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Rangelands of the Karakum desert, Turkmenistan

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  • I

    DARCA(INCO-COPERNICUS / RTD Project: ICA2-CT-2000-10015)

    DESERTIFICATION AND REGENERATION: MODELING THE IMPACT OF MARKET REFORM

    ON CENTRAL ASIAN RANGELAND

    FINAL REPORT GROUND-BASED VEGETATION ASSESSMENT

    (Work Package 2)

    RANGELANDS OF THE RAVNINA REGION IN THE KARAKUM DESERT (TURKMENISTAN):

    CURRENT CONDITION AND UTILIZATION

    byG. Gintzburger 1, S. Sadi 2 and V. Soti 3

    1 Coordinator Work Package 2 (DARCA), Range Ecology and Management, Range Survey in Turkmenistan, INRA-CIRAD, Montpellier, France

    ([email protected]) 2 Range Ecology, GIS and Satellite Imagery Processing, Consultant

    ([email protected]) 3 Satellite Imagery Processing and GIS, Range Survey in Turkmenistan, Consultant

    ([email protected])

    In collaboration withHodja Hanchaev, Atajan Cherkezov, and Abdul Jabbar Ustad Juma

    (Institute of Animal Husbandry and Veterinary Medicine, Ashkabat, Turkmenistan), who made valuable contributions to field surveys and logistic support

    in Turkmenistan

    August 2009(2nd Edition)

    Contact: G. Gintzburger11 Lee-Steere DriveMariginiup WA 6065

    [email protected]

  • II

    Copyright 2009 ICARDA (International Center for Agricultural Research in the Dry Areas)

    All rights reserved.

    ICARDA encourages fair use of this material for non-commercial purposes, with proper citation.

    Citation:

    Gintzburger, G., S. Sadi and V. Soti. 2009. Rangelands of the Ravnina Region in the Karakum Desert (Turkmenistan): Current Condition and Utilization. Sustainable Agriculture in Central Asia and the Caucasus Series, No. 2. CGIAR-PFU, Tashkent, Uzbekistan. 102 pp.

    ISSN: 0254-8318

    International Center for Agricultural Research in the Dry Areas (ICARDA)P.O. Box 5466, Aleppo, Syria. Tel: (963-21) 2213433Fax: (963-21) 2213490E-mail: [email protected]: www.icarda.org

    The views expressed are those of the authors, and not necessarily those of ICARDA. Where trade names are used, it does not imply endorsement of, or discrimination against, any product by the Center. Maps have been used to support research data, and are not intended to show political boundaries.

  • III

    Foreword

    In 1995, I was privileged to begin the first ICARDA collaboration with rangeland scientists in Central Asia. Over the years, the ICARDA team I worked with developed a strong partnership with colleagues from the Karakul Sheep Institute in Samarkand, Uzbekistan, resulting in a unique and well-received book, Rangelands of the Arid and Semi-arid Zones of Uzbekistan, bringing the steppe and desert landscapes, flora and fauna, to non-Russian speaking scientists. Concurrently, I initiated work with scientists from the Institute of Animal Husbandry and Veterinary Sciences in Ashgabat, Turkmenistan, and the Pasture and Rangelands Institute in Alma-Ata, Kazakhstan, which developed into an internationally funded European project. While working for CIRAD-INRA, France, I was then fortunate to enrol Dr Slim Sadi and Ms Valrie Soti, both Remote Sensing and GIS specialists. With rangeland colleagues from Ashgabat, Hodja Hanchaev, Atajan Cherkezov and Abdul Jabbar Ustad Juma, we concentrated on the ex-Ravnina farm in the south eastern Karakum desert. Starting with the range vegetation survey, we soon realized that much more could be done with the RS and GIS techniques and software. This triggered a chain of events rarely seen in projects in similar environments. While we trained and worked in the field with our knowledgeable and efficient Turkmen colleagues, we undertook a full survey of the Ravnina range resources during three years of field and laboratory work. In addition, with available data from the ex-Ravnina sovkhoz, we could explore and review past Ravnina range management, and compare it with current utilization using GIS technology. Finally, with the GIS facilities we computed and mapped the spatial and precise distribution of desertification risk on the Ravnina region rangeland using the Rain Use Efficiency factor. To our knowledge, this was a first, that opens up the possibility for quantitative desertification and revegetation studies in arid and semi-arid environments in other countries. This collaboration with colleagues from Central Asia made us aware of their invaluable range experience, and the needs for such a book as this. We sincerely hope that it will help all rangeland colleagues in the ICARDA mandate region to also use the simple techniques we have employed here, and be aware that much more can be done, providing a multidisciplinary approach is promoted.

    Gustave (Gus) Gintzburger Range Ecology and Management (Mediterranean Regions and Central Asia) [email protected]

  • IV

    Preface

    The Karakum Desert occupies some three quarters of Turkmenistan, roughly 350,000 square kilometers, located between the Caspian Sea to the west, the Aral Sea to the north, and the Kyzylkum Desert and Amu Darya river to the east and northeast. Mean annual rainfall is less than 100 mm per year, though areas may also not experience any rain for several consecutive years. Temperatures go from over 50C in the summer to -40C in the winter, and in mountainous areas the mean daily temperature can stay below zero for eight months of the year.

    Even in such an inhospitable environment, semi-nomadic Turkmen tribes manage to make a living, mostly from raising Karakul sheep and camels. However, increases in the number of livestock and changes in flock movements have been causing land degradation, so this timely study was conceived, to accurately measure the impacts on the composition and productivity of rangeland plants.

    Work began in 2001 around Ravnina Farm in south-eastern Turkmenistan, and this book describes in details internationally-agreed rangeland vegetation survey methods, and reports the results from the concurrent survey work and mapping. This has led to the most comprehensive vegetation map of the area ever produced, covering 10,000 square kilometers to a resolution of 30 meters. The authors also trained scientists from Turkmenistan, Uzbekistan and Kazakhstan in the methods used, adding much needed research capacity to local partners. Furthermore, this report provides unambiguous and very valuable data on rangeland degradation and desertification.

    This is an important contribution to rangeland research in Central Asia, as rangelands represent the majority of the land cover in this region and are often highly degraded. Improving the sustainability of rangeland environments is part of the CGIARs research program in Central Asia and the Caucasus (CAC), and this is an important step towards achieving their aims.

    This book is an extremely valuable addition to the new series of titles on Sustainable Agriculture in Central Asia and the Caucasus, launched in 2009 by the Central Asia and the Caucasus Program office in Tashkent. The series now has five publications, all highly relevant to agricultural development in the region. I hope that this present book and the others in this series, will find the wide readership they deserve.

    Mahmoud Solh Chairman, CGIAR Task Force for Central Asia and the Caucasus (CAC) July 2009

  • V

    ACKNOWLEDGEMENTS

    We are grateful to: Mr Attakurban and the people from Ravnina village for their hospitality and help during the field

    mission in Turkmenistan. Dr M. Durikov and Dr B. Mamedov (Desert Institute, Ashkabat) for their fruitful support and

    discussion during our stay in Ashkabat, Professor Ilya I. Alimaev, Mrs Guliya Kildibekova, Dr Zekksambay Sissatov, Dr Volodia Yurchenko,

    and Dr Quanish Kushenov (Kazakhstan Research Institute of Pasture and Fodder, Almaty) and Sayat Temirbekov (Geobotanic Institute, Almaty, Kazakhstan) for their collaboration during the field surveys in Kazakhstan,

    Dr K. Toderich and Dr B. Mardonov and many collaborators from the Desert Ecology Department and Water Resources Research (Uzbeck Academy of Sciences, Samarkand Branch, Uzbekistan) and the Karakul Research Institute (Samarkand) who introduced us to the Middle Asian rangelands in 1995 and granted us their unfailing friendship.

    Dr H. N. Le Hourou and Dr Ph. Daget who kindly helped us throughout this study with many comments, rare bibliography, and numerous and fruitful discussions,

    Mrs Isolde de Zborowski (CIRAD) and Franois Gintzburger for their talented support on technical cartographic issues,

    Dr A. Bgu and M. Despinoy, Maison de la Tldtection, GEOTROP, AMIS/CIRAD, Montpellier, France, for their collaboration on data processing of satellite imagery.

    Dr R. Behnke (Coordinator DARCA), Mrs C. Kerven and especially M.G. Davidson (Administrative support staff/DARCA project MLURI Aberdeen) for facilitating communication within the DARCA project,

    Mr Jrme Gintzburger who built a special super-fast bi-processor computer that we successfully used to process the massive amounts of Ravnina data, images and GIS.

    Dr Christopher Martius (ICARDA, Tashkent) for supporting the publication of this second edition, and Mr Nicholas Pasiecznik and Mr George Chouha (ICARDA, Aleppo) for editing and design.

    Finally, we are very much indebted to the European citizens who financed our activities through the DARCA project (INCO- COPERNICUS/RTD Project: ICA2-CT-2000-10015), and most of all, to all the friends from Turkmenistan, Kazakhstan, and Uzbekistan who offered their precious collaboration, hospitality, and time during our visits in their fascinating countries.

