基于GEE平台的1986-2021年黄土高原植被覆盖度时空演变及影响因素

doi:10.16258/j.cnki.1674-5906.2022.11.003

生态环境学报 ›› 2022, Vol. 31 ›› Issue (11): 2124-2133.DOI: 10.16258/j.cnki.1674-5906.2022.11.003

基于GEE平台的1986-2021年黄土高原植被覆盖度时空演变及影响因素

赵安周(), 田新乐^*()

河北工程大学矿业与测绘工程学院，河北邯郸 056038

收稿日期:2022-08-03 出版日期:2022-11-18 发布日期:2022-12-22
通讯作者: *田新乐（1998年生），女，硕士研究生，主要研究方向为植被动态遥感监测。E-mail: 1713647311@qq.com
作者简介:赵安周（1985年生），男，副教授，博士，主要研究方向为城市扩张对生态环境的影响。E-mail: zhaoanzhou@126.com
基金资助:
国家自然科学基金项目(42171212);河北省自然科学基金项目(D2022402030);河北省自然科学基金项目(D2021402007)

Spatiotemporal Evolution and Influencing Factors of Vegetation Coverage in the Loess Plateau from 1986 to 2021 Based on GEE Platform

ZHAO Anzhou(), TIAN Xinle^*()

School of Mining and Geomatics, Hebei University of Engineering, Handan 056038, China

Received:2022-08-03 Online:2022-11-18 Published:2022-12-22

摘要/Abstract

摘要：

基于Google Earth Engine（GEE）云平台，对1986-2021年黄土高原的Landsat地表反射率数据进行去云和融合处理，计算得到归一化植被指数（Normalized Difference Vegetation Index，NDVI），并利用像元二分模型估算植被覆盖度（Fractional Vegetation Coverage，FVC），在此基础上，辅以趋势分析、偏相关和残差分析等方法分析了不同时间段（1986-1999、2000-2021和1986-2021年）黄土高原FVC时空变化及其影响因素。结果表明，（1）时间上，1986-2021年黄土高原FVC呈显著增加的趋势（Trend=0.0044 a^-1，P<0.01）。从不同时间段看，2000-2021年FVC的增加趋势（Trend=0.0058 a^-1，P<0.01）快于1986-1999年（Trend=0.0038 a^-1，P<0.01）。黄土高原所有植被类型的FVC均呈显著上升的趋势（P<0.01），其中草地的上升趋势最大（Trend=0.0066 a^-1，P<0.01）。（2）空间上，黄土高原FVC呈东南向西北递减的趋势，1986-2021、1986-1999和2000-2021年FVC呈显著上升的面积分别为53.65%、18.38%和48.12%。（3）地形因子中，高程和坡度对FVC的影响较显著。FVC值随高程呈“下降-上升-下降”的变化趋势，最大值（0.7790）出现在3000-3500 m。FVC随坡度的增加呈上升的趋势，最大值（0.7025）出现在25°-45°。中高等级FVC和高等级FVC比例随坡度呈正相关关系，其中15°-25°面积占比最大（73.93%）。（4）1986-2021年黄土高原FVC与年降水量、年平均气温和太阳辐射（Solar Radiation，RAD）的偏相关系数分别为0.239、0.093和-0.006，其中呈显著正相关（P<0.05）的像元占比分别为48.50%、22.51%和5.96%。残差分析结果表明，人类活动是黄土高原植被动态变化的主要驱动因素，且起正向作用的像元比例为73.20%。

关键词: GEE, FVC, 时空演变, 影响因素, 黄土高原

Abstract:

