Leah Orlovsky

Aerosol optical properties and large‐scale transport of air masses: Observations at a coastal and a semiarid site in the eastern Mediterranean during summer 1998

Spectral measurements of the aerosol particle scattering coefficient σs and the aerosol optical depth τa were conducted at Ouranoupolis (Greece, 40°23′N, 23°57′E) and at Sde Boker (Israel, 30°51′N, 34°47′E) between June and September 1998. Measurements were related to 5-day three-dimensional back trajectories at 950, 850, and 550 hPa to assess the influence of long-range transport from particular source regions to the aerosol load at the two sites. Our measurements show that the eastern Mediterranean basin is moderately to highly polluted during summer. Daily average σs values at 550 nm were typically in the range of 30–200 Mm−1 at both sites. The range obtained for the summer regional aerosol optical depth τa was 0.03–0.52 at 500 nm. Enhanced aerosol extinction was related to transport of polluted air masses from western and eastern Europe. High-altitude transport of mineral dust from northern Africa was observed at both sites, particularly in Israel.

Interrelationships between aerosol characteristics and light scattering during late winter in an Eastern Mediterranean arid environment

An intensive field campaign involving measurement of various aerosol physical, chemical, and radiative properties was conducted at Sde Boker (also written as Sede Boqer) in the Negev Desert of Israel, from 18 February to 15 March 1997. Nephelometer measurements gave average background scattering coefficient values of about 25 Mm−1 at 550 nm wavelength, but strong dust events caused the value of this parameter to rise up to about 800 Mm−1. Backscattering fractions did not depend on aerosol loading and generally fell in the range of 0.1 to 0.25, comparable to values reported for marine and Arctic environments. Chemical analysis of the aerosol revealed that in the coarse size range (2–10 μm equivalent aerodynamic diameter (EAD)), calcium (Ca) was by far the most abundant element followed by silicon (Si), both of which are indicators for mineral dust. In the fine size fraction (<2 μm EAD), sulfur (S) generally was the dominant element, except during high dust episodes when Ca and Si were again the most abundant. Furthermore, fine black carbon (BC) correlates with S, suggesting that they may have originated from the same sources or source regions. An indication of the short-term effect of aerosol loading on radiative forcing was provided by measurements of global and diffuse solar radiation, which showed that during high-turbidity periods (strong dust events), almost all of the solar radiation reaching the area is scattered or absorbed.

Temporal dynamics and productivity of biogenic soil crusts in the central Karakum desert, Turkmenistan

Biogenic soil crusts (BSCs), consisting of different combinations of mosses, lichens, liverworts, fungi and cyanobacteria, are a widespread phenomenon in arid and semi-arid environments. BSCs are found throughout those areas unaffected by grazing and other anthropogenic activities. Current economic conditions in the newly Independent Central Asian States favor the development of BSCs, which may significantly influence plant communities. The data from 40-years of field observations in a protected area in the Karakum Desert, Turkmenistan, together with a sequence of aerial photographs of the same area may contribute to the study of the establishment of BSCs and understanding their role in vegetation communities. The fenced-off Karrykul area can serve as a model for the current processes in the vast desert areas in Central Asia, which are underused due to the financial constraints. It is hypothesized that after some 15 years, the role of BSCs changes from a positive (rehabilitating) effect on the first stages of protection, to a negative effect on the structure and biomass of vegetation communities. Therefore, undergrazing, as well as overgrazing, should be considered as a desertification factor.

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.

Monitoring Droughts and Pastures Productivity in Mongolia Using NOAA-AVHRR Data

The significant part of Mongolian economy is pastoral agriculture, which is a traditional scope of activity and main source of income for the rural population. Study of the natural vegetation dynamics is of essential interest both for decision-makers and herdsmen. During the last decades, Mongolia has suffered from prolonged droughts in combination with extensive grazing in many areas. This situation requires frequent monitoring environmental conditions and the state of pastures. This is an important and challenging security task for Mongolia since weather station network is limited for effective special monitoring and providing services and advises to decision-makers and herdsmen. During 1985–2004, the NOAA-AVHRR Global Vegetation Index (GVI) data set and its Vegetation Health (VH) products have been studied and used for analysis of pastoral changes in Mongolia. This paper discusses application of VH for early drought detection (one of the leading environmental disasters), monitoring drought impacts on pasture conditions and estimation of biomass production.

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.

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.