Please wait a minute...
 
Remote Sensing for Natural Resources    2024, Vol. 36 Issue (4) : 229-241     DOI: 10.6046/zrzyyg.2023138
|
2000-2020 spatiotemporal variations of different vegetation types in the Yellow River basin and influencing factors
WEI Xiao1,2,3(), ZHANG Lifeng1,2,3(), HE Yi1,2,3, CAO Shengpeng1,2,3, SUN Qiang1,2,3, GAO Binghai1,2,3
1. School of Surveying and Mapping and Geographic Information, Lanzhou Jiaotong University, Lanzhou 730070, China
2. National and Local Joint Engineering Research Center for Geographic Monitoring Technology Application, Lanzhou 730070, China
3. Engineering Laboratory of Geographic Monitoring of Gansu Province, Lanzhou 730070, China
Download: PDF(13766 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

Understanding the spatiotemporal characteristics of vegetation growth in the Yellow River basin and their influencing factors is crucial for the conservation and development of the ecology. However, existing studies rarely focus on the latest spatiotemporal characteristics of different vegetation types in the basin and their relationships with their influencing factors. Using the 2000-2020 time series remote sensing data of MODIS normalized difference vegetation index (NDVI), along with methods including trend analysis, correlation analysis, partial correlation analysis, and residual analysis, this study investigated the spatiotemporal characteristics of various vegetation types in the Yellow River basin. Accordingly, this study clarified the mechanisms behind the impacts of temperature and precipitation on annual and monthly scales and explored the influence of human activities on the spatiotemporal characteristics of different vegetation types. The results indicate that from 2000 to 2020, the NDVI of different vegetation types in the Yellow River basin trended upward overall, particularly in cultivated land and forest land. However, the increasing trends trended downward at different degrees with increasing elevation. Over the 21 years, various vegetation types were improved in most areas in the basin. However, a few areas exhibited degraded vegetation types, primarily including grassland and cultivated land. The proportion of areas with anti-continuous future trends in various vegetation types notably increased. Temperature and precipitation produced positive impacts on the growth of various vegetation types in the Yellow River basin. Nevertheless, various vegetation types exhibited greater responses to precipitation than to temperature, and the responses featured notable time lags. Furthermore, grassland and shrub growth were more sensitive to precipitation and temperature. Human activities had positive impacts on the vegetation of the Yellow River basin overall. However, some negative effects were also observed in grassland and cultivated land, warranting attention in future planning. Overall, most areas exhibited improved vegetation in the Yellow River basin in the 20 years. Given that partial grassland and cultivated land experienced degradation, it is necessary to protect typical degradation areas. The findings of this study will provide scientific data and theoretical support for ecological construction and economic development in the Yellow River basin.

Keywords vegetation type      Hurst index      residual analysis      remote sensing      Yellow River basin     
ZTFLH:  X87  
  TP79  
Issue Date: 23 December 2024
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Xiao WEI
Lifeng ZHANG
Yi HE
Shengpeng CAO
Qiang SUN
Binghai GAO
Cite this article:   
