Study On Quantitative Retrieval With Hyperspectral Resolution Remote Sensing For Desertification Monitoring

The study is the part of State Natural Science Funds project "Research on spectral rebuilt and quantitative retrieval of features in desertification area " (No.30070604).The paper is mainly to study the possibility and adaptability of application of hyperspectral resolution imaging spectrometer data in desertification monitoring. In the light of field features in desertification areas, the data quality and band combination of different bands are assessed, the indicators, principles and methods for data assessment and band option are put forward to, and optional band combination for desertification monitoring is determined primarily. The data pre-treatment model for desertification monitoring is developed after studying the pre-treatment algorithm of restoration and rebuilding of hyperspectral resolution data. The spectral features and variation rules of different objects in experimental areas are understood and analyzed. The different classified methods are given and discussed on the basis of characteristics of desertification region. The remote sensing quantitative retrieval model for the appraisable factors of desertification monitoring are founded.In Naiman country, Inner Mongolia, the experimental area, the hyperspectral imaging spectrometer data in plant growth seasons are collected by state-produced airborne hyperspectral imaging spectrometer OMIS-I, and the ETM+ image is also obtained. Meanwhile, ground investigation and measurement are made, which include the measurement on reflection feature of different kinds of geo-targets and the ground investigation data necessary for spectral rebuilding and retrieval models of the appraisable factors of desertification monitoring. Spectrum of calibration objects and main desertification types are measured through advanced spectral measurement equipment. Plot location and hyperspectral image registration is accurately done by GPS position methods.According to the nature and features of imaging spectrometer data and the field investigation and other data, on the basis of the pre-treatment of imaging spectrometer data, desertification information collection are implemented from the aspects of theoretical analysis and empirical fitting. The following results are made from this research: Assessing data quality and band combination of different bands in line with geo-target features in desertification areas, and determining basic bands for desertification monitoring. And understanding and studying the spectral features and variation rules of geo-targets in the experimental area, raising that it is the basis of geo-targets information collection with imaging spectrometer data to understand spectral features and variation rules of geo-targets, realizing that in a great extent spectral-integrated-form-based classification method can remove the phenomenon of "different spectrum with same objects" resulted from reflection ratio curve translation because of the angle change among sensor, targets and observation direction, and the average and variance images can be introduced to solve the problem of two kinds of geo-target with similar spectral forms and much different values of whole reflectionratio. It is suggested that "red edge" range bands of vegetation, which has close relationship with vegetation cover and biomass, is the main characteristic bands and important basis for careful vegetation classification and quantitative retrieval, and pixel-based derivative spectral analysis is very useful for removing the effects of soil background values and quantitatively retrieving vegetation biomass and cover. The remote sense quantitative retrieval model is developed for main appraisable factors of desertification monitoring assessment with imaging spectrometer data and then the applicability of model is analyzed.