Phenological dynamics of irrigated and natural drylands in Central Asia before and after the USSR collapse

Agriculture, Ecosystems and Environment 162 (2012) 77–89

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Phenological dynamics of irrigated and natural drylands in Central Asia before and after the USSR collapse Jahan Kariyeva a,b,∗ , Willem J.D. van Leeuwen a,b a

School of Geography and Development, The University of Arizona, Tucson, AZ 85721, USA School of Natural Resources and the Environment – Office of Arid Lands Studies – Arizona Remote Sensing Center, 1955 E. Sixth Street, The University of Arizona, Tucson, AZ 85721, USA b

a r t i c l e

i n f o

Article history: Received 1 November 2011 Received in revised form 30 May 2012 Accepted 10 August 2012 Available online 28 September 2012 Keywords: Remote sensing Phenology Land use change Socio-economic change Geography Uzbekistan Turkmenistan

a b s t r a c t Central Asia has experienced drastic socio-economic, geopolitical, and ecological transitions within the last few decades. The USSR collapse in 1991 has led to widespread changes in land cover and land use due to economic and political transformations within the region. Management practices during and after the Soviet era have intensified ecological problems and demands on resources. Satellite derived vegetation greenness data offer insights into these dynamics by providing measurements linked to vegetation productivity and the timing of vegetation growth cycles, including the timing of greenness onset, peak, and senescence. The main research goals are to examine the impact of socio-economic and bioclimatic factors by characterizing interannual dynamics of regional land surface phenology. One of the longest available records (1981–2006) of geospatial time-series data of the biweekly Normalized Difference Vegetation Index (NDVI) were used to derive annual pheno-metrics for sites in Uzbekistan and Turkmenistan. Land cover types include irrigated agriculture, riparian zones, and arid desert regions. Statistical analysis showed significant differences between pre- and post-Soviet collapse seasonal NDVI trajectories and interannual variation in greenness onset and vegetation response. Changes in satellite-based land surface phenological information are attributed to differences in prevailing land management, climate, and socio-economic factors before and after the USSR collapse. Published by Elsevier B.V.

1. Introduction Central Asia is an arid region that covers about four million km2 in the middle of the Eurasian continent and has very diverse physiographic conditions ranging from high mountains and glaciers to vast steppe and desert areas (Gleason, 1997). The region borders Russia on the north and west, Iran and Afghanistan on the south, and the People’s Republic of China on the east (Fig. 1). It includes five ex-Soviet and since 1991 independent states, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan, the boundaries of which were established in 1924–1936 (Elhance, 1997). Availability of water resources in Central Asia has for centuries been shaping its land use patterns and affecting its socio-economic and political landscape. While various leaders and systems have tried to establish governance by redrawing national and political borders of Central Asia, water has been the key focal point linking the diverse populations living in the region. From a hydrologic

∗ Corresponding author at: School of Geography and Development, The University of Arizona, Tucson, AZ 85721, USA. Tel.: +1 780 531 4282. E-mail addresses: [email protected] (J. Kariyeva), [email protected] (W.J.D. van Leeuwen). 0167-8809/$ – see front matter. Published by Elsevier B.V. http://dx.doi.org/10.1016/j.agee.2012.08.006

point of view, the region is an area of a special concern as most of its territory represents a semi-arid steppe (Gleason, 1997) with low precipitation (Small et al., 1999) where a large fraction of the population relies on irrigated agriculture from former commonpool and now shared transboundary water resources (ICG, 2005; Stanchin and Lerman, 2007). The complex geopolitical history of the region complicates regional cooperation in the water and energy sectors and management of current and future national and trans-national ecological processes and problems. A further understanding of challenges that the Central Asian region faces since the USSR collapse is crucial for designing sensible adaptation mechanisms to global change impacts in the broader context of sustainable development of the region. Improving the detection and characterization of land cover change and enhancing our ability to objectively attribute these changes due to human–environment interactions under changing climatic and socio-economic conditions will directly aid local and regional societal, ecological, and scientific development goals of Central Asia. Such improvements will further allow addressing the implications of the changing climate and institutional conditions for water use efficiency, an essential constituent for societal and food security of the region. Thus, in the light of recent institutional, administrative, and socio-economic changes in Central Asia,

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Fig. 1. Study site location: colored area inside the globe image represents Central Asia extent and includes all five countries (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan), where area of Turkmenistan and Uzbekistan is colored into darker color. Map demonstrates extent of Turkmenistan and Uzbekistan and location of the studied land surface and land use types: agriculture sites are identified by green color; and non-agriculture sites are identified by red color and include protected areas of nature reserves (NR) and NAG sites (see Table 1 for site description). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

there is a pressing need to develop new landscape-scale monitoring and management tools to inform local populations, land managers, agricultural and natural resource decision-making institutions, and administrative entities in charge of water and land allocation. Furthermore, although the causes and impacts of changing vegetation dynamics in arid ecosystems at regional to global scales have been studied extensively, an assessment of interplay between terrestrial vegetation responses and socio-economic linkage to shifting water and land use patterns in the Central Asian arid ecosystems has received far less emphasis in the literature. Vegetation is an important part of regional and global ecosystem dynamics and is tuned to the seasonal water and energy fluxes of the environment. Vegetation phenology science, the study of periodically recurring biological events in response to climate and environment, provides a valuable means of understanding and measuring the effects of global environmental change on ecological processes for a given ecosystem (Lloyd, 1990; White et al., 2005a). Remotely sensed land surface phenology provides a means to quantify seasonal and interannual changes in vegetation growth patterns and is a key part of research in bioclimatic interactions, climate change, and global change ecology. Numerous studies have shown how climate operating at interannual to decadal time scales (Peters et al., 2003; White et al., 2005b) and land use decisions (de Beurs and Henebry, 2004; Bradley and Mustard, 2008; Kariyeva and van Leeuwen, 2011) affect land surface phenology. Through the development and application of temporal geospatial methods and data, this research addresses the need to assess terrestrial vegetation responses to changing land use practices in the drylands of Central Asia within the context of climatic variation and socio-economic conditions before and after the USSR collapse. Specifically, this research examines the application of remotely sensed time series and vegetation response metrics to characterize and quantify changes in land and water use practices in two Central Asian countries, Turkmenistan and Uzbekistan, which share water from the Amu Darya River. Furthermore, this research will offer a unique means to develop a series of baseline maps for representative phenological metrics and a new approach to identify areas