Xiao WEI,Lifeng ZHANG,Yi HE, et al. 2000-2020 spatiotemporal variations of different vegetation types in the Yellow River basin and influencing factors[J]. Remote Sensing for Natural Resources, 2024, 36(4): 229-241.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023138     OR     https://www.gtzyyg.com/EN/Y2024/V36/I4/229
Fig.1  Maps of administrative and vegetation cover classification of study area
Fig.2  Annual average of NDVI variation for each vegetation zone in Yellow River basin from 2000 to 2020
Fig.3-1  Spatial distribution of vegetation cover classes for vegetation zones in Yellow River basin from 2000 to 2020
Fig.3-2  Spatial distribution of vegetation cover classes for vegetation zones in Yellow River basin from 2000 to 2020
植被类型 植被覆盖等级
低植被区 较低植被区 中等植被区 高植被区
草地 49 31 19 1
耕地 16 51 30 3
灌木 10 27 52 11
林地 3 14 36 47
Tab.1  Area share of different vegetation NDVI vegetation cover classes in the Yellow River Basin from 2000 to 2020(%)
Fig.4  Trends of NDVI variation for vegetation zones in Yellow River basin from 2000 to 2020
植被类型 变化趋势等级
严重退化 轻微退化 稳定不变 轻微改善 明显改善
草地 0.2 1.2 4.5 16.9 77.2
耕地 0.8 1.5 1.7 5.9 90.1
灌木 0.1 0.9 3.5 11.9 83.6
林地 0.1 0.3 0.4 2.5 96.7
Tab.2  Area share of different vegetation NDVI trend classes in the Yellow River Basin from 2000 to 2020(%)
Fig.5  Future trends of NDVI variation for vegetation zones in Yellow River basin from 2000 to 2020
植被类型 未来变化趋势等级
持续严
重退化
持续轻
微退化
持续稳
定不变
持续轻
微改善
持续明
显改善
不确定
草地 0.1 0.5 1.6 5.8 40.1 51.9
耕地 0.7 1.2 1.2 4.1 53.3 39.5
灌木 0.1 0.5 2.1 6.9 51.6 39.5
林地 0.1 0.2 0.4 1.8 53.8 43.7
Tab.3  Area share of different vegetation NDVI future trend classes in the Yellow River Basin(%)
Fig.6  Changes of distribution and growth trend of NDVI of different vegetation along elevation gradient
Fig.7-1  Spatial distribution of bias correlation coefficients between NDVI and temperature for vegetation zones in Yellow River basin from 2000 to 2020
Fig.7-2  Spatial distribution of bias correlation coefficients between NDVI and temperature for vegetation zones in Yellow River basin from 2000 to 2020
植被类型 偏相关系数等级
[-0.76,
-0.3)
[-0.3,0) [0,0.3) [0.3,0.5) [0.5,
0.89]
草地 0.14 5.52 35.10 40.26 18.98
耕地 0.30 10.68 43.65 32.77 12.60
灌木 0.18 7.73 27.56 39.62 25.91
林地 0.28 16.02 36.92 29.52 17.26
Tab.4  Area ratio of bias correlation classes between different vegetation NDVI and temperature in Yellow River Basin from 2000 to 2020(%)
Fig.8  Spatial distribution of bias correlation coefficients between NDVI and precipitation for vegetation zones in Yellow River basin from 2000 to 2020
植被类型 偏相关系数等级
[-0.73,
-0.3)
[-0.3,0) [0,0.3) [0.3,0.5) [0.5,
0.91]
草地 1.35 6.95 16.99 36.27 38.44
耕地 0.11 4.97 33.29 34.48 27.15
灌木 0.28 3.56 26.35 32.99 36.82
林地 0.02 2.08 40.44 32.89 24.57
Tab.5  Area ratio of bias correlation classes between different vegetation NDVI and precipitation in Yellow River basin from 2000 to 2020(%)
植被类型 气候因子 NDVI与气候因子相关系数
4月 5月 6月 7月 8月 9月
草地 P0 0.462* 0.362 0.435* 0.706** 0.5* -0.146
P1 0.051 0.372* 0.431* 0.365 0.706* 0.415*
P2 0.044 0.175 0.316 0.431* 0.202 0.507**
P3 -0.081 -0.042 0.123 0.214 0.233 0.265
T0 0.598** -0.044 0.108 -0.214 0.443* 0.115
T1 0.634** 0.524** -0.094 0.196 -0.175 0.433*
T2 0.054 0.457* 0.307 0.098 0.206 -0.381*
T3 0.273 -0.006 0.359 0.276 0.211 -0.053
耕地 P0 0.481* 0.371* 0.092 0.444* 0.387* -0.039
P1 0.161 0.507** 0.297 0.006 0.433* 0.372*
P2 -0.074 0.162 0.451* 0.395* -0.157 0.255
P3 -0.080 0.000 0.228 0.284 0.249 -0.087
T0 0.437* -0.238 -0.115 -0.304 0.42* 0.047
T1 0.488* 0.230 -0.172 -0.037 0.275 0.252
T2 0.048 0.085 0.153 -0.059 0.026 -0.437*
T3 0.378 -0.007 0.195 0.226 -0.005 -0.136
灌木 P0 0.473* 0.441* 0.316 0.588** 0.406* -0.172
P1 0.132 0.413* 0.425* 0.19 0.631** 0.309
