Assessment of pasture degradation in Turkmenistan using remote sensing

Shai Kaplan; Lea Orlovsky; Dan G. Blumberg; Elmar Mamedov

doi:10.1117/12.689580

20 October 2006 Assessment of pasture degradation in Turkmenistan using remote sensing

Shai Kaplan, Lea Orlovsky, Dan G. Blumberg, Elmar Mamedov

Proceedings Volume 6366, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VI; 636603 (2006) https://doi.org/10.1117/12.689580
Event: SPIE Remote Sensing, 2006, Stockholm, Sweden

Abstract

Nomadic and semi-nomadic livestock breeding is a significant income for Turkmenistan's economy. Thus, natural vegetation is an important resource for the area. The natural desert pastures of Turkmenistan have limited carrying capacity, and any changes of the fragile balance can lead to the destruction of this valuable resource. Since the 1980's, no research has been carried out concerning the ongoing changes of vegetation cover despite dramatic political and economical changes that took place throughout central Asia. The primary results of this research show the potential of remote sensing in general and specifically Spectral Mixture Analysis (SMA) for vegetation mapping. The information on vegetation change is important to quantify man-nature relationship. From this information vegetation cover is a useful indicator of the magnitude of change. Landsat TM and ETM+ images were processed, and maps of land use/land cover changes in northern Turkmenistan were produced. From the 1970's about 4000 km² of natural pastures were transformed into irrigated agricultural land, theoretically increasing the grazing pressure in the remaining areas. By applying SMA based on field end-members signatures, a sub-pixel fraction was obtained for each end-member. Our results indicate that most of the desert experiences vegetation rehabilitation.

Citation Download Citation

Shai Kaplan, Lea Orlovsky, Dan G. Blumberg, and Elmar Mamedov "Assessment of pasture degradation in Turkmenistan using remote sensing", Proc. SPIE 6366, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VI, 636603 (20 October 2006); https://doi.org/10.1117/12.689580

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