Temporal and Spatial Distribution Characteristics and Influencing Factors of Net Primary Productivity of Vegetation in Typical Basin Entering the Sea in Beibu Gulf

doi:10.16258/j.cnki.1674-5906.2021.05.006

Ecology and Environment ›› 2021, Vol. 30 ›› Issue (5): 938-948.DOI: 10.16258/j.cnki.1674-5906.2021.05.006

• Research Articles • Previous Articles Next Articles

Temporal and Spatial Distribution Characteristics and Influencing Factors of Net Primary Productivity of Vegetation in Typical Basin Entering the Sea in Beibu Gulf

TIAN Yichao¹^,²^,³(), YANG Tang¹, XU Xin¹

1. College of Resources and Environment, Beibu Gulf University, Qinzhou 535011, China
2. Key Laboratory of Marine Geographic Information Resources Development and Utilization in the Beibu Gulf, Beibu Gulf University, Qinzhou 535011, China
3. Guangxi Key Laboratory for Geospatial Informatics and Geomatics Engineering, Guilin University of Technology, Guilin 541004, China

Received:2020-02-15 Online:2021-05-18 Published:2021-08-06

北部湾典型入海流域植被净初级生产力时空分布特征及其影响因素

田义超¹^,²^,³(), 杨棠¹, 徐欣¹

1.北部湾大学资源与环境学院,广西钦州 535000
2.北部湾大学海洋地理信息资源开发利用重点实验室,广西钦州 535000
3.桂林理工大学/广西空间信息与测绘重点实验室,广西桂林 541004

作者简介:田义超（1986年生）,男,副教授,博士,主要从事资源环境遥感及海岸带生态环境监测的相关研究。E-mail:tianyichao1314@yeah.net
基金资助:
广西自然科学基金联合资助培育项目(2018JJA150135);广西创新驱动发展专项(AA18118038);广西基地和人才项目(2019AC20088);北部湾大学高层次人才引进项目(2019KYQD28);广西壮族自治区北部湾大学2018年大学生创新创业训练项目(201811607086)

Abstract

Abstract:

Beibu Gulf into the sea basin is located in the typical estuarine coastal interaction zone. It is a typical single flow river into the sea in the south subtropical region of China. The quantitative study on the net primary productivity (NPP) of vegetation can provide scientific basis for carbon cycle research and ecological restoration of typical single flow into the sea basin in south subtropical region of China. Based on CASA model, combined with Theil-Sen trend, Hurst index, partial correlation analysis and other mathematical statistical methods, this paper quantitatively analyzed the spatial and temporal variation characteristics, future trend and influencing factors of NPP in the Beibu Gulf Basin from 2000 to 2017. The results showed that: regarding temporal variations, the annual average NPP of the basin ranged from 8.50 to 95.86 g∙m^-2·a^-1 (in terms of C, the same below), showing an inverted “V” structure. Regarding inter-annual variation, the annual NPP of vegetation showed a fluctuating upward trend, with the growth rate of 8.83 g∙m^-2∙a^-1, which was faster than that in Guangxi Zhuang Autonomous Region (the growth rate was 0.001 g∙m^-2∙a^-1). Regarding spatial distribution, the NPP of typical estuarine basins showed obvious regional differentiation. The high value areas of NPP were mainly located in the south foot of Shiwan Mountain in the southwest, and the low value areas were scattered in the vicinity of Qinjiang River, the upstream of Nanliujiang River Basin and the southwest of downstream. The NPP of vegetation in the basin wasmainly improved, and the NPP increase area was 3.05 times of the decrease area. The Hurst index of NPP ranged from 0.20973 to 1, which was mainly a continuous sequence, indicating that NPP in the study area would continue to increase in the future. In terms of influencing factors, temperature was the dominant factor affecting NPP. NPP had a positive correlation with annual average temperature and a negative correlation with precipitation. NPP showed significant hellish differentiation and spatiotemporal differences in different vegetation types, altitudes and slope gradients. The results show that NPP in the typical watershed of Beibu Gulf is increasing, and the temperature is the dominant factor of NPP in climate factors.

