Impacts of climate gradients on the vegetation phenology of major land use types in Central Asia (1981–2008)

doi:10.1007/s11707-012-0315-1

Front Earth Sci

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Issue () : 206-225 https://doi.org/10.1007/s11707-012-0315-1

RESEARCH ARTICLE

Impacts of climate gradients on the vegetation phenology of major land use types in Central Asia (1981–2008)

Jahan KARIYEVA^1,2(

), Willem J.D. van LEEUWEN^1,2, Connie A. WOODHOUSE¹

1. School of Geography and Development, The University of Arizona, Tucson, AZ 85721, USA; 2. School of Natural Resources and the Environment – Office of Arid Lands Studies – Arizona Remote Sensing Center; The University of Arizona, Tucson, AZ 85721, USA

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Abstract

Time-series of land surface phenology (LSP) data offer insights about vegetation growth patterns. They can be generated by exploiting the temporal and spectral reflectance properties of land surface components. Interannual and seasonal LSP data are important for understanding and predicting an ecosystem’s response to variations caused by natural and anthropogenic drivers. This research examines spatio-temporal change patterns and interactions between terrestrial phenology and 28 years of climate dynamics in Central Asia. Long-term (1981–2008) LSP records such as timing of the start, peak and length of the growing season and vegetation productivity were derived from remotely sensed vegetation greenness data. The patterns were analyzed to identify and characterize the impact of climate drivers at regional scales. We explored the relationships between phenological and precipitation and temperature variables for three generalized land use types that were exposed to decade-long regional drought events and intensified land and water resource use: rainfed agriculture, irrigated agriculture, and non-agriculture. To determine whether and how LSP dynamics are associated with climate patterns, a series of simple linear regression analyses between these two variables was executed. The three land use classes showed unique phenological responses to climate variation across Central Asia. Most of the phenological response variables were shown to be positively correlated to precipitation and negatively correlated to temperature. The most substantial climate variable affecting phenological responses of all three land use classes was a spring temperature regime. These results indicate that future higher temperatures would cause earlier and longer growing seasons.

Keywords phenology land use climate variability

Corresponding Author(s): KARIYEVA Jahan,Email:jahank@email.arizona.edu

Issue Date: 05 June 2012

Cite this article:

Jahan KARIYEVA,Willem J.D. van LEEUWEN,Connie A. WOODHOUSE. Impacts of climate gradients on the vegetation phenology of major land use types in Central Asia (1981–2008)[J]. Front Earth Sci, 0, (): 206-225.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-012-0315-1
https://academic.hep.com.cn/fesci/EN/Y0/V/I/206

Fig.1 Map (35°-55° N; 50°-85° E) of the northern (purple outline), central (orange outline), and mountain (olive outline) climate subregions redrawn from Small et al. (). The map also shows elevation differences of the region and areas of rainfed (purple) and irrigated (orange) agriculture, created from the International Water Management Institute data (). Non-irrigated areas are non-colored areas of the map. A separate graph displays long-term (1981-2008) monthly averaged precipitation in mm/month () for the northern (green line), central (orange line), and mountain (blue line) climate subregions

Fig.2 Example of phenological metrics derived from interannual and seasonal NDVI time-series data. Top image represents real NDVI time-series data extracted for the Tejen River delta in Turkmenistan and bottom image represents example of replicated NDVI trajectories (for visualization purposes) and derived phenological metrics used in the analysis

Tab.1 List of the phenological (four metrics: rows) and climate (five seasons: columns) variables used in the simple regression analyses to estimate slope coefficient during 28 years of observed measurements (1981-2008) and combination of the variables (X) for which correlation coefficients were derived; i.e., correlation between winter temperature and start of growing season across time (1981-2008)

Fig.3 Median images of pheno-metrics (top) and their significant (-value<0.05) trend images (bottom) for 1981-2008: (a) start of growing season; (b) peak of growing season; (c) length of growing season, and (d) productivity (large integrated NDVI value) metric. Start and peak metrics are in day of year (Julian day) units, while pixels in length of season metric show season duration in number of days. Slope outputs are divided into the eight following classes: values equal to |0.75|-|1| show a very high slope coefficient; slope () values from |0.3|-|0.75| -a high slope coefficient; slope () values of |0.1|-|0.3| show medium strength slope coefficient; and slope () values from 0-|0.1| mean very low or almost no trend in change; negative (blue colored) slope values mean earlier dates of start and peak, shorter length periods, and decrease in NDVI-based productivity values and positive (red colored) slope values mean later dates of start and peak, longer length periods, and increase in productivity values; white areas are the pixels with non-significant slope values

