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Soil salinity is the most common land degradation agent that impairs soil functions, ecosystem services and negatively affects agricultural production in arid and semi-arid regions of the world. Therefore, reliable methods are needed to estimate spatial distribution of soil salinity for the management, remediation, monitoring and utilization of saline soils. This study investigated the potential of Landsat 8 OLI satellite data and vegetation, soil salinity and moisture indices in estimating surface salinity of 1014.6 ha agricultural land located in Dushak, Turkmenistan. Linear regression model was developed between land measurements and remotely sensed indicators. A systematic regular grid-sampling method was used to collect 50 soil samples from 0–20 cm depth. Sixteen indices were extracted from Landsat-8 OLI satellite images. Simple and multivariate regression models were developed between the measured electrical conductivity values and the remotely sensed indicators. The highest correlation between remote sensing indicators and soil EC values in determining soil salinity was calculated in SAVI index (r = 0.54). The reliability indicated by R2 value (0.29) of regression model developed with the SAVI index was low. Therefore, new model was developed by selecting the indicators that can be included in the multiple regression model from the remote sensing indicators. A significant (r = 0.74) correlation was obtained between the multivariate regression model and soil EC values, and salinity was successfully mapped at a moderate level (R2: 0.55). The classification of the salinity map showed that 21.71% of the field was non-saline, 29.78% slightly saline, 31.40% moderately saline, 15.25% strongly saline and 1.44% very strongly. The results revealed that multivariate regression models with the help of Landsat 8 OLI satellite images and indices obtained from the images can be used for modeling and mapping soil salinity of small-scale lands.

No abstract

Soil salinity is a major land degradation process reducing biological productivity in arid and semi-arid regions. Therefore, its effective monitoring and management is inevitable. Recent developments in remote sensing technology have made it possible to accurately identify and effectively monitor soil salinity. Hence, this study determined salinity levels of surface soils in 2650 ha agricultural and natural pastureland located in an arid region of central Anatolia, Turkey. The relationship between electrical conductivity (EC) values of 145 soil samples and the dataset created using Landsat 5 TM satellite image was investigated. Remote sensing dataset for 23 variables, including visible, near infrared (NIR) and short-wave infrared (SWIR) spectral ranges, salinity, and vegetation indices were created. The highest correlation between EC values and remote sensing dataset was obtained in SWIR1 band (r = -0.43). Linear regression analysis was used to reveal the relationship between six bands and indices selected from the variables with the highest correlations. Coefficient of determination (R2 = 0.19) results indicated that models obtained using satellite image did not provide reliable results in determining soil salinity. Microtopography is the major factor affecting spatial distribution of soil salinity and caused heterogeneous distribution of salts on surface soils. Differences in salt content of soils caused heterogeneous distribution of halophytes and led to spectral complexity. The dark colored slickpots in small-scale depressions are common features of sodic soils, which are responsible for spectral complexity. In addition, low spatial resolution of Landsat 5 TM images is another reason decreasing the reliability of models in determining soil salinity.

Tarihin izahı ve yorumunda dikkate alınması gereken önemli unsurlardan bir tanesi de salgın hastalıklardır. Salgın hastalıkların mahiyeti, korunma yolları ve tedavileri uzun süre tam olarak bilinmemiştir. İnsanlar salgın hastalığın mahiyetini öğreninceye kadar onu Tanrı’nın bir gazabı olarak görmüşler, çoğu zaman da üzerine bir kutsiyet yüklemişlerdir. Eski Çağ’dan günümüze kadar endemik, epidemik ve pandemik karakterli çok sayıda salgın hastalık yaşan-mış ve bu salgınlarda milyonlarca insan hayatını kaybetmiştir. Veba, sıtma, kolera, sarı humma, çiçek, grip, verem, tifo, tifüs ve frengi hastalıkları kitlesel ölümlere yol açan önemli salgın hastalıklardır. Ancak bunların hepsi aynı anda dünyanın birkaç kıtasını etkisi altına alacak ölçekte salgınlar üretememiştir. Sadece veba, kolera ve grip hastalıklarının insanları etkileyen ve küresel ola-rak bilinen pandemik salgın hastalıklar olduğunu söyleyebiliriz. Dünya, Eski Çağ’dan günümüze kadar, üç veba, yedi kolera ve bugünlerde yaşadığımız sal-gınla birlikte 10’dan fazla grip pandemisi yaşamıştır. Epidemik boyutta kalan ancak çok sayıda insanın ölümüne sebep olan çiçek, sıtma ve sarı humma sal-gınları da en az bu üç hastalık kadar etkili olmuşlardır.Yaşanan bütün küresel salgınların tahribatının yüksek olmasının sebebi, has-talığın tam olarak tanınmamasıdır. Bu yüzden ölüm oranları yüksek, yayılma alanları geniş olmuştur. Salgın hastalıklar; ekonomik, sosyal, psikolojik, kül-türel, siyasi, dinî, coğrafî ve daha birçok bakımdan toplumsal hayatı derinden etkilemiştir.