This paper explores the potential of modeling variations in soil salinity via the EMD and Random Forest modeling of remote sensing based environmental factors.
El Baroudy (2011), monitoring land degradation through remote sensing, assessed the causative factors, degree, and land degradation status using GIS techniques in the middle of the Nile Delta, Egypt.
"And it enables the discovery of relationships between tree density, remote sensing measurements and environmental factors".
Average fuel-based emission factors derived from the remote sensing measurements were compared with corresponding emission factors derived from IVE calculations for urban, hot stabilized condition.
In the present study, causal factors included remote sensing imagery derived land/use land/cover map, photo-lineament map and DEM derived slope, aspect and relative relief map.
This represents the highest achievable accuracy using the remote sensing raster grid, where other factors influencing classification such as classification algorithm, radiometric calibration and sensor characteristics are excluded.
Remote sensing data is an important factor in the decision-making system in order to identify and monitor oil spills, and provides an operational downlink (real time) for night-time operational direction of resources (ships) in clean-up.