    The results, ideas and conclusions expressed in this report are the sole responsibility of Dr G. Gintzburger (in charge of DARCA Ground-based Vegetation Assessment Work Package 2).

  • VI

    Abstract

    Our objective was to evaluate the vegetation and monitor the degradation or restoration processes on a large area (110 km 90 km) of the Karakum Desert used for range development and livestock rearing. The qualitative and quantitative evaluation and monitoring of the vegetation cover (vegetation cover) of the territory of Ravnina Farm (Niyazov Peasants Association Farm) in the Karakum (southeast Turkmenistan) were tested using a combination of three seasons of field surveys (20012003), satellite imagery (LANDSAT and SPOT) and remote-sensing processing coupled with GIS techniques. Phyto-ecological and biomass maps (20022003) were produced. We used two approaches, first studying the main vegetation types using image classification and field surveys, and second, the detection of bare ground linked to vegetation trends on Ravnina Farm.

    We produced updated vegetation maps (Phase 1) and biomass maps (Phase 2) by testing various vegetation indices (normalized difference vegetation index NDVI, transformed soil adjusted vegetation index TSAVI, and perpendicular vegetation index PVI), and using field measurements of annual and perennial vegetation (annual plant and ephemeroid biomass and line intercept measurements on perennial vegetation). They were carried out during the springs of 2001, 2002, and 2003, in relation to radiometric values of a SPOT image obtained at the end of May 2002 and LANDSAT imagery from May 2003. NDVI was definitely not an appropriate vegetation index (VI) for low (< 2025%) vegetation cover assessment in the arid Ravnina rangelands. The PVI (and TSAVI) was much more discriminating for this type of vegetation cover, and was therefore used for the rest of the study. In spite of constraints of image quality, limited dates available, and the difficulty in discriminating between vegetation and soil in this arid environment, the results were encouraging. High-resolution satellite images for this type of vegetation study were worthwhile, providing the appropriate VI was used.

    The third part is related to stocking rate, range use factor (RUF), and rain use efficiency (RUE) of wells combining GIS and field data. It shows that shepherds know their range conditions in terms of biomass available to avoid overstocking, in spite of possible and occasional high RUFs in some territories. The spatial distribution on the RUE map of Ravnina Farm clearly shows a range degradation gradient north of the Karakum Canal and immediately south (up to 3545 km south of Ravnina village) on the pre-1990 rangelands, used when the pontoon bridge was still in operation. The range condition improved out to the furthest wells (40100 km south of Ravnina village) of Ravnina Farm. These rangelands were exploited only recently and mainly after the collapse of the pontoon bridge on the Karakum Canal in the mid-1990s. This most likely led shepherds to abandon the degraded rangelands closest and immediately south of the canal, and to access the remote rangelands with better vegetation condition; however, these are now only accessible from Ravnina with a 7080 km detour through Zahmet. On this route water is difficult to obtain (wells are 70100 m deep) and terrain is rough (high fixed sand dunes) but covered with dense rangeland vegetation.

    The vegetation type, biomass and RUE maps are now available to local authorities of Ravnina Farm and researchers of the Institute of Animal Husbandry and Veterinary Medicine (Ashkabat, Turkmenistan). From this work, much can be learned on how to conduct rangeland surveys in other dryland areas, making optimal use of remote sensing, GIS and field work. The work thus constitutes an international public good. This study may be exploited as a reference for future work and as an example of what can be done in range survey and monitoring, contributing to range management plans, and desertification control in Middle Asia.

    Keywords: Land-cover, vegetation cover, desert, desertization1, remote sensing, RUE (rain use efficiency), RUF (range use factor), land degradation, vegetation index, NDVI, TSAVI, PVI, vegetation map, aboveground biomass, Turkmenistan, Middle Asia, Central Asia.

    1There is no agreed definition of desertization. The word desertification is something used instead of desertization. Some consider desertification to be man-made desertization desertization encompassing both natural and man-induced causes of extension of deserts. In the context of the project it is proposed to use the following definition: the development outside desert areas and within arid and semi-arid areas, of landscape features of the deserts. (http://www.fao.org/docrep/x5870e/x5870e08.htm - Sept 2008)

  • VII

    Rsum

  • VIII

  • IX

    Table of contents

    INTRODUCTION .................................................................................................................................................XIIIVegetation cover and utilization ........................................................................................................................................ XIIIStudy objectives and overview ........................................................................................................................................... XIV

    1) MATERIAL AND METHODS ..............................................................................................................................11.1) Field vegetation assessment ...........................................................................................................................................1

    1.1.1) Perennial plants (vegetation cover of perennials) ..............................................................................................11.1.1.1) Line Intercept Method (LIM) ............................................................................................................................11.1.1.2) Quadrat method (QM) ...................................................................................................................................3

    1.1.2) Biomass of annual plants and ephemeroids ........................................................................................................51.1.3) Selection of vegetation monitoring sites .............................................................................................................5

    1.1.3.1) Sites in Kazakhstan ...........................................................................................................................................81.1.3.2) Site in Turkmenistan (Figs 6a and b) ................................................................................................................8

    1.2) Satellite imagery, data, and software tools available ..................................................................................................81.3) Vegetation indices ..........................................................................................................................................................10

    1.3.1) NDVI (Normalized Difference Vegetation Index) ..............................................................................................111.3.2) PVI (Perpendicular Vegetation Index) ................................................................................................................131.3.3) TSAVI (Transformed Soil Adjusted Vegetation Index) ........................................................................................131.3.4) Results and discussion of the NDVI, PVI, and TSAVI maps ...............................................................................13

    2) RESULTS AND DISCUSSION ...........................................................................................................................172.1) Grazing territories and utilization of wells on Ravnina Farm (2002) ............................................................................17

    2.1.1) Well locations, depths, and operation ...............................................................................................................172.1.2) Water quality ..........................................................................................................................................................172.1.3) Period of wells utilization ......................................................................................................................................212.1.4) Grazing territories ..................................................................................................................................................21

    2.2) Phyto-ecological map of Ravnina Farm and region .................................................................................................212.3) Annual plant and ephemeroid biomass (APEB) ..........................................................................................................24

    2.3.1) APEB 2002 ...............................................................................................................................................................252.3.2) APEB 2003 ...............................................................................................................................................................29

    2.4) Range resources utilization by livestock .......................................................................................................................292.4.1) Sheep equivalent per well ...................................................................................................................................332.4.2) Current stocking rates (20022004) .....................................................................................................................352.4.3) Flock movements (walking or grazing) ..............................................................................................................392.4.4) Grazing days: A case study on Dash-Guyi well .................................................................................................42

    2.5) Range degradation and desertification on Ravnina Farm .......................................................................................442.5.1) Dynamic of bare soils, vegetation cover, and invariant in 19872000 .................................................44

    2.5.1.1) The vegetation approach..........................................................................................................................442.5.1.2) The bare soil approach ..............................................................................................................................45

    2.5.2) Range degradation around wells? Case study for Birinje Yingrimbesh, Dash-Guyi, and Allabaren wells 482.5.3) Range Use Factor (RUF): managing the range resources ...............................................................................502.5.4) Rangeland Rainwater Use Efficiency (RUE) for the season 20022003: The case of the Ravnina wells ......52

    2.5.4.1) RUE on the Ravnina wells used in 2003 .........................................................................................................532.5.4.2) RUE for all Ravnina wells in 2003 ....................................................................................................................54

    2.5.5) Spatial distribution of the RUEs on Ravnina Farm ...............................................................................................55

    3) CONCLUSIONS .............................................................................................................................................59

    BIBLIOGRAPHY .................................................................................................................................................61

    ANNEXES ............................................................................................................................................................65

    ABBREVIATIONS .................................................................................................................................................95

    GLOSSARY ........................................................................................................................................................96

    INDEX .................................................................................................................................................................98

  • X

    List of TablesTable 1: Satellite images available for spring vegetation survey in the Ravnina region ........... 10Table 2: Satellite images available for the vegetation changes of the Ravnina region

    (19872000). .......................................................................................................................... 10Table 3: Number of annual plant and ephemeroid biomass (APEB) sites field harvested

    during springs of 20012003. .............................................................................................. 24Table 4: Conversion factors for various livestock (Turkmenistan) into sheep equivalent (SE)

    of a sheep of 45 kg live body weight. ............................................................................... 33Table 5: Live weight (LW) and sheep equivalent (SE) for various local livestock on Ravnina

    Farm. ...................................................................................................................................... 33Table 6: Total sheep equivalent (SE) per well in 20022004 on Ravnina Farm. ........................... 35Table 7: Means of stocking rates, sheep equivalent (SE) and percentage of private SE

    (% Pvt SE) per well in various years on Ravnina Farm. .................................................... 35Table 8: Mean stocking rate (ha/SE) per well in 20022004 on Ravnina Farm. ........................... 39Table 9: Distance matrix (day, hour, min) between wells while grazing (0.36 km/h) or

    walking without grazing (1.8 km/h) ................................................................................... 41Table 10: Changes in bare soil, revegetation with perennials, and invariant areas (ha) for

    four periods during 19872000, using summer period satellite LANDSAT imagery on Ravnina Farm. ..................................................................................................................... 46