Key words: net primary productivity, CASA model, Theil-Sen trend, Hurst index, influencing factors, typical basin entering the sea in Beibu Gulf

摘要：

北部湾入海流域处于典型河口-近海岸相互作用地带,是中国南亚热带地区典型的独流入海河流,对该地区的植被净初级生产力（NPP）进行定量化研究,可为中国南亚热带地区典型独流入海流域碳循环研究及其生态恢复提供科学依据。以北部湾典型入海流域为研究对象,基于CASA模型并结合Theil-Sen趋势、Hurst指数、偏相关分析等数理统计方法,定量化分析了流域2000—2017年NPP的时空变化特征、未来趋势及其影响因素。结果表明,时间尺度上,流域多年月均NPP介于8.50—95.86 g∙m^-2（以C计,下同）之间,呈倒“V”型结构;年际变化上,多年NPP总体呈现波动上升趋势,增速为8.83 g∙m^-2∙a^-1,快于广西壮族自治区（增速为0.001 g∙m^-2∙a^-1）。空间尺度上,典型入海流域NPP呈明显地域分异规律,NPP高值区主要位于西南部的十万大山南麓,低值区散布钦江河道附近、南流江流域的上游以及下游的西南部地区。流域NPP总体上以改善为主,NPP增加区域是减少区域的3.05倍。流域NPP的Hurst指数范围介于0.20973—1之间,总体上以持续序列为主,预示研究区NPP未来处于持续增加趋势。影响因素上,气温是影响NPP的主导因素,NPP与多年平均气温存在正相关关系,与降水存在负相关关系。NPP在不同覆被类型、海拔和坡度梯度上均呈现出显著的地域分异规律以及时空差异性特征。该研究表明北部湾入海典型流域NPP呈逐年增加趋势,气温是流域NPP的主导因素。

关键词: 净初级生产力, CASA模型, Theil-Sen趋势, Hurst指数, 影响因素, 北部湾入海典型流域

CLC Number:

Q948
X17

TIAN Yichao, YANG Tang, XU Xin. Temporal and Spatial Distribution Characteristics and Influencing Factors of Net Primary Productivity of Vegetation in Typical Basin Entering the Sea in Beibu Gulf[J]. Ecology and Environment, 2021, 30(5): 938-948.

田义超, 杨棠, 徐欣. 北部湾典型入海流域植被净初级生产力时空分布特征及其影响因素[J]. 生态环境学报, 2021, 30(5): 938-948.

