Nikolai Orlovsky

Effect of salinity on seed germination, growth and ion content in dimorphic seeds of Salicornia europaea L. (Chenopodiaceae)

The halophyte Salicornia europaea L. is a widely distributed salt-tolerant plant species that produces numerous dimorphic seeds. We studied germination and recovery in dimorphic seeds of Central Asian S. europaea under various salinity conditions. We also tested the effects of various salts on Na+ and K+ accumulation during plant development from germination to anthesis under greenhouse conditions. We found good germination (close to control) of large seeds under NaCl between 0.5 and 2%, Na2SO4 and 2NaCl + KCl + CaCl between 0.5 and 3%, and 2Na2SO4 + K2SO4 + MgSO4 between 0.5 and 5%. For the small seeds, we found stimulating effects of chloride salts (both pure and mixed) under 0.5–1% concentrations, and sulfate salts under 0.5–3%. Both types of seeds showed high germination recovery potential. Salt tolerance limits of the two seed types during germination and at the later stages of development were very similar (4–5%). During plant growth the optimal concentrations of mixed chloride and sulfate salts ranged from 0.5 to 2%. The mechanisms of salt tolerance in the two seed types of S. europaea appear to differ, but complement each other, improving overall adaptation of this species to high salinity.

Monitoring land cover dynamics in the Aral Sea region by remote sensing

The Aral Sea ecological crisis resulted from the USSR government decision in 1960s to deploy agricultural project for cotton production in Central Asia. Consequently water flow in the Aral Sea decreased drastically due to the regulation of Amydarya and Syrdarya Rivers for irrigation purposes from 55-60 km3 in 1950s to 43 km3 in 1970s, 4 km3 in 1980s and 9-10 km3 in 2000s. Expert land cover classification approach gives the opportunity to use the unlimited variable for classification purposes. The band algebra (band5/band4 and Band4/Band3) and remote sensing indices (Normalized differential Salinity Index (NDSI), Salt Pan Index (SPI), Salt Index (SI), Normalized difference Vegetation Index (NDVI), Albedo, Crust Index) utilized for the land cover classification has shown satisfactory result with classification overall accuracy 86.9 % and kappa coefficient 0.85. Developed research algorithm and obtained results can support monitoring system, contingency planning development, and improvement of natural resources rational management.

Severe dust storms in Central Asia

The study of the spatial distribution of the severe and very severe dust storms over the Central Asian area has been carried out. The dust storm event can be considered as severe if it lasts 3–12 h, storms with wind speed 10–14 m/s and meteorological visibility in the range of 500–1000 m. The extremely severe dust storms last more than 12 h, with the wind speed exceeding 15 m/s; the dust storms with meteorological visibility less than 50 m are considered as very severe regardless to duration and wind speed. The data of daily meteorological observations from 144 meteorological stations of Kazakhstan and 29 meteorological stations in Uzbekistan and Turkmenistan for the period 1936–1972 had been analyzed, and number of days with severe and very severe dust storms had been calculated using above criteria. Relation between the number of days with dust events and number of severe and very severe storms was calculated, and map of spatial distribution of severe events in Central Asia was compiled using this relation. The analysis of extremely severe dust storms, which developed under the exits of southern cyclones, was done.

Sarykamysh Lake: Collector of Drainage Water – The Past, the Present, and the Future

Sarykamysh is one of about 2,500 artificial lakes-collectors of drainage water in Central Asia. The Lake is located in a natural depression in the northwestern part of Turkmenistan, it receives irrigation surpluses and soil washing drainage water from Dashoguz and Khoresm oases. The area of the Lake has grown from 12 km2 in 1962 to 3,955 km2 in 2006. In terms of volume the change is from 0.6 km3 to 68.56 km3, respectively. Currently, the national plan is to create a new lake-accumulator in the Karashor depression – the Golden Age Lake. Nowadays, less water is being discharged into the Lake, and in the future its area/level will decrease significantly. With average annual evaporation rates of 1.2–1.4 m/year, the drying process is expected to be rapid. The study attempts to model the possible scenarios in the development of the Lake following a change of inflow. This research deals with the retrospective study of the parameters of the lake in the past 40 years using GIS and remote sensing methods in order to suggest a forecast of these parameters. The forecasted parameters will enable the mitigation of the negative regional impacts of the Lake’s changes. A three-dimensional model of the Sarykamysh depression was built using the 1940s topographic maps. Topex/Poseidon altimeter data, early Corona satellite images, and time-series of the Landsat satellite images were applied on Digital Elevation Model (DEM) together with ground measurements of the parameters of the Lake and meteorological data. The model was calibrated and validated, and the water balance of the Lake was calculated, enabling us to suggest with higher accuracy, an optimal future inflow.