where changes in phenology can be attributed to shifts in land and water use practices caused by socio-economic change. The main goal of this study is to quantify seasonal and interannual changes in vegetation growth patterns in Turkmenistan and Uzbekistan in light of the institutional changes before and after the USSR collapse. To attain this goal we formulated three objectives. The first objective of this study is to characterize region specific seasonal and interannual phenological trajectories for a range of land cover types in the context of changing land use patterns after 1991. The second objective is to examine local scale phenological responses for non-agricultural and agricultural (irrigated and riparian) land use types prior to (1981–1991) and after (1992–2006) the USSR collapse in areas representative of the range of land-cover and use within the region. The last objective is to perform a regional scale phenological assessment for the entire study area by quantifying significant trends and differences in vegetation phenology and productivity dynamics caused by changes in crop cultivation practices. We pose that differences in vegetation response vary considerably between land cover types in different administrative and institutional regimes due to shifting economic and food security priorities including changes in crop cultivation preferences (e.g., change from cash crop to food crop production) and management of water resources that are no longer part of a common-pool resource. We use measurements that characterize the interannual land surface phenological dynamics to test this hypothesis for non-agricultural and agricultural (irrigated and riparian) land use types. Land surface vegetation phenology (LSP) is used as a proxy for vegetation response to water availability and redistribution due to changes in land use practices and cropping preferences.

2. Data and methods 2.1. Study area 2.1.1. Background and history of land use Until the second half of the 20th century, agricultural practices in Central Asia had limited impact on its ecosystems, allowing people to sustain traditional oasis farming and nomadic grazing for centuries to thousands of years (Lewis, 1962; Micklin, 1988; Libert, 1995; Geist and Lambin, 2004). Key ancient irrigated areas in the region included the lower Amu Darya, Zeravshan, and Ferghana valleys (Lewis, 1962), which, along with the Syr Darya, Murgab, and Tejen valleys, are the main perennial river valleys serving the Aral Sea basin. Because its predominantly arid climate supports rainfed agriculture mainly in the semi-steppe and steppe areas of the northern part of Central Asia (Thenkabail et al., 2009), the agricultural production within the central and southern parts has been largely dependent on irrigation along with the key annual stream flows of the Aral Sea basin. Like many socio-ecological practices involving land–human relationships, Soviet governance sought to instill an ideology of expansion of industrial and agricultural production in the republics to implement the grand development plans of the Communist era (Libert, 1995). While under the Soviet influence (1930s–1991) irrigated agriculture expanded, especially after 1970s, resulting in a 70% increase in irrigated lands for the entire region (Saiko and Zonn, 2000). The Soviet agricultural policies and their continued legacy in newly independent states of Central Asia have made irrigated agriculture a mainstay for their economies and adversely affected long-term land cover dynamics and the environment in the basin (Micklin, 1988; Elhance, 1997; Saiko and Zonn, 2000). The growing water demand of the increasing population in Central Asia has also contributed to the expansion of agricultural land use, withdrawal of more water for irrigation, and further increases in the

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agricultural labor force, especially in Uzbekistan and Turkmenistan (Stanchin and Lerman, 2007). Expansion of irrigated lands has further led to problems of overgrazing on the decreasing acreages of pasture lands and desiccation of natural riparian corridors (Libert, 1995). Freshwater resources of the basin have declined considerably as a result of water withdrawal from the tributaries, the Syr Darya and Amu Darya, causing a fall in water levels and desiccation (Saiko and Zonn, 2000). Following the disintegration of the USSR in 1991, shared water has become one of the most important and conflict-prone natural resource in the region, turning from a domestic issue into a subject of international dispute (Elhance, 1997; Glantz, 2005). Tightly integrated economies and infrastructure, dependence on shared natural resources, and redrawn national and political boundaries inherited from the Soviet times aggravate tensions among the Central Asian countries. Demarcation of political borders during the Soviet era has resulted in complex political relationships in modern Central Asia since these borders do not fully take into consideration national and cultural legacies of local populations. The territory of the Ferghana Valley, for example, one of the major cottonproducing and most fertile and populated areas of the region, was divided among Kyrgyzstan, Tajikistan, and Uzbekistan between 1924 and 1928 (Megoran, 2004). Although these boundaries disrupted the valley’s natural passage, the borders were not important as long as Soviet rule lasted. The entire region was part of a single economy geared to cotton production and the available water used to irrigate cotton in Uzbekistan came from the dams and water reservoirs located within Kyrgyzstan, while cotton from Kyrgyzstan was delivered through the territory of Tajikistan to be processed in Uzbekistan (Megoran, 2004). 2.1.2. Water resources: the Amu Darya Snow packs and glaciers of Tajikistan and Kyrgyzstan mountains contain large quantities of fresh water that feed numerous rivers that meander through Central Asia’s rugged terrain of mountains, deserts, and steppes. The high mountains of the Tien Shan and Pamir constitute about 70% and 21% of the total fresh water resources of the Central Asian region, respectively (Rahmatulina, 2008), and are the source of two river bodies, which are the main source of irrigation in the Aral Sea basin. The Amu Darya is formed in the Pamir Mountains of Afghanistan and Tajikistan and flows into the southern part of the Aral Sea, while the Syr Darya originates in the Tien Shan Mountains of Kyrgyzstan and flows into the northern part of the Aral Sea (Waltham and Sholji, 2001). The Amu Darya is a transboundary river that forms the border between Turkmenistan and Uzbekistan and is the main irrigation source for Turkmenistan (Hanmamedov and Rejepov, 2007). The annual water flow of the Amu Darya averages approximately 54–68 billion cubic meters (Hanmamedov and Rejepov, 2007). In January of 1996, Turkmenistan and Uzbekistan signed the “Agreement between Turkmenistan and Uzbekistan on Water Resources Collaboration” that regulates the amount of water withdrawn from the Amu Darya to 22 billion cubic meters for each country (Hanmamedov and Rejepov, 2007). The Karakum canal in Turkmenistan, one of the longest canals in the world (over 1300 km), has opened vast tracks of land to agriculture, using about one quarter of the water from the Amu Darya (Carlisle, 1997; Glantz, 2005). Construction of the open canal has caused loss of almost half of the water volume en route through evaporation and infiltration and intense soil salinization through a rise of ground water and ineffective leaching of soils (O’Hara, 1997; Saiko and Zonn, 2000; Waltham and Sholji, 2001). Regulated stream flow and withdrawal of Amu Darya water for irrigation have led to changes in the hydrological regime of the river, desiccation and salinization of the delta, degradation of the