P2 0.081 0.277 0.320 0.477 0.053 0.502*
P3 -0.093 -0.047 0.232 0.271 0.341 0.164
T0 0.662** -0.102 -0.015 -0.261 0.442* 0.109
T1 0.606** 0.497* -0.121 0.108 -0.268 0.392*
T2 0.078 0.375* 0.345 0.100 0.093 -0.493*
T3 0.206 0.049 0.283 0.298 0.128 -0.231
林地 P0 0.327 0.451* 0.019 0.377* 0.262 -0.135
P1 0.113 0.413* 0.379* -0.049 0.411* 0.253
P2 0.002 0.265 0.359 0.423* -0.162 0.312
P3 -0.182 0.031 0.321 0.304 0.253 0.009
T0 0.596** -0.202 -0.149 -0.321 0.414* 0.111
T1 0.439* 0.284 -0.196 -0.037 -0.299 0.285
T2 0.097 0.033 0.193 -0.112 -0.039 -0.482*
T3 0.231 0.005 0.057 0.237 -0.029 -0.239
Tab.6  Correlation coefficients between NDVI in same month and temperature and precipitation in previous 0~3 months for different vegetation zones in Yellow River basin
Fig.9  Spatial distribution of effects of human activities on NDVI in Yellow River basin from 2000 to 2020
植被类型 影响等级
[-0.02,
-0.006)
[-0.006,
-0.000 6)
[-0.000 6,
0.000 6)
[0.000 6,
0.006)
[0.006,
0.026]
草地 0.02 1.45 15.78 76.43 6.32
耕地 0.17 2.06 3.39 69.81 24.57
灌木 0.01 0.93 11.34 77.94 9.78
林地 0.05 0.48 1.45 83.89 14.13
Tab.7  Area ratio of effects of human activities on NDVI in Yellow River basin from 2000 to 2020(%)
[1] Wang X, Biederman J A, Knowles J F, et al. Satellite solar-induced chlorophyll fluorescence and near-infrared reflectance capture complementary aspects of dryland vegetation productivity dynamics[J]. Remote Sensing of Environment, 2022, 270:112858.
[2] 张逸如, 刘晓彤, 高文强, 等. 天然林保护工程区近20年森林植被碳储量动态及碳汇(源)特征[J]. 生态学报, 2021, 41(13):5093-5105.
[2] Zhang Y R, Liu X T, Gao W Q, et al. Dynamic changes of forest vegetation carbon storage and the characteristics of carbon sink (source) in the Natural Forest Protection Project region for the past 20 years[J]. Acta Ecologica Sinica, 2021, 41(13):5093-5105.
[3] 王菲, 曹永强, 周姝含, 等. 黄河流域生态功能区植被碳汇估算及其主要气象要素分析[J]. 生态学报, 2023, 43(6)1-14.
[3] Wang F, Cao Y Q, Zhou S H, et al. Estimation of vegetation carbon sink in the Yellow River Basin ecological function area and analysis of its main meteorological elements[J]. Acta Ecologica Sinica, 2023, 43(6)1-14.
[4] He Y, Yan H W, Ma L, et al. Spatiotemporal dynamics of the vegetation in Ningxia,China using MODIS imagery[J]. Front Earth Sci, 2020, 14(1):221-235.
[5] 胡盈盈, 戴声佩, 罗红霞, 等. 2001—2015年海南岛橡胶林物候时空变化特征分析[J]. 自然资源遥感, 2022, 34(1):210-217.doi:10.6046/zrzyyg.2021110.
[5] Hu Y Y, Dai S P, Luo H X, et al. Spatio-temporal change characteristics of rubber forest phenology in Hainan Island during 2001—2015[J]. Remote Sensing for Natural Resources, 2022, 34(1):210-217.
[6] 徐嘉昕, 房世波, 张廷斌, 等. 2000—2016年三江源区植被生长季NDVI变化及其对气候因子的响应[J]. 国土资源遥感, 2020, 32(1);237-246.doi:10.6046/gtzyyg.2020.01.32.
[6] Xu J X, Fang S B, Zhang T B, et al. NDVI changes and its correlation with climate factors of the Three River-Headwater region in growing seasons during 2000—2016[J]. Remote Sensing for Natural Resources, 2020, 32(1):237-246.doi:10.6046/gtzyyg.2020.01.32.
[7] Ma Y R, Guan Q Y, Sun Y F, et al. Three-dimensional dynamic characteristics of vegetation and its response to climatic factors in the Qilian Mountains[J]. CATENA, 2022, 208:105694.
[8] 张乐艺, 李霞, 冯京辉, 等. 2000—2018年黄河流域NDVI时空变化及其对气候和人类活动的双重响应[J]. 水土保持通报, 2021, 41(5):276-286.
[8] Zhang L Y, Li X, Feng J H, et al. Spatial-temporal changes of NDVI in Yellow River basin and its dual response to climate change and human activities during 2000—2018[J]. Bulletin of Soil and Water Conservation, 2021, 41(5):276-286.
[9] 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.
[9] 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.
[10] 陈晨, 王义民, 黎云云, 等. 黄河流域1982—2015年不同气候区植被变化规律及其影响因素[J]. 长江科学院院报, 2022, 39(2):57-62.
