Research on the shift change of vegetation greenness line in the Loess Plateau based on GEE
XIE Pei-jun1,2, SONG Xiao-yan3, SUN Wen-yi1,4, MU Xing-min1,4, GAO Peng1,4
1. Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China; 2. University of Chinese Academy of Sciences, Beijing 100049, China; 3. College of Water Resources and Architectural Engineering, Northwest A & F University, Yangling 712100, China; 4. State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A & F University, Yangling 712100, China
Abstract：The temporal and spatial evolution patterns of the vegetation greenness index and its spatial distribution in the Loess Plateau hold significant scientific importance in gaining comprehensive insights into the dynamic changes of vegetation and assessing the effectiveness of ecological restoration. Although previous studies have explored the factors influencing spatiotemporal variations in vegetation greenness, the limited spatial resolution of remote sensing products poses challenges in capturing fine-scale characteristics and dynamic shifts in vegetation greenness lines. In the study, the Google Earth Engine (GEE) cloud platform, combined with a series of Landsat remote sensing images, is used to calculate the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), along with their Vegetation Greenness Lines, as indicators to characterize the spatiotemporal evolution of vegetation greenness during the growing season on the Loess Plateau from 1987 to 2020. The results show that the annual average growth rates of NDVI and EVI in the Loess Plateau were 0.0042a-1 (P<0.01) and 0.0023a-1 (P<0.01) from 1987 to 2020, respectively. Notable, the average growth rates after the year 2000 were approximately three to four times higher than those observed before 2000. In terms of spatial distribution, the trends in NDVI and EVI predominantly exhibited significant and highly significant increases, with respective area percentages of 78.78% and 69.21%. Over the period from 1987 to 2020, the vegetation greenness lines VGLNDVI and VGLEVI exhibited a northward movement in the Loess Plateau at rates of 5.52 and 4.59 km/year, respectively. Over the same period, the average northward movements for VGLNDVI and VGLEVI were 173.93km and 131.62km, respectively. The most significant northward shift was primarily observed between 2005 and 2010, with the largest movement of the VGLNDVI and VGLEVI occurring in the Yulin and Yan'an areas of Shaanxi Province, northwest Shanxi Province, and central and southern Inner Mongolia, reaching maximum distances of 532.12km and 471.57km, respectively.
谢佩君, 宋小燕, 孙文义, 穆兴民, 高鹏. 基于GEE的黄土高原植被绿度线推移变化研究[J]. 中国环境科学, 2023, 43(12): 6518-6529.
XIE Pei-jun, SONG Xiao-yan, SUN Wen-yi, MU Xing-min, GAO Peng. Research on the shift change of vegetation greenness line in the Loess Plateau based on GEE. CHINA ENVIRONMENTAL SCIENCECE, 2023, 43(12): 6518-6529.
[1] Wu X P, Zhang R Z, Bento V A, et al. The effect of drought on vegetation gross primary productivity under different vegetation types across China from 2001 to 2020[J]. Remote Sensing, 2022,14(18): 4658. [2] Zhang R, Qi J, Leng S, et al. Long-term vegetation phenology changes and responses to preseason temperature and precipitation in Northern China[J]. Remote Sensing, 2022,14(6):1396. [3] 袁和第.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, 2020. Yuan H D. The research on model of soil erosion comprehensive control in typical small watershed in Hilly and Gully Loess Region[D]. Beijing: Beijing Forestry University, 2022. [4] Peters G P, Andrew R M, Boden T, et al. The challenge to keep global warming below 2℃[J]. Nature Climate Change, 2013,3(1):4-6. [5] Xu B, Qi B, Ji K, et al. Emerging hot spot analysis and the spatial-temporal trends of NDVI in the Jing River Basin of China[J]. Environmental Earth Sciences, 2022,81:1-15. [6] Zhang Y L, Song C H, Band L E, et al. Reanalysis of global terrestrial vegetation trends from MODIS products: Browning or greening?[J]. Remote Sensing of Environment, 2017,191:145-155. [7] Piao S L, Yin G D, Tan J G, et al. Detection and attribution of vegetation greening trend in China over the last 30years[J]. Global Change Biology, 2015,21(4):1601-1609. [8] 张宝庆,吴普特,赵西宁.近30a黄土高原植被覆盖时空演变监测与分析[J]. 