Investigating aerosol vertical distribution using CALIPSO time series over the Middle East and North Africa (MENA), Europe, and India: A BFAST-based gradual and abrupt change detection

Brakhasi, Foad; Hajeb, Mohammad; Mielonen, Tero; Matkan, A A; Verbesselt, Jan

doi:10.1016/j.rse.2021.112619

Cited by 15 publications

(4 citation statements)

References 142 publications

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“…According to the BFAST principle [52,54], this study adopts the BFAST's improved model, called BFAST01. Compared with the BFAST model, the BFAST01 model considers both seasonal and trend models.…”

Section: Bfast Mutation Testmentioning

confidence: 99%

“…Some studies have shown that the accuracy of the BFAST method in NDVI time series detection can reach 56% to 84% [66], and it can be applied to other disciplines dealing with seasonal or non-seasonal time series, such as hydrology, climatology, and econometrics [51]. Compared with other mutation detection methods, BFAST is less affected by seasonal differences and noise in time series and can detect changes faster [54]. Verbesselt et al and Verbesselt et al [52,67] presented a relatively complete description and introduction of this method.…”

Section: Sen-mk Trend and Nonlinear Mutation Of Bfast01mentioning

confidence: 99%

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Linear and Nonlinear Characteristics of Long-Term NDVI Using Trend Analysis: A Case Study of Lancang-Mekong River Basin

Zhong

Wang

et al. 2022

Remote Sensing

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Vegetation is the main body of the terrestrial ecosystem and is a significant indicator of environmental changes in the regional ecosystem. As an essential link connecting South Asia and Southeast Asia, the Lancang-Mekong River Basin(LMRB) can provide essential data support and a decision-making basis for the assessment of terrestrial ecosystem environmental changes and the research and management of hydrology and water resources in the basin by monitoring changes in its vegetation cover. This study takes the Lancang-Mekong River Basin as the study area, and employs the Sen slope estimation, Mann–Kendall test, and Hurst exponent based on the MODIS NDVI data from 2000 to 2021 to study the spatial and temporal evolution trend and future sustainability of its NDVI. Besides, the nonlinear characteristics such as mutation type and mutation year are detected and analyzed using the BFAST01 method. Results demonstrated that: (1) In the past 22 years, the NDVI of the Lancang-Mekong River Basin generally exhibited a fluctuating upward trend, and the NDVI value in 2021 was the largest, which was 0.825, showing an increase of 4.29% compared with 2000. However, the increase rate was different: China has the most considerable NDVI growth rate of 7.25%, followed by Thailand with an increase of 7.21%, Myanmar and Laos as the third, while Cambodia and Vietnam have relatively stable vegetation changes. The overall performance of NDVI is high in the south and low in the north, and is dominated by high and relatively high vegetation coverage, of which the area with vegetation coverage exceeding 0.8 accounts for 62%. (2) The Sen-MK trend showed that from 2000 to 2021, the area where the vegetation coverage in the basin showed a trend of increase and decrease accounted for 66.59% and 18.88%, respectively. The Hurst exponent indicated that the areas where NDVI will continue to increase, decrease, and remain unchanged in the future account for 60.14%, 25.29%, and 14.53%, respectively, and the future development trend of NDVI is uncertain, accounting for 0.04%. Thus, more attention should be paid to areas with a descending future development trend. (3) BFAST01 detected eight NDVI mutation types in the Lancang-Mekong River Basin over the past 22 years. The mutations mainly occurred in 2002–2018, while 2002–2004 and 2014–2018 were the most frequent periods of breakpoints. The mutation type of “interruption: increase with negative break” was changed the most during this period, which accounts for 36.54%, and the smallest was “monotonic decrease (with negative break)”, which only accounts for 0.65%. This research demonstrates that combining the conventional trend analysis method with the BFAST mutation test can more accurately analyze the spatiotemporal variation and nonlinear mutation of NDVI, thus providing a scientific reference to develop ecological environment-related work.

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Section: Bfast Mutation Testmentioning

confidence: 99%

Section: Sen-mk Trend and Nonlinear Mutation Of Bfast01mentioning

confidence: 99%

Linear and Nonlinear Characteristics of Long-Term NDVI Using Trend Analysis: A Case Study of Lancang-Mekong River Basin

Zhong

Wang

et al. 2022

Remote Sensing

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“…Mehta et al. (2018) found a global trend of decreasing particulate matter column loads based on CALIOP data, with an increase in the total aerosols in India and significant decreases in the total aerosol loads in North America, South America, eastern China and Australia (Brakhasi et al., 2021; Ratnam et al., 2021). Moreover, the vertical distribution of global aerosols significantly differs among various regions (Amiridis et al., 2010; Kim et al., 2021; Liu et al., 2021; Ma & Yu, 2014; VanCuren, 2003).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Long‐Term Trends in the Vertical Distribution and Transport Paths of Atmospheric Aerosols in Typical Regions of China Using 15 Years of CALIOP Data