[10] Chen C, Wang Y M, Li Y Y, et al. Vegetation changes and influencing factors in different climatic regions of the Yellow River Basin from 1982 to 2015[J]. Journal of Yangtze River Scientific Research Institute, 2022, 39(2):57-62
[11] 刘静, 温仲明, 刚成诚. 黄土高原不同植被覆被类型NDVI对气候变化的响应[J]. 生态学报, 2020, 40(2):678-691.
[11] Liu J, Wen Z M, Gang C C. Normalized difference vegetation index of different vegetation cover types and its responses to climate change in the Loess Plateau[J]. Acta Ecologica Sinica, 2020, 40(2):678-691.
[12] 解晗, 同小娟, 李俊, 等. 2000—2018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, 2022, 42(11):1-14.
[12] Xie H, Tong X J, Li J, et al. 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, 2022, 42(11):1-14.
[13] 易扬, 胡昕利, 史明昌, 等. 基于MODIS NDVI的长江中游区域植被动态及与气候因子的关系[J]. 生态学报, 2021, 41(19):7796-7807.
[13] Yi Y, Hu X L, Shi M C, et al. Vegetation dynamics and its relationship with climate factors in the middle reaches of the Yangtze River based on MODIS NDVI[J]. Acta Ecologica Sinica, 2021, 41(19):7796-7807.
[14] 张志强, 刘欢, 左其亭, 等. 2000—2019年黄河流域植被覆盖度时空变化[J]. 资源科学, 2021, 43(4):849-858.
doi: 10.18402/resci.2021.04.18
[14] Zhang Z Q, Liu H, Zuo Q T, et al. Spatiotemporal change of fractional vegetation cover in the Yellow River Basin during 2000—2019[J]. Resources Science, 2021, 43(4):849-858.
[15] 邱丽莎, 何毅, 张立峰, 等. 祁连山MODIS LST时空变化特征及影响因素分析[J]. 干旱区地理, 2020, 43(3):726-737.
[15] Qiu L S, He Y, Zhang L F, et al. Spatiotemporal variation characteristics and influence factors of MODIS LST in Qilian Mountains[J]. AridLand Geography, 2020, 43(3):726-737.
[16] 杨艳萍, 陈建军, 覃巧婷, 等. 2000—2018年广西植被时空变化及其对地形、气候和土地利用的响应[J]. 农业工程学报, 2021, 37(17):234-241.
[16] Yang Y P, Chen J J, Qin Q T, et al. Temporal and spatial variation of vegetation and its response to topography,climate and land use in Guangxi during 2000—2018[J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(17):234-241.
[17] 尹振良, 冯起, 王凌阁, 等. 2000—2019年中国西北地区植被覆盖变化及其影响因子[J]. 中国沙漠, 2022, 42(4):11-21.
doi: 10.7522/j.issn.1000-694X.2021.00200
[17] Yin Z L, Feng Q, Wang L G, et al. Vegetation coverage change and its influencing factors across the northwest region of China during 2000—2019[J]. Journal of Desert Research, 2022, 42(4):11-21.
[18] 王雄, 张翀, 李强. 黄土高原植被覆盖与水热时空通径分析[J]. 生态学报, 2023, 43(2):719-730.
[18] Wang X, Zhang C, Li Q. Path analysis between vegetation coverage and climate factors in the Loess Plateau[J]. Acta Ecologica Sinica. 2023, 43(2):719-730.
[19] 王志鹏, 张宪洲, 何永涛, 等. 2000—2015年青藏高原草地归一化植被指数对降水变化的响应[J]. 应用生态学报, 2018、 29(1):75-83.
doi: 10.13287/j.1001-9332.201801.014
[19] Wang Z P, Zhang X Z, He Y T, et al. Responses of normalized difference vegetation index (NDVI) to precipitation changes on the Grassland of Tibetan Plateau from 2000 to 2015[J]. Chinese Journal of Applied Ecology, 2018、 29(1):75-83.
[20] Vicente-Serrano S M, Camarero J J, Azorin-Molina C. Diverse responses of forest growth to drought time-scales in the Northern Hemisphere[J]. Global Ecology and Biogeography, 2014, 23(9):1019-1030.
[21] Yamori W, Hikosaka K., Way D A. Temperature response of photosynthesis in C3,C4,and CAM plants:Temperature acclimation and temperature adaptation[J]. Photosynth Research. 2014 119,101-117.
[22] 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):961-974.
doi: 10.11821/dlxb202005006
[22] 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):961-974.
