Spatio-temporal variations of vegetation ecological quality in Zhejiang Province and their driving factors

doi:10.6046/zrzyyg.2022070

Abstract
Figures/Tables
References
Related Articles
Metrics

Download: PDF(6272 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Zhejiang Province is both the birthplace of the theory that both the mountain of gold and silver and the lushmountain with lucid waters are required (also known as the Two Mountains theory) and the first ecological province in China. The study on the vegetation ecological quality of Zhejiang can be used as an important reference for the construction of ecological civilization. Based on multi-source remote sensing data and meteorological observation data, this study investigated the spatio-temporal variations of vegetation ecological quality in Zhejiang during 2000—2020, as well as their response to climate factors and human activities. The results show that: ① Both the fractional vegetation cover (FVC) and the net primary production (NPP) in Zhejiang showed an upward trend during 2000—2020, with significantly increased vegetation greenness; ② The vegetation eco-environmental quality in Zhejiang showed a fluctuating upward trend during 2000—2020, with the vegetation ecological quality indices (VEQIs) of mountainous areas significantly higher than those of basin and plain areas; ③ The dominant factor driving the VEQI variations in Zhejiang during 2000—2020 is human activities, while climate factors occupied a dominant position only in some areas of southwestern Zhejiang. This study deepens the understanding of the spatio-temporal variations of vegetation ecological quality in Zhejiang and their driving factors and, thus, is of great significance for the construction of ecological civilization in Zhejiang and even other regions in China.

Keywords ecology remote sensing net primary production vegetation MODIS

ZTFLH:

TP79

Issue Date: 07 July 2023

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	He FANG
	Yuhui ZHANG
	Yue HE
	Zhengquan LI
	Gaofeng FAN
	Dong XU
	Chunyang ZHANG
	Zhonghua HE

Cite this article:

He FANG,Yuhui ZHANG,Yue HE, et al. Spatio-temporal variations of vegetation ecological quality in Zhejiang Province and their driving factors[J]. Remote Sensing for Natural Resources, 2023, 35(2): 245-254.

URL:

https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022070 OR https://www.gtzyyg.com/EN/Y2023/V35/I2/245

Fig.1 Land cover in 2020 and digital elevation and district and county representative meteorological station distribution over Zhejiang Province

Fig.2 Variation of NDVI and FVC in Zhejiang Province from 2000 to 2020

Fig.3 Average value and improvement of FVC in Zhejiang Province from 2000 to 2020

Fig.4 Variation of NPP in Zhejiang from 2000—2020

Fig.5 Average value and variation trend spatial distribution of NPP in Zhejiang Province from 2000 to 2020

Fig.6 Chang trend of the comprehensive ecological quality index of vegetation from 2000 to 2020 in Zhejiang Province

Fig.7 Average value and variation trend spatial distribution of VEQI in Zhejiang Province from 2000 to 2020