    Table 11: Rain use efficiency (RUE) for the 26 Ravnina wells used in 2003. .................................. 53Table 12: Mean rain use efficiency (RUE) for the 46 wells (both used and unused) during the

    20022003 season on Ravnina Farm. ................................................................................. 54Table 13: Distribution of rain use efficiencies (RUEs) on the Ravnina Farm area in the

    20022003 season. .............................................................................................................. 57

  • XI

    List of figuresFigure 1. Field layout of the line intercept method (LIM) ...............................................................................................2Figure 2. Details of the perennial vegetation intercept measurements made with the LIM .....................................3Figure 3. Quadrate measurement (QM) layout ..............................................................................................................4Figure 4. Layout of Annual Plants and Ephemeroids (APEB) Biomass measurements ................................................6Figure 5. Wells layout developed during the Soviet period and expected location of vegetation monitoring

    biomass sites .........................................................................................................................................................6Figure 6a. Physiographic map of Middle Asia ...................................................................................................................9Figure 6b. Selected region for vegetation assessment in Turkmenistan .........................................................................9Figure 7. A simple and preliminary physiognomic - vegetation map using the NDVI ..................................................12Figure 8. A simple and preliminary physiognomic - vegetation map using the PVI .....................................................14Figure 9. A simple and preliminary physiognomic - vegetation map using the TSAVI .................................................16Figure 10. Well location, depth and operation on Ravnina Farm .................................................................................18Figure 11. Water quality of the wells on Ravnina Farm ...................................................................................................19Figure 12. Period of wells utilization on Ravnina Farm .....................................................................................................20Figure 13. Hierarchical classification after a Correspondence Analysis performed on the 2002 LIM Ravnina

    data to identify the vegetation types .............................................................................................................22Figure 14. Phyto-ecological map of Ravnina Farm based on the Line Intercept Measurement (LIM) of

    perennial vegetation ........................................................................................................................................23Figure 15. Biomass map on SPOT vs LANDSAT images on the Ravnina region (APEB-2002) ......................................26Figure 16a. Relationship between APEB 2002 and the PVI ...............................................................................................27Figure 16b. Relationship between APEB 2002 and the TSAVI ...........................................................................................27Figure 17. Annual Plants and Ephemeroid (APE) biomass map 2002 on SPOT Image ...............................................28Figure 18. Limit of Ravnina Farm using the grazing territories around each well with a 5 km buffer zone on

    the border of the farm ......................................................................................................................................30Figure 19. Sites of Annual Plants and Ephemeroid field measurements in 2003 on Ravnina Farm ...........................31Figure 20. Annual Plants and Ephemeriod Biomass 2003 map for Ravnina farm ........................................................32Figure 21a. Number of sheep equivalents (SE) per well on Ravnina Farm (2002-2004) ................................................34Figure 21b. Stocking rate (ha/sheep equivalent) applied on each well on Ravnina Farm (2002-2004) ....................34Figure 22. Stocking rate map on wells used on Ravnina Farm (2002) ..........................................................................36Figure 23. Stocking rate map on wells used on Ravnina Farm (2003) ..........................................................................37Figure 24. Stocking rate map on wells used on Ravnina Farm (2004) ..........................................................................38Figure 25a. Optimum flock tracks from Dash-Guyi to other wells using the Ravnina Digital Elevation Model ...........40Figure 25b. Speed correction taking account the terrain configuration (DEM) ............................................................40Figure 26a. Dash-Guyi rangeland zones available to each shepherd and his flock at various distance-time

    from the well shaft and camp .........................................................................................................................42Figure 26b. APE biomass (total biomass / ring) and surface of the Dash-Guyi rangeland sliced in one-hour

    grazing time ring with the flock grazing while moving slowly at 0.36 km/h ................................................43Figure 26c. Abacus for the different grazing management options of Dash-Guyi .......................................................43Figure 27. NDVI time trend (April 1998 - February 2002) on three test-zones of the Ravnina Farm (annual,

    perennials, irrigated) .........................................................................................................................................45Figure 28a. Changes in bare soil, revegetated areas and invariant between may 1987 and May

    1990 using the NDVI from the Vegetation Satellite ....................................................................................47Figure 28b. Changes on Chopan-Guyi well between May 1987 and May 1990 ..........................................................47Figure 28c. Changes on Moskva well between May 1987 and May 1990 .....................................................................47Figure 29. Ravnina range degradation and revegetation (1987 - 2000): A bare soil approach using

    LANDSAT satellite imagery ................................................................................................................................49Figure 30. Range APEB at different distance (km) from the shaft of Allabaren, Dash Guyi and Birinji

    Yingrimbesh wells ...............................................................................................................................................51Figure 31. Rain Use Efficiency (RUE) as a function of distance from Ravnina village to all the wells ........................55Figure 32. Rain Use Efficiency Map for Ravnina Farm. ................................................................ (Folded in back cover)

  • XIII

    INTRODUCTION

    The pastoralists of the world use native pastures and rangeland from different ecological zones in each season to maximize free forage harvesting and minimize feed storage. In Middle and Central Asia, these transhumant or nomadic systems were remodeled during the Soviet period. Sovkhoz1 and Kolkhoz2 were organized; nomadic populations settled and fixed on limited grazing territories with a systematic and tight network of new wells and water points. Forage and feed were provided at high cost during the cold winter and drought periods from remote higher rainfall or irrigated zones.

    Since the fall of the Soviet system and decollectivization of state livestock in 1991 (Annaklycheva 2001; Ellis and Lee 2002; Kerven 2003; Gintzburger et al. 2005), the systematic exploitation of the rangeland collapsed, mostly due to lack of extra support and feed supply to rangeland areas. The pauperization of the local population induced the number of animals to fall drastically and stopped livestock mobility in 19911995. Large rangeland areas were no longer grazed. The restructuring period of 19952000 led to concentrations of livestock close to settlements and wells and increased fuel wood collection, inducing desertification (Kharin 1994) around populated areas, but the regeneration of remote rangeland. After 45 years of reorganization at the village and regional levels, pastoralists and graziers are increasing their animal numbers and attempting to re-colonize abandoned rangelands. They are now reconsidering seasonal livestock movement as the most efficient way of exploiting sparse rangeland resources, and to minimize external feed dependence.

    Vegetation cover and utilization The distinctive features of sandy deserts that differentiate them from other desert types are due to the properties of sand, i.e. high water infiltration rates, mobile substrate, significant condensation ability, and low salinity. Moreover, sand differs from other substrates in having easily available water stored in the soil profile that allows a long period of plant growth. Conversely, a number of negative aspects affect the plant cover on sandy soils, such as sand mobility that limits plant establishment, poor soil structure, and low organic matter and hence soil fertility. Trampling by grazing livestock easily loosens sand, and it is subject to high temperatures during summer when the soil surface may reach 6070C. The psammophytes, the most common plants in the Karakum, are adapted to the particular conditions of sandy soils in a desert environment, and include a variety of plant forms, i.e. annuals, ephemeroids, shrubs, and trees. The landscape physiognomy and vegetation structure is similar to the fixed sand dunes of the Jeffara plain in Libya (Gintzburger 1986) in spite of noticeable differences in climate (Jeffara has no frost, but identical precipitation).

    Many plants from the Middle Asian arid and semi-arid zones have some summer growth, although annual and ephemeral vegetation grows during the end of winter and in spring. Many woody spe-cies (represented by Haloxylon aphyllum and Haloxylon persicum) are specifically summer active, very likely because of their ability to extend their roots deep (1030 m) into soil, and into the high or perched water tables, a common occurrence in the Karakum and adjacent Kyzylkum Deserts. This is a definite environmental advantage that few other plants have in other hot Mediterranean semi-arid and arid zones of the world (Gintzburger and Le Hourou 2003).

    The sandy desert rangelands of Middle Asia provide most of the fodder for Karakul sheep and cam-els, with an essential feature of these sandy desert pastures, being their relative yield stability and year round use, but with low productivity (in spring, 0.20.5, rarely 0.60.9 t dry matter/ha). Small ruminants mostly use the rangelands of the southeastern Karakum during the spring vegetation flush for lamb-ing. In autumnwinter, the ranges with dense perennial vegetation cover (vegetation cover) offer welcome protection to small ruminants against cold winter storms. The range productivity is unreliable and varies between 0.10.9 t dry matter/ha/yr, depending on annual rainfall, vegetation cover, and diversity of palatable plants (Momotov 1973; Nechaeva 1985; Ellis and Lee 2002; Gintzburger et al. 2003).

    1 Sovkhoz = [, ], Soviet farm or state-run farm2 Kolkhoz = [, ], Collective Farm Cooperative Farm

  • XIV

    G. G

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    There are about 60 basic plant associations in the sandy desert, with ligneous associations making up 30% of the Karakum flora (Babaev 1994; Fet and Atamuradov 1994; Rustamov 1994; Gintzburger et al. 2003).

    Micro-nanophanerophytes: Haloxylon persicum, Haloxylon aphyllum, Ammodendron conollyi, Salsola richteri, and Salsola paletzkiana over sand dunes.

