[Land salinization information extraction method based on HSI hyperspectral and TM imagery].

This paper chose the typical salinization area in Kenli County of the Yellow River Delta as the study area, selected HJ-1A satellite HSI image at March 15, 2011 and TM image at March 22, 2011 as source of information, and pre-processed these data by image cropping, geometric correction and atmospheric correction. Spectral characteristics of main land use types including different degree of salinization lands, water and shoals were analyzed to find distinct bands for information extraction Land use information extraction model was built by adopting the quantitative and qualitative rules combining the spectral characteristics and the content of soil salinity. Land salinization information was extracted via image classification using decision tree method. The remote sensing image interpretation accuracy was verified by land salinization degree, which was determined through soil salinity chemical analysis of soil sampling points. In addition, classification accuracy between the hyperspectral and multi-spectral images were analyzed and compared. The results showed that the overall image classification accuracy of HSI was 96.43%, Kappa coefficient was 95.59%; while the overall image classification accuracy of TM was 89.17%, Kappa coefficient was 86.74%. Therefore, compared to multi-spectral TM data, the hyperspectral imagery could be more accurate and efficient for land salinization information extraction. Also, the classification map showed that the soil salinity distinction degree of hyperspectral image was higher than that of multi-spectral image. This study explored the land salinization information extraction techniques from hyperspectral imagery, extracted the spatial distribution and area ratio information of different degree of salinization land, and provided decision-making basis for the scientific utilization and management of coastal salinization land resources in the Yellow River Delta.

[Estimation of chlorophyll content in apple tree canopy based on hyperspectral parameters].

The hyperspectral reflectance of apple tree canopy during spring shoots stopping growth period was measured using ASD FieldSpec3 field spectrometer. Original spectral data were processed in deviation forms, and significant spectrum parameters correlated with chlorophyll content were found out with correlation analysis. The best vegetation indices were chosen and the apple canopy chlorophyll content estimation model was established by analyzing vegetation index of two-band combination in the sensitive region 400-1 350 nm. The result showed that (1) The sensitive band region of apple canopy chlorophyll content is 400-1 350 nm. (2) The vegetation index CCI(D(794)/D(763)) can commendably estimate the apple canopy chlorophyll content. (3) The model with CCI(D(794)/D(763)) as the independent variables was determined to be the best for chlorophyll content prediction of apple tree canopy. Therefore, using hyperspectral technology can estimate apple canopy chlorophyll content more rapidly and accurately, and provides a theoretical basis for rapid apple tree canopy nutrition diagnosis and growth monitoring.