Fig.8 Scatter diagram between simulated and observed VEQI in different year

Fig.9 Driving force analysis of VEQI variation in Zhejiang Province

[1]	Chen C, Park T J, Wang X H, et al. China and India lead in greening of the world through land-use management[J]. Nature Sustainability, 2019(2):122-129. doi: 10.1038/s41893-019-0220-7 pmid: 30778399
[2]	Zuo L J, Zhang Z X, Carlson K M, et al. Progress towards sustainable intensification in China challenged by land-use change[J]. Nature Sustainability, 2018(1):304-313.
[3]	Ahrends A, Hollingsworth P M, Beckschafer P, et al. China’s fight to halt tree cover loss[C]// Proceedings of the Royal Society Biological Sciences, 2017, 284(1854):20162559.
[4]	Xu D, Yang F, Yu L, et al. Quantization of the coupling mechanism between eco-environmental quality and urbanization from multisource remote sensing data[J]. Journal of Cleaner Production, 2021, 321:128948. doi: 10.1016/j.jclepro.2021.128948 url: https://linkinghub.elsevier.com/retrieve/pii/S0959652621031413
[5]	Guo E, Liu X, Zhang J, et al. Assessing spatiotemporal variation of drought and its impact on maize yield in northeast China[J]. Journal of Hydrology, 2017:231-247.
[6]	赵苗苗, 刘熠, 杨吉林, 等. 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, 2019, 28(2):215-225. doi: 10.16258/j.cnki.1674-5906.2019.02.001
[6]	Zhao M M, Liu Y, Yang J L, et al. Spatio-temporal patterns of NPP and its relations to climate in China based on HASM[J]. Ecology and Environmental Sciences, 2019, 28(2):215-225.
[7]	李登科, 王钊. 退耕还林后陕西省植被覆盖度变化及其对气候的响应[J]. 生态学杂志, 2020, 39(1):1-10.
[7]	Li D K, Wang Z. Changes of fractional vegetation coverage after returning farmland to forests and its response to climate in Shaanxi[J]. Chinese Journal of Ecology, 2020, 39(1),1-10.
[8]	贾俊鹤, 刘会玉, 林振山. 中国西北地区植被NPP 多时间尺度变化及其对气候变化的响应[J]. 生态学报, 2019, 39(14) :5058-5069.
[8]	Jia J H, Liu H Y, Lin Z S. Multi-time scale changes of vegetation NPP in six provinces of northwest China and their responses to climate change[J]. Acta Ecologica Sinica, 2019, 39(14) :5058-5069.
[9]	Liu C, Dong X, Liu Y. Changes of NPP and their relationship to climate factors based on the transformation of different scales in Gansu,China[J]. Catena, 2015, 125:190-199. doi: 10.1016/j.catena.2014.10.027 url: https://linkinghub.elsevier.com/retrieve/pii/S0341816214003105
[10]	陈舒婷, 郭兵, 杨飞, 等. 2000—2015年青藏高原植被NPP时空变化格局及其对气候变化的响应[J]. 自然资源学报, 2020, 35(10):2511-2527. doi: 10.31497/zrzyxb.20201016
[10]	Chen S t, Guo F, Yang F, et al. Spatial and temporal patterns of NPP and its response to climate change in the Qinghai-Tibet Plateau from 2000 to 2015[J]. Journal of Natural Resources, 2020, 35(10):2511-2527. doi: 10.31497/zrzyxb.20201016 url: http://www.jnr.ac.cn/EN/10.31497/zrzyxb.20201016
[11]	周伟, 刚成诚, 李建龙, 等. 1982—2010年中国草地覆盖度的时空动态及其对气候变化的响应[J]. 地理学报, 2014, 69(1):15-30.
[11]	Zhou W, Gang C C, Li J L, et al. Spatial-temporal dynamics of grassland coverage and its response to climate change in China during 1982—2010[J]. Acta Geographica Sinica, 2014, 69(1):15-30.
[12]	郭永强, 王乃江, 褚晓升, 等. 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, 2019, 039(11):4804-4811.
[12]	Guo Y Q, Wang N J, Chu X S, et al. Analyzing vegetation coverage changes and its reasons on the Loess Plateau based on Google Earth Engine[J]. China Environmental Science, 2019, 39(11):4804-4811.
[13]	Liu H Y, Zhang M Y, Lin Z S, et al. Spatial heterogeneity of the relationship between vegetation dynamics and climate change and their driving forces at multiple time scales in southwest China[J]. Agricultural and Forest Meteorology, 2018, 256:10-21.
[14]	毛留喜, 李朝生, 侯英雨, 等. 2006年上半年全国生态气象监测与评估研究[J]. 气象, 2006(11):105-112.
[14]	Mao L X, Li C S, Hou Y Y, et al. China Meteorologically-driven ecological monitoring and assessment in the first half of 2006[J]. Meteorological Monthly, 2006(11):105-112.
[15]	毛留喜, 钱拴, 侯英雨, 等. 2006年夏季川渝高温干旱的生态气象监测与评估[J]. 气象, 2007(3):83- 88,132-133.
