جستجوی مقالات مرتبط با کلیدواژه "spectral indices" در نشریات گروه "آبخیزداری، بیابان، محیط زیست، مرتع"

Soil salinity and alkalinity are among the most important soil degradation processes, especially in arid and semi-arid regions. The purpose of this study is to evaluate spectral indicators as well as use the data of the EM38 for identifying saline soils and spatial changes. The study area is Ghahavand plain that is located in Hamedan Province. In this study, Landsat 8 satellite data were used. Soil sampling of 37 points was performed and 86 points were read using an electromagnetic induction device. Using protomorphic units based on visual interpretation of OLI 543 false-color composite image and field observations, a total of 9 homogeneous units were identified in the region using these units as training regions for supervised classification. The results showed that the detection of soil salinity in the visible spectrum (blue, green, and red band) is feasible. The bands 5, 6, and 7 can be useful in differentiating salty white crust lands from salty gray crust lands. In the reflective bands, the white and smooth crust exhibits the highest reflectance. The results of classification accuracy showed that the highest total accuracy was 90.0 and the kappa coefficient was 80.45 when bands 1, 2, 3, 4, 5, 6, 7, 10, and 11 were used and shallow and abandoned plowed lands had the least accuracy. Also, the final model of salinity estimation showed that SI6 and SI11 indicators and electromagnetic induction vertical measurements (EMv) are the most suitable variables for estimating salinity spatial changes.

Soil salinity undergoes significant spatial and temporal variations; therefore, salinity mapping is difficult, expensive, and time consuming. However, researchers have mainly focused on arid soils (bare) and less attention has been paid to halophyte plants and their role as salinity indicators. Accordingly, this paper aimed to investigate the relationship between soil properties, such as electrical conductivity of the saturation extract (ECe) and the spectral reflectance of vegetation species and bare soil, to offer a method for providing salinity map using remote sensing. Various vegetation species and bare soil reflectance were measured. Spectral Response Index (SRI) for bare soil and soil with vegetation was measured via the Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and salinity indexes. The electrical conductivity of the saturated extract, texture, and organic matter of soil samples were determined. The correlation coefficient of soil salinity with SRI, SAVI, and salinity indexes were obtained, and a model was presented for soil salinity prediction. EC map was estimated using the proposed model. The correlation between SRI and EC was higher than other models (0.97). The results showed that the salinity map obtained from the model had the highest compliance (0.96) with field findings. In general, in this area and similar areas, the SRI index is an acceptable indicator of salinity and soil salinity mapping.