Based on the cloud platform of Google Earth Engine (GEE), Landsat surface reflectance data of Loess Plateau from 1986 to 2021 were de-clouding and fused. The Normalized Difference Vegetation Index (NDVI) was calculated, and fractional vegetation coverage (FVC) was estimated using the pixel dichotomy model. On this basis, with the help of trend analysis, partial correlation analysis and residual analysis, the temporal and spatial variations of FVC and its influencing factors in the Loess Plateau in different time periods (1986-2021, 1986-1999 and 2000-2021) were analyzed. The results show that (1) in terms of time, FVC on the Loess Plateau increased significantly from 1986 to 2021 (Trend=0.0044 a^-1, P<0.01). In different time periods, the increasing trend from 2000 to 2021 (Trend=0.0058 a^-1, P<0.01) was faster than that from 1986 to 1999 (Trend=0.0038 a^-1, P<0.01). The FVC of all vegetation types on the Loess Plateau showed a significant increasing trend (P<0.01), among them, the grassland had the largest increasing trend (Trend=0.0066 a^-1, P<0.01). (2) Spatially, FVC decreased from southeast to northwest in the Loess Plateau. The FVC of the total area showed significant improvement in 1986-2021, 1986-1999 and 2000-2021 by 53.65%, 18.38% and 48.12%, respectively. (3) Elevation and Trend of topographic factors had significant effects on FVC. The value of FVC first decreased, then rose and finally decreased with the elevation, with the maximum value (0.7790) between 3000 m and 3500 m. FVC increased with the increase of Trend, with the maximum value (0.7025) appeared in the range of 25°-45°. The proportion of middle and high coverage and high coverage increased with the increase of Trend, in which the area proportion was the largest in the range of 15°-25° (73.93%). (4) The biased relationship between FVC and annual precipitation, annual mean temperature and solar radiation in the Loess Plateau from 1986 to 2021 were 0.239, 0.093 and -0.006, respectively. The proportions of pixels with significant positive correlations (P<0.05) accounted for 48.50%, 22.51% and 5.96% of the total area. The results of residual analysis showed that human activities was the main driving factor of vegetation dynamic change on the Loess Plateau, and the proportion of pixels that played a positive role was 73.20%.

Key words: GEE, fractional vegetation coverage, spatiotemporal evolution, influencing factors, Loess Plateau

中图分类号:

Q948
X173

赵安周, 田新乐. 基于GEE平台的1986-2021年黄土高原植被覆盖度时空演变及影响因素[J]. 生态环境学报, 2022, 31(11): 2124-2133.

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.

图/表 10

图1 研究区位置和植被类型图

Figure 1 Location and vegetation type of Loess Plateau

图2 1986-2021年Landsat 5/7/8用于年合成采用的影像数量

Figure 2 Number of scenes of Landsat 5/7/8 used for annual composite formation in 1986-2021

图3 1986-1999、2000-2021和1986-2021年黄土高原FVC年际变化

Figure3 The variation of average annual vegetation cover of the Loess Plateau in 1986-2021, 1986-1999 and 2000-2021

表1 1986-1999、2000-2021和1986-2021年黄土高原不同植被类型的FVC变化趋势

Table 1 Trends of FVC changes between different vegetation types in the Loess Plateau in 1986-2021, 1986-1999 and 2000-2021

植被类型 Vegetation type	年份 Year
植被类型 Vegetation type	1986-1999	2000-2021	1986-2021
耕地 Cropland	0.0033^**	0.0056^**	0.0039^**
林地 Woodland	0.0024^**	0.0032^**	0.0028^**
草地 Grassland	0.0052^**	0.0066^**	0.0055^**
灌丛 Thicket	0.0033^**	0.0036^**	0.0030^**

图4 1986-1999、2000-2021和1986-2021年黄土高原FVC均值空间分布及不同等级FVC像元占比 (a)-(c) 图中的虚线表示FVC低于或高于0.60分界线

Figure 4 Spatial distribution and the frequencies of FVC on the Loess Plateau in 1986-2021, 1986-1999 and 2000-2021 The dotted line in the (a-c) figure indicates that the FVC is below or above the 0.60 dividing line

图5 1986-1999、2000-2021和1986-2021年黄土高原FVC空间趋势

Figure 5 Spatial distribution of FVC trends on the Loess Plateau in 1986-2021, 1986-1999 and 2000-2021