农业工程学报, 2011,27(4):287-293,400. Zhang B Q,, Wu P T, Zhao X N. Detecting and analysis of spatial and temporal variation of vegetation cover in the Loess Plateau during 1982~2009[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2011,27(4):287-293. [9] Tang L L, Chen X L, Cai X B, et al. Disentangling the roles of land-use-related drivers on vegetation greenness across China[J]. Environmental Research Letters, 2021,16(12):124033. [10] Chen C, He B, Yuan W P, et al. Increasing interannual variability of global vegetation greenness[J]. Environmental Research Letters, 2019,14(12):124005. [11] 解晗,同小娟,李俊,等.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, 2022,42(11):4536-4549. 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): 4536-4549. [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, 2019,39(11):4804-4811. 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] Kumar L, Mutanga O, Google Earth Engine applications since inception: Usage, trends, and potential[J]. Remote Sensing, 2018,10(10):1509. [14] 王晓蕾,石守海.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, 2022,24(6):1087-1098. Wang X L, Shi S H. Spatio-temporal changes of vegetation in the Yellow River Basin and related effect of landform based on GEE[J]. Journal of Geo-information Science, 2022,24(6):1087-1098. [15] Fu B J, Wang S, Liu Y, et al. Hydrogeomorphic ecosystem responses to natural and anthropogenic changes in the Loess Plateau of China.[J]. Annual Review of Earth and Planetary Sciences, 2017,45(1):223-243. [16] 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 A Z, Tian X L. Spatiotemporal evolution and influencing factors of vegetation coverage in the Loess Plateau from 1986 to 2021 based on GEE platform[J]. Ecology and Environmental Sciences, 2022,31(11):2124-2133. [17] 杨灿,魏天兴,李亦然,等.黄土高原水蚀风蚀交错区退耕还林工程前后NDVI时空变化特征[J]. 北京林业大学学报, 2021,43(6): 83-91. Yang C, Wei T X, Li Y R, et al. Spatiotemporal variations of NDVI before and after implementation of Grain for Green Project in wind-water erosion crisscross region of the Loess Plateau[J]. Journal of Beijing Forestry University, 2021,43(6):83-91. [18] 王一,郝利娜,许强,等.2001~2019年黄土高原植被覆盖度时空演化特征及地理因子解析[J]. 生态学报, 2023,43(6):1-11. Wang Y, He L N, Xu Q, et al. Spatio-temporal variations of vegetation coverage and its geographical factors analysis on the Loess Plateau from 2001 to 2019[J]. Acta Ecologica Sinica, 2023,43(6):1-11. [19] Chen Y P, Wang K B, Lin Y S, et al. Balancing green and grain trade[J]. Nature Geoscience, 2015,8(10):739-741. [20] Li Z, Zheng F L, Liu W Z, et al. Spatial distribution and temporal trends of extreme temperature and precipitation events on the Loess Plateau of China during 1961~2007[J]. Quaternary International, 2010, 226(1):92-100. [21] Sun W Y, Song X Y, Mu X M, et al. Spatiotemporal vegetation cover variations associated with climate change and ecological restoration in the Loess Plateau[J]. Agricultural and Forest Meteorology, 2015,209: 87-99. [22] Sun Q H, Miao C Y, Duan Q Y, et al. Temperature and precipitation changes over the Loess Plateau between 1961 and 2011, based on high-density gauge observations[J]. Global and Planetary Change, 2015,132:1-10. [23] Zhang X P, Zhang L, Zhao J, et al. Responses of streamflow to changes in climate and land use/cover in the Loess Plateau, China[J]. Water Resources Research, 2008,44(7):W00A07. [24] Lü Y, Fu B J, Feng X M, et al. A policy-driven large scale ecological restoration: quantifying ecosystem services changes in the Loess Plateau of China[J]. PloS One, 2012,7(2):e31782. [25] Li J J, Peng S Z, Li Z. Detecting and attributing vegetation changes on China's Loess Plateau[J]. Agricultural and Forest Meteorology, 2017, 247:260-270. [26] Huete A, Didan K, et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices[J]. Remote Sensing of Environment, 2002,83(1/2):195-213. [27] Yue Y, Liu H F, Mu X X, et al. Spatial and temporal characteristics of drought and its correlation with climate indices in Northeast China[J]. PLoS One, 2021,16(11):e0259774. [28] An S, Zhu X L, Shen M G, et al. Mismatch in elevational shifts between satellite observed vegetation greenness and temperature isolines during 2000~2016 on the Tibetan Plateau[J]. Global Change Biology, 2018,24(11):5411-5425. [29] Tabari H, Marofi S, Aeini A, et al. Trend analysis of reference evapotranspiration in the western half of Iran[J]. Agricultural and Forest Meteorology, 2011,151(2):128-136. [30] Glenn E P, Huete A R, Nagler P L, et al. Relationship between remotely-sensed vegetation indices, canopy attributes and plant physiological processes: What vegetation indices can and cannot tell us about the landscape[J]. Sensors, 2008,8(4):2136-2160. [31] Na R, Na L, Du H, et al. Vegetation greenness variations and response to climate change in the arid and semi-arid transition zone of the Mongo-Lian Plateau during 1982~2015[J]. Remote Sensing, 2021,13: 4066. [32] Zeng X B, Dickinson R E, Walker A, et al. Derivation and evaluation of global 1-km fractional vegetation cover data for land modeling[J]. Journal of Applied Meteorology, 2000,39(6):826-839. [33] Fang J Y, Piao S L, He J S, et al. Increasing terrestrial vegetation activity in China, 1982~1999[J]. Science in China Series C: Life Sciences, 2004,47:229-240. [34] Zhou L M, Tucker C J, Kaufmann R K, et al. Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999[J]. Journal of Geophysical Research: Atmospheres, 2001,106(D17):20069-20083. [35] Montandon L M, Small E E, The impact of soil reflectance on the quantification of the green vegetation fraction from NDVI[J]. Remote Sensing of Environment, 2008,112(4):1835-1845. [36] Li X B, Chen Y H, Shi P J, et al. Detecting vegetation fractional coverage of typical steppe in northern China based on multi-scale remotely sensed data[J]. ACTA BOTANICA SINICA-CHINESE EDITION-, 2003,45(10):1146-1156. [37] Jeong S J, Ho C H, Brown M E, et al. Browning in desert boundaries in Asia in recent decades[J]. Journal of Geophysical Research: Atmospheres, 2011,116(D2):D02103. [38] Li X S, Zhang J, Derivation of the green vegetation fraction of the whole China from 2000 to 2010 from MODIS data[J]. Earth Interactions, 2016,20(8):1-16. [39] Wei X D, Wang S N, Wang Y K, Spatial and temporal change of fractional vegetation cover in North-western China from 2000 to 2010[J]. Geological Journal, 2018,53:427-434. [40] Carlson T N, Ripley D A, On the relation between NDVI, fractional vegetation cover, and leaf area index[J]. Remote sensing of Environment, 1997,62(3):241-252. [41] Song W Y, Mu X M, Ruan G Y, et al. Estimating fractional vegetation cover and the vegetation index of bare soil and highly dense vegetation with a physically based method[J]. International Journal of Applied Earth Observation and Geoinformation, 2017,58:168-176. [42] Hashim H, Latif Z A, Adnan N A, Urban vegetation classification with NDVI threshold value method with very high resolution (VHR) Pleiades imagery[J]. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019,42:237-240. [43] Aryal J, Sitaula C, Aryal S, NDVI threshold-based urban green space mapping from Sentinel-2A at the local governmental area (LGA) level of victoria, Australia[J]. Land, 2022,11(3):351. [44] Crist E P, Cicone R C, A physically-based transformation of Thematic Mapper data---The TM Tasseled Cap[J]. IEEE Transactions on Geoscience and Remote Sensing, 1984,(3):256-263. [45] Sharaf El Din E, A novel approach for surface water quality modelling based on Landsat-8tasselled cap transformation[J]. International Journal of Remote Sensing, 2020,41(18):7186-7201. [46] Han T, Wulder M A, White J C, et al. An efficient protocol to process Landsat images for change detection with tasselled cap transformation[J]. IEEE Geoscience and Remote Sensing Letters, 2007,4(1):147-151. [47] Kou P L, Xu Q, Jin Z, et al. Complex anthropogenic interaction on vegetation greening in the Chinese Loess Plateau[J]. Science of The Total Environment, 2021,778:146065. [48] Kong D X, Miao C Y, Borthwick A G, et al. Spatiotemporal variations in vegetation cover on the Loess Plateau, China, between 1982and 2013: possible causes and potential impacts[J]. Environmental Science and Pollution Research, 2018,25(14):13633-1344. [49] 张含玉,方怒放,史志华.黄土高原植被覆盖时空变化及其对气候因子的响应[J]. 生态学报, 2016,36(13):3960-3968. Zhang H Y, Fang N F, Shi Z H. Spatio-temporal patterns for the NDVI and its responses to climatic factors in the Loess Plateau[J]. Acta Ecologica Sinica, 2016,36(13):3960-3968. [50] Xin Z B, Xu J X, Zheng W. Spatiotemporal variations of vegetation cover on the Chinese Loess Plateau (1981~2006): Impacts of climate changes and human activities[J]. Science in China Series D: Earth Sciences, 2008,51(1):67-78. [51] Wang Y, Peng D L, Shen M G, et al. Contrasting effects of temperature and precipitation on vegetation greenness along elevation gradients of the Tibetan Plateau[J]. Remote Sensing, 2020,12:2751. [52] 程昌武.黄河流域1982~2015年植被变化及其影响因素分析[D]. 西安:长安大学, 2020. Cheng C H. Yellow River Basin during 1982 to 2015 Vegetation dynamics and its influential factors in the[D]. Xi¢an: Chang'an University, 2020. [53] 朱玉英,张华敏,丁明军,等.青藏高原植被绿度变化及其对干湿变化的响应[J]. 植物生态学报, 2023,47(1):51-64. Zhu Y Y, Zhang H M, Ding M J, et al. Changes of vegetation greenness and its response to drought-wet variation on the Qingzang Plateau[J]. Chinese Journal of Plant Ecology, 2023,47(1):51-64.