    Nano-phanerophytes: Salsola arbuscula, Salsola arbusculiformis, Calligonum spp., Ephedra strobilacea, Astragalus unifoliolatus, Astragalus paucijugus, and Astragalus villosissimus on fixed dunes.

    Chamaephytes: Convolvulus divaricatus, Convolvulus hamadae (characteristic of inter-dune depressions with compacted sands), Mausolea eriocarpa, Artemisia diffusa, Halothamnus subaphylla, Acantophyllum borsczowii, Acantophyllum elatius, Acantophyllum turanica, and Salsola orientalis.

    Ephemeroids and grasses (about 10%): Carex physodes and Poa bulbosa which frequently make a continuous carpet over sand dunes with a 24 cm mat of superficial densely interwoven roots dominating the vegetation cover. Also remarkable is Ferula assa-foetida, a large plant of the Apiaceae.

    Perennial grasses (20%) dominated by Aristida pennata and Aristida karelinii, growing over mobile sand; they are good sand-fixing pioneer plants.

    Annual summer plants make up about 40% of sandy desert flora: Agriophyllum latifolium, Agriophyllum minus, Corispermum lehmannianum, Salsola paulsenii, Salsola praecox, Salsola aperta, Climacoptera lanata, Climacoptera crassa, and Climacoptera turkomanica.

    Ephemerals including Eremopyrum distans, Eremopyrum orientale, Eremopyrum bonaepartis, Senecio subdentalis, Malcolmia grandiflora, Malcolmia africana, Isatis violascens, Isatis minima, Tetracme recurvata, Stretoloma desertorum, and Matricaria lamellata.

    Study objectives and overviewThe objective of this study was to monitor and detect quantifiable impacts on rangeland composition and productivity on the most important rangeland types, following recent changes in livestock numbers and flock movements. Ground-based vegetation assessment techniques was used in relation to satellite imagery to estimate vegetation types, vegetation cover, trends, and forage availability in areas potentially exposed to degradation (Gintzburger and Saidi 2008). The use of a Geographic Information System (GIS) linked to satellite image analysis and processing was essential to this study.

    The study involved: Establishing long-term and reliable field methods for measuring perennial vegetation cover,

    biomass of annual plants and ephemeroids, range condition, and trends, Developing geo-referenced databases and GIS to support the integration of ground and remote-

    sensed vegetation assessments, Producing current vegetation and biomass maps of the rangelands of the Niyazov Peasant

    Association Farm (hereon called Ravnina Farm) south of Ravnina village, Comparing contemporary and historical data to determine grazing-land use, vegetation cover,

    and potential degradationdesertification trends, Training colleagues from Turkmenistan and Kazakhstan to use these approaches and methods

    after the conclusion of this DARCA project.

  • 1

    RANGELANDS OF THE RAVNINA REGION IN THE KARAKUM DESERT (TURKMENISTAN)RANGELANDS OF THE RAVNINA REGION IN THE KARAKUM DESERT (TURKMENISTAN)

    1) MATERIAL AND METHODS

    1.1) Field vegetation assessment Field vegetation assessments (Gintzburger and Saidi 2008) were based on vegetation surveys (ecol-ogy, floristic, percentage of perennial vegetation cover using intercept data, aboveground biomass measurements, etc.). These were collected for the main vegetation community type. These homo-geneous vegetation types were identified by satellite image processing using unsupervised analysis, completed and refined by feedback from field vegetation and ecological surveys. The vegetation groups and types were identified using a Correspondence Analysis and a Hierarchical Classification taking into consideration the perennial species line intercept method (LIM) on each site. Vegetation mapping was then processed using a LANDSAT satellite image (3 September 2000, Path 157, Row 34) in relation to vegetation groups/site location and the unsupervised image classification.

    1.1.1) Perennial plants (vegetation cover of perennials)A team of field workers recorded intercept data on perennial plants, bare soil, and rocks at ground level along 50100 m of measuring tape or rope (four replicates/site) using the LIM and simplified CEFE (Centre dEcologie Fonctionnelle et Evolutive, Centre National de la Recherche Scientifique, France) techniques (Canfield 1941; Daget and Poissonet 1971; Gintzburger 1986) and the quadrat method (QM) we developed specifically. The initial purpose of this work was to document and quantify the homogeneity of vegetation and available biomass (annuals and perennials). Conducted over a number of seasons and years at a site accurately identified with Global Positioning System, these techniques document changes in species composition, and the prevalence of bare ground and mo-bile sand, as an indication of degradation or regeneration trends. We refined our work with satellite imagery using a similar field methodology used in Australia (Tongway and Hindley 1995; Caccetta et al. 2000; Karfs et al. 2000) and data processing and technologies that we developed specifically.

    We used two methods: Line Intercept Method (LIM) in Turkmenistan and Kazakhstan and/or Quadrat Method (QM) in Kazakhstan

    1.1.1.1) Line Intercept Method (LIM)The LIM is a modified technique from Canfield (1941). Four permanent intercept lines (each 50100 m long) allow that quantitative measurements of perennial vegetation were established on selected and representative vegetation type or sites. This gives an estimate of the measured intercept along a line of a pre-defined length. We developed this method for vegetation cover where micro-phanero-phytes (small trees) and nano-phanerophytes (tall shrubs) were dominant.

    The four permanent intercept lines radiating north, east, south and west, from a GPS-located central point (Fig. 1) were established and monitored at least once a year, at the end of summer or in au-tumn.

    Each intercept (Fig. 2) consisted of a 50100 m long transect delineated using a simple rope. The in-tercepts of the projections of each perennial plant (species 1, species 2, species 3, species X) along the transect were measured and recorded.

    This field operation usually took about an hour per site for three operators working together; one mea-suring along the rope, one recording, and one moving and placing the rope.

    LIM calculationThe Percentage Perennial Vegetation Intercept (%PVI) for each species (%PVI of species 1, %PVI of species 2, %PVI of species 3, .., %PVI of species X ) was then calculated for each transect and site according to the following:

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    %PVI of species 1 = [(1.1 + 1.2 + 1.3 + 1.4 + + 1.a) / (Length A B)] 100 = %PVI 1 + %PVI of species 2 = [(2.1 + + 2.b) / (Length A B)] 100 = %PVI 2 + %PVI of species 3 = [(3.1 + 3.2 + 3.3 + + 3.c) / (Length A B)] 100 = %PVI 3 + + + %PVI of species X = [(X.1 + X.2 + X.3 + + X.m) / (Length A B)] 100 = %PVI m*= TOTAL %PVI = %PVI (1, 2, 3,, m) * m = species X

    The perennial species occurrence (a = occurrence of species 1, b = occurrence of species 2, c = oc-currence of species 3, , m = occurrence of species X) is the number of times the perennial species 1, 2, 3 , or m was recorded on each transect line.

    The species frequency is the percentage of occurrence of a specific perennial species (a, b, c, , m) relative to the total number of occurrences (a + b + c + . + m) of all perennial species (1, 2, 3, , X) recorded on each transect. The total of the perennial species frequencies must equal 100%.

    The perennial species frequency was calculated as follows:

    Frequency of plant 1 = [a/(a + b + c + . + m)] 100), Frequency of plant 2 = [b/(a + b + c + . + m)] 100), --------------------------------------------------------------------------------------------------------------------------------------------------------------Frequency of plant X = [m/(a + b + c + . + m)] 100)

    Figure 1. Field layout of the line intercept method (LIM)

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    RANGELANDS OF THE RAVNINA REGION IN THE KARAKUM DESERT (TURKMENISTAN)

    Seasonal measurements of perennial plants were usually carried out at the end of the growing season, usually the end of summer. Perennial biomass measurement is time consuming, difficult, and requires a large field team (Gintzburger 1986). In the present study, the perennial biomass was not measured, as the small ruminants dominant on the Ravnina rangelands essentially consume only the annuals and ephemeroids.

    1.1.1.2) Quadrat method (QM) QM was used for low or small perennial vegetation such as Artemisia spp., Eurotia spp., and Salsola spp. or with chamaephytes (small shrubs) < 50 cm high. QM is a combination of LIM and aboveg-round biomass measurement for small perennial plants (see chapter 1.1.2) when the harvesting of all perennial plants is bulky and cumbersome.

    The measurement was carried out with a rectangular quadrat to minimize vegetation heterogeneity. The quadrat was 2 m wide by up to 2025 m long (Fig. 3) delimited by four pegs linked by a rope and located in a homogeneous vegetation type. The width of the quadrat was sufficiently narrow to eas-ily count all perennial shrubs contained. Individual plants on the edge/limit of the quadrat were also counted as if included in the quadrat.

    QM calculation The specific plant density was estimated by counting the number of perennial plants of the same

    species (e.g.: Plant a = Artemisia sp., plant b = Eurotia sp.) within the quadrat. The specific plant density of plant a (SPDa) was then calculated and reported as number of plant a / unit of area (m or ha). The same procedure was used for all other species (b, c, d, etc.).

