Detecting dryland degradation using Time Series Segmentation and Residual Trend analysis (TSS-RESTREND)

Burrell, Arden; Evans, Jason P.; Liu, Yi

doi:10.1016/j.rse.2017.05.018

Cited by 134 publications

(135 citation statements)

References 90 publications

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“…The Guadiamar site (Spain, Site E in Table 1) (Bai, Dent, Olsson, & Schaepman, 2008). However, doubts arose concerning quality of the assessment over nonwaterlimited regions (Wessels, 2009).…”

Section: Soil Contaminationmentioning

confidence: 99%

“…Enhanced methodology should account for non-linearity and legacy effect in the response of the ecosystem to changing pressure (Horion, Cornet, Erpicum, & Tychon, 2013) as well as for potential regime shifts in EF (Scheffer et al, 2009). In that sense, segmented trend analysis has proven to be an effective method when applied on environmental time series (Burrell et al, 2017;de Jong et al, 2012;Horion et al, 2016). Yet there is a need for considering a higher level of complexity in relation to change in EF (i.e., more than one abrupt shift).…”

Section: Conclusion and Future Research Prioritiesmentioning

confidence: 99%

“…Hence, simple linear models are likely to only provide partial insights on the mechanism of change, which is in fact essential for an appropriate attribution of drivers of changes. Piecewise linear models-such as Break For Additive Season and Trend (BFAST) (Verbesselt et al, 2010) or Detecting Breakpoints and Estimating Segments in Trend (DBEST) (Jamali et al, 2015)-allow for separating environmental time series into individual segments, capturing specific vegetation conditions (de Jong, Verbesselt, Schaepman, & Bruin, 2012;de Jong, Verbesselt, Zeileis, & Schaepman, 2013) or stages of degradation through time (Burrell, Evans, & Liu, 2017;Horion et al, 2016). Horion et al (2016) applied segmented trend analysis on time series of rain-use efficiency (Le Houérou, 1984) in order to identify turning points in EF, thereby revealing degradation processes such as soil salinization, increased grazing pressure, or drought-induced loss in ecosystem productivity.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mapping European ecosystem change types in response to land‐use change, extreme climate events, and land degradation

et al. 2019

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Extreme climate events and nonsustainable land use are important drivers altering the functioning of European ecosystems, resulting in loss of the services provided. Yet a consensus method for regular continental scale assessment of ecosystem condition in relation to land degradation (LD) is still lacking. Here, we propose a new remote sensing‐based approach allowing for improved, repeated assessment of changing pressure on terrestrial ecosystems. On the basis of segmented trend analysis of water‐use efficiency (WUE), a map of ecosystem change type (ECT) was produced over Europe for the period 1999 to 2013. Results were related to drought and change in land use and land cover and to known cases of soil degradation (LD case‐studies). More than 30% of the European ecosystems experienced significant changes in WUE, of which more than 20% were categorized as abrupt. Large‐scale positive reversals in WUE were observed over regions with increasing crop yield and intensification of wood production, whereas decreased WUE was observed over grassland areas coinciding with high farmland abandonment. Evidence of drought pressure on ecosystem functioning (EF) was observed, with abrupt changes in functioning observed during major European drought events. The ECTs also provided relevant information on the location and type of change in EF over the LD case studies. We conclude that mapping of gradual and abrupt changes in EF is expected to be valuable tool for ecosystem condition assessment that is essential for assessing the success of reaching the LD neutrality objectives set by the United Nations Convention to Combat Desertification.

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Section: Soil Contaminationmentioning

confidence: 99%

Section: Conclusion and Future Research Prioritiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mapping European ecosystem change types in response to land‐use change, extreme climate events, and land degradation

et al. 2019

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“…Human activities have changed the geographical extent and quality of vegetation growth on a short time scale and have had a major impact on regional ecosystem services and functions [4,5]. An in-depth analysis of the impact of climate change and human activities on changes to vegetation cover can provide a quantitative assessment of the contribution rate of the two types of driving factors that have been key issues in global change research but are also challenging to research [6,7]. Some biophysical parameters that have been obtained by satellite remote sensing inversion, such as the normalized difference vegetation index (NDVI), Albedo, and the leaf area index (LAI), can characterize the specific nature of ecosystems so that researchers can use satellite remote sensing inversion and simulation techniques to conduct long-term ecological monitoring and evaluation.…”