图6 黄土高原不同地形因子的FVC等级占比及其均值变化

Figure 6 Proportion and FVC means of vegetation cover at different levels of terrain factors

图7 1986-2021年FVC与气候因子偏相关系数的空间分布显著性水平0.01、0.05、0.1分别对应的偏相关系数为±0.42、±0.32和±0.27

Figure 7 Spatial distribution of partial correlation coefficients between FVC and climate factors in 1986-2021 The levels of significance of 0.01, 0.05 and 0.1 represent partial correlation coefficient of ±0.42, ±0.32 and ±0.27, respectively

图8 1986-2021年黄土高原人类活动对FVC影响的空间分布

Figure 8 Spatial distribution of human activities impacts on FVC in the Loess Plateau in 1986-2021

图9 1986-2021、1986-1999和2000-2021年黄土高原年降水量及趋势

Figure 9 Trends of precipitation in the Loess Plateau in 1986-2021, 1986-1999 and 2000-2021

参考文献 41

[1]	EVANS J, GEERKEN R, 2004. Discrimination between climate and human-induced dryland degradation[J]. Journal of Arid Environments, 57(4): 535-554. DOI URL
[2]	FENG X M, FU B J, PIAO S L, et al., 2016. Revegetation in China’s Loess Plateau is approaching sustainable water resource limits[J]. Nature Climate Change, 6(11): 1019-1022. DOI URL
[3]	GAO W D, ZHENG C, LIU X H, et al., 2022. NDVI-based vegetation dynamics and their responses to climate change and human activities from 1982 to 2020: A case study in the Mu Us Sandy Land, China[J]. Ecological Indicators, 137: 108745. DOI URL
[4]	HAN H Z, BAI J J, MA G, 2022. Seasonal responses of net primary productivity of vegetation to phenological dynamics in the Loess Plateau, China[J]. Chinese Geographical Science, 32(2): 340-357. DOI URL
[5]	KOU P L, XU Q, JIN Z, et al., 2021. Complex anthropogenic interaction on vegetation greening in the Chinese Loess Plateau[J]. Science of Total Environment, 778(7): 65-146.
[6]	LIU Z Z, WANG H, LI N, et al., 2020. Spatial and temporal characteristics and driving forces of vegetation changes in the Huaihe River Basin from 2003 to 2018[J]. Sustainability, 12(6): 21-98. DOI URL
[7]	WANG Y P, YAN W M, HAN X Y, et al., 2021. Changes in deep soil water content in the process of large-scale apple tree planting on the Loess[J]. Tableland of China. Forests, 12(2): 123.
[8]	XU B, QI B, JI K, et al., 2022. Emerging hot spot analysis and the spatial temporal trends of NDVI in the Jing River Basin of China[J]. Environmental Earth Sciences, 81(2): 1-15. DOI URL
[9]	YU H C, BIAN Z F, MU S G, et al., 2020. Effects of climate change on land cover change and vegetation dynamics in Xinjiang, China[J]. International Journal of Environmental Research and Public Health, 17(13): 48-65. DOI URL
[10]	ZHAO A Z, YU Q Y, WANG D L, et al., 2022. Spatiotemporal dynamics of ecosystem water use efficiency over the Chinese Loess Plateau base on long-time satellite data[J]. Environmental Science and Pollution Research, 29(2): 2298-2310. DOI URL