    Figure 2. Details of the perennial vegetation intercept measurements made with the LIM

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    Along the length (2 m 25m in our example) of the rope marking the border of quadrat, we de-termined: - LIM of each species which gave %PVI, occurrence, frequency relative to other species, and

    the total %PVI, - the dimensions of each individual dominant shrub intercepting the quadrat (D1 = maximum

    diameter, D2 = minimum diameter, and H = height, all measurements in cm), - all shrubs intercepting the border of the quadrat were harvested at soil level and packed in

    separate paper bags, marked with the dimensions (D1, D2, and H) of the harvested shrub. In the laboratory, the green and woody parts of each individual shrub were separated, dried and precisely weighed individually (mg). Dry matter (DM) collected and sorted were kept in their original bags for further checking and plants analysis if necessary.

    From this data, we obtained the average dimension to compute the biovolume (Thomson et al. 1998) and Woody (W), Green (G) and total biomass (G+W) of the dominant shrub. It must be noted that the average ratio (G / G+W) for each species indicates the condition of the range and the use of species during the growing season. A low ratio may indicate either a senescing population of the shrub considered or its heavy use by livestock. A high ratio would indicate either undergrazing or that the shrub is not palatable. This is usually confirmed by other field observa-tions.

    It is simple to calculate from the above information (note that this example is for plant a but the same procedure was applied to the other perennials in the quadrat):

    For the estimated Specific Cover of plant a = SPDa mean soil projection of plant a (computed from mean [D1 D2]) / total quadrat area (i.e. 50 m),

    The Estimated Plant Biomass of plant a / ha = (mean W biomass of plant a + mean G Biomass of plant a) SPDa / ha.

    This field operation usually took 7590 minutes per site for a team of three operators.

    Figure 3. Quadrate measurement (QM) layout

    Peg 1 Peg 2

    Peg 3Peg 4

    PVI Perennial Vegetation Intercept (Replicate 1)

    PVI Perennial Vegetation Intercept (Replicate 2)

    Gintzburger 2001

    Perennial Species a

    Perennial Species b

    Perennial Species c

    Quadrate / Intercept length = 20 or 25 m

    Qua

    drat

    e w

    idth

    = 2

    m

    Rope on 4 pegs

    Rocks/stones

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    RANGELANDS OF THE RAVNINA REGION IN THE KARAKUM DESERT (TURKMENISTAN)

    1.1.2) Biomass of annual plants and ephemeroidsHuman resources permitting and using a simplified QM, the annual plant and ephemeroid biomass (APEB) was estimated for each vegetation type to determine if APEB and plant density increased or decreased over the study period as a result of climatic variation or grazing. Large quadrats were used to sample woody perennials (see chapter 1.1.1.2), and smaller quadrats for APEB; their size deter-mined by the minimum area method to ensure a representative sample area for each vegetation type (Mueller-Dombois and Ellenberg 1974). Within each sampled and individual quadrat, vegetation structure allowing, annual plants and ephemeroids were counted to determine their relative fre-quency and density, and then cut at ground level, dried, and weighed to determine aboveground biomass.

    APEB was evaluated by aboveground biomass measurement in a 1 m quadrat repeated 10 times and spaced at 10 m intervals (i.e. over a 100 m length) along an identified and GPS-located transect (Fig. 4). Measurements included: Above ground biomass specific contribution (DM g/m) for the dominant species or families (e.g.

    Graminaceae, Cyperaceae, and Asteraceae), Specific plant density (no. of plants per species/m); this was important to evaluate the level of

    range degradation using the density of Poa bulbosa, Carex physodes, Carex pachystylis, Bromus spp., and other annual plants.

    This field operation usually took two operators 6075 minutes per site.

    The precise timing of sampling was determined by plant phenology and the local grazing season. Spring is most crucial for small ruminant flocks as this is the lambing time, thus the priority was on spring. Annual plants were therefore measured at the peak standing biomass, usually at the end of spring (end of April to mid-May) when the annuals began to dry. This time was logistically difficult to forecast, due to no prediction method for the season proceeding or stopping abruptly.

    Standard reporting formats were developed to accurately record observations. Kazakh and Turkmen colleagues were trained in field data measurements, collection, and data entry on spreadsheets.

    The vegetation assessment field methods of annual and perennial plants were presented, qualita-tively (environment and vegetation description) and quantitatively (biomass and permanent LIM) on thematic maps.

    The environment and vegetation description includes: Site location (GPS coordinates), Geomorphology, Soil description (soil surface, type, color, texture, structure, and horizon depth), Vegetation physiognomy (type, dominant species i.e. annual and perennials, dominant plant

    list with scientific and local names, etc.), LIM to evaluate the perennial vegetation cover that stabilizes the landscape against wind and

    water erosion, Biomass measurements of annual plants and ephemeroids that feed flocks in spring and early

    summer.

    1.1.3) Selection of vegetation monitoring sites After thorough preparing of topographical, vegetation, soil, and climate map information, and pro-cessing satellite images (geometric and radiometric corrections) with unsupervised classification, we then explored the study area.

    When we first arrived, we spent a number of days traveling and using the unsupervised satellite im-agery process to check the homogeneous vegetation areas. We avoided the usual and traditional camp sites and wells (the standard guided tour for official visitors) and searched for areas that had little or no grazing, to obtain reference sites with vegetation in good condition. This was not always possible due to rough terrain and logistics.

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    Figure 4. Layout of Annual Plants and Ephemeroids (APEB) Biomass measurements

    Figure 5. Wells layout developed during the Soviet period and expected location of vegetation monitoring biomass sites

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    RANGELANDS OF THE RAVNINA REGION IN THE KARAKUM DESERT (TURKMENISTAN)

    Having secured the necessary authorizations from the local authorities, we traveled cross-country across difficult terrain with a well-equipped and reliable four-wheel drive vehicle to check the veg-etation along transects across the usual tracks and roads. This gave a broad idea of the vegetation (absolute and relative) condition of the area. We used a reliable local guide familiar with the area, willing and having time to go cross-country and camp overnight, who knew who, where, and when the people were grazing, about wood collection for fuel, and local plant names and uses of the veg-etation by desert dwellers. During these trips, we collected plant specimens for a work herbarium and for correct plant identification, confirmed later by a resident professional botanist.

    We used a GPS for all navigation and monitoring site locations; we tracked all exploration using a GPS III plus (GARMIN). All sites and trail/tracks were marked on the base maps from MAPSOURCE WorldMap (GARMIN) for future visits. The guiding principle of vegetation sampling in Ravnina and other similar areasWater is essential to livestock in arid environments and especially to small ruminants. Any small rumi-nant flock needs to be watered nearly every day; they will only walk a maximum of 46 km from a well every day, thus they will graze within 56 km of a given well. Therefore we expected a gradient of vegetation degradation (i.e. a grazing gradient) around these wells, with the most degraded area close to the well, and the range in fair/good condition about 56 km further away. Shepherds also collect the wood of perennial plants around the well and their villages, adding to desertification.

    During the Soviet era, a network of wells was established at 1015 km intervals. This is very clear south of Ravnina in Turkmenistan and south of Malye Kamkalye in Kazakhstan.

    We tried to establish vegetation-monitoring sites (Fig. 5): close to wells (within 300500 m), at the limit of the grazing territory of each well (some 46 km from the well, i.e. midway between

    wells), at the barycenter of adjacent wells and/or in remote areas where shepherds indicated there was

    little or no grazing. This was not always possible as travel to some areas was difficult. Adapting these principles to specific conditions - Positioning of the monitoring sites - Information was obtained from shepherds, hunters, and other local people on the currently

    grazed territory (such as frequency, dates, intensity, grazing tracks, and fuel wood collecting sites). We avoided tracks and main migration routes that were usually heavily degraded.

    - Operational timing of LIM, QM, and annual biomass measurement Near wells we mostly monitored the annual plants and ephemeroids (specific biomass, plant-

    density, and floristic composition) as perennials had been eradicated by overgrazing and fuel wood collection.

    Far from wells, we monitored as a priority the perennial plants using the LIM and QM, and mea-sured annuals as necessary, when their contribution was important according to shepherds. The floristic composition of the annuals and ephemeroids was important, as biomass of annuals and ephemeroids could be similar to that near wells but with a different species composition.

    - Soil conditions When the bare soil surface (wind or water erosion), loose sand (dunes), or rocks were impor-tant on a site, their line transect-intercepts were recorded. This gave a percentage intercept of soil condition over the years to indicate increasing or decreasing vegetation and so vegeta-tion colonization or degradation related to grazing pressure or fuel wood collection.

    Within the DARCA project, we were assigned two remote rangeland areas in Kazakhstan and in Turk-menistan.

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    1.1.3.1) Sites in Kazakhstan The area of our study was between Malyye-Kamkaly along the Chu River, west of Balkhash Lake and in the hills and low mountains between Almata and Bishkek.

    The climate is extremely harsh. The mean annual precipitation (MAP) is about 170 mm with range 21327 mm (arid Mediterranean climate with extremely cold winter). As the precipitation is mostly in winterspring, this represents an extreme eastern Mediterranean climate (Gintzburger et al. 2003).