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riparian ecosystems, and further rapid shrinkage of the Aral Sea because only restricted quantities of water reach the sea (O’Hara, 1997; Glantz, 2005; Micklin, 2006). The large areas of exposed sea bed are the source of major dust and salt storms that cause substantial ecological and agricultural damage for hundreds of kilometers inland and make available water much less suitable for drinking and irrigation (Micklin, 1988; Libert, 1995). The poor quality of drinking water causes serious health problems for the population of Karakalpakistan, an autonomous republic of Uzbekistan, and irrigation water must be used to wash out excessive salt (Libert, 1995). While returning the Aral Sea to its initial state is not a realistic option in the near future, there were some restoration efforts undertaken to raise the sea level for the northern part of the sea by creating dikes and dams on the Syr Darya (Pala, 2006). With the rehabilitation work, the Syr Darya capacity has been steadily rising in recent years (Pala, 2006), which resulted in increased water release that led to sea level rise in the northern part of the Aral Sea. 2.1.3. Area extent and climate The areas of the Amu Darya River and Karakum canal zones were examined to assess local and regional scale changes in terrestrial phenology and productivity caused by altered patterns of water resources allocation and land use practices. A range of representative agricultural and non-agricultural study sites throughout the area of interest were chosen to provide insight on local scale impacts of land and water management practices. The territories of Turkmenistan and Uzbekistan were used to assess these phenomena at regional scales because the primary sources of water in the region are located within these two countries and are now vital for their individual economies (Fig. 1). Turkmenistan and Uzbekistan are about the same size and have relatively similar physiographic conditions, ranging from flat desert terrain that comprises about 80% of their land area, to mountainous landscapes. Territories of both countries have a semi-arid climate with limited precipitation (Small et al., 1999; Barlow et al., 2002; Tippett et al., 2003). Maximum precipitation takes place during late winter and early spring due to increased water vapor flux caused by orographic features that capture the winter precipitation from eastward propagating mid-latitude cyclones from the North Atlantic region and increased cyclonic storm frequency from the Mediterranean/Black Sea region (Martyn, 1992; Small et al., 1999). The patterns of precipitation and drought conditions in Central Asia have been directly linked to ENSO phases (Barlow et al., 2002). Warm ENSO phases result in an intensified precipitation signal that has a north-south direction, while cold ENSO phases result in drought conditions in the region (Barlow et al., 2002; Syed et al., 2006). The arid interfluve lowlands of the Amu Darya and Syr Darya are already experiencing the effects of climate change with increased drought frequency and glacier recession (IPCC, 2007). The warming climate (Podrezov et al., 2001; Alamanov et al., 2006; IPCC, 2007) has increased melting of glaciers and snow packs and will likely cause temporary increases in water runoff over the next couple of decades. This could promote further expansion of agricultural land use that is likely unsustainable in the long-term. 2.2. Satellite, climate, and crop production time series data To evaluate vegetation response and land use change, long-term (1981–2006) time-series data of vegetation greenness were used to assess vegetation response trajectories and to derive annual phenological metrics (pheno-metrics) for representative agricultural and non-agricultural sites in Uzbekistan and Turkmenistan. These datasets represent twice-a-month composited images of the Normalized Difference Vegetation Index (NDVI; at 8 km spatial resolution). The images were acquired from the Global Inventory Modeling and Mapping Studies (GIMMS) data set derived from

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Table 1 Land use classes examined in the study. #

Name

Location and water source

Before-after 1991

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

NR 1 NR 2 NR 3 NR 4 NR 5 NR 6 NAG 1 NAG 2 NAG 3 NAG 4 AG 1 AG 2 AG 3 AG 4 AG 5 AG 6 AG 7 AG 8 AG 9 AG 10

Uzbekistan: Baday-Tugay NR, the Amu Darya Uzbekistan: Kyzylkum NR, the Amu Darya Turkmenistan: Amu Darya NR, the Amu Darya Uzbekistan: Surkhan NR, the Amu Darya Turkmenistan: Koytendag NR, the Amu Darya Turkmenistan: Syunt-Hasardag NR, the Amu Darya Uzbekistan: site in the Amu Darya zone Uzbekistan: site in the Zeravshan river zone Turkmenistan: site in the Murgab river delta Turkmenistan: site in the Tejen river delta Uzbekistan: Baday-Tugay NR, the Amu Darya Uzbekistan: Kyzylkum NR, the Amu Darya Turkmenistan: Amu Darya NR, the Amu Darya Uzbekistan: Surkhan NR, the Amu Darya Turkmenistan: Koytendag NR, the Amu Darya Turkmenistan: Syunt-Hasardag NR, the Amu Darya Uzbekistan: the Amu Darya delta Turkmenistan: the Amu Darya and Karakum canal Turkmenistan: the Karakum canal zone Turkmenistan: the Karakum canal zone

NR-NR NR-NR NR-NR NR-NR NR-NR NR-NR NAG-NAG NAG-NAG NAG-NAG NAG-NAG AG-AG NAG-AG AG-AG AG-AG AG-AG AG-AG AG-NAG NAG-AG AG-AG NAG-AG

