工大学建筑学院,广东广州 510641
2.亚热带建筑科学国家重点实验室,广东广州 510641
3.长江大学医学部,湖北荆州 434023

通讯作者: 冯娴慧
作者简介:冯娴慧（1977年生）,女,副教授,博士,研究方向为城市与区域生态、绿地生境机理、城市微气候等。E-mail: xhfeng@scut.edu.cn
基金资助:
国家自然科学基金项目(51978276);国家自然科学基金项目(51878288)

Abstract

Abstract:

In the past 20 years, the characteristics and dynamic changes of vegetation cover in The Guangdong-Hong Kong-Macao Greater Bay Area have been affected by a variety of natural factors. Based on the MODIS NDVI remote sensing data from 2001 to 2020, enhanced vegetation index (EVI) was adopted in this study, combined with Sen trend analysis, Mann-Kendall test, Pearson correlation coefficient method, hierarchical analysis method, and geographical detector driving force analysis model. The vegetation cover characteristics and the changing trend of the Greater Bay Area over a 20-year time series were analyzed, and the influences and driving forces of climatic and non-climatic natural factors on vegetation cover characteristics and the changing trend were further studied by combining meteorological data, elevation, slope, aspect and soil data. The results showed that (1) the overall vegetation coverage of the greater bay area showed an improvement trend. Among different land use types, the vegetation changing trend showed extremely significant improvement. Among them, the trend of vegetation change in urban and rural construction land, water area and cultivated land showed the coexistence of improvement and degradation, and the most significant degradation trend of urban and rural construction land was about 18.59%. (2) The climate change in the Greater Bay Area was mainly due to the slow rise of temperature and relative humidity. Climate factors such as relative humidity, wind speed and temperature had influenced vegetation cover change. In terms of spatial distribution, the influence area of air temperature was closely related to the intensive urban construction area. (3) Among the non-climatic factors, elevation, soil type and slope were closely related to vegetation distribution characteristics. In terms of the changing trend of vegetation, the area with the highest improvement degree was concentrated in the height between 82 and 394 m, while the elevation of degraded area was concentrated in the height ≤82 m. Significant vegetation coverage degradation occurred in paddy soil, sandbank and coastal saline soil. (4) According to the analysis of the driving force model, average annual temperature, average annual relative humidity and wind speed were the main factors affecting vegetation spatial characteristics. Among the non-climatic factors, elevation and soil type were the main factors affecting the spatial characteristics of vegetation. In the two-factor interaction verification, the two-factor driving power of temperature, relative humidity and each factor was about 50%, indicating that the high temperature and high humidity climate had positive effects on vegetation growth in the Greater Bay Area. The interaction of elevation, soil factors and other natural factors showed synergistic enhancement of the driving force.

Key words: EVI, change trends, natural factors, climate, GBA

摘要：

粤港澳大湾区的植被覆盖特征与变化趋势受到多种自然因子的影响。基于2001-2020年的MODIS NDVI遥感数据,采用增强植被指数（EVI）,结合Sen趋势分析和Mann-Kendall检验、Pearson相关系数法、分级分析法、地理探测器驱动力分析模型等方法,分析20年长时间序列大湾区植被覆盖特征与变化趋势,并进一步结合气象数据、高程、坡度、坡向、土壤等数据,研究气候与非气候自然因子对植被覆盖特征与变化趋势的影响和驱动力。结果表明,（1）大湾区植被覆盖整体呈极显著改善趋势。在不同土地利用类型上,植被变化趋势均表现为极显著改善。但城乡建设用地、水域、耕地的植被变化趋势表现改善、退化等7种不同程度的变化趋势共存,城乡建设用地的极显著退化趋势最高,占比面积约18.59%。（2）相对湿度、风速、气温因子对植被覆盖变化具有影响力;在空间分布上,气温影响区域与城市建设密集区域紧密相关。（3）在非气候因子中,高程、土壤类型、坡度与植被分布特征紧密相关。在植被的变化趋势上,改善程度最高的区域集中在82-394 m高程之间,退化区域集中在≤82 m的高程区域。植被覆盖发生显著退化占比面积比较大的有水稻土、沙洲和滨海盐土。（4）通过驱动力模型分析,气候因子中,气温、相对湿度、风速是影响植被覆盖特征的主要因子;非气候因子中,高程、土壤类型是影响植被覆盖特征的主要因子。在双因子交互验证中,气温、相对湿度与其他各因子的双因子驱动解释力均在50%左右,表明高温、高湿的气候对大湾区的植被生长具有正向驱动作用。高程、土壤因子与其他自然因子的交互作用均表现为驱动力协同增强。

