Combining gradual and abrupt analysis to detect variation of vegetation greenness on the loess areas of China

Abstract

The annual peak growth and trend shift of vegetation are critical in characterizing the carbon sequestration capacity of ecosystems. As the well-known area with the fastest vegetation growth in the world, the Loess Plateau (LP) lands find an enhanced greening trend in the annual and growing-season. However, the spatiotemporal dynamics of vegetation peak growth and breakpoints characteristics on time series still needs to be explored. Here, we performed tendency analysis to characterize recent variations in annual peak vegetation growth through a satellite-derived vegetation index (NDVI_max, Maximum Normalized Difference Vegetation Index) and then applied breakpoint analysis to capture abrupt points on the time series. The results demonstrated that the vegetation peak trend had been significantly increasing, with a growth rate at 0.68×10^–2·a^–1 during 2001–2018, and most pixels (70.81%) have a positive linear greening trend over the entire LP. In addition, about 83% of the breakpoint type on the monthly NDVI time series is a monotonic increase at the pixel level, and most pixels (57%) have detected breakpoints after 2010. Our results also showed that the growth rate accelerates in the northwest and decelerates in the southeast after the breakpoint. This study indicates that combining abrupt analysis with gradual analysis can describe vegetation dynamics more effectively and comprehensively. The findings highlighted the importance of breakpoint analysis for monitor timing and shift using time series satellite data at a regional scale, which may help stakeholders to make reasonable and effective ecosystem management policies.

Keywords

vegetation greenness / gradual trend / breakpoint / BFAST algorithm / the Loess Plateau area

References

[1] Andela N, Liu Y, van Dijk A I J M, de Jeu R A M, McVicar T R (2013). Global changes in dryland vegetation dynamics (1988–2008) assessed by satellite remote sensing: comparing a new passive microwave vegetation density record with reflective greenness data. Biogeosciences, 10(10): 6657–6676

[2] Brandt M, Rasmussen K, Peñuelas J, Tian F, Schurgers G, Verger A, Mertz O, Palmer J R B, Fensholt R (2017). Human population growth offsets climate-driven increase in woody vegetation in sub-Saharan Africa. Nat Ecol Evol, 1(4): 0081

[3] Cao S, Chen L, Shankman D, Wang C, Wang X, Zhang H (2011). Excessive reliance on afforestation in China’s arid and semi-arid regions: lessons in ecological restoration. Earth Sci Rev, 104(4): 240–245

[4] Chen C, Park T, Wang X, Piao S, Xu B, Chaturvedi R K, Fuchs R, Brovkin V, Ciais P, Fensholt R, Tømmervik H, Bala G, Zhu Z, Nemani R R, Myneni R B (2019). China and India lead in greening of the world through land-use management. Nat Sustain, 2(2): 122–129

[5] de Jong R, Verbesselt J, Zeileis A, Schaepman M(2013). Shifts in global vegetation activity trends. Remote Sens, 5: 1117–1133

[6] Deng H, Pepin N C, Liu Q, Chen Y (2018). Understanding the spatial differences in terrestrial water storage variations in the Tibetan Plateau from 2002 to 2016. Clim Change, 151(3): 379–393

[7] Fang X, Zhu Q, Ren L, Chen H, Wang K, Peng C (2018). Large-scale detection of vegetation dynamics and their potential drivers using MODIS images and BFAST: a case study in Quebec Canada. Remote Sens Environ, 206(1): 391–402

[8] Fensholt R, Langanke T, Rasmussen K, Reenberg A, Prince S D, Tucker C, Scholes R J, Le Q B, Bondeau A, Eastman R, Epstein H, Gaughan A E, Hellden U, Mbow C, Olsson L, Paruelo J, Schweitzer C, Seaquist J, Wessels K (2012). Greenness in semi-arid areas across the globe 1981–2007 — an Earth Observing Satellite based analysis of trends and drivers. Remote Sens Environ, 121(1): 144–158

[9] Fu B, Wang S, Liu Y, Liu J, Liang W, Miao C (2017). Hydrogeomorphic ecosystem responses to natural and anthropogenic changes in the Loess Plateau of China. Annu Rev Earth Planet Sci, 45(1): 223–243