    The temperature varied from 40C in January to +46C in July during the 19561977 climatic period, with a daily mean of 11C for the coldest and +26.5C for the hottest month. The average number of days per year without frost is about 140160.

    A constant wind of an average speed of 2.5-3.5 m/s blows all year, on country that is mostly flat. It usually comes from the east in MarchApril and SeptemberNovember, and from the northwest for the rest of the year.

    After two field expeditions to this area, and in spite of its interesting features, we realized that it was neither workable nor logistically feasible to monitor and carry out the ground-based vegetation as-sessment on such a large, undefined and remote area, even with the combined human resources available in the DARCA Work Package 2.

    The preliminary description of the Kazakhstan sites is presented in Annex 1.

    1.1.3.2) Site in Turkmenistan (Figs 6a and b)The assigned survey sites were in the Ravnina region about 85 km northeast of Mary (N373600 E614959), and in the Gk-Tepe (N380912 E575715) region, 2070 km northwest of Ashkabat.

    The available logistical support for ground-based vegetation assessment only allowed us to concen-trate and complete our work in the Ravnina region. This was a reasonable choice as this region had a large area once managed as a Sovkhoz and therefore had livestock management and production records potentially available.

    The whole area is sandy, mostly with high (1030 m) fixed sand dunes covered with medium height Haloxylon aphyllum (23 m) and Astragalus unifoliatus vegetation, and in some places with Aristida karelinii growing on moving sand (Rustamov 1994). All soils were deep sands.

    Signs of past degradation were obvious, as some areas were totally devoid of tall shrubs that have been long collected for fuel. The cover of annual plants such as Bromus tectorum and Bromus dan-thoniae with Peganum harmala indicate an ultimate stage of overgrazing and range degradation close to the wells. There was often shifting and mobile sand dunes covering the tracks, making driving and navigation difficult.

    The monitored sites were chosen as much as possible along transects between wells to highlight the different facets of vegetation degradation. The preliminary description of the Turkmenistan sites is in Annex 2.

    1.2) Satellite imagery, data, and software tools availableFor the vegetation and/or biomass mapping, we used the satellite images (Table 1) and the following information: Ten days Normalized Difference Vegetation Index (NDVI) synthesis (www.vgt.vito.be) with low

    resolution (1 km) from September 1998 to February 2002, GPS position of wells on the Ravnina rangelands, Floristic listing, APEB measurements, and LIM from the three springs of 20012003, Limited climatic data (Orlovsky 1994) of the area (mostly of Uch-Adji and Repetek, courtesy of

    Desert Institute, Askabat), Livestock data and well utilization (our own and some provided by R. Benhke).

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    RANGELANDS OF THE RAVNINA REGION IN THE KARAKUM DESERT (TURKMENISTAN)

    Figure 6b. Selected region for vegetation assessment in Turkmenistan

    Figure 6a. Physiographic map of Middle Asia

    Takl

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    Mountain

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    Table 1: Satellite images available for spring vegetation survey in the Ravnina region

    Date 16 May 1987 11 May 1990 3 September 2000 31 May 2002 7 May 2003

    Satellite LANDSAT 5 TM LANDSAT 5 TM LANDSAT 7 ETM+ SPOT 4Special mission (ISIS SPOT)

    LANDSAT 7 ETM+

    Spatial Resolution 30 m 30 m 30 m 20 m 30 m

    Path/row 157/34 157/34 157/34 KJ 172-27 4/4 157/34

    CommentsFrom USA From USA From USA From France

    Special ISIS CNES/CIRAD order

    From USA

    For the vegetation trends on the whole Ravnina Farm, we used five LANDSAT images (bought on CIRAD-INRA budget; Table 2).

    Table 2: Satellite images available for the vegetation changes of the Ravnina region (19872000).

    Date 23 August 1987 17 June 1989 3 September 1991 24 August 1999 3 September 2000

    Satellite LANDSAT 5 TM LANDSAT 5 TM LANDSAT 7 ETM+ LANDSAT 7 ETM+ LANDSAT 7 ETM+

    Spatial resolution 30 m 30 m 30 m 30 m 30 m

    Path/row 157/34 157/34 157/34 157/34 157/34

    Comments From USA From USA From USA From USA From USA

    For processing the satellite imagery, the software used was ERDAS 8.5 IMAGINE (on UNIX) and Map-Info 6.5 (on PC) at the Maison de la Tldtection de Montpellier (http://www.teledetection.fr/MTD). The maps were then prepared using Adobe Photoshop and Adobe Illustrator.

    1.3) Vegetation indicesBelow are some general considerations regarding satellite data processing and analysis, especially on choosing between NDVI, PVI, and TSAVI (Vonder and Clevers 1998; http://cgi.girs.wageningen-ur.nl/cgi/projects/bcrs/multisensor/report1 chapters 4 and 12 last accessed August 2003).

    To elaborate the vegetation and APEB maps, we used an unsupervised classification (beginning with 60 classes) of different vegetation indices (VIs) processed on the SPOT Image (31 May 2002) and subsequent classes aggregation after field surveys. We finally settled for a simple presentation of the dominant vegetation types with a combination of the SPOT May 2002 image classification and infor-mation from the VIs processed. An index is a quantitative and synthetic variable that characterizes the intensity or the extent of an observable fact and that is too complex to be described and quanti-fied by a limited number of parameters (Caloz and Collet 2001).

    We used three different VIs: the NDVI (Normalized Difference Vegetation Index), mostly used when the vegetation cover was

    high, the PVI (Perpendicular Vegetation Index), and the TSAVI (Transformed Soil Adjusted Vegetation Index).

    The two last VIs were developed for low vegetation cover when the soil signal is high compared to the vegetation signal. The differences were quite clear on the different representations of the veg-etation cover obtained. We obtained very similar vegetation cover representation with TSAVI and PVI, later confirmed by field surveys. There was more discriminating capability of PVI in differentiating between annual and perennial vegetation cover as confirmed by field visits in the southeastern zone of the Ravnina Farm.

    We strongly emphasize that NDVI is not appropriate for appraisal of vegetation cover and photosyn-thetic activities in desert conditions when the vegetation cover is low (< 2030% threshold) and when bare soil areas are important (Kennedy 1989; Vonder and Clevers 1998). NDVI was developed for full

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    RANGELANDS OF THE RAVNINA REGION IN THE KARAKUM DESERT (TURKMENISTAN)

    vegetation cover of crops in favorable environments and not for arid and semi-arid zones, as in the Karakum Desert.

    Several attempts were made to assess vegetation condition in the southeast Karakum where vegeta-tion cover is poor and usually < 2025%. Our objective was to elaborate a simple and reliable meth-odology allowing a smooth differentiation between the poor cover of the different vegetation types and the bare soils, and to relate the satellite imagery information available on the SPOT image with field surveys and data (APEB and LIM).

    Remotely sensed data may be used to map and monitor vegetation. Various research has focused on VIs that exploit the differences in reflection and absorption of radiation by vegetation in different wavebands. The reflectance properties of green photosynthetically active vegetation are widely documented (Tucker and Sellers 1986; Belward 1991). Typically vegetation gives low reflectance in red (R) wavelengths (0.60.7 m) due to absorption of radiation by chlorophyll-a, chlorophyll-b, and carotenoids in the leaves, and high reflectance in the near-infrared (NIR) wavelengths (0.71.1 m) due to the internal structure of leaves and resultant refractivereflective scattering.

    Vegetation detection in arid regions is strongly affected by the background soil where vegetation is sparse and soil reflectance is high. VIs such as PVI or TSAVI may be more appropriate as they all include terms to account for soil.

    In our study, the first step was to determine an appropriate VI to identify, locate, and quantify the vegetation cover and biomass on the SPOT or the LANDSAT images of the Ravnina area where the vegetation is sparse (< 2530% perennial cover). The VIs were computed from a combination of the spectral band (usually R and NIR) available on satellite imagery. The vegetation absorbs R of solar radiation and has high reflectance of NIR. The aim was to quantify these differences and to translate the vegetation changes in terms of quantifiable biophysical variables (Leblond 2004).

    Two VIs were tested: one that takes account of the soil effect and one that does not. The soil effect was considered by calculating the soil line slope using a simple linear regression between the R and NIR of pre-selected pixels for bare soil. The PVI was then computed for each pixel in a formula using the slope and the intercept of the soil line.

    In our preliminary study, we computed the NDVI that is most often used on vegetated areas, then the PVI and the TSAVI; the last two are mostly used when dominant bare soil affects the signal recorded by the satellite sensors, and disrupts the VIs. We then performed non-supervised classification on the results for each computed VI.

    1.3.1) NDVI (Normalized Difference Vegetation Index)The NDVI (Rouse et al. 1974; Leblond 2004), was computed as follows:

    NIR = near infra-red band valueR = red band value

    The typical range of actual NDVI values is about 0.1 for bare soils to 0.9 for dense vegetation.

    NDVI is not directly correlated with biomass. It varies exponentially with green vegetation density and saturates with a thick and dense vegetation cover. Furthermore, it does not discriminate the vegeta-tion well when cover is < 2030%. Even integrating NDVI over the growing season is not a very robust proxy variable for biomass (Diouf and Lambin 2001). However, we attempted to use NDVI extracted for each field site, as we had the spring SPOT image (30 May 2002) at the peak standing biomass when photosynthetic activity was greatest.