[11]	ZHENG H, LIN H, ZHOU W J, et al., 2019. Revegetation has increased ecosystem water-use efficiency during 2000-2014 in the Chinese Loess Plateau: Evidence from satellite data[J]. Ecological Indicators, 102: 507-518. DOI URL
[12]	常铮, 李崇贵, 张家政, 等, 2022. 基于GEE云平台的陕北黄土高原生态修复前后植被变化及原因[J]. 西安理工大学学报, 117(5): 1-10.
	CHANG Z, LI C G, ZHANG J Z, et al., 2022. Analysis of vegetation changes and causes before and after ecological restoration projects on the Loess Plateau in northern Shaanxi based on Google Earth Engine[J]. Journal of Xi’an University of Technology, 117(5): 1-10.
[13]	郭永强, 王乃江, 褚晓升, 等, 2019. 基于Google Earth Engine分析黄土高原植被覆盖变化及原因[J]. 中国环境科学, 39(11): 4804-4811.
	GUO Y Q, WANG N J, CHU X S, et al., 2019. Analyzing vegetation coverage changes and its reasons on the Loess Plateau based on Google Earth Engine[J]. China Environmental Science, 39(11): 4804-4811.
[14]	侯学煜, 2001. 中国植被图集(1꞉1000000)[Z]. 北京: 科学出版社.
	HOU X Y, 2001. Vegetation map (1꞉1,000,000) in the Heihe River basin[Z]. BeiJing: Science Press.
[15]	何亮, 2021. 黄土高原植被覆盖变化特征及驱动力分析[D]. 呼和浩特: 内蒙古农业大学: 51-60.
	HE L, 2021. Analysis of vegetation cover change characteristics and driving forces in Loess Plateau[D]. Huhhot: Inner Mongolia Agricultural University: 51-60.
[16]	黄栋, 李鹏, 董南, 2021. 近20a环渤海地区GS_NDVI时空分异及其对气候变化和LUCC的响应[J]. 生态环境学报, 30(12):2275-2284.
	HUANG D, LI P, DONG N, 2021. Spatial-temporal Differentiation of GS_NDVI in Recent 20 Years and Its Responses to Climate Change and LUCC in the Bohai Coastal Region[J]. Ecology and Environmental Sciences, 30(12): 2275-2284.
[17]	解晗, 同小娟, 李俊, 等, 2022. 2000-2018年黄河流域生长季植被指数变化及其对气候因子的响应[J]. 生态学报, 42(11): 4536-4549.
	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): 4536-4549.
[18]	金凯, 王飞, 韩剑桥, 等, 2020. 1982-2015年中国气候变化和人类活动对植被NDVI变化的影响[J]. 地理学报, 75(5): 961-974. DOI
	JIN K, WANG F, HAN J Q, et al., 2020. Contribution of climatic change and human activities to vegetation NDVI change over China during 1982-2015[J]. Acta Geographica Sinica, 75(5): 961-974. DOI
[19]	李辉霞, 刘国华, 傅伯杰, 2011. 基于NDVI的三江源地区植被生长对气候变化和人类活动的响应研究[J]. 生态学报, 31(19): 5495-5504.
	LI H X, LIU G H, FU B J, 2011. Response of vegetation to climate change and human activity based on NDVI in the Three-River Headwaters region[J]. Acta Ecologica Sinica, 31(19): 5495-5504.
[20]	李晶, 闫星光, 闫萧萧, 等, 2021. 基于GEE云平台的黄河流域植被覆盖度时空变化特征[J]. 煤炭学报, 46(5): 1439-1450.
	LI J, YAN X G, YAN X X, et al., 2021. Temporal and spatial variation characteristic of vegetation coverage in the Yellow River Basin based on GEE cloud platform[J]. Journal of China Coal Society, 46(5): 1439-1450.
[21]	李苗苗, 吴炳方, 颜长珍, 等, 2004. 密云水库上游植被覆盖度的遥感估算[J]. 资源科学, 26(4): 153-159.