关键词: EVI, 变化趋势, 自然因子, 气候, 粤港澳大湾区

CLC Number:

Q948
X17

FENG Xianhui, ZENG Zhilin. Natural Driving Forces of Vegetation Cover Characteristics and Change Trends in the Guangdong-Hong Kong-Macao Greater Bay Area[J]. Ecology and Environment, 2022, 31(9): 1713-1724.

冯娴慧, 曾芝琳. 粤港澳大湾区植被覆盖特征与变化趋势的自然驱动力研究[J]. 生态环境学报, 2022, 31(9): 1713-1724.

Figures/Tables 14

References 33

[1]	FENSHOLT R, LANGANKE T, RASMUSSEN K, et al., 2012. Greenness in semi-arid areas across the globe 1981-2007-an Earth Observing Satellite based analysis of trends and drivers[J]. Remote Sensing of Environment, 121: 144-58. DOI URL
[2]	GITELSON A A, KAUFMAN Y J, STARK R, et al., 2002. Novel algorithms for remote estimation of vegetation fraction[J]. Remote Sensing of Environment, 80(1): 76-87. DOI URL
[3]	HU M, XIA B, 2019. A significant increase in the normalized difference vegetation index during the rapid economic development in the Pearl River Delta of China[J]. Land Degradation & Development, 30(4): 359-370.
[4]	MYNENI R B, KEELING C D, TUCKER C J, et al., 1997. Increased plant growth in the northern high latitudes from 1981 to 1991[J]. Nature, 386(6626): 698-702. DOI URL
[5]	PENG J, LIU Z H, LIU Y H, et al., 2012. Trend analysis of vegetation dynamics in Qinghai-Tibet Plateau using Hurst Exponent[J]. Ecological Indicators, 14(1): 28-39. DOI URL
[6]	PIAO S L, WANG X H, CIAIS P, et al., 2011. Changes in satellite-derived vegetation growth trend in temperate and boreal Eurasia from 1982 to 2006[J]. Global Change Biology, 17(10): 3228-3239. DOI URL
[7]	TUCKER C J, SLAYBACK D A, PINZON J E, et al., 2001. Higher northern latitude normalized difference vegetation index and growing season trends from 1982 to 1999[J]. International Journal of Biometeorology, 45(4): 184-190. PMID
[8]	XIAO J F, MOODY A, 2005. A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA[J]. Remote Sensing of Environment, 98(2-3): 237-250. DOI URL
[9]	陈超男, 朱连奇, 田莉, 等, 2019. 秦巴山区植被覆盖变化及气候因子驱动分析[J]. 生态学报, 39(9): 3257-3266.
	CHEN C N, ZHU L Q, TIAN L, et al., 2019. Spatial-temporal changes in vegetation characteristics and climate in the Qinling-Daba Mountains[J]. Acta Ecologica Sinica, 39(9): 3257-3266.
[10]	陈丹, 钱峻屏, 张虹鸥, 等, 2006. 基于遥感数据的广东植被指数时空变化研究[J]. 生态科学, 25(5): 412-416.
	CHEN D, QIAN J P, ZHANG H O, et al., 2006. The Spatio-temporal Patterns of NDVI in Guangdong Based RS Data[J]. Ecologic Science, 25(5): 412-416.
[11]	陈云浩, 李晓兵, 史培军, 2001. 1983-1992年中国陆地NDVI变化的气候因子驱动分析[J]. 植物生态学报, 25(6): 716-720.
	CHEN Y H, LI X B, SHI P J, 2001. Variation in NDVI driven by climate factors across China, 1983-1992 [J]. Acta Phytoecologica Sinica, 25(6): 716-720.
[12]	程红芳, 章文波, 陈锋, 2008. 植被覆盖度遥感估算方法研究进展[J]. 国土资源遥感, 75(1): 13-18.
	CHENG H F, ZHANG W B, CHEN F, 2008. Advance in researches on application of remote sensing method to estimating vegetation coverage[J]. Remote sensing for Land & Resources, 75(1): 13-18.