Figures/Tables 14

References 37

[1]	ALESSANDRI A, NAVARRA A, 2008. On the coupling between vegetation and rainfall inter-annual anomalies: Possible contributions to seasonal rainfall predictability over land areas[J]. Geophysical Research Letters, 35(2): 209-212.
[2]	CHEN X F, CHEN J M, AN S Q, et al., 2007. Effects of topography on simulated net primary productivity at landscape scale[J]. Journal of Environmental Management, 85(3): 585-596. DOI URL
[3]	CRAMER W, FIELD C B, 1995. Comparing global models of terrestial net primary productivity (NPP): Introduction[J]. Global Change Biology, 5(s1): 3-4.
[4]	FIELD C B, RANDERSON J T, MALMSTROEM C M, 1995. Global net primary production: Combining ecology and remote sensing[J]. Remote Sensing of Environment, 51(1): 74-88. DOI URL
[5]	HURST H E, 1951. Closure to long-term storage capacity of reservoirs[J]. Transactions of the American Society of Civil Engineers, 116: 804-808. DOI URL
[6]	MONTEITH J L, MOSS C J, 1977. Climate and the Efficiency of Crop Production in Britain and Discussion[J]. Philosophical Transactions of the Royal Society of London, 281(980): 277-294.
[7]	MONTEITH J L, 1972. Solar Radiation and Productivity in Tropical Ecosystems[J]. Journal of Applied Ecology, 9(3): 747-766. DOI URL
[8]	PENG S L, GUO Z H, WANG B S, 2000. Use of GIS and RS to estimate the light utilization efficiency of the vegetation in Guangdong, China[J]. Acta Ecologica Sinica, 20(6): 903-909.
[9]	POTTER C S, RANDERSON J T, FIELD C B, et al., 1993. Terrestrial ecosystemproduction: a process model based on global satellite and surface data[J]. Global Biogeochemical Cycles, 7(4): 811-841. DOI URL
[10]	UCHIJIMA Z, SEINO H, 1985. Agroclimatic Evaluation of Net Primary Productivity of Natural Vegetations:(1) Chikugo Model for Evaluating Net Primary Productivity[J]. Journal of Agricultural Meteorology, 40(4): 343-352. DOI URL
[11]	车风, 黄国清, 刘韬, 等, 2019. 2004—2015年湖北省植被NPP时空分布特征及其与气候因素关系[J]. 水土保持研究, 26(6): 198-204, 225.
	CHE F, HUANG G Q, LIU T, et al., 2019. Spatiotemporal distribution of net primary productivity and its correlation with meteorological factors in Hubei province from 2004 to 2015 [J]. Research of Soil and Water Conservation, 26(6): 198-204, 225.
[12]	陈强, 陈云浩, 王萌杰, 等, 2014. 2001—2010年黄河流域生态系统植被净第一性生产力变化及气候因素驱动分析[J]. 应用生态学报, 25(10): 2811-2818.
	CHEN Q, CHEN Y H, WANG M J, et al., 2014. Change of vegetation net primary productivity in Yellow River watersheds from 2001-2010 and its climatic driving factors analysis [J]. Chinese Journal of Applied Ecology, 25(10): 2811-2818.
[13]	董丹, 倪健, 2011. 利用CASA模型模拟西南喀斯特植被净第一性生产力[J]. 生态学报, 31(7): 1855-1866.
	DONG D, NI J, 2011. Modeling changes of net primary productivity of karst vegetation in southwestern China using the CASA model[J]. Acta Ecologica Sinica, 31(7): 1855-1866.
[14]	韩王亚, 张超, 曾源, 等, 2018. 2000—2015年拉萨河流域NPP时空变化及驱动因子[J]. 生态学报, 38(24): 8787-8798.
	HAN W Y, ZHANG C, ZENG Y, et al., 2018. Spatio-temporal changes and driving factors in net primary productivity of Lhasa River Basin from 2000 to 2015 [J]. Acta Ecologica Sinica, 38(24): 8787-8798.
