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

doi:10.1007/s11707-021-0891-z

Front. Earth Sci.

2022, Vol. 16

Issue (2) : 368-380 https://doi.org/10.1007/s11707-021-0891-z

RESEARCH ARTICLE

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

Panxing HE^1,², Zongjiu SUN¹(

), Dongxiang XU³, Huixia LIU¹, Rui YAO⁴, Jun MA²(

)

¹. Ministry of Education Key Laboratory for Western Arid Region Grassland Resources and Ecology, College of Grassland and Environment Sciences, Xinjiang Agricultural University, Urumqi 830000, China
². Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, Institute of Biodiversity Science, Fudan University, Shanghai 200438, China
³. School of Earth and Environmental Sciences, University of Queensland, Queensland 4072, Australia
⁴. Hubei Key Laboratory of Critical Zone Evolution, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China

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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

Corresponding Author(s): Zongjiu SUN,Jun MA

About author: Tongcan Cui and Yizhe Hou contributed equally to this work.

Online First Date: 19 July 2021 Issue Date: 26 August 2022

Cite this article:

Panxing HE,Zongjiu SUN,Dongxiang XU, et al. Combining gradual and abrupt analysis to detect variation of vegetation greenness on the loess areas of China[J]. Front. Earth Sci., 2022, 16(2): 368-380.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-021-0891-z
https://academic.hep.com.cn/fesci/EN/Y2022/V16/I2/368

Fig.1 Biome distribution of the Loess Plateau, the biome type is from MCD12Q1 products.

Fig.2 Interannual variation of NDVI_max and total greenness of Loess Plateau during 2001–2018. (a) Annual max NDVI; (b) annual total greenness and its distribution of different regions. GS: Gansu, HN: Henan, NMG: Inner Mongolia, NX: Ningxia, QH: Qinghai, SHX: Shaanxi, SX: Shanxi; (c) annual total greenness and its distribution of different region biomes: FR: forests, SS: shrubs and savannas, GL: grasslands, CP: croplands, UB: urban and built-up lands, BR: barren land.

Fig.3 Spatial patterns of annual linear trends of MODIS NDVI_max over the Loess Plateau for the period 2001–2018. Blue represents a positive change (trend>0), and red represents a negative change (trend<0). (a) Spatial trend of NDVI_max; (b) significance of NDVI_max. In addition, pixels with a linear trend are statistically significant at P<0.01, P<0.05, and P<0.1.

Fig.4 Spatial distribution of per-pixel NDVI breakpoints detected by breaks for additive season and trend (BFAST). (a) Spatial distribution of six breakpoint types;(b) spatial distribution of the year of breakpoint detection and histogram of pixels with different breakpoint type and year were shown in the sidebar.

Fig.5 Spatial distribution of (a) NDVI magnitudes before breakpoint year; (b) NDVI magnitudes after breakpoint year; and (c) difference of NDVI magnitudes before and after breakpoint year.

Fig.6 Difference of NDVI before and after breakpoint.

Fig.7 Junger coalmine pixel with abrupt changes. (a) BFAST method detected the significance on the pixel scale over the Loess Plateau; (b) location of the Junger coal mine; (c) BFAST decomposition results between 2000 and 2018; (d) annual max NDVI (30 m) of Landsat satellites is processed and downloaded on the Google Earth Engine platform from 2001 to 2018, and blue indicates the higher the value of NDVI, while red indicates the lower.

Fig.8 Hongsibu irrigation area with abrupt human-induced changes. (a) BFAST method detected the stability on the pixel scale over the Loess Plateau; (b) location of the Hongsibu; (c) BFAST decomposition results between 2000 and 2018; (d) annual max NDVI (30 m) of Landsat satellites is processed and downloaded on the Google Earth Engine platform from 2001 to 2018, and blue indicates the higher the value of NDVI, while red indicates the lower.

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