Detecting Land Degradation in Eastern China Grasslands with Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) and GIMMS NDVI3g Data

Liu, Caixia; Mélack, John M.; Tian, Ye; Huang, Huabing; Jiang, Jinxiong; Xiao, Fu; Zhang, Zhouai

doi:10.3390/rs11091014

Cited by 29 publications

(25 citation statements)

References 55 publications

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“…NDVI has negative values in the place covered by cloud, water and snow, and thus will be affected by the atmosphere (cloud, water-vapour, and aerosols). To minimize the influence of the atmosphere and of the viewing and illumination conditions, the maximum value composite (MVC) technique was developed by Holben (1986), which has been widely used in many previous studies to deal with the NDVI3g data [56][57][58][59]. In this study, MVC was used to aggregate the original biweekly NDVI series into monthly series.…”

Section: Data Sourcesmentioning

confidence: 99%

Relationship of Abrupt Vegetation Change to Climate Change and Ecological Engineering with Multi-Timescale Analysis in the Karst Region, Southwest China

Liu

Zhen-shan

et al. 2019

Remote Sensing

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Vegetation is known to be sensitive to both climate change and anthropogenic disturbance in the karst region. However, the relationship between an abrupt change in vegetation and its driving factors is unclear at multiple timescales. Based on the non-parametric Mann-Kendall test and the ensemble empirical mode decomposition (EEMD) method, the abrupt changes in vegetation and its possible relationships with the driving factors in the karst region of southwest China during 1982–2015 are revealed at multiple timescales. The results showed that: (1) the Normalized Difference Vegetation Index (NDVI) showed an overall increasing trend and had an abrupt change in 2001. After the abrupt change, the greening trend of the NDVI in the east and the browning trend in the west, both changed from insignificant to significant. (2) After the abrupt change, at the 2.5-year time scale, the correlation between the NDVI and temperature changed from insignificantly negative to significantly negative in the west. Over the long-term trend, it changed from significantly negative to significantly positive in the east, but changed from significantly positive to significantly negative in the west. The abrupt change primarily occurred on the long-term trend. (3) After the abrupt change, 1143.32 km2 farmland was converted to forests in the east, and the forest area had significantly increased. (4) At the 2.5-year time scale, the abrupt change in the relationships between the NDVI and climate factors was primarily driven by climate change in the west, especially rising temperatures. Over the long-term trend, it was caused by ecological protection projects in the east, but by rising temperatures in the west. The integration of the abrupt change analysis and multiple timescale analysis help assess the relationship of vegetation changes with climate changes and human activities accurately and comprehensively, and deepen our understanding of the driving mechanism of vegetation changes, which will further provide scientific references for the protection of fragile ecosystems in the karst region.

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Section: Data Sourcesmentioning

confidence: 99%

Relationship of Abrupt Vegetation Change to Climate Change and Ecological Engineering with Multi-Timescale Analysis in the Karst Region, Southwest China

Liu

Zhen-shan

et al. 2019

Remote Sensing

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“…Previous studies have used land degradation reflected by vegetation to monitor desertification [7][8][9] as vegetation cover is a highly unstable and delicate portion of the ecosystem [10]. Due to the biophysical response of plant respiration, evapotranspiration, and photosynthesis, vegetation has an obvious relationship with climatic factors [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Vegetation Dynamic and Persistence under Climatic and Anthropogenic Factors

