Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan

Beurs, Kirsten M. de; Henebry, Geoffrey M.

doi:10.1016/j.rse.2003.11.006

Cited by 433 publications

(243 citation statements)

References 31 publications

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“…Kazakhstan underwent a considerable transition in agricultural land use in the postSoviet era, marked by a sharp decline in total rainfed grain area from 25 million ha in 1983 to 14 million ha in 2003, particularly in the country's northern part (De Beurs and Henebry, 2004). Today, the area is largely covered by abandoned cropland returning to original land cover types prevalent before their conversion to cultivation (Schierhorn et al, 2013), mainly grassland.…”

Section: Map Of Degradation Of Soils Of a Test Sitementioning

confidence: 99%

Distribution, typology and assessment of degraded soils Piedmont Plains Zhetysu Ridge, Kazakhstan

Kussainova¹,

Pachikin²,

Erokhina³

2017

Eurasian Journal of Soil Science (Ejss)

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Section: Map Of Degradation Of Soils Of a Test Sitementioning

confidence: 99%

Distribution, typology and assessment of degraded soils Piedmont Plains Zhetysu Ridge, Kazakhstan

Kussainova¹,

Pachikin²,

Erokhina³

2017

Eurasian Journal of Soil Science (Ejss)

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“…Several studies discussed the effects of climate variability on land surface change [33][34][35]. The monthly mean temperature (TMP) and precipitation total (PRE) datasets were obtained from the Climate Research Unit (CRU).…”

Section: Environmental Variablesmentioning

confidence: 99%

Towards an Improved Environmental Understanding of Land Surface Dynamics in Ukraine Based on Multi-Source Remote Sensing Time-Series Datasets from 1982 to 2013

et al. 2016

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Ukraine has experienced immense environmental and institutional changes during the last three decades. We have conducted this study to analyze important land surface dynamics and to assess processes underlying the changes. This research was conducted in two consecutive steps. To analyze monotonic changes we first applied a Mann-Kendall trend analysis of the Normalized Difference Vegetation Index (NDVI3g) time series. Gradual and abrupt changes were studied by fitting a seasonal trend model and detecting the breakpoints. Secondly, essential environmental factors were used to quantify their possible relationships with land surface changes. These factors included soil moisture as well as gridded air temperature and precipitation data. This was done using partial rank correlation analysis based on annually aggregated time-series. Our results demonstrate that positive NDVI trends characterize approximately one-third of Ukraine's land surface, located in the northern and western areas of the country. Negative trends occurred less frequently, covering less than 2% of the area and are distributed irregularly across the country. Monotonic trends were rarely found; shifting trends were identified with a greater frequency. Trend shifts were seen to occur with an increased frequency following the period of the 2000s. We determined that land surface dynamics and climate variability are functionally interdependent; however, the relative influence of the drivers varies in different locations. Among the factors analyzed, the air temperature variable explains the largest portion of NDVI variability. High air temperature/NDVI correlation coefficients (r = 0.36´0.77) are observed over the entire country. The soil moisture content is of significant influence in the eastern portion of Ukraine (r = 0.68); precipitation (r = 0.65) was most influential in the central regions of the country. These results increase our understanding of ecosystem responses to climatic changes and anthropogenic activities.

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“…To minimize cloud and atmospheric contamination, the maximum value composite (MVC) (Holben, 1986) and best index slope extraction (BISE) (Viovy et al, 1992) are commonly applied to create weekly, biweekly, or monthly composites. To further reduce noise, time series of VI data are often smoothed using a variety of different methods including Fourier harmonic analysis (Moody and Johnson, 2001), asymmetric Gaussian function-fitting (Jonsson and Eklundh, 2002), piece-wise logistic functions (Zhang et al, 2003), SavitzkyGolay filters (Chen et al, 2004), degree-day based quadratic models (de Beurs and Henebry, 2004), and polynomial curve fitting (Bradley et al, 2007). In mid-and high latitudes, vegetation signals are also contaminated by snow cover during winter.…”

Section: Algorithm Of Phenology Detectionmentioning

confidence: 99%

“…Phenology observed from satellite data is usually defined as land surface phenology (de Beurs and Henebry, 2004;Friedl et al, 2006) because an annual cycle of satellite data reflects seasonal variation composed of vegetation, atmosphere, snow cover, water conditions, and other land disturbance. However, vegetation seasonal dynamics are generally the parameters of interest to retrieve, whereas the abiotic signals in the temporal satellite data are considered to be noise.…”

Section: Global Vegetation Phenological Metricsmentioning

confidence: 99%

Long-Term Detection of Global Vegetation Phenology from Satellite Instruments

Zhang¹,

Friedl²,

Tan³

et al. 2012

Phenology and Climate Change

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Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan

Cited by 433 publications

References 31 publications

Distribution, typology and assessment of degraded soils Piedmont Plains Zhetysu Ridge, Kazakhstan

Distribution, typology and assessment of degraded soils Piedmont Plains Zhetysu Ridge, Kazakhstan

Towards an Improved Environmental Understanding of Land Surface Dynamics in Ukraine Based on Multi-Source Remote Sensing Time-Series Datasets from 1982 to 2013

Long-Term Detection of Global Vegetation Phenology from Satellite Instruments

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