A High-Precision Remote Sensing Identification Method on Saline-Alkaline Areas Using Multi-Sources Data

Soil salinization is a widespread and important environmental problem. We propose high-precision remote sensing identification method for saline-alkaline areas using multi-source data, which of some significance improving ecological problems on global scale have been caused by soil salinization. Its principle to identify from imagery decision tree model combining four spectral indices named NDSI34 (Normalized Difference Spectral Index Band 3 4), NDSI25 2 5), NDSI237 4) NDSInew (New Normalized Salt Index) that can distinguish other features. In this method, the complementary information within data used improve classification accuracy. The main steps include acquisition, adaptive feature fusion integrated expression area fine area, accuracy verification. Taking Minqin County, Gansu Province, China as study we use based GF-2, GF-6/WFV DEM data. results show overall 88.11%, 7.69% higher than traditional methods, indicating it could effectively distribution areas, thus provide scientific technique quick in large regions.