Expert assessments for crop and range productivity of very-large arid and semiarid areas worldwide are ever more in demand and these studies require greater sensitivity in delineating the different grades or levels of soil salinity. In conjunction with field study in arid southeastern Oregon, we assess the merit of adding decision-tree analysis (DTA) to a commonly used remote-sensing method. Randomly sampled surface soil horizons were analyzed for saturation percentage, field capacity, pH and electrical conductivity (EC). IFSAR data were acquired for terrain analysis and surficial geological mapping, followed by derivation of layers for analysis. Significant correlation was found between EC values and surface elevation, bands 1, 2, 3 and 4 of the Landsat TM image, and brightness and wetness indices. Maximum-likelihood supervised classification of the Landsat images yields two salinity classes: non-saline soils (EC < 4 dSm -1 ), prediction accuracy of 97%, and saline soils (EC > 4 dSm -1 ), prediction accuracy 60%. Addition of DTA results in successful prediction of five classes of soil salinity and an overall accuracy of about 99%. Moreover, the calculated area of salt-affected soil was overestimated when mapped using remote sensing data only compared to that predicted by additionally using DTA. DTA is a promising approach for mapping soil salinity in more productive and accurate ways compared to only using remote-sensing analysis.
OPEN ACCESSRemote Sens. 2010, 2 152
Burullus Lake is one of most important lakes in north Delta of Egypt. It is exposed to huge amounts of serious pollutants especially heavy metals. The sediments within the lake aid in the dispersion of these metals. The main objectives of this research were to evaluate and map the spatial distribution of heavy metals in Burullus Lake sediments. Accordingly, 37 locations were randomly distributed within the lake. Sediment samples were taken from these locations. These samples were analyzed for seven metals including Fe, Cu, Zn, Cr, Co, Cd and Pb. Also, five indices were used to identify the status of metal pollutants in the Lake. These indices are: enrichment factor (EF), contamination factor (CF), degree of contamination (DC), pollution load index (PLI) and geo-accumulation index (Igeo). Ordinary Kriging was used to interpolate the spatial distribution of the studied elements within the lake. The obtained results indicated that cadmium was the most enriched element in the lake sediments due to industrial and agricultural wastes drained into the lake. The Igeo index revealed that Cd and Pb were the common pollutants in lake sediments. The DC values ranged between low (near El-Boughaz) and moderate (near drainage areas). The spatial distribution of pollutants within the lake indicated that the highly polluted areas are located close to the drains, whereas as the less polluted areas were close to El-Boughaz.
Contamination with heavy metals is one of the most serious problems in the aquatic environments. In Egypt, Manzala Lake is suffering from this problem. The objective of this work was to assess heavy metals pollutants and their spatial distribution in Manzala Lake using GIS technique. Georeferenced water and sediment samples were randomly collected from the lake. The detected heavy metals were: Fe, Pb, Cu, Cd, Cr, Zn and Co. The obtained results indicated that the highest concentrations of heavy metals were observed in the northeastern and the southern parts of the lake nearby drains. This could be attributed to industrial, agricultural and municipal wastes coming through the drains especially Bahr El-Baqar drain and the industrial wastes coming from Port Said drains. From the geo-accumulation index, it was noticed that the lake is more polluted with cadmium and lead in the hydrosoils samples. All metals in water are within the EPA standard limit except for cadmium. Geostatistics provides effective methods to quantify the contaminated waters and sediments which support decision-making about redevelopment scenarios or remediation techniques.
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