[13]	邓玉娇, 王捷纯, 徐杰, 等, 2021. 广东省NDVI时空变化特征及其对气候因子的响应[J]. 生态环境学报, 30(1): 37-43.
	DENG Y J, WANG J C, XU J, et al., 2021. Spatiotemporal variation of NDVI and its response to climatic factors in Guangdong Province[J]. Ecology and Environmental Sciences, 30(1): 37-43.
[14]	董晨炜, 曹宇, 谭永忠, 2017. 基于夜间灯光数据的环杭州湾城市扩张及植被变化[J]. 应用生态学报, 28(1): 231-238. DOI
	DONG C W, CAO Y, TAN Y Z, 2017. Urban expansion and vegetation changes in Hangzhou Bay area using night-light data[J]. Chinese Journal of Applied Ecology, 28(1): 231-238.
[15]	冯娴慧, 曾芝琳, 张德顺, 2022. 基于MODIS NDVI数据的粤港澳大湾区植被覆盖时空演变[J]. 中国城市林业, 20(1): 1-6, 28.
	FENG X H, ZENG Z L, ZHANG D S, 2022. Temporal-spatial evolution of vegetation coverage in the Guangdong-Hong Kong-Macao Greater Bay Area on MODIS NDVI data[J]. Journal of Chinese Urban Forestry, 20(1): 1-6, 28.
[16]	甘春英, 王兮之, 李保生, 等, 2011. 连江流域近18年来植被覆盖度变化分析[J]. 地理科学, 31(8): 1019-1024.
	GAN C Y, WANG X Z, LI B S et al., 2011. Changes of vegetation coverage during recent 18 years in Lianjiang River watershed[J]. Scientia Geographica Sinica, 31(8): 1019-1024. DOI
[17]	高照忠, 魏海霞, 黄铁兰, 2021. 粤港澳大湾区土地覆盖及景观格局时空变化分析[J]. 测绘通报 (5): 25-29.
	GAO Z Z, WEI H X, HUANG T L, 2021. Analysis of spatial-temporal changes of land cover and landscape pattern in Guangdong-Hong Kong-Macao Greater Bay Area[J]. Bulletin of Surveying and Mapping (5): 25-29.
[18]	何全军, 2019. 基于MODIS数据的珠三角地区NDVI时空变化特征及对气象因素的响应[J]. 生态环境学报, 28(9): 1722-1730.
	HE Q J, 2019. Spatio-temporal variation of NDVI and its response to meteorological factors in Pearl River Delta based on MODIS data[J]. Ecology and Environmental Sciences, 28(9): 1722-1730.
[19]	贺忠华, 张育慧, 何月, 等, 2020. 浙江省近20年植被变化趋势及驱动因子分析[J]. 生态环境学报, 29(8): 1530-1539.
	HE Z H, ZHANG Y H, HE Y, et al., 2020. Trends of vegetation change and driving factor analysis in recent 20 years over Zhejiang province[J]. Ecology and Environmental Sciences, 29(8): 1530-1539.
[20]	贾坤, 姚云军, 魏香琴, 等, 2013. 植被覆盖度遥感估算研究进展[J]. 地球科学进展, 28(7): 774-782. DOI
	JIA K, YAO Y J, WEI X Q, et al., 2013. A review on fractional vegetation cover estimation using remote sensing[J]. Advances in Earth Science, 28(7): 774-782. DOI
[21]	李卓, 孙然好, 张继超, 等, 2017. 京津冀城市群地区植被覆盖动态变化时空分析[J]. 生态学报, 37(22): 7418-7426.
	LI Z, SUN R H, ZHANG J C et al., 2017. Temporal-spatial analysis of vegetation coverage dynamics in Beijing-Tianjin-Hebei metropolitan regions[J]. Acta Ecologica Sinica, 37(22): 7418-7426.
[22]	刘宪锋, 杨勇, 任志远, 等, 2013. 2000-2009年黄土高原地区植被覆盖度时空变化[J]. 中国沙漠, 33(4): 1244-1249.
	LIU X F, YANG Y, REN ZH Y, et al., 2013. Changes of vegetation coverage in the Loess Plateau in 2000-2009[J]. Journal of Desert Research, 33(4): 1244-1249.
[23]	刘彦随, 李进涛, 2017. 中国县域农村贫困化分异机制的地理探测与优化决策[J]. 地理学报, 72(1): 161-173. DOI
	LIU Y S, LI J T, 2017. Geographic detection and optimizing decision of the differentiation mechanism of rural poverty in China[J]. Acta Geographica Sinica, 72(1): 161-173. DOI