NOAA (National Oceanic and Atmospheric Administration) satellite platforms (7, 9, 11, 14, 16 and 17) with the Advanced Very High Resolution Radiometer (AVHRR) sensors (Tucker et al., 2005). Sensor derived vegetation indices, a measure of vegetation greenness and productivity in landscape dynamics studies are commonly used to quantify phenological response of vegetation (Justice et al., 1985; Reed et al., 1994; White and Nemani, 2006; Zhang et al., 2006). The ability of NDVI to detect and quantify global spatiotemporal vegetation characteristics (Anyamba and Eastman, 1996; Myneni et al., 1998; Tucker et al., 2001, 2005) by exploiting the spectral contrast between red and near-infrared reflectance values to detect the presence of vegetation greenness (Tucker, 1979) has been well documented. The advantage of this dataset (24 gridded NDVI images per year) is its quarter of a century temporal extent that allows detection and quantification of the trends and anomalies in vegetation variability over relatively long study periods (Myneni et al., 1998; Tucker et al., 2001, 2005). These multi-sensor GIMMS NDVI data were ready to be used and have been preprocessed at the University of Maryland Global Land Cover Facility: radiometrically calibrated and composited to optimize data continuity and minimize atmospheric effects (Tucker et al., 2005). Records from the United States Department of Agriculture (USDA) Foreign Agriculture Service (FAS) crop production data (1987–2008) were used to examine another line of evidence for assessing trends in overall vegetation productivity and crop preferences between the Soviet and post-Soviet eras for agricultural and non-agricultural land cover types in the study area. The USDA-FAS (2008) datasets include records on crop areas and crop production.

Trends of ENSO phases were analyzed to detect anomalous patterns of precipitation and drought conditions across the study region (Barlow et al., 2002). Time series of the sea surface temperature anomalies in the equatorial Pacific Ocean from the Climate Prediction Center, NOAA/National Weather Service (NOAA/NWS, 2008) were used to define ENSO phases and events for the period 1981–2006. Neutral ENSO phases have temperature anomalies within the range of ±0.4 ◦ C (Trenberth, 1997) and warm and cold phases have temperature anomalies above and below 0.4 ◦ C, respectively. 2.3. Data sampling and analysis methods NDVI data and pheno-trajectories were extracted for irrigated and riparian agriculture sites that are of primary interest to this study. The sites include the Amu Darya delta (Uzbekistan), where the river flows into the Aral Sea; sections of irrigated zones of the Amu Darya in Uzbekistan and Turkmenistan, i.e., Amu Darya entrance zone into Turkmenistan; and sections of the irrigated zones of the Karakum canal (Turkmenistan). The land cover types examined include agricultural sites (AG: ten sites), as well as nonagricultural (NAG: four sites) and reference sites that represent riparian and desert land cover classes (Table 1). Reference sites, hereafter nature reserve (NR: six sites) areas, differ from NAG sites in that the former are within nature reserves and represent areas in which the impact of human activity was presumed to be minimal during the study period (Table 1). Therefore, vegetation response patterns of NR sites are expected to correspond most closely to climate variability. Vegetation change will likely depend on rainfall events in these naturally water-limited areas and/or on moisture

Table 2 Pairs of comparison sites with similar natural biome classification type but different land use type. #

NR-AG pair

NR location

NR biome type

1 2 3 4 5 6

NR NR NR NR NR NR

Baday-Tugay NR area Kyzylkum NR area Amu Darya NR area Surkhan NR area Koytendag NR area Syunt-Hasardag NR area

Herbaceous with sparse tree and shrub cover Sparse herbaceous with shrubs cover Sparse herbaceous with shrubs cover Herbaceous with sparse tree and shrub cover Herbaceous with sparse tree and shrub cover Herbaceous with sparse tree and shrub cover

1-AG 2-AG 3-AG 4-AG 5-AG 6-AG

1 2 3 4 5 6

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Fig. 2. Interannual and seasonal NDVI time series data and derived phenological metrics: (A) an example of a smoothed and curve-fitted NDVI time-series for the agriculture zone in Uzbekistan where the Amu Darya departs Turkmenistan (AG 2), which explains and highlights eleven derived pheno-metrics for one year; (B) an example of the eleven image-based (spatially explicit) pheno-metrics that were derived for the entire study site extent for 1993.

availability due to close proximity of natural water bodies for riparian ecosystems. Six sites were selected within the protected areas of the nature reserves (Table 2). All sites were selected based on expert knowledge and the Global Irrigated Area Map (GIAM) (Thenkabail et al., 2009) for Central Asia. 2.3.1. NDVI-based seasonal and interannual trajectories (1981–2006) There are several stages of phenological assessments, ranging from the extraction of NDVI time series data for particular areas and phenological information retrieval to the actual analysis of these data in the context of the land cover and land use change. The first step for this study was to extract the vegetation greenness time-series data to assess NDVI-based pheno-trajectories for then

20 local study sites (Table 1). These pheno-trajectories were used to evaluate temporal patterns of bioclimatic synchronies and humaninduced impact to land surface dynamics (Table 2). To reduce single pixel noise, NDVI pheno-trajectories were based on average value of two-by-two pixel blocks (16 km × 16 km) for each of the 20 local study sites. Values extracted for these blocks were categorized as local scale observations. Records extracted for the areal extent of Turkmenistan and Uzbekistan were categorized as regional scale observations. 2.3.2. Derivation of phenological metrics TIMESAT time-series analysis software (Jönsson and Eklundh, 2004) was used to develop pheno-metrics from the extracted NDVI time-series for Turkmenistan and Uzbekistan. The method