[15]	侯英雨, 柳钦火, 延昊, 等, 2007. 我国陆地植被净初级生产力变化规律及其对气候的响应[J]. 应用生态学报, 18(7): 1546-1553.
	HOU Y Y, LIU Q H, YAN H, et al., 2007. Variation trends of China terrestrial vegetation net primary productivity and its responses to climate factors in 1982-2000 [J]. Chinese Journal of Applied Ecology, 18(7): 1546-1553.
[16]	李燕丽, 潘贤章, 王昌昆, 等, 2014. 2000—2011年广西植被净初级生产力时空分布特征及其驱动因素[J]. 生态学报, 34(18): 5220-5228.
	LI Y L, PAN X Z, WANG C K, et al., 2014. Change of vegetation net primary productivity and its driving factors from 2000 to 2011 in Guanxi, China[J]. Acta Ecologica Sinica, 34(18): 5220-5228.
[17]	刘海江, 尹思阳, 孙聪, 等, 2015. 2000—2010年锡林郭勒草原NPP时空变化及其气候响应[J]. 草业科学, 32(11): 1709-1720.
	LIU H J, YIN S Y, SUN C, et al., 2015. Temporal and variation of net primary productivity (NPP) and its presponses with climatic changes in the Xilingol grassland from 2000-2010 [J]. Pratacultural Science, 32(11): 1709-1720.
[18]	刘洋, 李诚志, 刘志辉, 等, 2016. 1982—2013年基于GIMMS-NDVI的新疆植被覆盖时空变化[J]. 生态学报, 36(19): 6198-6208.
	LIU Y, LI C Z, LIU Z H, et al., 2016. Assessment of spatio-temporal variations in vegetation cover in Xinjiang from 1982 to 2013 based on GIMMS-NDVI[J]. ActaEcologica Sinica, 36(19): 6198-6208.
[19]	苗茜, 黄枚, 李仁强, 2010. 长江流域植被净初级生产力对未来气候变化的相应[J]. 自然资源学报, 25(8): 1296-1305.
	MIAO Q, HUANG M, LI R Q, 2010. The impacts of climate change on vegetation net primary productivity of the Yangtze River Basin[J]. Journal of Natural Resources, 25(8): 1296-1305.
[20]	莫建飞, 莫伟华, 陈燕丽, 2019. 基于净初级生产力的广西喀斯特区生物多样性维护功能评价[J]. 科学技术与工程, 19(29): 371-377.
	MO J F, MO W H, CHEN Y L, 2019. Evaluation of biodiversity maintenance function in karst area of Guangxi based on NPP[J]. Science Technology and Engineering, 19(29): 371-377.
[21]	苏宗明, 1998. 广西植被的自然环境条件对广西植被的影响[J]. 广西科学, 5(1): 51-57.
	SU Z M, 2019. Influence of natural environmental conditions on the vegetation in Guangxi[J]. Guangxi Science, 5(1): 51-57.
[22]	索玉霞, 王正兴, 刘闯, 等, 2009. 中亚地区1982年至2002年植被指数与气温和降水的相关性分析[J]. 资源科学, 31(8): 1422-1429.
	SUO Y X, WANG Z X, LIU C, et al., 2009. Relationship between NDVI and Precipitation and Temperature in Middle Asia during 1982-2002 [J]. Resources Science, 31(8): 1422-1429.
[23]	宋艺, 李小军, 江涛, 2017. 2008—2014年植被覆盖变化对黑河流域净初级生产力的影响研究[J]. 水土保持研究, 24(4): 204-209, 218.
	SONG Y, LI X J, JIANG T, 2017. Effects of vegetation coverage change on net primary productivity of Heihe river basin during 2008-2014 [J]. Research of Soil and Water Conservation, 24(4): 204-209, 208.
[24]	孙红雨, 王长耀, 牛铮, 等, 1998. 中国地表植被覆盖变化及其与气候因子关系——基于NOAA时间序列数据分析[J]. 遥感学报, 2(3): 204-210.
	SUN H Y, WANG C Y, NIU Z, et al., 1998. Analysis of the vegetation cover change and the relationship between NDVI and environment factors by using NOAA time series data[J]. Journal of Remote Sensing, 2(3): 204-210.
[25]	田慧文, 毕如田, 朱洪芬, 等, 2019. 汾河流域植被净初级生产力的驱动因素及梯度效应[J]. 生态学杂志, 38(10): 3066-3074.
	TIAN H W, BI R T, ZHU H F, et al., 2019. Driving factors and gradient effect of net primary productivity in Fenhe River Basin[J]. Chinese Journal of Ecology, 38(10): 3066-3074.
[26]	田义超, 黄远林, 张强, 等, 2019. 北部湾南流江流域植被净初级生产力时空分布及其驱动因素[J]. 生态学报, 39(11): 1-16.