[24]	穆少杰, 李建龙, 陈奕兆, 等, 2012. 2001-2010年内蒙古植被覆盖度时空变化特征[J]. 地理学报, 67(9): 1255-1268.
	MU S J, LI J L, CHEN Y Z, et al., 2012. Spatial differences of variations of vegetation coverage in Inner Mongolia during 2001-2010[J]. Acta Geographica Sinica, 67(9): 1255-1268. DOI
[25]	王劲峰, 徐成东, 2017. 地理探测器: 原理与展望[J]. 地理学报, 72(1): 116-134. DOI
	WANG J F, XU C D, 2017. Geodetector: Principle and prospective[J]. Acta Geographica Sinica, 72(1): 116-134. DOI
[26]	王思, 张路路, 林伟彪, 等, 2022. 基于MODIS-NDVI的广东省植被覆盖与土地利用变化研究[J]. 生态学报, 42(6): 1-15.
	WANG S, ZHANG L L, LIN W B, et al., 2022. Study on vegetation coverage and land-use change of Guangdong Province based on MODIS-NDVI[J]. Acta Ecologica Sinica, 42(6): 1-15.
[27]	解晗, 同小娟, 李俊, 等, 2022. 2000-2008年黄河流域生长季NDVI、EVI变化及其对气候因子的响应[J]. 生态学报, 42(11): 1-16.
	XIE H, TONG X J, LI J, et al., 2022. Changes of NDVI and EVI and their responses to climatic variables in the Yellow River Basin during the growing season of 2000-2018[J]. Acta Ecologica Sinica, 42(11): 1-16.
[28]	徐勇, 黄雯婷, 靖娟利, 等, 2020. 京津冀地区植被NDVI动态变化及其与气候因子的关系[J]. 水土保持通报, 40(5): 319-327.
	XU Y, HUANG W T, JIANG J L, et al., 2020. Dynamic variation of vegetation cover and its relation with climate variables in Beijing-Tianjin-Heibei region[J]. Bulletin of soil and water conversation, 40(5): 319-327.
[29]	晏利斌, 刘晓东, 2011. 1982-2006年京津冀地区植被时空变化及其与降水和地面气温的联系[J]. 生态环境学报, 20(2): 226-32.
	YAN L B, LIU X D, 2011. Spatial-temporal changes in vegetation cover and their relationships with precipitation and surface air temperature over the Beijing-Tianjin-Hebei region from 1982 to 2006[J]. Ecology and Environmental Sciences, 20(2): 226-232.
[30]	张顾萍, 陈国民, 邵怀勇, 等, 2021. 近16年金沙江流域植被覆盖时空特征及其对气候的响应[J]. 长江流域资源与环境, 30(7): 1638-1648.
	ZHANG G P, CHEN G M, SHAO H Y, et al., 2021. Spatial-temporal characteristics of vegetation coverage and its response to climate from 2000 to 2015 in Jinsha River Basin, China[J]. Resources and Environment in the Yangtze Basin, 30(7): 1638-1648.
[31]	张杰, 李清泉, 吴祥茵, 等, 2021. 基于土地利用的粤港澳大湾区生态系统服务价值及承载力演变分析[J]. 生态学报, 41(21): 8375-8386.
	ZHANG J, LI Q Q, WU X Y, et al., 2021. Evolution of the ecosystem services value and carrying capacity in the Guangdong-Hong Kong-Macao Greater Bay Area based on land use changes[J]. Acta Ecologica Sinica, 41(21): 8375-8386.
[32]	赵桔超, 张韶华, 尹晓雪, 等, 2022. 粤港澳大湾区植被覆盖变化及其影响因素分析[J]. 测绘科学, 47(3): 75-84.
	ZHANG J C, ZHANG S H, YIN X X, et al., 2022. Changes in vegetation coverage and its influencing factors across the Guangdong-Hong Kong-Macao Greater Bay Area[J]. Science of Surveying and Mapping, 47(3): 75-84.
[33]	张凯选, 范鹏鹏, 王军邦, 等, 2019. 西南喀斯特地区植被变化及其与气候因子关系研究[J]. 生态环境学报, 28(6): 1080-1091.
	ZHANG K X, FAN P P, WANG J B, et al., 2019. Study on vegetation changes and climate factors in a karst region of southwest China[J]. Ecology and Environmental Sciences, 28(6): 1080-1091.