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applies a Savitzky-Golay smoothing filter and moving polynomial fitting functions to derive the phenological metrics from the NDVI time series data. The Savitzky-Golay filter was tested during preliminary analyses and considered the most consistent at maintaining unique vegetation time-series curves while minimizing atmospheric effects like clouds. Eleven phenological metrics per year were derived from the vegetation index time-series data, four of which were timing metrics that identified dates of the vegetation growth cycle (phenological phases) and consist of start (SOS), peak (POS), end (EOS), and length (LNG) of a growing season (Fig. 2). Another seven metrics were greenness metrics that include NDVI values at the identified dates of pheno-phases and several seasonal metrics: base NDVI value (BS), peak NDVI value (PK), NDVI amplitude (AMP), NDVI value for the rate of green-up (LD: left derivative) and senescence (RD: right derivative), and greenness values integrated over the growing season (small integral that includes the area under the seasonal NDVI curve and above the average base NDVI value (SI); and large integral that includes the area under the seasonal NDVI curve and zero-NDVI value (LI)) (Fig. 2). A more detailed description of these pheno-metrics and their derivation methods can be found in Reed et al. (1994), Jönsson and Eklundh (2002) and van Leeuwen (2008). 2.3.2.1. Local scale phenology assessment. To assess phenological dynamics for identified land cover types on a local scale, eleven phenological metrics for 24 years were derived for each of the local scale study sites (Table 1). Although, the temporal extent of the NDVI data is 26 years (1981–2006), the first and last years were used as buffers to avoid anomalous results related to the application of the time-series algorithms. Six pairs of study sites were used to examine anthropogenic impacts on the dynamics in land surface phenology. Table 2 provides information on land use classes, listing pairs of land surface types that were compared between nature reserves and agricultural sites. Agricultural land cover classes (Table 2) were obtained from the Global Irrigated Area Map (GIAM) irrigated agriculture datasets (Thenkabail et al., 2009) for Central Asia. All six agricultural areas (Table 2) had the same land cover class: irrigated with surface water double crop, rice–wheat–cotton (Thenkabail et al., 2009). A two-sample t-test was used to test for significant differences between the sampled land use types, using responses of the phenological metrics for agricultural and nature reserve sites located within the same biome types. Response of each pheno-metric for AG and NR areas were tested over the 24 years (1982–2005). Another series of two-sample t-tests were performed for different land cover types on the local scale study sites to determine whether the pheno-metrics demonstrated significantly different responses in land surface phenology due to institutional and administrative changes that followed the collapse of the USSR. For these analyses the pheno-metrics were divided into two periods: era of the Soviet influence (mean values of pheno-metrics from 1982 to 1991), and era after the USSR collapse (mean values of pheno-metrics from 1992 to 2005). 2.3.2.2. Regional scale phenology assessment. Image-based phenometrics for Turkmenistan–Uzbekistan were derived from the original NDVI (at 8 km resolution) time series data to perform several spatially explicit analyses. Changes in land surface phenology were characterized for a period before (1981–1991) and a period after (1992–2006) the USSR collapse. This was performed using an image-based pixel-by-pixel change detection technique: the same pixel of the first date image is subtracted from a second date image (Jensen, 1981). Pheno-metrics were averaged for the pre-collapse (1981–1991) and post-collapse (1992–2006) periods. The “before” pheno-metric images were subtracted from

Fig. 3. NDVI time-series based phenological trajectories for non-agriculture (NAG) sites located in Uzbekistan (A) and Turkmenistan (B). The Amu Darya zone is NAG 1 site, the Zeravshan river tributary zone is NAG 2 site, the Murgab river delta zone is NAG 3 site, and the Tejen river delta zone is NAG 4 site. Years from 1989 to 1995 had a prolonged warm ENSO phase and increased NDVI response when compared to other years. Note: NDVI time-series were plotted across time using composite periods (bottom x-axis), which were represented in years (top x-axis) for reference purposes.

the “after” images to create an image that highlighted areas of change that exceeded the user specified change threshold based on a “binary change mask” as discussed in Jensen (2005). Three threshold values were selected for analysis: 5%, 10%, and 15% increase and decrease in the values of all vegetation phenology metrics. Although, the 5% threshold detected more pixels with change, many of the observed differences were observed as isolated pixels. The 15% threshold value demonstrated almost the same change results as those based on a 10% threshold. Therefore, the 10% threshold was used to identify areas with increases and decreases in the vegetation phenology metrics. The implemented change detection analysis offered an instant visual representation of the changes in vegetation responses before and after 1991. However, change detection techniques do not always fully account for changes in biophysical parameters and processes (Lambin, 2000), therefore, there was a need for more comprehensive analyses of the change processes in the given ecosystems. Long-term trends in the interannual phenological dynamics were evaluated for each of the pheno-metrics. Each of the phenological metrics was fitted across time (26 years) using simple linear regression models, where time is the explanatory variable and the pheno-metrics are the response variables. Relationships were tested for significance at ˛ ≤ 0.05.

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Fig. 4. (A) Averaged annual NDVI response over the entire study site extent and time no 3.4 series of the Sea Surface Temperatures (SST) anomalies (1981–2006) from Ni˜ Region dataset spanning 5◦ N–5◦ S and 170–120◦ West used to define ENSO phases. (B) Averaged annual NDVI response in four river zones (AD: Amu Darya, Uzbekistan; ZV: Zeravshan, Uzbekistan; MG: Murgab, Turkmenistan; and TJ: Tejen, Turkmenistan) and time series of SST anomalies. Note: ENSO phase thresholds at ±0.4 ◦ C (Trenberth, 1997).

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Fig. 5. NDVI time-series based phenological trajectories for NR (black colored line) and AG (gray colored line) sites with the same biome type (Table 2) located within the Amu Darya water allocation zones in Uzbekistan (A: Baday Tugay NR area) and Turkmenistan (B: Amu Darya NR area). Years from 1989 to 1995 had a prolonged warm ENSO phase. Note: NDVI time-series were plotted across time using composite periods (bottom x-axis), which were represented in years (top x-axis) for reference purposes.