[15]	Mao L X, Qian S, Hou Y Y, et al. Study on the meteorologically-driven ecological monitoring and assessment of high temperature and drought of Sichuan-Chongqing area in summer 2006[J]. Meteorological Monthly, 2007(3):83-88,132-133.
[16]	钱拴, 毛留喜, 侯英雨, 等. 北方草地生态气象综合监测预测技术及其应用[J]. 气象, 2008(11):62-68.
[16]	Qian S, Mao L X, Hou Y Y, et al. Technology and application of ecology meteorological synthetic monitoring and predicting for northern grassland in China[J]. Meteorological Monthly, 2008(11):62-68.
[17]	吴宜进, 赵行双, 奚悦, 等. 基于MODIS的2006—2016年西藏生态质量综合评价及其时空变化[J]. 地理学报, 2019, 74(7):1438-1449. doi: 10.11821/dlxb201907012
[17]	Wu Y J, Zhao X S, Xi Y, et al. Comprehensive evaluation and spatial-temporal changes of eco-environmental quality based on MODIS in Tibet during 2006—2016[J]. Acta Geographic Sinica, 2019, 74(7):1438-1449.
[18]	杨绘婷, 徐涵秋. 基于遥感空间信息的武夷山国家级自然保护区植被覆盖度变化与生态质量评估[J]. 应用生态学报, 2020, 31(2):187-196.
[18]	Yang H T, Xu H Q. Assessing fractional vegetation cover changes and ecological quality of the Wuyi Mountain National Nature Reserve based on remote sensing spatial information[J]. Chinese Journal of Applied Ecology, 2020, 31(2):187-196
[19]	李洪伟. 浙江省植被覆盖的时空变化研究[D]. 金华: 浙江师范大学, 2010.
[19]	Li H W. Study on the spatial-temporal change of vegetation in Zhejiang Province[D]. Jinhua: Zhejiang Normal University, 2010.
[20]	高大伟, 张小伟, 蔡菊珍, 等. 浙江省植被覆盖时空动态及其与生态气候指标的关系[J]. 应用生态学报, 2010(6):171-175.
[20]	Gao D W, Zhang X W, Cai J Z, et al. Spatiotemporal variations of vegetation cover in Zhejiang Province and their relations to ecoclimatic indices[J]. Chinese Journal of Applied Ecology, 2010(6):171-175.
[21]	何月, 樊高峰, 张小伟, 等. 浙江省植被NDVI 动态及其对气候的响应[J]. 生态学报, 2012, 32(14) :4352-4362.
[21]	He Y, Fan G F, Zhang X W, et al. Variation of vegetation NDVI and its response to climate change in Zhejiang Province[J]. Acta Ecologica Sinica, 2012, 32(14) :4352-4362. doi: 10.5846/stxb url: http://www.ecologica.cn/
[22]	钱拴, 延昊, 吴门新, 等. 植被综合生态质量时空变化动态监测评价模型[J]. 生态学报, 2020, 40(18):6573-6583.
[22]	Qian S, Yan H, Wu M X, et al. Dynamic monitoring and evaluation model for spatio-temporal change of comprehensive ecological quality of vegetation[J]. Acta Ecologica Sinica, 2020, 40(18):6573-6583.
[23]	曹云, 钱永兰, 孙应龙, 等基于MODIS NDVI 的西南森林植被时空变化特征及其气候响应分析[J]. 生态环境学报, 2020, 29(5):857-865. doi: 10.16258/j.cnki.1674-5906.2020.05.001
[23]	Cao Y, Qian Y L, Sun Y L, et al. Spatial-temporal variations of forest vegetation and climatic driving force analysis in southwest China based on MODIS NDVI and climate data[J]. Ecology and Environmental Sciences, 2020, 29(5):857-865.
[24]	Yan H, Wang S Q, Billesbach D, et al. Improved global simulations of gross primary product based on a new definition of water stress factor and a separate treatment of C3 and C4 plants[J]. Ecological Modelling, 2015, 297:42-59. doi: 10.1016/j.ecolmodel.2014.11.002 url: https://linkinghub.elsevier.com/retrieve/pii/S0304380014005602
[25]	金凯, 王飞, 韩剑桥, 等. 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, 2020, 75(5):75-88.
[25]	Jin K, Wang F, Han J Q, et al. Contribution of climatic change and human activities to vegetation NDVI change over China during 1982—2015[J]. Acta Geographica Sinica, 2020, 75(5):75-88.
[26]	田智慧, 任祖光, 魏海涛. 2000—2020年黄河流域植被时空演化驱动机制[J]. 环境科学, 2022, 43(2):743-751.
[26]	Tian Z H, Ren Z G, Wei H T. Driving mechanism of the spatiotemporal evolution of vegetation in the Yellow River basin from 2000 to 2020[J]. Environmental Science, 2022, 43(2):743-751. doi: 10.1021/es801135v url: https://pubs.acs.org/doi/10.1021/es801135v
[27]	顾羊羊, 邹长新, 乔旭宁, 等. 2000—2015年黔西南州植被覆盖时空变化及影响因素分析[J]. 生态与农村环境学报, 2021, 37(11):1413-1422.
[27]	Gu Y Y, Zou C X, Qiao X N, et al. Spatio-temporal variations of fractional vegetation coverage and influencing factors in Qianxinan Prefecture from 2000 to 2015[J]. Journal of Ecology and Rural Environment, 2021, 37(11) :1413-1422.