β_EVI	Z值 Z value	趋势变化 EVI trends	面积占比 Area proportion/%
S>0.0005	>2.58	极显著改善	66.98
S>0.0005	1.96<\|Z\|≤2.58	显著改善	6.83
S>0.0005	\|Z\|≤1.96	不显著改善	9.91
-0.0005≤S≤0.0005	-	稳定不变	4.14
S<-0.0005	\|Z\|≤1.96	不显著退化	4.73
S<-0.0005	1.96<\|Z\|≤2.58	显著退化	1.73
S<-0.0005	\|Z\|>2.58	极显著退化	5.70

β_EVI	Z值 Z value	趋势变化 EVI trends	面积占比 Area proportion/%
S>0.0005	>2.58	极显著改善	66.98
S>0.0005	1.96<\|Z\|≤2.58	显著改善	6.83
S>0.0005	\|Z\|≤1.96	不显著改善	9.91
-0.0005≤S≤0.0005	-	稳定不变	4.14
S<-0.0005	\|Z\|≤1.96	不显著退化	4.73
S<-0.0005	1.96<\|Z\|≤2.58	显著退化	1.73
S<-0.0005	\|Z\|>2.58	极显著退化	5.70

EVI等级 EVI value	总面积占比 Proportion/%	土地利用类型 Land use type						植被类型 Vegetation type
EVI等级 EVI value	总面积占比 Proportion/%	耕地 Croplands	林地 Forest	草地 Grass	水域 Water	城乡建设用地 Urban area	未利用土地 Unused land	阔叶林 Broad-leaved forest	针叶林 Coniferous forest	灌丛 Brush	草丛 Grass cluster	栽培植被 Cultivation of vegetation	高山植被 Alpine vegetation	其他 Others
EVI<0.2 (Ⅰ)	3.92	2.01	0.26	0.93	21.12	10.03	28.57	0.04	0.45	0.66	0.43	5.05	62.96	15.52
0.2≤EVI<0.4 (Ⅱ)	17.97	20.81	2.60	5.66	46.44	58.80	57.14	0.36	3.26	6.13	4.54	36.80	25.93	25.86
0.4≤EVI<0.6 (Ⅲ)	37.43	56.85	32.77	49.87	28.05	27.63	14.29	15.85	28.32	37.71	26.36	45.26	11.11	41.38
0.6≤EVI<0.8 (Ⅳ)	40.66	20.33	64.34	43.53	4.38	3.53	0.00	83.74	67.97	55.46	68.67	12.90	0.00	17.24
EVI≥0.8 (Ⅴ)	0.02	0.00	0.03	0.00	0.00	0.01	0.00	0.00	0.00	0.04	0.00	0.00	0.00	0.00

EVI等级 EVI value	总面积占比 Proportion/%	土地利用类型 Land use type						植被类型 Vegetation type
EVI等级 EVI value	总面积占比 Proportion/%	耕地 Croplands	林地 Forest	草地 Grass	水域 Water	城乡建设用地 Urban area	未利用土地 Unused land	阔叶林 Broad-leaved forest	针叶林 Coniferous forest	灌丛 Brush	草丛 Grass cluster	栽培植被 Cultivation of vegetation	高山植被 Alpine vegetation	其他 Others
EVI<0.2 (Ⅰ)	3.92	2.01	0.26	0.93	21.12	10.03	28.57	0.04	0.45	0.66	0.43	5.05	62.96	15.52
0.2≤EVI<0.4 (Ⅱ)	17.97	20.81	2.60	5.66	46.44	58.80	57.14	0.36	3.26	6.13	4.54	36.80	25.93	25.86
0.4≤EVI<0.6 (Ⅲ)	37.43	56.85	32.77	49.87	28.05	27.63	14.29	15.85	28.32	37.71	26.36	45.26	11.11	41.38
0.6≤EVI<0.8 (Ⅳ)	40.66	20.33	64.34	43.53	4.38	3.53	0.00	83.74	67.97	55.46	68.67	12.90	0.00	17.24
EVI≥0.8 (Ⅴ)	0.02	0.00	0.03	0.00	0.00	0.01	0.00	0.00	0.00	0.04	0.00	0.00	0.00	0.00