et al. 2020

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Land degradation reflected by vegetation is a commonly used practice to monitor desertification. To retrieve important information for ecosystem management accurate assessment of desertification is necessary. The major factors that drive vegetation dynamics in arid and semi-arid regions are climate and anthropogenic activities. Progression of desertification is expected to exacerbate under future climate change scenarios, through precipitation variability, increased drought frequency and persistence of dry conditions. This study examined spatiotemporal vegetation dynamics in arid regions of Sindh, Pakistan, using annual and growing season Normalized Difference Vegetation Index (NDVI) data from 2000 to 2017, and explored the climatic and anthropogenic effects on vegetation. Results showed an overall upward trend (annual 86.71% and growing season 82.7%) and partial downward trend (annual 13.28% and growing season 17.3%) in the study area. NDVI showed the highest significant increase in cropland region during annual, whereas during growing season the highest significant increase was observed in savannas. Overall high consistency in future vegetation trends in arid regions of Sindh province is observed. Stable and steady development region (annual 48.45% and growing 42.80%) dominates the future vegetation trends. Based on the Hurst exponent and vegetation dynamics of the past, improvement in vegetation cover is predicted for a large area (annual 44.49% and growing 30.77%), and a small area is predicted to have decline in vegetation activity (annual 0.09% and growing 3.04%). Results revealed that vegetation growth in the study area is a combined result of climatic and anthropogenic factors; however, in the future multi-controls are expected to have a slightly larger impact on annual positive development than climate whereas positive development in growing season is more likely to continue in future under the control of climate variability.

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“…Compared with similar studies [19,23,25], here, we used MOD-R to study vegetation changes in small areas. The results showed that MOD-TR had detected more pixels with a significant decrease than when using MOD-R in small areas.…”

Section: Validation Of the Mod-tr In Prdmentioning

confidence: 99%

“…TSS-RESTREND mainly employed GIMMS NDVI data (i.e., GIM-TR) to study large areas. A previous study used GIM-TR to analyze vegetation change in a relatively small study area [25], but its results showed that GIMMS NDVI was too coarse to detect enough breakpoints by BFAST, which would lead to an underestimation of the vegetation decrease. However, data from a new generation of satellites, namely Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data, with a higher resolution (i.e., 250 m, 500m and 1000m) is more commonly used to detect the vegetation change in small study areas [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Detecting Vegetation Change in the Pearl River Delta Region Based on Time Series Segmentation and Residual Trend Analysis (TSS-RESTREND) and MODIS NDVI

Zhu

Kuang

et al. 2020

Remote Sensing

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Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) can detect an abrupt change that was undetected by Residual Trend analysis (RESTREND), but it is usually combined with the Global Inventory for Mapping and Modeling Studies (GIMMS) Normalized Difference Vegetation Index (NDVI), which cannot detect detailed vegetation changes in small areas. Hence, we used Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI (MOD-TR) to analyze the vegetation dynamic of the Pearl River Delta region (PRD) in this study. To choose the most suitable MODIS NDVI from MOD13Q1 (250 m), MOD13A1 (500 m), and MOD13A2 (1 km), whole and local comparison of results of the break year and MOD-TR were used. Meanwhile, a comparison of vegetation change at the city-scale was also implemented. Moreover, to reduce insignificant trend pixels in TSS-RESTREND, a combination method of TSS-RESTREND and RESTREND (CTSS-RESTREND) was proposed. We found that: (1) MOD13Q1 and MOD13A1 two NDVI were suitable for combination with TSS-RESTREND to detect vegetation change in PRD, but MOD13Q1 was a better choice when considering the accuracy of local detailed vegetation change; (2) CTSS-RESTREND could detect more pixels with a significant change (i.e., significant increase and significant decrease) than those of TSS-RESTREND and RESTREND. Also, its effectiveness could be verified by Landsat data; (3) at the city-scale, the CTSS-RESTREND detected that only vegetation decreases in Shenzhen, Foshan, Dongguan, and Zhongshan were higher than vegetation increases, but, significant vegetation changes (i.e., decreases and increases) were mainly concentrated in Huizhou, Jiangmen, Zhaoqing, and Guangzhou.

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Detecting Land Degradation in Eastern China Grasslands with Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) and GIMMS NDVI3g Data

Cited by 29 publications

References 55 publications

Relationship of Abrupt Vegetation Change to Climate Change and Ecological Engineering with Multi-Timescale Analysis in the Karst Region, Southwest China

Relationship of Abrupt Vegetation Change to Climate Change and Ecological Engineering with Multi-Timescale Analysis in the Karst Region, Southwest China

Spatio-Temporal Vegetation Dynamic and Persistence under Climatic and Anthropogenic Factors

Detecting Vegetation Change in the Pearl River Delta Region Based on Time Series Segmentation and Residual Trend Analysis (TSS-RESTREND) and MODIS NDVI

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