[10] Geng L, Che T, Wang X, Wang H (2019). Detecting spatiotemporal changes in vegetation with the BFAST model in the Qilian Mountain Region during 2000–2017. Remote Sens, 11(2): 103

[11] Gholamnia M, Khandan R, Bonafoni S, Sadeghi A (2019). Spatiotemporal analysis of MODIS NDVI in the semif-arid region of Kurdistan (Iran). Remote Sens, 11(14): 1723

[12] Guo Y, Peng C, Zhu Q, Wang M, Wang H, Peng S, He H (2019). Modelling the impacts of climate and land use changes on soil water erosion: model applications, limitations and future challenges. J Environ Manage, 250(15): 109403

[13] Han Z, Huang S, Huang Q, Bai Q, Leng G, Wang H, Zhao J, Wei X, Zheng X (2020). Effects of vegetation restoration on groundwater drought in the Loess Plateau China. J Hydrol (Amst), 591(1): 125566

[14] He P, Sun Z, Han Z, Ma X, Zhao P, Liu Y, Ma J (2020a). Divergent trends of water storage observed via gravity satellite across distinct areas in China. Water, 12(10): 2862

[15] He Y, Yan H, Ma L, Zhang L, Qiu L, Yang S (2020b). Spatiotemporal dynamics of the vegetation in Ningxia China using MODIS imagery. Front Earth Sci., 14(1): 221–235

[16] Holben B (1986). Characteristics of maximum-value composite images from temporal AVHRR data. Int J Remote Sens, 7(11): 1417–1434

[17] Huang K, Xia J, Wang Y, Ahlström A, Chen J, Cook R B, Cui E, Fang Y, Fisher J B, Huntzinger D N, Li Z, Michalak A M, Qiao Y, Schaefer K, Schwalm C, Wang J, Wei Y, Xu X, Yan L, Bian C, Luo Y (2018). Enhanced peak growth of global vegetation and its key mechanisms. Nat Ecol Evol, 2(12): 1897–1905

[18] Kaptué A T, Prihodko L, Hanan N P (2015). On regreening and degradation in Sahelian watersheds. Proc Natl Acad Sci USA, 112(39): 12133–12138

[19] La Fevor M C (2014). Restoration of degraded agricultural terraces: rebuilding landscape structure and process. J Environ Manage, 138(1): 32–42

[20] Li G, Sun S, Han J, Yan J, Liu W, Wei Y, Lu N, Sun Y (2019). Impacts of Chinese grain for green program and climate change on vegetation in the Loess Plateau during 1982–2015. Sci Total Environ, 660(1): 177–187

[21] Li Z, Pan J (2018). Spatiotemporal changes in vegetation net primary productivity in the arid region of northwest China 2001 to 2012. Front Earth Sci, 12(1): 108–124

[22] Liu Z, Wu C, Liu Y, Wang X, Fang B, Yuan W, Ge Q (2017). Spring green-up date derived from GIMMS3g and SPOT-VGT NDVI of winter wheat cropland in the North China Plain. ISPRS J Photogramm Remote Sens, 130(1): 81–91

[23] Lu F, Hu H, Sun W, Zhu J, Liu G, Zhou W, Zhang Q, Shi P, Liu X, Wu X, Zhang L, Wei X, Dai L, Zhang K, Sun Y, Xue S, Zhang W, Xiong D, Deng L, Liu B, Zhou L, Zhang C, Zheng X, Cao J, Huang Y, He N, Zhou G, Bai Y, Xie Z, Tang Z, Wu B, Fang J, Liu G, Yu G (2018). Effects of national ecological restoration projects on carbon sequestration in China from 2001 to 2010. Proc Nat Acad Sci USA, 115(16): 4039–4044

[24] Lu X, Liao Y (2017). Effect of tillage practices on net carbon flux and economic parameters from farmland on the Loess Plateau in China. J Clean Prod, 162(20): 1617–1624

[25] Ma J, Xiao X, Miao R, Li Y, Chen B, Zhang Y, Zhao B (2019). Trends and controls of terrestrial gross primary productivity of China during 2000–2016. Environ Res Lett, 14(8): 084032