相关性 Correlation	气温影响区域 Temperature drive area/%	降水影响区域 Precipitation drive area/%	风速影响区域 Wind speed drive area/%	相对湿度影响区域 Relative humidity drive area/%	日照时数影响区域 Sunshine duration drive area/%
显著正相关 Significant positive correlation (r>0, P<0.05)	7.15	2.51	32.99	37.74	0.44
不显著正相关 No-significant positive correlation (r>0, P≥0.05)	42.01	74.14	32.75	42.49	28.25
不显著负相关 No-significant negative correlation (r<0, P≥0.05)	36.43	22.63	25.06	14.00	68.22
显著负相关 Significant negative correlation (r<0, P<0.05)	14.40	0.71	9.20	5.77	3.09

Natural Driving Forces of Vegetation Cover Characteristics and Change Trends in the Guangdong-Hong Kong-Macao Greater Bay Area

粤港澳大湾区植被覆盖特征与变化趋势的自然驱动力研究

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 14

References 33

Related Articles 15

Recommended Articles

Metrics

Comments

EVI值 EVI value	高程梯度 Elevation gradients/m					坡度 Slope/(°)
EVI值 EVI value	<82	82-217	217-394	394-653	>653	≤2	2-6	6-15	15-25	>25
EVI<0.2 (Ⅰ)	6.69	0.11	0.00	0.00	0.00	13.10	4.30	1.76	0.42	0.35
0.2≤EVI<0.4 (Ⅱ)	30.37	1.38	0.12	0.08	0.12	41.28	30.03	10.42	2.09	1.20
0.4≤EVI<0.6 (Ⅲ)	47.79	3.02	14.76	10.49	11.53	37.79	47.51	40.84	28.02	21.82
0.6≤EVI<0.8 (Ⅳ)	15.14	65.44	85.12	89.43	88.35	7.82	18.17	46.97	69.44	76.59
EVI≥0.8 (Ⅴ)	0.01	0.05	0.01	0.00	0.00	0.00	0.00	0.01	0.03	0.04

Z值 Z value	趋势变化 EVI trends	高程梯度 Elevation gradients/m					坡度 Slope/(°)
Z值 Z value	趋势变化 EVI trends	<82	82-217	217-394	394-653	>653	≤2	2-6	6-15	15-25	>25
>2.58	极显著改善	57.02	82.29	82.61	77.86	63.40	45.75	56.69	74.29	81.22	79.97
1.96<\|Z\|≤2.58	显著改善	6.74	6.14	6.91	8.38	11.18	7.10	6.90	6.52	6.65	7.34
\|Z\|≤1.96	不显著改善	11.25	7.02	7.53	9.74	16.54	13.31	11.34	8.56	7.75	8.53
-	稳定不变	5.82	1.78	1.46	1.89	3.72	8.11	5.52	2.99	1.79	1.81
\|Z\|≤1.96	不显著退化	7.01	1.58	1.12	1.62	3.84	9.09	7.16	3.26	1.59	1.62
1.96<\|Z\|≤2.58	显著退化	2.74	0.40	0.17	0.24	0.64	3.63	2.76	1.14	0.34	0.34
\|Z\|>2.58	极显著退化	9.42	0.79	0.20	0.26	0.69	12.99	9.63	3.25	0.68	0.40