Chen

Huang

et al. 2023

JGR Atmospheres

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Long‐range transport and vertical distribution of aerosols are important factors for assessing the uncertainty in aerosol radiative forcing. This paper reveals the vertical distribution and trends of aerosol optical properties in China using 15 years of Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) data. The Hybrid Single‐Particle Lagrangian integrated trajectory model was used to analyze the transport and trends of aerosols in the layer with the highest occurrence frequencies of dust, polluted dust, polluted continental and elevated smoke aerosols. The results indicated that (a) there were significant regional and seasonal differences in the vertical distribution of aerosols. The aerosol optical depth (AOD) trend in a given region depends on the changes in the aerosol type with the highest frequency and the layer corresponding to the largest AOD in the vertical profile. The frequency of polluted dust aerosols was the highest in Beijing‐Tianjin‐Hebei (BTH) and Central China. The considerable decrease in the 0–2 km AOD led to a significant trend of the column AOD. (b) The changes of AOD and main aerosol types in a region are also affected by the changes of aerosol sources and long‐range transport pathways. In the BTH, dust aerosols originated from the Mongolian Plateau, accounting for 57.88% of the total trajectories. The Pearl River Delta was dominated by elevated smoke aerosols, with trajectories mainly originating from the Myanmar and Vietnam, accounting for 27.38% and 29.59%, respectively. The trend of 15‐year backward trajectories of dust aerosols on the Tibetan Plateau indicated that the trajectory from India is increasing.

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“…De Jong et al (2013) proposed a model named BFAST01 to detect major (gradual and abrupt) changes in vegetation activity trends, their associated types (four monotonic changes, two interruptions, and two reversals), timing, and magnitude [27]. The BFAST01 algorithm has been used to detect the changes in temperature trends [28] and aerosol vertical distribution [29] at national and regional scales, respectively. In addition, the BFAST model has been used to decompose the components of time-series NTL [30] at the national level.…”

Section: Introductionmentioning

confidence: 99%

Evolution of Gradual and Abrupt Trends in Nighttime Lights and Responses to Land Drivers via BFAST01 and Geographically Weighted Regression

Guo,

Hu,

Wang

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Nighttime lights (NTLs) provide an unparalleled view for understanding urban environments. The high-frequency NTL intensity changes (NTLICs) and their land drivers in megaregions are less investigated. Here, monthly Visible Infrared Imaging Radiometer Suite-Day Night Band (VIIRS-DNB) NTL images from 2014 to 2020 were regarded as time series modeling input data. Furthermore, the 7-year mean values of five types of Points of Interest Density (POID), Road Density (RD), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI) were used to extract features as land drivers of NTLIC. A simplified version of the Breaks For Additive Season and Trend (BFAST01) algorithm was adopted to detect major changes in monthly NTL intensity at the pixel level in the Beijing-Tianjin-Hebei (BTH) megaregion, China. Geographically weighted regression (GWR) was used to address the spatial nonstationarity of the land drivers. The results showed that (1) increasing trends (63.35%) and decreasing trends (4.57%) were mainly observed in central and coastal cities, respectively. This indicates that the spatial characteristics of the NLTIC were unbalanced; (2) reversal change type 8 (37.29% of abrupt changes) was the dominant type, mainly occurring around April 2017, which may be related to the non-capital function redistribution policy; (3) NDVI and RD were the best one of two physical drivers and six human activity drivers, respectively; and (4) the R 2 of the GWR reached 0.60, a 20% improvement over ordinary least squares regression. This study provides an insightful understanding of the dynamics of NTL intensity and its response mechanisms in megaregions.

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Investigating aerosol vertical distribution using CALIPSO time series over the Middle East and North Africa (MENA), Europe, and India: A BFAST-based gradual and abrupt change detection

Cited by 15 publications

References 142 publications

Linear and Nonlinear Characteristics of Long-Term NDVI Using Trend Analysis: A Case Study of Lancang-Mekong River Basin

Linear and Nonlinear Characteristics of Long-Term NDVI Using Trend Analysis: A Case Study of Lancang-Mekong River Basin

Analysis of Long‐Term Trends in the Vertical Distribution and Transport Paths of Atmospheric Aerosols in Typical Regions of China Using 15 Years of CALIOP Data

Evolution of Gradual and Abrupt Trends in Nighttime Lights and Responses to Land Drivers via BFAST01 and Geographically Weighted Regression

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