	TIAN Y C, HUANG Y L, ZHANG Q, et al., 2019. Spatiotemporal distribution of net primary productivity and its driving factors in the Nanliu River basin in the Beibu Gulf[J]. Acta Ecologica Sinica, 39(21): 1-16. DOI URL
[27]	田义超, 梁铭忠, 2016. 北部湾沿海地区植被覆盖对气温和降水的旬响应特征[J]. 自然资源学报, 31(3): 488-502.
	TIAN Y C, LIANG M Z, 2016. The NDVI Characteristics of Vegetation and Its Ten-day Response to Temperature and Precipitation in Beibu Gulf Coastal Region[J]. Journal of Natural Resources, 31(3): 488-502.
[28]	信忠保, 许炯心, 郑伟, 2007. 气候变化和人类活动对黄土高原植被覆盖变化的影响[J]. 中国科学 (D辑),37(11): 1504-1514.
	XIN Z B, XU J X, ZHENG W, 2007. Effects of climate change and human activities on vegetation cover change in the Loess Plateau[J]. Science in China (Series D: Earth Sciences), 37(11): 1504-1514.
[29]	王强, 张婷斌, 易桂花, 等, 2017. 横断山区2004—2014年植被NPP时空变化及其驱动因子[J]. 生态学报, 37(9): 3084-3095.
	WANG Q, ZHANG T B, YI G H, et al., 2017. Tempo-spatial variation and driving factors analysis of net primary productivity in the Hengduan mountain area from 2004 to 2014 [J]. Acta Ecologica Sinica, 37(9): 3084-3095.
[30]	许静, 陈迪, 李文龙, 等, 2019. 基于光能利用率模型的甘南州植被净初级生产力研究[J]. 草业科学, 36(10): 2455-2465.
	XU J, CHEN D, LI W L, et al., 2019. Study of vegetation net primary productivity in Gannan based on light use efficiency model[J]. Pratacultural Science, 36(10): 2455-2465.
[31]	赵国帅, 王军邦, 范文义, 等, 2011. 2000—2008年中国东北地区植被净初级生产力的模拟及季节变化[J]. 应用生态学报, 22(3): 621-630.
	ZHAO G S, WANG J B, FAN W Y, et al., 2011. Vegetation net primary productivity in Northeast China in 2000-2008 [J]. Chinese Journal of Applied Ecology, 22(3): 621-630.
[32]	赵苗苗, 刘熠, 杨吉林, 等, 2019. 基于HASM的中国植被NPP时空变化特征及其与气候的关系[J]. 生态环境学报, 28(2): 215-225.
	ZHAO M M, LIU Y, YANG J L, et al., 2019. Spatio-temporal patterns of NPP and its ralations to climate in China based on HASM[J]. Ecology and Environmental Sciences, 28(2): 215-225.
[33]	周爱萍, 向悟生, 姚月锋, 等, 2014. 广西植被净初级生产力 (NPP) 时空演变及主要影响因素分析[J]. 广西植物, 34(5): 622-628.
	ZHOU A P, XIANG W S, YAO Y F, et al., 2014. Analyzing variation characteristics of vegetation net primary productivity (NPP) in Guangxi[J]. Guihaia, 34(5): 622-628.
[34]	周广胜, 张新时, 1996. 全球气候变化的中国自然植被的净第一性生产力研究[J]. 植物生态学报, 20(1): 11-19.
	ZHOU G S, ZHANG X S, 1996. Study on NPP of natural vegetation in china under global climate change[J]. Acta Phytoecologica Sinica, 20(1): 11-19.
[35]	朱士华, 艳燕, 邵华, 等, 2017. 1980—2014年中亚地区植被净初级生产力对气候和CO₂变化的响应[J]. 自然资源学报, 32(11): 1844-1856.
	ZHU S H, YAN Y, SHAO H, et al., 2017. The responses of the net primary productivity of the dryland ecosystems in central Asia to the CO₂ and climate changes during the past 35 years[J]. Journal of Natural Resources, 32(11): 1844-1856.
[36]	朱文泉, 潘耀忠, 阳小琼, 等, 2007a. 气候变化对中国陆地植被净初级生产力的影响分析[J]. 科学通报, 52(21): 2535-2541.
	ZHU W Q, PAN Y Z, YANG X Q, et al., 2007. Impact of climate change on net primary productivity of terrestril vegetation in china[J]. Chinese Science Bulletin, 52(3): 2535-2541.
[37]	朱文泉, 潘耀忠, 张锦水, 2007b. 中国陆地植被净初级生产力遥感估算[J]. 植物生态学报, 31(3): 413-424.
	ZHU W Q, PAN Y Z, ZHANG J S, 2007. Estimation of net primary productivity of chinese terrestrial vegetation based on remote sensing[J]. Journal of Plant Ecology, 31(3): 413-424.