土壤类型 Soil type	EVI值 EVI value					EVI变化趋势 EVI change trends
土壤类型 Soil type	EVI<0.2 (Ⅰ)	0.2≤EVI<0.4 (Ⅱ)	0.4≤EVI<0.6 (Ⅲ)	0.6≤EVI<0.8 (Ⅳ)	EVI≥0.8 (Ⅴ)	极显著改善	显著改善	不显著改善	稳定不变	不显著退化	显著退化	极显著退化
水稻土 Paddy soil	4.47	33.34	46.28	15.91	0.00	52.07	6.85	11.77	6.01	8.00	3.19	12.11
赤红壤 Latosolic red soil	0.82	7.69	39.88	51.58	0.03	77.62	6.28	8.02	2.41	2.66	0.85	2.17
红壤 Red soil	0.00	0.15	11.60	88.25	0.00	76.58	8.56	10.01	2.12	1.90	0.30	0.53
风沙土 Aeolian sandy soil	0.00	23.01	56.64	20.35	0.00	74.34	5.31	5.31	5.31	4.42	1.77	3.54
石灰(岩)土 Limestone soil	0.00	0.00	36.73	63.27	0.00	85.09	4.82	6.80	1.75	0.77	0.33	0.44
紫色土 Purple soil	0.00	0.19	65.55	34.27	0.00	64.24	8.44	11.87	4.58	4.31	1.57	4.98
石质土 Rocky soil	0.30	20.08	48.93	30.69	0.00	72.88	7.39	8.68	3.47	3.82	1.14	2.63
粗骨土 Skeletal soil	0.19	2.87	45.35	51.58	0.00	78.57	7.16	8.52	2.14	2.44	0.49	0.68
山地草甸土 Mountain meadow soil	0.00	0.00	0.83	99.17	0.00	46.40	16.76	25.62	4.43	5.26	1.11	0.42
潮土 Moisture soil	13.66	64.18	20.67	1.49	0.00	52.12	7.33	12.72	7.10	7.48	3.37	9.88
滨海盐土 Seashore saline soil	42.06	37.67	19.83	0.43	0.00	41.13	5.78	13.36	9.10	11.43	4.92	14.29
酸性硫酸盐土 Acid sulphate soil	63.55	10.53	16.96	8.97	0.00	29.63	6.24	13.84	21.05	12.67	6.82	9.75
黄壤 Yellow soil	0.00	0.15	6.49	93.37	0.00	60.21	12.08	17.40	4.47	3.98	0.95	0.91
城区 Urban area soli	41.43	51.19	5.66	1.71	0.00	80.48	6.86	6.71	3.43	2.16	0.15	0.22
湖泊、水库 Lake and reservoir soil	6.36	31.02	45.92	16.70	0.00	68.85	6.77	11.03	3.78	4.62	1.46	3.50
江、河 River soil	30.31	47.25	20.04	2.41	0.00	44.62	7.26	14.34	9.26	9.51	3.68	11.32
沙洲、岛屿 Cay and island soil	33.33	49.48	17.19	0.00	0.00	44.79	3.65	15.10	6.77	12.50	2.60	14.58
滨海盐场、养殖场 Coastal saltworks and farm soil	0.00	65.00	35.00	0.00	0.00	25.00	1.67	11.67	13.33	11.67	10.00	26.67

编号 Code	影响因子 Impact factor	影响因子类型 Type of impact factor
X₁	年均气温	气候类自然因子 Climate factor
X₂	年均降水量
X₃	年均风速
X₄	年均相对湿度
X₅	年均日照时数
X₆	高程	非气候类自然因子 Non-climate factor
X₇	坡度
X₈	坡向
X₉	土壤类型