3. Results and discussion 3.1. Changes in seasonal and interannual pheno-trajectories (1981–2006) Pheno-trajectories derived from 26 years of NDVI time-series data for the local scale analysis revealed dissimilar seasonal and interannual dynamics for different land cover types suggesting that land use patterns play an important role in temporal patterns of bioclimatic synchronies (Figs. 3–6). The seasonal and interannual vegetation greenness trajectories were shown to be similar for all NAG sites near the four rivers with a prolonged increase in NDVI values from 1990 to 1995 (Fig. 3). The drought of the last decade (Durdyev, 2006; NOAA/NWS, 2008) resulted in relatively low NDVI values compared to the fifteen years before 1996 (Fig. 3). Increased precipitation in Central Asia is linked to warm ENSO phases (Syed et al., 2006) and the 1990–1995 ENSO event was the longest warm ENSO phase since 1882 that would be expected to occur only once in 2000 years for stationary climate conditions (Trenberth and Hoar, 1995). There is correspondence between ENSO phases and annual averaged NDVI for both regional scale case studies, the Turkmenistan–Uzbekistan extent (Fig. 4A), and for each of the NAG sites along the four rivers (Fig. 4B) that represent local scale case studies. NDVI values tend to increase and decrease with warm and cold ENSO phases respectively, with the annual NDVI values often slightly lagging behind the ENSO phase (Fig. 4). As it was expected, seasonal and interannual NDVI response for precipitation-driven NR areas are much more variable than the NDVI response

trajectories for irrigated AG areas (Fig. 5). An example for two of the six pairs (Table 2) of examined pheno-trajectories with different land use types is shown in Fig. 5. The NDVI response for protected areas (black colored lines in Fig. 5) demonstrated clear seasonal and interannual variation that is somewhat related to such environmental controls as ENSO phases (Fig. 4) and rainfall distribution. The seasonal and interannual NDVI signals for irrigated AG sites (gray colored lines in Fig. 5) were relatively consistent, suggesting a reliable water supply during the entire study period. Institutional change following the USSR collapse in 1991 has resulted in increased NDVI response for the sites that were converted from mostly NAG into AG (Fig. 6A and B). For the site located in the Amu Darya delta and which used to be an agriculture zone during the Soviet regime (Fig. 6B), there was, however, a noticeable decrease in the NDVI immediately after 1991, followed by a gradual drop-off in NDVI (i.e., decrease in vegetation cover) after 1997. The aforementioned salinization and environmental degradation of the Amu Darya delta (Glantz, 2005; Micklin, 2006) are likely contributors to this effect. 3.2. Phenological trends and response to societal and environmental changes 3.2.1. Local scale phenology assessment Different land and water use patterns result in variability of vegetation response for the sites with the same potential

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Fig. 6. NDVI time-series based phenological trajectories for agriculture (AG) sites located in Turkmenistan (A) and Uzbekistan (B) areas of the Amu Darya water allocation zones. Sites AG 8 and AG 10 (A: Turkmenistan) and AG 7 (B: Uzbekistan) are located along the Amu Darya that goes through Turkmenistan and then enters Uzbekistan before flowing into the Aral Sea at the Amu Darya delta zone (AG 7: B). Images of NDVI time series for AG 8 and AG 10 sites (A) show an increase in NDVI response after the USSR collapse in 1991 and a decrease in NDVI response after 1991 for site AG 7 (B). Note: NDVI time-series were plotted across time using composite periods (bottom x-axis), which were represented in years (top x-axis) for reference purposes.

Fig. 7. Summary of two sample t-test based on significant two-sided p-values (˛ ≤ 0.05, i.e., 95% probability) of pheno-metrics for six pairs of local study sites that were compared to detect variability in phenological metrics due to different land use patterns (see Table 2). Values of each pheno-metric for agricultural (AG) and nature reserve (NR) areas were tested for 24 years (1982–2005). Top image (A) represents t-test results for four timing metrics (start (SOS), peak (POS), end (EOS), and length (LNG) of a growing season) (see Fig. 2). Bottom image (B) displays t-test results for seven greenness metrics (NDVI values at base (BS), peak (PK), amplitude (AMP), green-up (LD), senescence (RD: right derivative), and NDVI value integrated over growing season (SI and LI)) (see Fig. 2). Note: DOY–day of year (phenological metric unit).

biome type (Fig. 7). The majority of the productivity metrics for almost every examined NR-AG pair displayed strong differences for 1982 through 2005: higher greenness values for agricultural sites and lower greenness values for non-agricultural and protected areas (Fig. 7). For timing based pheno-metrics, measured in DOY (day of year) units, only three out of six examined NR-AG pairs demonstrated significant evidence of timing differences between evaluated sites (Fig. 7). The remotely sensed phenology data used in this study cannot fully describe the vegetation phenology of individual plant types, because unlike single-species phenology, land surface phenology often deals with mixtures of vegetation communities and land cover types and reflects spectral properties of the canopy at a coarse scale. However, these AVHRR NDVI-based phenological data provided excellent biophysical and seasonal measures for vegetation growth cycles and change patterns across landscapes, which is important to detect composition alteration of vegetation communities as a response to climate and humaninduced changes in land use. Timing metrics for NAG and AG sites had a mixed response. On the other hand, greenness and productivity metrics demonstrated the greatest evidence of differences between the two institutional periods (Fig. 8). NDVI base (BS), NDVI peak (PK), and large integrated value (LI) metrics are associated with the highest number of sites with strong evidence of differences between the pre- and

post-collapse periods (Fig. 8). NAG sites had less variability in greenness pheno-metrics than agriculture sites (Fig. 8). Furthermore, the NR areas, intended as reference areas for climate variability, show large differences in phenological response between the institutional regimes (Fig. 8). However, drought conditions of the last decade (Durdyev, 2006) followed the wet period of the warm 1990–1995 ENSO event attributed to the observed lower NDVI results in both NR and NAG (Figs. 4 and 5). Turkmenistan and Uzbekistan have increased the extent of irrigated wheat crop area since 1992–1993 (Fig. 9). Turkmenistan reduced cotton crop areas to utilize more area for wheat production (personal communication with Ministry on Nature Protection of Turkmenistan and Fig. 9A). Cotton crop areas in Uzbekistan have not changed considerably, while areas that are used for irrigated wheat crops have increased (Fig. 9A). Times-series of wheat and cotton crops verify that both countries have increased their annual wheat production since 1992–1993 (Fig. 9B): Uzbekistan from about 500 metric tons (MT) in the 1990s to 6000 MT in 2008, and Turkmenistan from about 100 MT in the 1990s to about 1600 MT (USDA-FAS, 2008). However, for cotton production, Turkmenistan has decreased its production while Uzbekistan kept production rates relatively consistent (Fig. 9B). The results of the USDA-FAS (2008) data displayed in Fig. 9 support results obtained from the local-scale phenological analysis that AG sites experienced noticeable differences between the Soviet and post-Soviet institutional control, and that the