[1] QU Haicheng, LIANG Xu. Building extraction from high-resolution images using a hybrid attention mechanism combined with multi-scale feature enhancement[J]. Remote Sensing for Natural Resources, 2024, 36(4): 107-116.
[2] KANG Hui, DOU Wenzhang, HAN Lingyi, DING Ziyue, WU Liangting, HOU Lu. Rapid monitoring of surface water based on remote sensing data and DeepLabv3+ model[J]. Remote Sensing for Natural Resources, 2024, 36(4): 117-123.
[3] ZHANG Dongyun, WU Tianjun, LI Manjia, GUO Yifei, LUO Jiancheng, DONG Wen. Remote sensing-based classification of crops on a farmland parcel scale and uncertainty analysis[J]. Remote Sensing for Natural Resources, 2024, 36(4): 124-134.
[4] PAN Junjie, SHEN Li, YAN Xin, NIE Xin, DONG Kuanlin. An adversarial learning-based unsupervised domain adaptation method for semantic segmentation of high-resolution remote sensing images[J]. Remote Sensing for Natural Resources, 2024, 36(4): 149-157.
[5] ZHAO Jinling, HUANG Jian, LIANG Zijun, ZHAO Xuedan, JIN Tao, GE Hanghang, WEI Xiaoyan, SHAO Yuanzheng. BDANet-based assessment of building damage from earthquake disasters[J]. Remote Sensing for Natural Resources, 2024, 36(4): 193-200.
[6] ZHUANG Huifu, WANG Peng, SU Yanan, ZHANG Xiang, FAN Hongdong. Dynamic monitoring of flood inundation in Zhuozhou, Hebei Province based on multi-temporal SAR data[J]. Remote Sensing for Natural Resources, 2024, 36(4): 218-228.
[7] ZHAO Yuling, YANG Jinzhong, SUN Weidong, YU Hao, XING Yu, CHEN Dong, MA Xinying, WANG Tixin, WANG Cong. Evaluation and analysis of geological environment of mines in the Ili Valley and countermeasures for ecological restoration and management[J]. Remote Sensing for Natural Resources, 2024, 36(4): 23-30.
[8] QI Changwei, DONG Ji’en, CHENG Xu, YE Gaofeng, HE Shuyue, DAI Wei, WANG Bing. Application of ZY-1 02D hyperspectral data to altered mineral mapping and ore prospecting in desert areas along the northern margin of the Qaidam Basin[J]. Remote Sensing for Natural Resources, 2024, 36(4): 31-42.
[9] FENG Lei, WANG Yi, LI Wenji, WANG Yanzuo, ZHENG Xiangxiang, WANG Shanshan, ZHANG Ling. Development and application of 3D geological hazard identification information platform[J]. Remote Sensing for Natural Resources, 2024, 36(4): 321-327.
[10] QIU Junting, LI Jiangkun, GE Tengfei, MU Hongxu, RUI Xinmin, YANG Yunhan, YANG Yanjie. Application of multi-source remote sensing data in the exploration of sandstone-type uranium deposits: A case study of the Yingen area, Bayingebi basin[J]. Remote Sensing for Natural Resources, 2024, 36(4): 43-54.
[11] LAI Shijiu, HU Jinshan, KANG Jianrong, WANG Xiaobing. Ecological evolution of coal resource-based regions: A case study of Shanxi Province[J]. Remote Sensing for Natural Resources, 2024, 36(4): 62-74.
[12] YE Lijuan, DUAN Xiaolong, LI Ting, ZHANG Jing, ZHANG Yun, CHEN Donglei. Distribution and rehabilitation status of lands destroyed by mining: A case study of Hebei Province[J]. Remote Sensing for Natural Resources, 2024, 36(4): 75-81.
[13] LUO Zhenhai, ZHANG Chao, FENG Shaoyuan, TANG Min, LIU Rui, KONG Jiying. Advances in research on methods for optical remote sensing monitoring of soil salinization[J]. Remote Sensing for Natural Resources, 2024, 36(4): 9-22.
[14] LING Xiaolu, CHEN Chaorong, GUO Weidong, QIN Kai, ZHANG Jinlong. Comprehensive evaluation of ESA CCI soil moisture data in eastern China[J]. Remote Sensing for Natural Resources, 2024, 36(4): 92-106.
[15] QIN Qiming, WU Zihua, YE Xin, WANG Nan, HAN Guhuai. Remote sensing-based exploration of coalbed methane enrichment areas:Advances in research and prospects[J]. Remote Sensing for Natural Resources, 2024, 36(3): 1-12.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech