Dryland monitoring in Turkmenistan using remote sensing

Transcription

1 Dryland monitoring in Turkmenistan using remote sensing Lea Orlovsky and Dan Blumberg Shai Kaplan, Shimrit Tirosh, Eldad Eshed, Offir Matsraffi Batyr Mamedov and Elmar Mamedov Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Israel National Institute of Deserts, Flora and Fauna, Turkmenistan

2 Research Area Research Area Research Area Background Objectives Outline Methods Results Analysis Conclusions Total area area of of Turkmenistan ,100 km km 2 2 Karakum Desert (Black Sands) occupies more than 80% 80%

3 Research Area Background Nomadic and semi-nomadic livestock breeding is a significant income for Turkmenistan's economy and traditional occupation of local population 90% of forage comes from the natural pastures Turkmenistan lies within the hydrological basin of the Aral and Caspian Seas Amu Darya is the region s largest river and main source of water for drinking, agriculture, and supply to the Karakum Canal After the fall of the Soviet regime the country experience the difficulties with the maintenance of engineering wells and water supply to distant pastures. Mean annual precipitation in the Kara-Kum desert is below 150 mm Objectives Outline Methods Results Analysis Conclusions

4 Agricultural potential of indigenous water harvesting systems in the Karakum Desert (USAID - C21-031) Research Area Background Monitoring Indigenous Water Harvesting Systems on Takyrs in Turkmenistan by Remote Sensing Objectives Outline Methods Results Analysis Conclusions

5 Takyrs (from Turk - barren land) Takyrs (from Turk - barren land) These are broad and shallow clayey depressions of varying size ranging from 0.5 to tens of km 2 in the Central Karakum Desert to tens and hundreds of square kilometers in western Turkmenistan Research Area Background Objectives Outline Methods Results Analysis Conclusions

6 Takyrs Due to the Takyr s properties (fine texture, infiltration rate and low slope), during rainfall events, runoff can be generated Precipitation Research Area Background Objectives Outline Methods Results Analysis Runoff Takyr Alluvial deposits Sand dune Conclusions Oytak

7 Takyrs as water resources Takyrs as water resources The ability of a Takyr to collect runoff Research Area Background Objectives Outline Methods Results Analysis Conclusions Non-degraded Takyrs Degraded Takyrs Degree of degradation

8 Research Objectives Research Objectives Research Area Background Objectives Outline Methods Results 1. Determine the spectral characteristics of 1. Takyrs and Solonchaks. 2. Determine the primary minerals of Takyr and Solonchak soils using X-Ray X Diffraction. 3. Detect and assess the location and area of Takyrs in south Turkmenistan from the satellite images. 4. Detect changes on the spatial distribution of Takyrs over time. Analysis Conclusions

9 Research Outline Research Outline Layers 11 Images of LandSat 1+2 MSS 1972/5 10 Images of LandSat 7 ETM+ 2002/3 QuickBird Images Research Area Pre processing Atmospheric and Geometric correction Classification Background Objectives Image processing Indices: NDVI, Albedo, Clay Mineral change detection,supervised classification Maps Outline Methods Output Maps Accuracy assessment Results Analysis Conclusions Soil measurements XRD, spectrometer (on soil samples) Field data GPS

10 Spectral Measurements Spectral Measurements FieldSpecFR ( nm) Research Area Background Objectives Outline Methods Results Analysis Conclusions

11 Spectrometric Results Spectrometric Results Weighting Method (Score: 0-1) Soil sample Mineral type Spectral Angle Mapper SAM Spectral Feature Fitting SFF Binary Encoding BE Average Score Research Area Background Takyr Illite Objectives Outline Methods Solonchak Halite Results Analysis Conclusions

12 Spectrometric Results Spectrometric Results Research Area Background Objectives Outline Methods Results Analysis Conclusions

13 X-Ray Diffraction Results X-Ray Diffraction Results Research Area Background Analysis of the Takyr spectra indicates the presence of: Quartz, Calcite, Illite, Albite or Orthoclase, Kaolinite and Halite. Objectives Outline Methods Results Analysis Conclusions Solonchak spectra analysis indicates that the main minerals composing this sample are: Quartz, Halite, Anorthite, Illite and Bassanite.

14 Image Classification Image Classification 1 Research Area Background Objectives Outline Methods 2 Landuse/cover classification Results Analysis Conclusions 3 Recode

15 Classification Results Classification Results Research Area Background Objectives Outline Methods Results Analysis Conclusions Clean takyr

16 Change Detection Change Detection Research Area Background Objectives Outline Methods Results Analysis Conclusions

17 Accuracy assessment Accuracy assessment Research Area Background Objectives Outline Methods Results Analysis Conclusions

18 Accuracy assessment Accuracy assessment Research Area Background Objectives Outline Methods Results Analysis Conclusions