[1]	WANG Jianqiang, ZOU Zhaohui, LIU Rongbo, LIU Zhisong. A method for extracting information on coastal aquacultural ponds from remote sensing images based on a U²-Net deep learning model[J]. Remote Sensing for Natural Resources, 2023, 35(3): 17-24.
[2]	TANG Hui, ZOU Juan, YIN Xianghong, YU Shuchen, HE Qiuhua, ZHAO Dong, ZOU Cong, LUO Jianqiang. River and lake sand mining in the Dongting Lake area: Supervision based on high-resolution remote sensing images and typical case analysis[J]. Remote Sensing for Natural Resources, 2023, 35(3): 302-309.
[3]	YU Hang, AN Na, WANG Jie, XING Yu, XU Wenjia, BU Fan, WANG Xiaohong, YANG Jinzhong. High-resolution remote sensing-based dynamic monitoring of coal mine collapse areas in southwestern Guizhou: A case study of coal mine collapse areas in Liupanshui City[J]. Remote Sensing for Natural Resources, 2023, 35(3): 310-318.
[4]	WANG Jing, WANG Jia, XU Jiangqi, HUANG Shaodong, LIU Dongyun. Exploring ecological environment quality of typical coastal cities based on an improved remote sensing ecological index: A case study of Zhanjiang City[J]. Remote Sensing for Natural Resources, 2023, 35(3): 43-52.
[5]	XU Xinyu, LI Xiaojun, ZHAO Heting, GAI Junfei. Pansharpening algorithm of remote sensing images based on NSCT and PCNN[J]. Remote Sensing for Natural Resources, 2023, 35(3): 64-70.
[6]	LIU Li, DONG Xianmin, LIU Juan. A performance evaluation method for semantic segmentation models of remote sensing images considering surface features[J]. Remote Sensing for Natural Resources, 2023, 35(3): 80-87.
[7]	NIU Xianghua, HUANG Wei, HUANG Rui, JIANG Sili. A high-fidelity method for thin cloud removal from remote sensing images based on attentional feature fusion[J]. Remote Sensing for Natural Resources, 2023, 35(3): 116-123.
[8]	DONG Ting, FU Weiqi, SHAO Pan, GAO Lipeng, WU Changdong. Detection of changes in SAR images based on an improved fully-connected conditional random field[J]. Remote Sensing for Natural Resources, 2023, 35(3): 134-144.
[9]	ZHANG Xian, LI Wei, CHEN Li, YANG Zhaoying, DOU Baocheng, LI Yu, CHEN Haomin. Research progress and prospect of remote sensing-based feature extraction of opencast mining areas[J]. Remote Sensing for Natural Resources, 2023, 35(2): 25-33.
[10]	MA Shibin, PI Yingnan, WANG Jia, ZHANG Kun, LI Shenghui, PENG Xi. High-efficiency supervision method for green geological exploration based on remote sensing[J]. Remote Sensing for Natural Resources, 2023, 35(2): 255-263.
[11]	WANG Ping. Application of thermal infrared remote sensing in monitoring the steel overcapacity cutting[J]. Remote Sensing for Natural Resources, 2023, 35(2): 271-276.
[12]	PANG Xin, LIU Jun. Effects of climate changes on the NDVI of vegetation in Asia[J]. Remote Sensing for Natural Resources, 2023, 35(2): 295-305.
[13]	LI Tianchi, WANG Daoru, ZHAO Liang, FAN Renfu. Classification and change analysis of the substrate of the Yongle Atoll in the Xisha Islands based on Landsat8 remote sensing data[J]. Remote Sensing for Natural Resources, 2023, 35(2): 70-79.
[14]	DIAO Mingguang, LIU Yong, GUO Ningbo, LI Wenji, JIANG Jikang, WANG Yunxiao. Mask R-CNN-based intelligent identification of sparse woods from remote sensing images[J]. Remote Sensing for Natural Resources, 2023, 35(2): 97-104.
[15]	ZHAO Hailan, MENG Jihua, JI Yunpeng. Application status and prospect of remote sensing technology in precise planting management of apple orchards[J]. Remote Sensing for Natural Resources, 2023, 35(2): 1-15.

Viewed

Full text

Abstract

Cited

Shared

Discussed