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    Figure 7. A simple and preliminary physiognomic - vegetation map using the NDVI

    Source & cartography:G. Gintzburger G & V. Soti - Sept 2002

    0 10 km

    WGS 84Projection UTM, zone 41 N

    Spot4 image, 31 May 2002

    P rog. IS IS - C NE S KJ 172-274/4

    470 000

    490 000

    510 0004 190 000

    4 170 000

    4 150 000

    4 180 000

    4 160 000

    4 140 000

    480 000

    500 000

    460 000

    Bare soils, mobile sand dunes with low vegetation cover

    Crops , Marshes and dense vegetation with strong chlorophyllian activity

    Vegetation with medium chlorophyllian activity / perennials

    Vegetation with low chlorophyllian activity / annuals and ephemeroids

    Karakum Canal

    Carte simplifie de la vgtation partir des valeurs de NDVI S-E Karakoum ( Rgion Ravnina-Turkmnistan)

    N

  • 13

    RANGELANDS OF THE RAVNINA REGION IN THE KARAKUM DESERT (TURKMENISTAN)

    We then processed an unsupervised classification with 60 classes on the NDVI image, with nine itera-tions and a convergence of 0.95, and regrouped the results in four classes (Fig. 7). This map processed with the NDVI indicated that much of the study area was devoid of vegetation. This was clearly not the case in the field, thus other approaches and other VIs needed to be tested.

    1.3.2) PVI (Perpendicular Vegetation Index)To account for the important influence of bare soil reflectance in an arid environment, we used PVI for each field site to minimize the soil effect and to better discriminate the vegetation cover. PVI as-sumes that the perpendicular distance of the pixel from the soil line is linearly related to vegetation cover (Richardson and Wiegand 1977). We therefore had to establish the soil line, a linear relationship between the NIR and R reflectance of bare soil as characterized by slope and intercept parameters (Fox et al. 2004; Leblond 2004). VIs such as PVI and TSAVI use soil line parameters. When a pixel has NIR vs R reflectance values located on the soil line, it characterizes bare soil. The further a pixel is from the soil line, the denser the vegetation is on this pixel.

    Once the soil line parameters (a and b) are calculated, the PVI is computed as follows:

    a = slope of the soil line b = intercept of the soil line on the X-axis

    When PVI > 0, there is vegetation cover on the pixel, When PVI = 0, there is bare soil on the pixel, When PVI < 0, this is mostly water, or very low mineral content or photosynthetic activity.

    We then processed an unsupervised classification with 60 classes on the PVI image, with nine it-erations and a convergence of 0.95, and regrouped the results in four classes (Fig. 8). With this PVI process, we obtained a closer image to the field situation. We then tried to process the image using TSAVI.

    1.3.3) TSAVI (Transformed Soil Adjusted Vegetation Index)The TSAVI, as does the PVI, compensates for the soil effect using the soil line parameters (Baret et al. 1993) and a 0.08 correction constant.

    a = slope of the soil line b = intercept of the soil line on the X-axisAfter computing TSAVI for each field site, we processed an unsupervised classification with 60 classes on the PVI image, with nine iterations and a convergence of 0.95, and regrouped the results in four classes (Fig. 9).

    1.3.4) Results and discussion of the NDVI, PVI, and TSAVI maps The NDVI gave a good and clear discrimination where the perennial vegetation was dense (e.g. southeast corner of Fig. 7). As expected and clearly seen on the NDVI map (Fig. 7), when compared to the PVI (Fig. 8) and TSAVI (Fig. 9) maps, the NDVI did not discriminate low vegetation cover (< 2025%). This is in accordance with comments of Huete et al. (1984) that soil noise can restrict the

  • 14

    G. G

    intz

    burg

    er, S

    . Sa

    di a

    nd V

    . Sot

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    Figure 8. A simple and preliminary physiognomic - vegetation map using the PVI

    0 10 km

    WGS 84Projection UTM, zone 41 N

    Spot4 image, 31 May 2002

    Prog. IS IS - C NE S KJ 172-274/4

    470 000

    490 000

    510 0004 190 000

    4 170 000

    4 150 000

    4 180 000

    4 160 000

    4 140 000

    480 000

    500 000

    460 000

    Carte simplifie de la vgtation partir des valeurs de PVI S-E Karakoum ( Rgion Ravnina - Turkmnistan)

    Source & cartography:G. Gintzburger G & V. Soti - Sept 2002

    Bare soils , mobile sand dunes with low vegetation cover

    Crops , Marshes and dense vegetation with strong chlorophyllian activity

    Vegetation with low chlorophyllian activity / annuals and ephemeroids

    Karakum Canal

    N

    Vegetation with medium chlorophyllian activity / perennials

  • 15

    RANGELANDS OF THE RAVNINA REGION IN THE KARAKUM DESERT (TURKMENISTAN)

    discrimination of green vegetation < 25% cover. Choudbury and Tucker (1987) also found that NDVI was not sensitive at < 20% green leaf ground cover in the Great Victoria and Great Sandy Deserts in Western Australia and the Kalahari. Kennedy (1989) used NDVI as an indicator for monitoring inter- and intra-annual variations in biomass and productivity in arid and semi-arid rangelands of southern Tunisia; she comments that the percentage soil contribution to total recorded reflectance was an important limiting factor beyond which NDVI was less reliable. Diouf and Lambin (2001) stressed the same point in the semi-arid Ferlo of Senegal, i.e. NDVI is not a perfect and robust proxy variable for biomass, because of strong bare soil contamination of the signal, especially when using the coarse spatial resolution (1.1 1.1 km pixel) of the AVHRR data.

    Such problems were clear from our results in the Ravnina area. All the area covered with annual and ephemeroid vegetation during spring 2002 appeared on the PVI and TSAVI maps but was depicted as bare soil by NDVI (center and northeast corner of Fig. 7). However, there was better discrimination when using NDVI between the annualephemeroid vs perennial vegetation, where the vegetation was thicker, as in the Ravnina south and southeast corner.

    The results of the PVI and TSAVI maps were visually quite similar, though PVI better discriminated the water line of the Karakum Canal and the surrounding swampy areas. We therefore used the PVI for each field site for all our results and data processing of the available images, as confirmed by ground truthing and performing APEB and LIM.

  • 16

    G. G

    intz

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    er, S

    . Sa

    di a

    nd V

    . Sot

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    Figure 9. A simple and preliminary physiognomic - vegetation map using the TSAVI

    0 10 km

    WGS 84Projection UTM, zone 41 N

    Spot4 image, 31 May 2002

    Prog. IS IS - C NE S KJ 172-274/4

    470 000

    490 000

    510 0004 190 000

    4 170 000

    4 150 000

    4 180 000

    4 160 000

    4 140 000

    480 000

    500 000

    460 000

    Carte simplifie de la vgtation partir des valeurs de TSAVI S-E Karakoum ( Rgion Ravnina - Turkmnistan)

    Source & cartography:G. Gintzburger G & V. Soti - Sept 2002

    Bare soils , mobile sand dunes with low vegetation cover

    Crops , Marshes and dense vegetation with strong chlorophyllian activity

    Vegetation with low chlorophyllian activity / annuals and ephemeroids

    Karakum Canal

    N

    Vegetation with medium chlorophyllian activity / perennials

  • 17

    RANGELANDS OF THE RAVNINA REGION IN THE KARAKUM DESERT (TURKMENISTAN)RANGELANDS OF THE RAVNINA REGION IN THE KARAKUM DESERT (TURKMENISTAN)

    2) RESULTS AND DISCUSSION

    2.1) Grazing territories and utilization of wells on Ravnina Farm (2002)A GIS of the Ravnina rangeland was developed with the information collected during our field surveys of 20002003 with the Work Package 2 Turkmen team and Behnke and Juma (2003). The information collected pertains to the well locations (GPS position) and depth, water quality, period of wells utili-zation, grazing territories, and current stocking rates for most wells of Ravnina Farm.

    All this information must be double-checked and should not be considered as final. The GIS was established on ARC-View software and presented on the 2003 phyto-ecological map (Fig. 14, sec-tion 2.2). It allows quick and reliable modification as new and double-checked information becomes available.

    We recorded 57 wells or watering points on Ravnina Farm: 53 were proper deep wells, and four were watering points from pipelines. The four watering points (Raziyezd, Tayli 1, Tayli 2, and Voyentchas) were northwest and west of Ravnina. The Cheraze well at the most northern point of the Ravnina rangelands (although not belonging to Ravnina Farm) was reportedly salty and often used by private flocks from Ravnina village.