Section: Introductionmentioning

confidence: 99%

Vegetation Change and Driving Factors: Contribution Analysis in the Loess Plateau of China during 2000–2015

Dao²,

Yang

2019

Sustainability

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Judging vegetation change and analyzing the impacts of driving factors on vegetation change are important bases on which to evaluate the effects of ecological engineering constructions on the Loess Plateau and to support ecological construction planning decisions. The authors applied time-section difference analysis and trend analysis methods to analyze the temporal–spatial characteristics of vegetation change on the Loess Plateau from 2000 to 2015. Then, complex linear regression analysis and residual analysis methods were applied to estimate the contribution rates of driving factors to regional vegetation changes. The results showed the following: (1) From 2000 to 2015, most areas of the Loess Plateau became “greener”. These areas were mainly distributed in the southern part of Shanxi Province, the northern and central parts of Shaanxi Province, and the eastern part of Gansu Province. (2) In 2015, the overall contribution rate of meteorological factors (temperature and precipitation) to normalized difference vegetation index (NDVI) in the Loess Plateau was as high as 87.7%. The average contribution rate of non-meteorological factors (mainly referring to human activities) to vegetation NDVI was 6.4%.

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“…Despite its relatively coarse spatial resolution these data correspond well with data captured with higher resolution sensors such as MODIS and SPOT (Fensholt et al 2006, Fensholt andProud 2012). The GIMMS3g data set is therefore generally perceived as the most accurate multi-decadal NDVI dataset (Ibrahim et al 2015, Burrell et al 2017. The latest version of the GIMMS NDVI3g.v1 data, available at https://ecocast.arc.nasa.…”

Section: Data Sources and Preprocessingmentioning

confidence: 70%

Regime shift of the hydroclimate–vegetation system in the Yellow River Delta of China from 1982 through 2015

Niu

Zhang

et al. 2020

Environ. Res. Lett.

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The Yellow River Delta (YRD) has been experiencing substantial climatic, hydrological, and anthropogenic stresses, and a sound understanding of the regime shift in its hydroclimate-vegetation system is of fundamental importance for maintaining the health and stability of its regional ecosystems. This study constructs and analyzes a 34-year-dataset (1982-2015) of hydro-climatic variables and satellite-based Normalized Difference Vegetation Index (NDVI) in the YRD. A seasonaltrend decomposition technique based on loess (STL), and a structural change analysis were coupled to detect regime shifts of regional hydroclimate and vegetation in the YRD from 1982 through to 2015. During this period the YRD exhibited a significant warmer-drier-greening trend and experienced four regime shifts of its hydroclimate-vegetation system, with the four shift periods roughly centered in 1989, 1998, 2004, and 2012. Partial correlation analysis revealed that temperature was the dominant factor promoting vegetative growth in spring and autumn (all P NDVI-TEM greater than 0.65), and streamflow impacted the NDVI mainly in summer. Temperature and precipitation were the dominant controls of vegetative growth during the growing season prior to 2002, and thereafter precipitation and streamflow alternately became the main moisture-influencing factors of vegetative growth. Streamflow played an important complementary role on vegetative growth, particularly in near riverine areas when drought exceeds a certain threshold. Additionally, climate shifts determined the changing trend of NDVI across the region, while the effect of land use change is localized and predominant in the northeastern part of the study region. These findings offer an insight into appropriate water regulation of the Yellow River and on climatic adaptation within the YRD.

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Detecting dryland degradation using Time Series Segmentation and Residual Trend analysis (TSS-RESTREND)

Cited by 134 publications

References 90 publications

Mapping European ecosystem change types in response to land‐use change, extreme climate events, and land degradation

Mapping European ecosystem change types in response to land‐use change, extreme climate events, and land degradation

Vegetation Change and Driving Factors: Contribution Analysis in the Loess Plateau of China during 2000–2015

Regime shift of the hydroclimate–vegetation system in the Yellow River Delta of China from 1982 through 2015

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