[26] Ning T, Liu W, Lin W, Song X (2015). NDVI variation and its responses to climate change on the northern Loess Plateau of China from 1998 to 2012. Adv Meteorol, 2015(1): 1–10

[27] Niu Q, Xiao X, Zhang Y, Qin Y, Dang X, Wang J, Zou Z, Doughty R B, Brandt M, Tong X, Horion S, Fensholt R, Chen C, Myneni R B, Xu W, Di G, Zhou X (2019). Ecological engineering projects increased vegetation cover production and biomass in semiarid and subhumid Northern China. Land Degrad Dev, 30(13): 1620–1631

[28] Piao S, Wang X, Park T, Chen C, Lian X, He Y, Bjerke J W, Chen A, Ciais P, Tømmervik H, Nemani R R, Myneni R B (2020). Characteristics drivers and feedbacks of global greening. Nat Rev Earth Environ, 1(1): 14–27

[29] Poulter B, Frank D, Ciais P, Myneni R B, Andela N, Bi J, Broquet G, Canadell J G, Chevallier F, Liu Y Y, Running S W, Sitch S, van der Werf G R (2014). Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature, 509(7502): 600–603

[30] Pütz S, Groeneveld J, Henle K, Knogge C, Martensen A C, Metz M, Metzger J P, Ribeiro M C, de Paula M D, Huth A (2014). Long-term carbon loss in fragmented Neotropical forests. Nat Commun, 5(5037): 5037

[31] Qiao J, Yu D, Wang Q, Liu Y (2018). Diverse effects of crop distribution and climate change on crop production in the agro-pastoral transitional zone of China. Front Earth Sci, 12(2): 408–419

[32] Sun W, Zhang Y, Mu X, Li J, Gao P, Zhao G, Dang T, Chiew F (2019). Identifying terraces in the hilly and gully regions of the Loess Plateau in China. Land Degrad Dev, 30(17): 2126–2138

[33] Tang G, Arnone J A III, Verburg P S J, Jasoni R L, Sun L (2015). Trends and climatic sensitivities of vegetation phenology in semiarid and arid ecosystems in the US Great Basin during 1982–2011. Biogeosciences, 12(23): 6985–6997

[34] Tong X, Brandt M, Yue Y, Horion S, Wang K, Keersmaecker W D, Tian F, Schurgers G, Xiao X, Luo Y, Chen C, Myneni R, Shi Z, Chen H, Fensholt R (2018). Increased vegetation growth and carbon stock in China karst via ecological engineering. Nat Sustain, 1(1): 44–50

[35] Verbesselt J, Hyndman R, Newnham G, Culvenor D (2010a). Detecting trend and seasonal changes in satellite image time series. Remote Sens Environ, 114(1): 106–115

[36] Verbesselt J, Hyndman R, Zeileis A, Culvenor D (2010b). Phenological change detection while accounting for abrupt and gradual trends in satellite image time series. Remote Sens Environ, 114(12): 2970–2980

[37] Wang F, Haiyan Z, Dong X (2016). Quantifying changes in multiple ecosystem services during 2000–2012 on the Loess Plateau China as a result of climate variability and ecological restoration. Ecol Eng, 97(1): 258–271

[38] Wang J, Zhang D, Nan Y, Liu Z, Qi D K (2020). Spatial patterns of net primary productivity and its driving forces: a multi-scale analysis in the transnational area of the Tumen River. Front Earth Sci, 14(1): 124–139

[39] Wang Q, Zeng J, Leng S, Fan B, Tang J, Jiang C, Huang Y, Zhang Q, Qu Y, Wang W, Shui W (2018a). The effects of air temperature and precipitation on the net primary productivity in China during the early 21st century. Front Earth Sci, 12(4): 818–833

[40] Wang X, Xiao F, Feng X, Fu B, Zhou Z, Chan C (2018b). Soil conservation on the Loess Plateau and the regional effect: impact of the ‘Grain for Green’ Project. Earth Environ Sci, 109(3–4): 461–471