Fig.4 Cumulative seasonal precipitation (mm) images averaged across 28 years (top; white areas are the pixels with no precipitation) and their significant (-value<0.05) trend images (bottom) for 1981-2008: winter (a); spring (b); summer (c); and fall (d). Slope outputs are divided into the same eight classes as described in Fig. 3: negative (blue colored) slope values mean decrease in precipitation and positive (red colored) slope values mean increase in precipitation

Fig.5 Average seasonal temperature (C°) images averaged across 28 years (top) and their significant (-value<0.05) trend images (bottom) for 1981-2008: winter (a); spring (b); summer (c); and fall (d). Slope outputs are divided into the same eight classes as discussed in Fig. 3: negative (blue colored) slope values mean decrease in temperature and positive (red colored) slope values mean increase in temperature

Fig.6 Correlation of start of season metric with (a) spring temperature (T); (b) fall T; and (c) winter precipitation (P). Graph (d) represents percentage of distribution of values (based on -value<0.05) for each of the seasonal climate metrics ( = precipitation; = temperature) correlated to start of season pheno-metric for rainfed agriculture (RAG), irrigated agriculture (IAG), and non-agriculture (NAG) zones; values above/below zero represent percentage of pixels with positive/negative values respectively; ovals outline correlation analysis outputs displayed in (a), (b), and (c) images. Ant.Fall: antecedent fall; Cr.Fall: coincident fall. Negative (blue colored) correlation coefficient values mean earlier dates of season start and positive (red colored) correlation coefficient values mean later dates of season start.

Fig.7 Correlation of peak of season metric with (a) spring temperature (T); (b) summer precipitation (P); and (c) summer T. Graph (d) represents percentage of distribution of values (based on -value<0.05) for each of the seasonal climate metrics ( = precipitation; = temperature) correlated to start of season pheno-metric for rainfed agriculture (RAG), irrigated agriculture (IAG), and non-agriculture (NAG) zones; values above/below zero represent percentage of pixels with positive/negative values respectively; ovals outline correlation analysis outputs displayed in (a), (b), and (c) images. Ant.Fall: antecedent fall; Cr.Fall: coincident fall. Negative (blue colored) correlation coefficient values mean earlier dates of season peak and positive (red colored) correlation coefficient values mean later dates of season peak.

Fig.8 Correlation of length of season metric with (a) fall temperature (T); (b) spring T; and (c) summer precipitation (P). Graph (d) represents percentage of distribution of values (based on -value<0.05) for each of the seasonal climate metrics ( = precipitation; = temperature) correlated to start of season pheno-metric for rainfed agriculture (RAG), irrigated agriculture (IAG), and non-agriculture (NAG) zones; values above/below zero represent percentage of pixels with positive/negative values respectively; ovals outline correlation analysis outputs displayed in (a), (b), and (c) images. Ant.Fall: antecedent fall; Cr.Fall: coincident fall. Negative (blue colored) correlation coefficient values mean shorter dates of season length and positive (red colored) correlation coefficient values mean longer dates of season length.

Fig.9 Correlation of total vegetation productivity measured over the season (large integrated seasonal NDVI) with (a) spring temperature (T); (b) summer T; and (c) fall T. Graph (d) represents percentage of distribution of values (based on -value<0.05) for each of the seasonal climate metrics ( = precipitation; = temperature) correlated to start of season pheno-metric for rainfed agriculture (RAG), irrigated agriculture (IAG), and non-agriculture (NAG) zones; values above/below zero represent percentage of pixels with positive/negative values respectively; ovals outline correlation analysis outputs displayed in (a), (b), and (c) images. Ant.Fall: antecedent fall; Cr.Fall: coincident fall. Negative (blue colored) correlation coefficient values mean decreased NDVI-based productivity values and positive (red colored) correlation coefficient values mean increased NDVI-based productivity values.

Fig.10 Summary of number of pixels with significant (based on -value<0.05) values for climate-phenology relationships over time (1981-2008): timing and productivity responses to precipitation and temperature patterns for three land use classes (a) rainfed agriculture; (b) irrigated agriculture; and (c) non-agriculture areas. Units are percentage from total number of pixels in each of the land use classes. Outputs representing less than 10% from total number of pixels are highlighted in gray color.

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