	LI M M, WU B F, YAN C Z, et al., 2004. Estimation of vegetation fraction in the upper basin of Miyun Reservoir by remote sensing[J]. Resources Science, 26(4): 153-159.
[22]	李依璇, 朱清科, 石若莹, 等, 2021. 2000-2018黄土高原植被覆盖时空变化及影响因素[J]. 中国水土保持科学, 19(4): 60-68.
	LI Y X, ZHU Q K, SHI R Y, et al., 2021. Spatial and temporal changes of vegetation cover and its influencing factors in the Loess Plateau from 2000 to 2018[J]. Science of Soil and Water Conservation, 19(4): 60-68.
[23]	马士彬, 安裕伦, 杨广斌, 等, 2019. 不同地形梯度上的植被变化趋势及原因分析[J]. 生态环境学报, 28(5): 857-864.
	MA S B, AN Y L, YANG G B, et al., 2019. The analysis of distribution characteristics and reasons of NDVI change trends along the terrain gradient[J]. Ecology and Environmental Sciences, 28(5): 857-864.
[24]	宁晓刚, 常文涛, 王浩, 等, 2022. 联合GEE与多源遥感数据的黑龙江流域沼泽湿地信息提取[J]. 遥感学报, 26(2): 386-396.
	NING X G, CHANG W T, WANG H, et al., 2022. Extraction of marsh wetland in Heilongjiang Basin based on GEE and multi-source remote sensing data[J]. National Remote Sensing Bulletin, 26(2): 386-396.
[25]	聂桐, 董国涛, 蒋晓辉, 等, 2022. 榆林地区植被时空分异特征及其影响因素研究[J]. 生态环境学报, 31(1): 26-36.
	NIE T, DONG G T, JIANG X H, et al., 2022. Spatio-temporal Variations and Influencing Factors of Vegetation in Yulin[J]. Ecology and Environment, 31(1): 26-36.
[26]	孙高鹏, 刘宪锋, 王小红, 等, 2021. 2001-2020年黄河流域植被覆盖变化及其影响因素[J]. 中国沙漠, 41(4): 205-212.
	SUN G P, LIU X F, WANG X H, et al., 2021. Changes in vegetation coverage and its influencing factors across the Yellow River Basin during 2001-2020[J]. Journal of Desert Research, 41(4): 205-212.
[27]	田智慧, 任祖光, 魏海涛, 等, 2022. 2000-2020年黄河流域植被时空演化驱动机制[J]. 环境科学, 43(2):743-751.
	TIAN Z H, REN Z G, WEI H T, et al., 2022. Driving mechanism of the spatiotemporal evolution of vegetation in the Yellow River Basin from 2000 to 2020[J]. Environmental Science, 43(2):743-751. DOI URL
[28]	王晓蕾, 石守海, 2022. 基于GEE的黄河流域植被时空变化及其地形效应研究[J]. 地球信息科学学报, 24(6): 1087-1098. DOI
	WANG X L, SHI S H, 2022. Spatio-temporal changes of vegetation in the Yellow River Basin and related effect of landform based on GEE[J]. Journal of Geo-information Science, 24(6): 1087-1098.
[29]	肖强, 陶建平, 肖洋, 2016. 黄土高原近10年植被覆盖的动态变化及驱动力[J]. 生态学报, 36(23): 7594-7602.
	XIAO Q, TAO J P, XIAO Y, 2016. Dynamic vegetation cover change over the past 10 years on the Loess Plateau, China[J]. Acta Ecologica Sinica, 36(23): 7594-7602.
[30]	徐建华, 2014. 计量地理学[M]. 北京: 高等教育出版社: 159-184.
	XU J H, 2014. Quantitative geography[M]. Beijing: Higher Education Press: 159-184.
[31]	徐勇, 黄雯婷, 卢梦缘, 等, 2022. 气候变化和人类活动对西南喀斯特地貌区植被NDVI变化相对作用[J]. 水土保持研究, 29(3): 292-299.
	XU Y, HUANG W T, LU M Y, et al., 2022. Vegetation cover change and the relative role of climate change and human activities in southwest Karst Areas[J]. Research of Soil and Water Conservation, 29(3): 292-299.