[41] Wang X, Wang B, Xu X, Liu T, Duan Y, Zhao Y (2018c). Spatial and temporal variations in surface soil moisture and vegetation cover in the Loess Plateau from 2000 to 2015. Ecol Indic, 95(1): 320–330

[42] Wang Y, Brandt M, Zhao M, Tong X, Xing K, Xue F, Kang M, Wang L, Jiang Y, Fensholt R (2018d). Major forest increase on the Loess Plateau China (2001–2016). Land Degrad Dev, 29(11): 4080–4091

[43] Watts L M, Laffan S W (2014). Effectiveness of the BFAST algorithm for detecting vegetation response patterns in a semi-arid region. Remote Sens Environ, 154(1): 234–245

[44] Wu Z, Wang M, Zhang H, Du Z (2019a). Vegetation and soil wind erosion dynamics of sandstorm control programs in the agro-pastoral transitional zone of northern China. Front Earth Sci, 13(2): 430–443

[45] Wu X, Wang S, Fu B, Feng X, Chen Y (2019b). Socio-ecological changes on the Loess Plateau of China after Grain to Green Program. Sci Total Environ, 678(15): 565–573

[46] Xia J, Niu S, Ciais P, Janssens I A, Chen J, Ammann C, Arain A, Blanken P D, Cescatti A, Bonal D, Buchmann N, Curtis P S, Chen S, Dong J, Flanagan L B, Frankenberg C, Georgiadis T, Gough C M, Hui D, Kiely G, Li J, Lund M, Magliulo V, Marcolla B, Merbold L, Montagnani L, Moors E J, Olesen J E, Piao S, Raschi A, Roupsard O, Suyker A E, Urbaniak M, Vaccari F P, Varlagin A, Vesala T, Wilkinson M, Weng E, Wohlfahrt G, Yan L, Luo Y (2015). Joint control of terrestrial gross primary productivity by plant phenology and physiology. Proc Nat Acad Sci USA, 112(9): 2788–2793

[47] Xiao J (2014). Satellite evidence for significant biophysical consequences of the “Grain for Green” Program on the Loess Plateau in China. J Geophys Res Biogeosci, 119(12): 2261–2275

[48] Xiong Y, Peng F, Zou B (2019). Spatiotemporal influences of land use/cover changes on the heat island effect in rapid urbanization area. Front Earth Sci, 13(3): 614–627

[49] Yang J, Dong J, Xiao X, Dai J, Wu C, Xia J, Zhao G, Zhao M, Li Z, Zhang Y, Ge Q (2019a). Divergent shifts in peak photosynthesis timing of temperate and alpine grasslands in China. Remote Sens Environ, 233(1): 111395

[50] Yang X, Na J, Tang G A, Wang T, Zhu A (2019b). Bank gully extraction from DEMs utilizing the geomorphologic features of a loess hilly area in China. Front Earth Sci, 13(1): 151–168

[51] Zhang S, Yang D, Yang Y, Piao S, Yang H, Lei H, Fu B (2018). Excessive afforestation and soil drying on China’s Loess Plateau. J Geophys Res Biogeosci, 123(3): 923–935

[52] Zhao A, Zhang A, Lu C, Wang D, Wang H, Liu H (2017). Spatiotemporal variation of vegetation coverage before and after implementation of Grain for Green Program in Loess Plateau China. Ecol Eng, 104(1): 13–22

[53] Zhao J, Huang S, Huang Q, Wang H, Leng G, Peng J, Dong H (2019). Copula-Based abrupt variations detection in the relationship of seasonal vegetation-climate in the Jing River Basin China. Remote Sens, 11(13): 1628

[54] Zhong Q, Ma J, Zhao B, Wang X, Zong J, Xiao X (2019). Assessing spatial-temporal dynamics of urban expansion vegetation greenness and photosynthesis in megacity Shanghai China during 2000–2016. Remote Sens Environ, 233(1): 111374

[55] Zhou L, Tian Y, Myneni R B, Ciais P, Saatchi S, Liu Y Y, Piao S, Chen H, Vermote E F, Song C, Hwang T (2014). Widespread decline of Congo rainforest greenness in the past decade. Nature, 509(7498): 86–90

RIGHTS & PERMISSIONS

Higher Education Press