Genetic (RAPD) Diversity Across Species Range: Core vs. Peripheral Populations of Wild Barley in Israel and Turkmenistan

Populations of wild barley, Hordeum spontaneum, were collected in two countries, Israel and Turkmenistan, in environments representing two similar sharp clines of aridity. This allowed us to use the same criteria to define species core and periphery in the two regions. Plants from 21 Israeli and 11 Turkmenian populations were analyzed for 59 putative loci by randomly amplified polymorphic DNA. Extent of variation was similar in populations at species border (periphery) and in populations inhabiting favorable environments away from the border (core). In contrast, the two regions (Israel and Turkmenistan) differed in extent of genetic diversity as estimated by mean number of alleles per locus, the proportion of polymorphic loci, and the percent of expected heterozygosity, with Israel harboring more variation than Turkmenistan. The genetic population structure revealed by RAPDs did not differ between species core and periphery in each region and between the two regions. The pattern of RAPD variation correspo...

Monitoring land use and land cover changes in Turkmenistan using remote sensing

In Turkmenistan the most prominent cause for desertification is inappropriate land use practices. The natural arid pastures have limited carrying capacity and any changes of the fragile balance can lead to the destruction of this valuable resource. One of the most appropriate tools for monitoring these processes is change detection through remote sensing imagery. Accurate monitoring of changes on the Earths surface is important to understand the relationship between man and nature and to provide decision makers with relevant information. The information on vegetation change is the most important of these relationships. Vegetation cover is also a useful indicator of the magnitude of land degradation that is easily assessed by multispectral remote sensing. The reduced vegetation cover causes an increase in albedo, which can also be monitored by remote sensing. The combination of these two parameters can give us a better map of the pasture status and its degradation rate. Landsat TM and ETM+ images were processed to maps of land use/land cover changes in northern Turkmenistan. The data were further processed in GIS and revealed the shrinking and the degradation of the pasture area. From the 1970s a total of ~4000km 2 of pasture were transformed into agricultural land, increasing the grazing pressure in the remaining areas. By applying advanced techniques for image based end-member retrieval and spectral mixture analysis a sub-pixel fraction was obtained for each end-member. The fractions of soil and vegetation emphasize the most degraded/rehabilitated sectors of the study area. Our results indicate the reduction of vegetation in specific areas while most of the desert experiences an increase in the vegetation cover. Our current study focuses on combining the spectral mixture analysis products with other degradation criteria such as change detection using albedo and vegetation indices to produce a more detailed assessment and understanding of the processes leading to these changes.

Classification-based mapping of trees in commercial orchards and natural forests

ABSTRACT Hyperspectral remote sensing (RS) and images of various spatial resolution open new vistas for classification and mapping trees. These approaches would improve plant classification in a complex population of forest trees of diverse species, genera, and families, as well as monitoring commercial orchards. In this work, we used new RS indices for cellulose, lignin, wax, chlorophyll, carotenoid, and anthocyanin for plant species classification in natural forests and commercial orchards. For proof of concept, the indices were applied to the classification and mapping of various horticultural crop orchards, where error due to the spatial mixing of different trees is minimal. The classification accuracy of the maps varied between 65 and 82%. This wide range was a result of the following factors: The RS index used, the season, and the spatial resolution of the hyperspectral images. The classification quality was highest when the full set of RS indices was used. The effect of the wax index on accuracy was significant. Furthermore, seasonality played an important role in the classification; the target species were better resolved in spring than in the summer. The higher spatial resolution of the images does not necessarily yield better classification and mapping results; it appeared to be case-specific and greatly depended on the species/crop and the unique environment.