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Fig. 8. Summary of two sample t-test based on significant two-sided p-values (˛ ≤ 0.05, i.e., 95% probability) derived from pheno-metrics (see Table 2) for twenty local scale study sites located within the proximity of the Amu Darya and Karakum canal water bodies. Values of each pheno-metric were divided into two periods: the era of the Soviet influence (1982–1991) and the post-Soviet era (1992–2005). Top image (A) represents t-test results for four timing metrics (start (SOS), peak (POS), end (EOS), and length (LNG) of a growing season) (see Fig. 2). Bottom image (B) displays t-test results for seven greenness metrics (NDVI values at base (BS), peak (PK), amplitude (AMP), green-up (LD), senescence (RD: right derivative), and NDVI value integrated over growing season (SI and LI)) (see Fig. 2). Note: DOY–day of year (phenological metric unit).

vegetation phenology responses are different between the NAG, NR, and AG study sites (Figs. 7 and 8). 3.2.2. Regional scale phenology assessment Spatially explicit phenological metrics were examined to assess shifts in landscape scale vegetation response caused by changing land and water management practices before and after 1991. To show the effect of pre- and post-Soviet collapse impacts on phenological metrics, the start of season (SOS) was used as a representative geospatial example and will be further discussed in this section (Fig. 10). The SOS changes between 1983 and 2003 (Fig. 10) highlight the expansion of irrigated crop areas in Turkmenistan in 2003 (white circles). The image also shows that these expanded crop areas are winter wheat which are greening up just before the winter sets in (bottom two white circles), and rice that greens up in July–August (personal communication with Ministry on Nature Protection of Turkmenistan) in the north of Turkmenistan (top white circle). In addition, SOS images for these two years reveal that the irrigated areas of the Turkmenistan–Uzbekistan study region showed more area with cotton (which generally greens up in April–May) in 1983 than in 2003 (black colored circles in Fig. 10). These findings agree with USDA-FAS (2008) information on crop production and harvested areas for wheat and cotton (Fig. 9).

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Fig. 9. USDA-FAS (2008) crop production datasets for Turkmenistan on the left side Y-axis (TN: black colored lines) and Uzbekistan on the right side Y-axis (UN: gray colored lines) from 1987 to 2008: (A) crop area in 1000 ha; (B) crop production in 1000 metric tons.

Two analyses (image differencing change detection and long-term variability trend) were conducted for each of the phenometrics to quantify the detected variability in regional scale vegetation response (phenology) due to altered land and water use patterns following the 1991 change of institutional and governmental systems in the region. Fig. 11 shows an example for both analyzed products derived for the SOS metric. It is striking that the highlighted area, i.e., the area with more than 10% change in either direction (positive and negative), clearly identified boundaries of the Turkmenistan–Uzbekistan study site from the neighboring countries that share the same climate systems (Fig. 11A). This result corroborates the hypothesis that observed differences in land surface phenology across the study region vary considerably between land cover types for different institutional regimes due to shifted economic and food security priorities including changed crop cultivation preferences. Products of both analyses demonstrate that areas of irrigated and riparian agriculture have a later start of the season (black colored circles in both images of Fig. 11). The areas with later SOS (Fig. 11) include irrigation zones of the Murgab and Tejen rivers in Turkmenistan; the junction of the Karakum canal with the Amu Darya; irrigation zones of the Zeravshan River, the former tributary of the Amu Darya; and Ferghana valley, one of the most fertile areas in Central Asia (Megoran, 2004). Furthermore, areas with later SOS patterns (Fig. 11) correspond with the observed 1983 and 2003 SOS signals (Fig. 10): black circles in the 1983 image (Fig. 10A) identified SOS dates for cotton (April–May), while the same circles in the 2003 image (Fig. 10B) identified SOS dates associated with other crops. These results revealed that land use patterns, such as altered crop cultivation practices, result in altered interannual pheno-variability that can be monitored with these time series of remotely sensed data.

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Fig. 10. Examples of imagery representing the dates for the start of the growing season (SOS) derived from the NDVI time series data for 1983 (A: pre-Soviet collapse period) and 2003 (B: post-Soviet collapse period). White circles highlight the newly irrigated and expanded areas in Turkmenistan and black circles identify traditionally cotton crop areas.

Black circles in both images of Fig. 11 show irrigated agriculture areas that have an earlier start of season detected by both image differencing and trend analyses. Orange circles in Fig. 11 show irrigated agriculture areas with earlier start of season detected by both analysis methods and have correspondence with areas that were identified as newly irrigated in the 2003 SOS image in Fig. 10B. These results support the hypothesis that various land cover types demonstrated shifts in landscape-scale vegetation response due to different institutional regimes. Fig. 12 shows a summary of the results for the image-based change analysis between the Soviet and post-Soviet eras of influence (Fig. 12A) as well as the directions of the trends that were derived from regression analysis of the entire temporal extent of pheno-observations (Fig. 12B). The graph values represent percentage of the total number of pixels that had values indicating at least 10% change between the two institutional eras (Fig. 12A) and those that had trend values that were significantly different (Fig. 12B). For example, for the SOS metric, after the USSR collapse