植被变化趋势 Vegetation change trend	NPP变化趋势 NPP change trend		面积 Area/km²	百分比 Proportion/%
植被变化趋势 Vegetation change trend	Sen趋势 Sen trend	P值 P value	面积 Area/km²	百分比 Proportion/%
明显改善 Significant improvement	>0	P<0.01	6225.88	0.33
中度改善 Moderate improvement	>0	0.01≤P<0.05	2195.31	0.12
轻微改善 Slight improvement	>0	P≥0.05	5667.00	0.31
轻微减少 Slightly decrease	<0	P≥0.05	3042.25	0.16
中度减少 Moderate decrease	<0	0.01≤P<0.05	612.88	0.03
严重减少 Seriously decrease	<0	P<0.01	970.31	0.05

植被变化趋势 Vegetation change trend	NPP变化趋势 NPP change trend		面积 Area/km²	百分比 Proportion/%
植被变化趋势 Vegetation change trend	Sen趋势 Sen trend	P值 P value	面积 Area/km²	百分比 Proportion/%
明显改善 Significant improvement	>0	P<0.01	6225.88	0.33
中度改善 Moderate improvement	>0	0.01≤P<0.05	2195.31	0.12
轻微改善 Slight improvement	>0	P≥0.05	5667.00	0.31
轻微减少 Slightly decrease	<0	P≥0.05	3042.25	0.16
中度减少 Moderate decrease	<0	0.01≤P<0.05	612.88	0.03
严重减少 Seriously decrease	<0	P<0.01	970.31	0.05

土地利用 land use	年份 Year				平均值 Average
土地利用 land use	2000	2005	2010	2015	平均值 Average
耕地 Arable land	383.16	428.70	451.52	468.61	433.00
灌木 Bushwood	605.96	669.00	721.35	798.66	698.74
林地 Woodland	651.18	732.47	759.20	846.37	747.31
草地 Grassland	345.15	389.14	385.58	338.38	364.56
湿地 Wetland	289.36	269.49	290.12	309.15	289.53
其他用地 Other land	256.50	273.68	211.55	187.14	232.22

土地利用 land use	年份 Year				平均值 Average
土地利用 land use	2000	2005	2010	2015	平均值 Average
耕地 Arable land	383.16	428.70	451.52	468.61	433.00
灌木 Bushwood	605.96	669.00	721.35	798.66	698.74
林地 Woodland	651.18	732.47	759.20	846.37	747.31
草地 Grassland	345.15	389.14	385.58	338.38	364.56
湿地 Wetland	289.36	269.49	290.12	309.15	289.53
其他用地 Other land	256.50	273.68	211.55	187.14	232.22

海拔类别 Gradient classfication	年份 Year				平均值 Average
海拔类别 Gradient classfication	2000	2005	2010	2015	平均值 Average
第一梯度 First gradient (<250 m)	441.23	495.07	529.13	566.05	500.62
第二梯度 Second gradient (250‒500 m)	799.81	863.38	870.13	971.28	853.54
第三梯度 Third gradient (500‒800 m)	828.25	884.71	883.75	974.88	865.89
第四梯度 Fourth gradient (>800 m)	768.98	848.74	750.80	890.09	776.52

Temporal and Spatial Distribution Characteristics and Influencing Factors of Net Primary Productivity of Vegetation in Typical Basin Entering the Sea in Beibu Gulf

北部湾典型入海流域植被净初级生产力时空分布特征及其影响因素

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 14

References 37

Related Articles 15

Recommended Articles

Metrics

Comments

坡度分类 Slope classification	坡度 Slope/ (°)	NPP_ave/ (g∙m^-2∙a^-1)	面积 Area/ km²	比例 Proportion/%
缓倾斜坡 Gentle slope	<5	454.95	13449.44	71.87
中等斜坡 Medium slope	5‒8	684.08	1868.75	9.98
斜坡 Slope	8‒15	789.30	2288.38	12.23
陡坡 Steep slope	15‒25	887.06	914.69	4.89
急坡 Sharp slope	25‒35	905.10	187.50	1.00
急陡坡 Sharp steep slope	>35	842.96	4.88	0.03