[32]	阎世杰, 王欢, 焦珂伟, 2019. 京津冀地区植被时空动态及定量归因[J]. 地球信息科学学报, 21(5): 767-780. DOI
	YAN S J, WANG H, JIAO K W, 2019. Spatiotemporal dynamic of NDVI in the Beijing-Tianjin-Hebei region based on MODIS data and quantitative attribution[J]. Journal of Geo-information Science, 21(5): 767-780.
[33]	杨灿, 魏天兴, 李亦然, 等, 2021. 黄土高原典型县域植被覆盖度时空变化及地形分异特征[J]. 生态学杂志, 40(6): 1830-1838.
	YANG C, WEI T X, LI Y R, et al., 2021. Spatiotemporal variations and topographic differentiation of fractional vegetation cover in typical counties of Loess Plateau[J]. Chinese Journal of Ecology, 40(6): 1830-1838.
[34]	杨嘉, 郭铌, 黄蕾诺, 等, 2008. 西北地区MODIS-NDVI指数饱和问题分析[J]. 高原气象, 27(4): 896-903.
	YANG J, GUO N, HUANG L N, et al., 2008. Ananlyses on MODIS-NDVI Index Saturation in Northwest China[J]. Plateau Meteorology, 27(4): 896-903.
[35]	易浪, 任志远, 张翀, 等, 2014. 黄土高原植被覆盖变化与气候和人类活动的关系[J]. 资源科学, 36(1): 166-174.
	YI L, REN Z Y, ZHANG C, et al., 2014. Vegetation cover, climate and human activities on the Loess Plateau[J]. Resources Science, 36(1): 166-174.
[36]	袁和第, 2020. 黄土丘陵沟壑区典型小流域水土流失治理技术模式研究[D]. 北京: 北京林业大学: 33-57.
	YUAN H D, 2020. The research on model of soil erosion comprehensive control in typical small watershed in hilly and gully loess region[D]. Beijing: Beijing Forestry University: 33-57.
[37]	张宝庆, 田磊, 赵西宁, 等, 2021. 植被恢复对黄土高原局地降水的反馈效应研究[J]. 中国科学: 地球科学, 51(7): 1080-1091.
	ZHANG B Q, TIAN L, ZHAO X N, et al., 2021. Feedbacks between vegetation restoration and local precipitation over the Loess Plateau in China[J]. Science China Earth Sciences, 64(6): 920-931. DOI URL
[38]	张宝庆, 吴普特, 赵西宁, 2011. 近30 a黄土高原植被覆盖时空演变监测与分析[J]. 农业工程学报, 27(4): 287-293, 400.
	ZHANG B Q, WU P T, ZHAO X N, 2011. Detecting and analysis of spatial and temporal variation of vegetation cover in the Loess Plateau during 1982-2009[J]. Transactions of the CSAE, 27(4): 287-293, 400.
[39]	张家政, 李崇贵, 王涛, 2022. 黄土高原植被覆盖时空变化及原因[J]. 水土保持研究, 29(1): 224-230, 241.
	ZHANG J Z, LI C G, WANG T, 2022. Dynamic changes of vegetation coverage on the Loess Plateau and its factors[J]. Research of Soil and Water Conservation, 29(1): 224-230, 241.
[40]	张园, 袁凤辉, 王安志, 等, 2020. 2001-2018年长白山自然保护区生长季NDVI变化特征及其对气候变化的响应[J]. 应用生态学报, 31(4): 1213-1222. DOI
	ZHANG Y, YUAN F H, WANG A Z, et al., 2020. Variation characteristics of NDVI and its response to climatic change in the growing season of Changbai Mountain Nature Reserve during 2001 and 2018[J]. Chinese Journal of Applied Ecology, 31(4): 1213-1222. DOI
[41]	赵安周, 张安兵, 刘海新, 等, 2017. 退耕还林(草)工程实施前后黄土高原植被覆盖时空变化分析[J]. 自然资源学报, 32(3): 449-460.
	ZHAO A Z, ZHANG A B, LIU H X, et al., 2017. Spatiotemporal variation of vegetation coverage before and after implementation of grain for green project in the Loess Plateau[J]. Journal of Natural Resources, 19(4): 60-68.

