Long-term trend of and correlation between vegetation greenness and climate variables in Asia based on satellite data

Lamchin, Munkhnasan; Lee, Woo‐Kyun; Jeon, Seong Woo; Wang, Sonam Wangyel; Lim, Chul-Hee; Song, Cholho; Sung, Minjun

doi:10.1016/j.mex.2018.07.006

Cited by 19 publications

(13 citation statements)

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“…Common methods for TS analysis include Fourier analysis [40,41], principal components analysis, [42,43] and the Mann-Kendall statistic [44]. The Mann-Kendall statistic has been used to identify the presence and nature of monotonic trends in vegetation time series [5,10,45]. It is a non-parametric statistic; thus, data does not have to conform to any specific distribution [46].…”

Section: Introductionmentioning

confidence: 99%

“…Besides, Mann-Kendall compares relative magnitudes of sample data instead of raw data values and therefore missing values are allowed in the analysis. The Mann-Kendall analysis is often followed by the Sen's slope estimator [47], which quantifies the strength of the monotonic trends [12,45]. The Sen's slope estimator calculates the median of the set of slopes generated from Mann-Kendall [47].…”

Section: Introductionmentioning

confidence: 99%

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Persistent Vegetation Greening and Browning Trends Related to Natural and Human Activities in the Mount Elgon Ecosystem

2020

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Many developing nations are facing severe food insecurity partly because of their dependence on rainfed agriculture. Climate variability threatens agriculture-based community livelihoods. With booming population growth, agricultural land expands, and natural resource extraction increases, leading to changes in land use and land cover characterized by persistent vegetation greening and browning. This can modify local climate variability due to changing land–atmosphere interactions. Yet, for landscapes with significant interannual variability, such as the Mount Elgon ecosystem in Kenya and Uganda, characterizing these changes is a difficult task and more robust methods have been recommended. The current study combined trend (Mann–Kendall and Sen’s slope) and breakpoint (bfast) analysis methods to comprehensively examine recent vegetation greening and browning in Mount Elgon at multiple time scales. The study used both Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and Climate Hazards group Infrared Precipitation with Stations (CHIRPS) data and attempted to disentangle nature- versus human-driven vegetation greening and browning. Inferences from a 2019 field study were valuable in explaining some of the observed patterns. The results indicate that Mount Elgon vegetation is highly variable with both greening and browning observable at all time scales. Mann–Kendall and Sen’s slope revealed major changes (including deforestation and reforestation), while bfast detected most of the subtle vegetation changes (such as vegetation degradation), especially in the savanna and grasslands in the northeastern parts of Mount Elgon. Precipitation in the area had significantly changed (increased) in the post-2000 era than before, particularly in 2006–2010, thus influencing greening and browning during this period. The greenness–precipitation relationship was weak in other periods. The integration of Mann–Kendall and bfast proved useful in comprehensively characterizing vegetation greenness. Such a comprehensive description of Mount Elgon vegetation dynamics is an important first step to instigate policy changes for simultaneously conserving the environment and improving livelihoods that are dependent on it.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Persistent Vegetation Greening and Browning Trends Related to Natural and Human Activities in the Mount Elgon Ecosystem

2020

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“…In fact, the Z Mann-Kendall test [112][113][114] is not only able to verify the existence of a trend, but also to identify its significance. Since the Theil-Sen estimator does not provide any information regarding the significance of the estimated trend, the nonparametric Mann-Kendall test is often used [115,116] associated with it. On the other hand, Z Mann Kendall despite being able to estimate the significance of a trend (separating the hypothesis H0 = absence of trend from H1 = presence of monotonous trend) is not able to identify by himself, if an increasing or decreasing trend is manifesting: thus the Theil-Sen slope (able to identify increases / decreases) is very often used with the Mann-Kendall test (able to identify the significance of trend) [78,117,118].…”

Section: Analysis Of Environmental Criticalitiesmentioning

confidence: 99%

Assessing Vegetation Decline due to Pollution from Waste Cycle Activities by a Multitemporal Remote-Sensing Approach