[1]	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.
[2]	LI Jianhui, DANG Zheng, CHEN Lin. Spatial-temporal Characteristics of PM_2.5 and Its Influencing Factors in the Yellow River Jiziwan Metropolitan Area [J]. Ecology and Environment, 2023, 32(4): 697-705.
[3]	ZHANG Lin, QI Shi, ZHOU Piao, WU Bingchen, ZHANG Dai, ZHANG Yan. Study on Influencing Factors of Soil Organic Carbon Content in Mixed Broad-leaved and Coniferous Forests Land in Beijing Mountainous Areas [J]. Ecology and Environment, 2023, 32(3): 450-458.
[4]	HE Yanhu, GONG Zhenjie, WU Haibin, CAI Yanpeng, YANG Zhifeng, CHEN Xiaohong. Spatiotemporal Evolution of Urban Eco-efficiency and Its Influencing Factors in Guangdong-Hong Kong-Macao Greater Bay Area [J]. Ecology and Environment, 2023, 32(3): 469-480.
[5]	HAO Jinhu, WEI Wei, LI Shengnan, MA Muyuan, LI Xiaoxia, YANG Hongguo, JIANG Qiyu, CHAI Peidong. GEE Based Evaluation of the Spatial-temporal Pattern and Drivers of Long-term Water Body in Beijing-Tianjin-Hebei [J]. Ecology and Environment, 2023, 32(3): 556-566.
[6]	ZHANG Li, LI Cheng, TAN Haoze, WEI Jiayi, CHENG Jiong, PENG Guixiang. Reduction Effect and Influencing Factors of Typical Urban Woodlands on Atmospheric Particulate Matter in Guangzhou [J]. Ecology and Environment, 2023, 32(2): 341-350.
[7]	YUAN Linjiang, LI Mengbo, LENG Gang, ZHONG Bingbing, XIA Dapeng, WANG Jinghua. Synergistic Effect of Sulfate Reduction and Ammonia Oxidation in Anaerobic Environment [J]. Ecology and Environment, 2023, 32(1): 207-214.
[8]	SU Yongsong, SONG Song, CHEN Ye, YE Ziqiang, ZHONG Runfei, WANG Zhaoyao. Temporal and Spatial Characteristics of Net Anthropogenic Nitrogen Input and Its Influencing Factors in the Pearl River Delta [J]. Ecology and Environment, 2022, 31(8): 1599-1609.
[9]	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.
[10]	JIANG Peng, QIN Mei’ou, LI Rongping, MENG Ying, YANG Feiyun, WEN Rihong, SUN Pei, FANG Yuan. Seasonal Variability of GPP and Its Influencing Factors in the Typical Ecosystems in China [J]. Ecology and Environment, 2022, 31(4): 643-651.
[11]	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.
[12]	ZHAO Anzhou, TIAN Xinle. Spatiotemporal Evolution and Influencing Factors of Vegetation Coverage in the Loess Plateau from 1986 to 2021 Based on GEE Platform [J]. Ecology and Environment, 2022, 31(11): 2124-2133.
[13]	LI Liangliang, DAI Liangyu, GAO Weichang, ZHANG Shuyi, LIU Taoze. The Occurrence Characteristics and Influencing Factors of Residual Mulching Film of Typical Farmland with Plastic Film in Guizhou Province [J]. Ecology and Environment, 2022, 31(11): 2189-2197.
[14]	YANG Yan, ZHOU Decheng, GONG Zhaoning, LIU Ziyuan, ZHANG Liangxia. Ecological Vulnerability and Its Drivers of the Loess Plateau Based on Vegetation Productivity [J]. Ecology and Environment, 2022, 31(10): 1951-1958.
[15]	LI Shengzeng, HAO Saimei, TAN Luyao, ZHANG Huaicheng, XU Biao, GU Shumao, PAN Guang, WANG Shuyan, YAN Huaizhong, ZHANG Guiqin. Characteristics of Spatiotemporal Variation, and Factors Influencing Secondary Components in PM_2.5 in Ji'nan [J]. Ecology and Environment, 2022, 31(1): 100-109.