[1]	XIAO Bo, WANG Shaojun, XIE Lingling, WANG Zhengjun, GUO Zhipeng, ZHANG Kunfeng, ZHANG Lulu, FAN Yuxiang, GUO Xiaofei, LUO Shuang, XIA Jiahui, LI Rui, LAN Mengjie, YANG Shengqiu. Effect of Ant Nesting Activity on Soil Nitrogen Component Allocation in the Xishuangbanna Tropical Forests [J]. Ecology and Environment, 2023, 32(6): 1026-1036.
[2]	HAO Lei, ZHAI Yongguang, QI Wenchao, LAN Qiongqiong. Spatial-temporal Dynamics of Vegetation Carbon Sources/sinks in Inner Mongolia from 2001 to 2020 and Its Response to Climate Change [J]. Ecology and Environment, 2023, 32(5): 825-834.
[3]	HOU Hui, YAN Peixuan, XIE Qinmi, ZHAO Hongliang, PANG Danbo, CHEN Lin, LI Xuebin, HU Yang, LIANG Yongliang, NI Xilu. Characterization of Arbuscular Mycorrhizal Fungal Community Diversity in the Rhizosphere Soils of Prunus mongolica Scrub of Helan Mountain [J]. Ecology and Environment, 2023, 32(5): 857-865.
[4]	ZHAO Hongbin, BAI Xue, FAN Yufeng, ZHANG Xiaofu, ZHANG Tao, LI Shufen. Cloning and Differential Expression Analysis of Stipa Breviflora StbCRY1 and StbCRY2 Genes [J]. Ecology and Environment, 2023, 32(5): 866-877.
[5]	CHEN Junfang, WU Xian, LIU Xiaolin, LIU Juan, YANG Jiarong, LIU Yu. Shaping Characteristics of Elemental Stoichiometry on Microbial Diversity under Different Soil Water Contents [J]. Ecology and Environment, 2023, 32(5): 898-909.
[6]	LI Hui, LI Bilong, GE Lili, HAN Chenhui, YANG Qian, ZHANG Yuejun. Temporal and Spatial Characteristics of Vegetation Evolution and Topographic Effects in Fenhe River Basin from 2000 to 2021 [J]. Ecology and Environment, 2023, 32(3): 439-449.
[7]	QI Yue, ZHANG Qiang, HU Shujuan, CAI Dihua, ZHAO Funian, ZHANG Kai, WANG Heling, WANG Runyuan. Climate Change and Its Impact on Winter Wheat Potential Productivity of Loess Plateau in China [J]. Ecology and Environment, 2022, 31(8): 1521-1529.
[8]	DENG Tianle, XIE Liyong, ZHANG Fengzhe, ZHAO Hongliang, JIANG Yutong. Competition for Growth Space between Barnyard Grass and Rice under Elevated Atmospheric CO₂ Concentration [J]. Ecology and Environment, 2022, 31(8): 1566-1572.
[9]	LU Yanyu, SUN Wei, FANG Yanqiu, TANG Weian, DENG Hanqing, HE Dongyan. Estimating the Climatic Potential Productivity and the Climatic Capacity of Food Security Based on the Cropping Structure in Anhui Province [J]. Ecology and Environment, 2022, 31(7): 1293-1305.
[10]	LI Dengke, WANG Zhao. Quantitative Analysis of the Impact of Climate Change and Human Activities on Vegetation NPP in Shaanxi Province [J]. Ecology and Environment, 2022, 31(6): 1071-1079.
[11]	CAO Xiaoyun, ZHU Cunxiong, CHEN Guoqian, SUN Shujiao, ZHAO Huifang, ZHU Wenbin, ZHOU Bingrong. Surface Greenness Change and Topographic Differentiation over Qaidam Basin from 2000 to 2021 [J]. Ecology and Environment, 2022, 31(6): 1080-1090.
[12]	GAO Siqi, DONG Guotao, JIANG Xiaohui, NIE Tong, GUO Xinwei, DANG Suzhen, LI Xinyu, LI Haoyang. Analysis of Vegetation Coverage Changes and Natural Driving Forces of Spatial Distribution in the Source Region of the Yellow River [J]. Ecology and Environment, 2022, 31(3): 429-439.
[13]	YE Youhua, XIAO Bing, FENG Hongjuan, HE Yulin, CHEN Ping, CHEN Xiaoyi, WANG Dandan, ZENG Zhixiang, GUO Xin. Researches on the Pattern and Route of Ecological Product Value Realization from the Perspective of Rural Revitalization [J]. Ecology and Environment, 2022, 31(2): 421-428.
[14]	CAO Yun, SUN Yinglong, JIANG Yueqing, WAN Jun. Analysis on Temporal-spatial Variations and Driving Factors of Net Ecosystem Productivity in the Yellow River Basin [J]. Ecology and Environment, 2022, 31(11): 2101-2110.
[15]	SHI Zhiyu, WANG Yating, ZHAO Qing, ZHANG Lianpeng, ZHU Changming. The Spatiotemporal Changes of NPP and Its Driving Mechanisms in China from 2001 to 2020 [J]. Ecology and Environment, 2022, 31(11): 2111-2123.