Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators

This research deals with monitoring irrigated saline soils of Faisalabad, Pakistan. The analysis is based on remote sensing data acquired from the Indian Remote Sensing satellite (IRS-1B) and using a Geographical Information System (GIS). We have examined how different remote sensing indicators work for salinity prone lands classification and assessment in part of the Indus basin of Pakistan, which is facing extremely hydrosalinized land degradation problems. The study has suggested some new but simple and practical approaches for assessing salinity. We have analyzed the effectiveness of several indicators for the presence of salts in the area in terms of salinity indices (SI), especially several combinations of the ratio of the signals received in the third spectral band to others. As salt-affected soils are also characterized by stressed vegetation, vegetation indices were also analyzed as concurrent indicators. The probability for obtaining a correct classification of the satellite images has shown to be strongly dependent on the season for all indicators analyzed. The best results can be achieved for the dry season (March–April), but not in humid or high temperature periods. The most difficult part in the classification processes was to distinguish between salt-affected areas and rural/village populated areas due to its muddy roofs producing similar reflection as of patchy saline and dry barren distributed soils. We have come-up with two original schemes of classification through the analysis of available satellite data for this specific test area. In the first case, we tried to produce a new set of composite and stretched images out of four channels data using special digital image processing (DIP) techniques and then analyzing their ratios. In the second scheme, we analyzed isoclustering functions that perform classification based on specifically created images (through principal component analysis (PCA) and salinity indices) instead of the common practice of using just satellite sensorʹs reflectance measurements. Both schemes have shown the ability to perform good classification and assessment for hydrosaline degraded lands in the study area using IRS-1B data.