19 Accuracy assessment Research Area Background Objectives Outline Methods Results Analysis Conclusions

20 Accuracy assessment Research Area Background Objectives Outline Methods Results Analysis Conclusions

21 GPS Research Area Background Objectives Outline Methods Results Analysis Conclusions

22 Conclusion The main mineral components of the Takyr soil and of the Solonchak as derived from the spectrometer are Illite and Halite, respectively. The XRD supports our spectrometric results. Research Area Background Objectives Outline An area of ~8000 km 2 that were previously occupied by Takyrs has been degraded mainly due to anthropogenic pressure, leaving an area of ~17,000 km 2 degraded Takyrs that is suitable for water harvesting. of non Methods Results Analysis Conclusions

23 Conclusion The difference of ~5000 km 2 detected in the ETM+ imagery is mainly due to spectral and spatial resolutions between the two sensors relative to the size of the Takyr patches. Research Area Background Objectives Outline According to this research, the available potential area suitable for water harvesting is strongly overestimated by local scientists and authorities. Methods Results Analysis Conclusions

24 Acknowledgements The research is supported by the United States Agency for International Development Thank you!

25 Monitoring Vegetation Dynamics in Mongolia Using Remote Sensing Indices E. Eshed, L.Orlovsky, E. Adar - BGU F. Kogan NOAA C.& E. Dugarjav, S. Tsooj, L. Jargalsaikhan, Sanjit, et al. Institute of Botany, Mongolia מ ו נגול יה

26 Research Aims Develop and implement a remote sensing system for monitoring seasonal vegetation dynamics in four different ecosystems in Mongolia Monitor grazing effects and trends in ecosystems מ ו נגול יה

27 Bulgan Desert Steppe Height, 1442 m`

28 Bayanunjuul Dry Steppe Height 1369 m`

29 Tumensogt Typical Steppe Height 1000 m`

30 Partizan Forest Steppe Height 1320 m`

31 VCI Normalized Indices Based on Historical Data Vegetation Condition Index (VCI) Month July Aug Sep MAX MIN = ( NDVIi NDV Imin) /( NDV Imax NDV Imin) VCI (July 1998)=( )/( )*100 VCI=34 The VCI expresses the relationship between physiological status of vegetation and moisture.

32 Temperature Condition Index (TCI) Month MAX MIN July Aug Sep TCI = ( BT max BTi) /( BT max BT min) 100 TCI (July 1998)=( )/( )*100 TCI=30 The TCI is received through NOAA-AVHRR channel 4, which measures the ground temperature

33 Vegetation Health Index (VHI) VHI = a VCI + (1 a) TCI Combines the two indices into one equation which assumes vegetation productivity is a result of both temperature and precipitation VHI = 0.5 VCI TCI

34 Biomass Anomaly Biomass Kg/hectare Month Ave July Biomass anomaly, % B = anomaly ( B B i mean )*100 Month July

35 Standard Precipitation Index (SPI) A normalized meteorological index developed for identifying and monitoring droughts Month STDEV Ave July 150mm 96mm 120mm 230mm 194mm mm SPI = ( X X mean) /σ i

36 Results

37 Tumensogt Research site Correlation between VH and Biomass anomaly in Tumentsogt and R 2 = 0.69 n=98 Satellite measurement of 48 km square VH Biomass anomaly (%)

38 Bayanunjuul Correlation between VH and Biomass anomaly in Bayanunjuul, VH R 2 = 0.80, n= Biomass anomaly (%)

39 Bulgan 90 Correlation between VH and Biomass anomaly in Bulgan, , inside fence R 2 = 0.84, n= VH Biomass anomaly (%)

40 Bulgan, SPI Correlation between VH and SPI in Bulgan sum R 2 = 0.65, n=61 VH SPI

41 Partizan Comparision between VH and Biomass anomaly in Partizan, VH R 2 = 0.71, n= Biomass anomaly (%)

42 Tov aimag 100 km Comparing seasonal average of Precipitation to seasonal average of VH for the Tov Aimag, Precipitation (mm) VH Precipitation VH Year

43 Conclusions The VH was found to be the best index for describing vegetation dynamics in all four sites and thus can be used for a real time monitoring of vegetation dynamics over Mongolia. All four research sites, Bulgan, Bayanunjuul, Tumesoght and Partizan are influenced by anthropogenic pressure. There is a trend of vegetation degradation in Tumensogt and the Tov aimaq area due to anthropogenic pressure.

44 Estimation of Seasonal Dynamics of Desert Pastures Productivity in Turkmenistan using NOAA/AVHRR Data USAID - CA

45 VCI R 2 = n= Biomass anomaly R 2 = x 3 window n=63 VH Chagyl R 2 = n= Biomass anomaly 80 VH Biomass anomaly

46 VH VH (16km) versus Biomass anomaly 120 R 2 = Biomass anomaly, % Biomass anomaly, % Lekker VH (3 pixels) versus Biomass anomaly R 2 = VH Biomass anomaly,%

47 Conclusions (very preliminary) Vegetation Health Index is a proper tool for monitoring and early detecting droughts in the different lithoedaphic types of the deserts Future work: Test the higher spatial resolution set of GVI Test correlation between the modeled vegetation and VI Take into account the bushes productivity

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49 Non- Degraded Takyr Non- Degraded Takyr

50 Degraded Takyrs Degraded Takyrs