Fig. 11. Examples of regional scale change analysis for start of growing season (SOS) pheno-metric: (A) image differencing change detection with more than 10% in SOS between Soviet and post-Soviet institutional regimes; (B) long-term trends (based on slope values) in interannual dynamics of SOS with 95% probability from 1981 to 2006. Black circles highlight irrigated and riparian agriculture areas that shown to have later start of season detected by both analysis methods. Light blue circles highlight irrigated agriculture areas that shown to have earlier start of season detected by both analysis methods. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

there were about 37% and 17% of total number of pixels that had earlier and later dates of season start, respectively (Fig. 12A). However, only 1.6% and 2.8% of the study area demonstrated having statistically significant earlier and later season start dates throughout the entire study period (Fig. 12B). Both graphs show that productivity metrics have more variability than the timing metrics (Fig. 12). The summarized outcome of the image-based analyses of change detection reveals that timing metrics have a more uniform distribution of the pixels with negative and positive tendencies (Fig. 12A) and productivity metrics have about ten times more pixels with increased greenness values (Fig. 12A). The interannual trends in pheno-metrics are less striking: timing metrics have more pixels with negative (earlier growing season) than positive (later growing season) trends (Fig. 12B). Greenness metrics demonstrate uniform pixel distribution with both negative and positive trends (Fig. 12B). These findings are consistent with the results of the local-scale phenology assessment. Land surface phenological events make it possible to detect responses not only to natural variation in environmental settings (Mott and McComb, 1975; Wallace, 1985; Nemani et al., 2003; Jolly et al., 2005), but also to altered land use and management practices (Justice et al., 1985; de Beurs

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Fig. 12. Summary of results from the regional scale change analyses for each of the eleven pheno-metrics: (A) Positive and negative changes for each pheno metric between Soviet and post-Soviet institutional regimes are expressed as the percentage of the total number of pixels; (B) trends (based on slope values that had 95% probability) in interannual dynamics of pheno-metrics for 1981–2006 are expressed as the percentage of the total number of pixels the study area.

and Henebry, 2004), and can be detected and assessed by means of remotely sensed data records. 4. Conclusions The end of the Soviet era resulted in a number of socio-economic and political alterations in the five now-independent Central Asian countries, including having to transform their centralized and planned economies into market-oriented economies and without having regional cooperation to manage large-scale ecological issues of the Aral Sea basin. One of the most prominent current regional concerns is related to the use of shared water resources, which have become highly politicized due to the continued and expanded irrigated agricultural practices in the Aral Sea basin. The study aimed to introduce land surface phenology as a resource assessment tool to various stakeholders in Turkmenistan and Uzbekistan, which share the water resources of Amu Darya as they are adapting to substantial socio-economic changes. These research findings are expected to be important to farmers, institutional and administrative entities in charge of water and land allocation, and agricultural and natural resource decision makers. The results of this research supported the hypothesis that differences in vegetation response differ substantially between land cover types in different administrative and institutional regimes due to altered economic priorities including changed crop growing and production preferences and lack of regional cooperation and management of water and land resources that are no longer a common-pool resource. Statistical analysis showed significant

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differences between pre- and post-Soviet collapse seasonal NDVIbased pheno-trajectories as well as seasonal and interannual variation in greenness onset and vegetation response. These trajectories have also demonstrated distinct temporal patterns of bioclimatic synchronies for different land use patterns. Changes in satellite-based land surface phenological records were attributed to differences in crop cultivation practices that were prevalent before and after the regime change in 1991 and to expansion of irrigated lands that amplified water withdrawal after the USSR disintegration. This study addressed interactive effects of natural (i.e., ENSO patterns) and human (i.e., altered crop preferences) driven land cover transformations and their implications for water related issues (changing patterns in water use and its distribution) in drylands of the Amu Darya and Karakum canal lowlands. Timely and precise measurements of pheno-dynamics are necessary to promote a deeper understanding of the terrestrial vegetation responses to projected climate change as shifts in phenological phases, i.e., the timing of the growing seasons, may affect carbon, water, and energy fluxes of a given ecosystem. This study demonstrates that satellite based phenological analysis is a powerful resource assessment tool to characterize landscape dynamics in light of recent institutional transitions in Central Asia. Although the extent of global change impacts on natural and human systems depends largely on adaptation capacities, not every societal approach to adaptation is mitigating or focused on sustainability. Therefore, having the tools to gain a greater understanding of the consequences of these strategies for both natural and human systems is important, particularly as they might affect allocation and availability of resources to these systems. Some groups within the Central Asian population have demonstrated adaptation and coping abilities to socio-economic changes through increased water withdrawal from the Amu Darya, expansion of irrigated lands, and altered crop cultivation practices to ensure food security and meet the needs of growing populations. Due to ever-increasing withdrawals of water from the Amu Darya for agricultural needs of some population groups, a further reduction of water reaching its delta and shrinkage of the Aral Sea takes place, exacerbating existing ecosystem and human health and water/food security issues of another local population group (i.e., Karakalpakistan). The ability to detect the aforementioned alteration in land cover and their implications for changes in water use efficiency and distribution patterns is vital in order to recognize ecological and societal needs of Central Asia and to develop appropriate adaptation mechanisms to address changes occurring in the region. Furthermore, temporally and spatially explicit measurements of land-surface phenology change dynamics due to natural and human-induced disturbances are important for understanding and visualizing the ecological and societal impacts of these perturbations in the broader context of sustainable development of Central Asia. This research offers novel means and methods to assess and characterize land cover change under rapidly shifting national to regional scale socio-economic priorities and projected climate change impacts in the drylands of the Aral Sea basin. Future research could focus on examining trends in vegetation response dynamics and phenology for different land cover types to identify areas susceptible to change. Additionally, future research could benefit from finer spatial and temporal resolution data sets and serve to complement the findings of the current research at both local and regional scales. In addition, human interventions combined with regional scale climate patterns (e.g., precipitation, temperature) and interactions with land surface phenology will need to be evaluated further to disentangle their impacts on land and water resource use and to develop sustainable land use strategies under changing climate and socio-economic pressures and regimes.

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Acknowledgments This research was supported by NCAR/Center for Capacity Building (CCB) and NASA MEaSUREs project “Vegetation Phenology and Vegetation Index Products from Multiple Long Term Satellite Data Records” grant and the U.S. Geological Survey “Interaction of Climate Change and Other Environmental Factors on Invasive Plant Infestation in the Arid West” project. Special thanks to Dr. Michael H. Glantz, Director of the CCB (Consortium for Capacity Building) at University of Colorado. Additional thanks to the Global Inventory Mapping and Modeling Systems (GIMMS) Group at NASA Goddard Space Flight Center for provided NDVI data and to Dr. Stuart Marsh and the reviewers for providing feedback on this manuscript.

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