    Abandoned wells Seventeen wells were abandoned in 19602002 for various reasons. They were Agash Guyi, Altinji Guyi, Barhan, Birinji Guyi, Bolchevik, Borjakli, Tcharchulat (Charycholak), Dolarik, Hodja Guyi, Mojekli, Tahtali-1, Taze Guyi, Uzin Beden, Yedinji Guyi, Yekegandim, Yubileynaya, and Zidanovic (Idanovich). From these wells, eight (Altinji Guyi, Birinji Guyi, Hodja Guyi, Uzin Beden, Yedinji Guyi, Hodja Chay, Ikinji Guyi, and Cheraze) were reportedly salty, and were mostly close to Ravnina village and near the southern bank of the canal and not usable in summer. Taze Guyi was reportedly salty and in bad condition.

    The remainder (Agash Guyi; Tcharchulat (Charycholak); Mojekli; Yekegandim; Yubileynaya; Zidanovic (Idanovich)) reputedly provide sweet water, but were abandoned as they were: good condition but with poor range conditions (Agash Guyi and Mojekli), not used in summer Tcharchulat (Charycholak) possibly due to mosquito infestation, needed repairs (Barhan, Bolchevik, Borjakli, Dolarik, and Tahtali-1), too deep to be economic (Dolarik at 120 m, and Yubileynaya at 110 m deep).

    2.1.1) Well locations, depths, and operation (Fig. 10) The well positions were GPS-located on the well shaft. The water is collected directly from the water table using either a tractor-driven pump or a simple engine and belt pump. The belt pump consists of a single-cylinder diesel engine running a simple and long 10-cm wide solid rubber belt dipped into the water table. As the engine runs, a thin layer of water adhering to the belt is lifted to the soil sur-face at speed. The water sticking to the belt is then simply collected by a rubber strip across the belt near the engine and flows into a water trough and tank. It is simple and efficient and can lift water from 8090 m deep. This old belt system is being slowly replaced by tractor-driven pumps.

    The depth of wells increased from 1520 m northeast near Ravnina to some 110 m to the southeast, at Yubileyenaya well.

    2.1.2) Water quality (Fig. 11)No water analysis was available, and the users of the wells reported the water quality and salinity. Between Ravnina and the Karakum Canal, all wells were shallow and saline. This may be due to the geomorphology of the region with Ravnina at the edge of a large flat (possibly salty but not sur-veyed) area to the northeast, with a large delta draining the Murgab River basin into the desert to the north.

  • 18

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    Figure 10. Well location, depth and operation on Ravnina Farm

    Grazing territory of w

    ells (ha)

    Depth of w

    ells (m)

    20 Km0

    10

    76 - 12048 - 7510 - 47

    0 - 50005001 - 9000

    Ravnina farm

    Darca project

    Haloxylon aphyllum

    & Calligonum

    eriopodum (no site surveyed)

    Haloxylon persicum

    dominant &

    Calligonum setosum

    Haloxylon persicum

    dominant

    Calligonum setosum

    dominant &

    Haloxylon persicum

    Calligonum setosum

    & Convolvulus divaricatus

    Calligonum caput-m

    edusae &

    Astragalus unifoliolatus

    Calligonum setosum

    , Salsola richteri & Sm

    irnowia turkestana

    Salsola richteri, Astragalus unifoliolatus, Convolvulus sp &

    Asparagus sp

    Calligonum eriopodum

    & Ephedra strobilacea

    VEGETATIO

    N TYPES

    Salty flats

    Karakum canal

    Large takyr flats

    Flats and small takyr

    Depression &

    flats

    Irrigated farming

    Free water

    Flodded flats

    MISCELLA

    NEO

    US

    Depth of w

    ells and Grazing territory

    N

    2020

    201016

    32

    48

    40

    40

    475651

    5844

    32

    70

    6848

    58

    8570

    86

    7575

    10490

    110

    85

    120

    10098

    7575

    85

    57

    70

    7575

    594068

    40

    26

    18

    CherazeU

    chadzi

    Tayli-2

    Yedinji Guyi

    Ravnina

    Voyentchas

    Tayli-1

    Ikinji Guyi

    Birinji- Guyi

    Altinji G

    uyi

    Raziyezd-61U

    zin BedenBrokenBridge

    Mojekli

    Egry

    Hodja G

    uyi

    Tahtali-1

    Tahtali-2BirinjeY

    ingrimbesh

    Sereteli

    Bolchevik

    Tcharchulat

    TcharvadarBorjakli

    Dashliseyid

    Tally

    YalpakYashilk

    ChopanG

    uyi

    Udarnik

    BarhanD

    ash Guyi

    Gagarin

    YekegandimPioner

    Zidanovic

    Ikinje Yingrim

    besh

    Sayed Guyi

    Agash G

    uyiH

    odjashai

    Dort

    Guruk

    KirkG

    ulatsh

    Yang Guyi

    Mir

    Allabaren

    TahtaKom

    somol

    Dolarik

    Moskva

    Taze Guyi

    Anasahet

    Yubileynaya

    Dash G

    uruk

    Tulty(A

    k Tully)W

    ells without inform

    ationSettem

    ent

    Ravnina field information collected in collaboration w

    ith H. H

    anchaev, A. Cherkezov, A

    -J Ustad, R. Behnke &

    C. Kerven (2001-2002)

    S

    ource and cartography: G. G

    intzburger, Slim

    Sadi, Valrie S

    oti Sept 2004

  • 19

    RANGELANDS OF THE RAVNINA REGION IN THE KARAKUM DESERT (TURKMENISTAN)

    Figure 11. Water quality of the wells on Ravnina Farm

    Grazing territory of w

    ells (ha)

    20 Km0

    10

    0 - 50005001 - 9000

    Ravnina farm

    Darca project

    N

    Haloxylon aphyllum

    & C

    alligonum eriopodum

    (no site surveyed)H

    aloxylon persicum dom

    inant & C

    alligonum setosum

    Haloxylon persicum

    dominant

    Calligonum

    setosum dom

    inant & H

    aloxylon persicumC

    alligonum setosum

    & C

    onvolvulus divaricatus

    Calligonum

    caput-medusae &

    Astragalus unifoliolatus

    Calligonum

    setosum, S

    alsola richteri & S

    mirnow

    ia turkestana S

    alsola richteri, Astragalus unifoliolatus, C

    onvolvulus sp & A

    sparagus spC

    alligonum eriopodum

    & E

    phedra strobilacea

    VEGETATIO

    N TYPES

    Salty flats

    Karakum canal

    Large takyr flats

    Flats and small takyr

    Depression &

    flats

    Irrigated farming

    Free water

    Flodded flats

    MISCELLA

    NEO

    US

    Water quality

    Ravnina field information collected in collaboration w

    ith H. H

    anchaev, A. Cherkezov, A

    -J Ustad, R. Behnke &

    C. Kerven (2001-2002)

    Grazing territory and w

    ells

    CherazeU

    chadzi

    Tayli-2

    Yedinji Guyi

    Ravnina

    Voyentchas

    Tayli-1

    Ikinji Guyi

    Birinji- Guyi

    Altinji G

    uyi

    Raziyezd-61U

    zin BedenBrokenBridge

    Mojekli

    Egry

    Hodja G

    uyi

    Tahtali-1

    Tahtali-2BirY

    ingrimbesh

    Sereteli

    Bolchevik

    Tcharchulat

    TcharvadarBorjakli

    Dashliseyid

    Tally

    YalpakYashilk

    ChopanG

    uyi

    Udarnik

    BarhanD

    ash Guyi

    Gagarin

    YekegandimPioner

    Zidanovic

    Ikkinje Yingrim

    besh

    Sayed Guyi

    Agash G

    uyiH

    odjashai

    Dort

    Guruk

    KirkG

    ulatsh

    Yang Guyi

    Mir

    Allabaren

    TahtaKom

    somol

    Dolarik

    Moskva

    Taze Guyi

    Anasahet

    Yubileynaya

    Dash G

    uruk

    Tulty(A

    k Tully)

    Settement

    Guest H

    ouse

    Moderatly salty

    Sweet

    Salty

    Free water from

    Karakum canal

    Tap water from

    pipe

    S

    ource and cartography: G. G

    intzburger, Slim

    Sadi, Valrie S

    oti Sept 2004

  • 20

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    Figure 12. Period of wells utilization on Ravnina Farm

    Grazing territory of w

    ells (ha)

    Period of well's use

    0 - 50005001 - 9000

    Ravnina farm

    Darca project

    N

    Haloxylon aphyllum

    & C

    alligonum eriopodum

    (no site surveyed)H

    aloxylon persicum dom

    inant & C

    alligonum setosum

    Haloxylon persicum

    dominant

    Calligonum

    setosum dom

    inant & H

    aloxylon persicumC

    alligonum setosum

    & C

    onvolvulus divaricatus

    Calligonum

    caput-medusae &

    Astragalus unifoliolatus

    Calligonum

    setosum, S

    alsola richteri & S

    mirnow

    ia turkestana S

    alsola richteri, Astragalus unifoliolatus, C

    onvolvulus sp &

    Asparagus sp

    Calligonum

    eriopodum &

    Ephedra strobilacea

    VEGETATIO

    N TYPES

    Salty flats

    Karakum canal

    Large takyr flats

    Flats and small takyr

    Depression &

    flats

    Irrigated farming

    Free water

    Flodded flats

    MISCELLA

    NEO

    US

    Period of well's


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