编辑推荐 0

Metrics

阅读次数

全文

484

HTML			PDF

最新录用	在线预览	正式出版	最新录用	在线预览	正式出版
0	0	34	0	0	450

来源	本网站	其他网站

次数	423	61
比例	87%	13%

摘要

866

最新录用	在线预览	正式出版

0	0	866

	来源	本网站

	次数	866
	比例	100%

基于GEE平台的1986-2021年黄土高原植被覆盖度时空演变及影响因素

Spatiotemporal Evolution and Influencing Factors of Vegetation Coverage in the Loess Plateau from 1986 to 2021 Based on GEE Platform

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 41

相关文章 15

编辑推荐 0

Metrics

本文评价

[1]	王琳, 卫伟. 黄土高原典型县域生态系统服务变化特征及驱动因素[J]. 生态环境学报, 2023, 32(6): 1140-1148.
[2]	巫晨煜, 许帆帆, 魏士博, 樊晶晶, 刘观鹏, 王坤. 渭河流域地表植被覆盖对气候变化的响应研究[J]. 生态环境学报, 2023, 32(5): 835-844.
[3]	王超, 杨倩楠, 张池, 刘同旭, 张晓龙, 陈静, 刘科学. 丹霞山不同土地利用方式土壤磷组分特征及其有效性[J]. 生态环境学报, 2023, 32(5): 889-897.
[4]	李建辉, 党争, 陈琳. 黄河几字弯都市圈PM_2.5时空特征及影响因素分析[J]. 生态环境学报, 2023, 32(4): 697-705.
[5]	李晖, 李必龙, 葛黎黎, 韩琛惠, 杨倩, 张岳军. 2000-2021年汾河流域植被时空演变特征及地形效应[J]. 生态环境学报, 2023, 32(3): 439-449.
[6]	张林, 齐实, 周飘, 伍冰晨, 张岱, 张岩. 北京山区针阔混交林地土壤有机碳含量的影响因素研究[J]. 生态环境学报, 2023, 32(3): 450-458.
[7]	何艳虎, 龚镇杰, 吴海彬, 蔡宴朋, 杨志峰, 陈晓宏. 粤港澳大湾区城市生态效率时空演变及影响因素[J]. 生态环境学报, 2023, 32(3): 469-480.
[8]	王嘉丽, 冯婧珂, 杨元征, 俎佳星, 蔡文华, 杨健. 南宁市主城区不透水面与热环境效应的空间关系研究[J]. 生态环境学报, 2023, 32(3): 525-534.
[9]	郝金虎, 韦玮, 李胜男, 马牧源, 李肖夏, 杨洪国, 姜琦宇, 柴沛东. 基于GEE平台的京津冀长时序水体时空格局及其影响因素[J]. 生态环境学报, 2023, 32(3): 556-566.
[10]	张莉, 李铖, 谭皓泽, 韦家怡, 程炯, 彭桂香. 广州典型城市林地对大气颗粒物的削减效应及影响因素[J]. 生态环境学报, 2023, 32(2): 341-350.
[11]	郑晓豪, 陈颖彪, 郑子豪, 郭城, 黄卓男, 周泳诗. 湖北省生态系统服务价值动态变化及其影响因素演变[J]. 生态环境学报, 2023, 32(1): 195-206.
[12]	袁林江, 李梦博, 冷钢, 钟冰冰, 夏大朋, 王景华. 厌氧环境下硫酸盐还原与氨氧化的协同作用[J]. 生态环境学报, 2023, 32(1): 207-214.
[13]	刘希林, 卓瑞娜. 崩岗崩积体坡面初始产流时间影响因素及其临界阈值[J]. 生态环境学报, 2023, 32(1): 36-46.
[14]	李威闻, 黄金权, 齐瑜洁, 刘小岚, 刘纪根, 毛治超, 高绣纺. 土壤侵蚀条件下土壤微生物生物量碳含量变化及其影响因素的Meta分析[J]. 生态环境学报, 2023, 32(1): 47-55.
[15]	齐月, 张强, 胡淑娟, 蔡迪花, 赵福年, 陈斐, 张凯, 王鹤龄, 王润元. 黄土高原地区气候变化及其对冬小麦生产潜力的影响[J]. 生态环境学报, 2022, 31(8): 1521-1529.