Mancino¹,

Console²,

Greco³

et al. 2021

Preprint

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The work consisted in identifying possible effects from heavy metals (HMs) pollution due to waste disposal activities in three potentially polluted sites located in Basilicata (Italy), where a release of pollutants with values over the thresholds allowed by the Italian legislation was detected. The potential variations in the physiological efficiency of vegetation have been analyzed through the multitemporal processing of satellite images. In detail, Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) images were used to calculate the Normalized Difference Vegetation Index (NDVI) trend over the years. Then, the multitemporal trends were analyzed using the median of Theil-Sen, a non-parametric estimator particularly suitable for the treatment of remote sensing data, being able to minimize the outlier effects due to exogenous factors. Finally, the subsequent application of the Mann-Kendall test on the trends identified by Theil-Sen slope allowed the evaluation of trends significance and, therefore, the areas characterized by the effects of contamination on vegetation. The application of the procedure to the three survey sites led to the exclusion of the presence of significant effects of HMs contamination on the vegetation surrounding the sites during the years of waste disposal activities.

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“…Alongside climate, vegetation can also affect the hydrological cycle of a watershed by altering certain processes, such as vegetation transpiration, canopy interception, and soil evaporation (Zhou et al, 2015;Zastrow, 2019). The normalized vegetation index (NDVI) is closely related to the leaf area index, photosynthetic capacity, and net primary productivity (Lamchin et al, 2018;Chu et al, 2019). In addition, this index compares the reflection intensity of visible light and near-infrared bands and is not easily affected by light conditions, which makes NDVI become the most commonly used indicator to characterize vegetation dynamics (Kumari et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

The impacts of natural and anthropogenic factors on vegetation change in the Yellow-Huai-Hai River Basin

Zhang

Bao

et al. 2022

Front. Earth Sci.

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The Yellow-Huai-Hai River Basin (YHHRB) is an important ecological barrier in China. There are obvious spatiotemporal differences and intrinsic drivers of vegetation coverage in this region. Using the Normalized Difference Vegetation Index (NDVI) and meteorological data, the spatiotemporal characteristics of vegetation change in the YHHRB from 1982 to 2015 and its related driving factors were analyzed. Combined with CAM5.1-1 degree model data, the impacts of natural and anthropogenic forcings on climate change were separated, and the relative roles of other driving factors, natural and anthropogenic forcings, in vegetation change were further distinguished by using a residual trend method. Results showed that the vegetation coverage increased during the study period, the NDVI increased with a slope of 0.014/10a, and the areas with NDVI significant increased accounted for 75.78%. NDVI was positively correlated with precipitation and temperature in the YHHRB, and the correlation between NDVI and temperature was higher than that of precipitation, indicating that vegetation growth was more sensitive to temperature. Residual analysis indicated that other driving factors, natural and anthropogenic forcings, were responsible for 48.52%, 26.36%, and 25.12% of NDVI variation, respectively. Natural forcing contributed most to vegetation change in the Huai River Basin, whereas anthropogenic forcing and other driving factors had large effects on vegetation coverage in the Hai River Basin. In addition, obvious spatial differences were observed in the relative roles of different driving forces on vegetation conditions. The areas where natural forcing contributed most were mainly distributed in the upper Yellow River Basin, while the areas where other driving factors played a significant role in vegetation restoration were mainly concentrated in Inner Mongolia, Shanxi Province, and northern Hebei Province. Climate change and active human activities both made positive impacts on vegetation restoration, and the change in land use was the main factor causing vegetation degradation. The results are meaningful for the ongoing ecological civilization construction project in the YHHRB.

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Long-term trend of and correlation between vegetation greenness and climate variables in Asia based on satellite data

Abstract: Graphical abstract

Cited by 19 publications

References 4 publications

Persistent Vegetation Greening and Browning Trends Related to Natural and Human Activities in the Mount Elgon Ecosystem

Persistent Vegetation Greening and Browning Trends Related to Natural and Human Activities in the Mount Elgon Ecosystem

Assessing Vegetation Decline due to Pollution from Waste Cycle Activities by a Multitemporal Remote-Sensing Approach

The impacts of natural and anthropogenic factors on vegetation change in the Yellow-Huai-Hai River Basin

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