Investigation on Physiological Status of Regional Vegetation Using Pushbroom Hyperspectral Imager Data

To extract vegetation pigment concentration and physiological status has been studied in two test areas covered with swamp and flourish vegetation using pushbroom hyperspectral imager (PHI) data which flied in September of 2000 at Daxing'anling district of Heilongjiang Province, China. The ratio analysis of reflectance spectra (RARS) indices, which were put forward by Chappelle et al (1992), are chosen in this paper owing to their effect and simpleness against both comparison with various methods and techniques for exploration of pigment concentration and characteristics of PHI data. The correlation coefficients between RARS indices and pigment concentration of vegetation were up to 0.8. The new RARS indices modes are established in the two test areas using both PHI data and spectra of different vegetations measured in the field. The indices' parameter images of chlorophyll a (Chl a), chlorophyll b (Chl b) and carotenoids (Cars) of the test areas covered with swamp and flourish vegetation are acquired by the new RARS indices modes. Furthermore, the regional concentration of Chl a and Chl b are extracted and quantified using regression equations between RARS indices and pigment concentrations, which were built by Blackburn (1998). The results showed the physiological status and variety clearly, and are in good agreement with the distribution of vegetation in the field.

An airborne pushbroom hyperspectral imager with wide field of view

An airborne pushbroom hyperspectrai imager （APHI） with wide field （42° field of view） is presented. It is composed of two 22° field of view （FOV） imagers and can provide 1304 pixels in spatial dimension, 124 bands in spectral dimension in one frame. APHI has a bandwidth ranging from 400 to 900 nm. The spectral resolution is 5 nm and the spatial resolution is 0.6 m at 1000-m height. The implementation of this system is helpful to overcome the restriction of FOV in pushbroom hyperspectral imaging in a more feasible way. The electronic and optical designs axe also introduced in detail.

Wavelet-based threshold denoising for imaging hyperspectral data

Imaging spectroradiometer is highly susceptible to noise.Accurately quantitative processing with higher quality is obligatory before any derivative analysis,especially for precision agricultural application.Using the self-developed Pushbroom Imaging Spectrometer(PIS),a wavelet-based threshold(WT)denoising method was proposed for the PIS imaging hyperspectral data.The WT with PIS was evaluated by comparing with other popular denoising methods in pixel scale and in regional scale.Furthermore,WT was validated by chlorophyll concentration retrieval based on red-edge position extraction.The result indicated that the determination coefficient R2 of the chlorophyll concentration inversion model of winter wheat leaves was improved from 0.586 to 0.811.It showed that the developed denoising method allowed effective denoising while maintaining image quality,and presented significant advantages over conventional methods.