基于数码相片Gamma校正的水稻叶面积指数估算

随着数码相机的日益普及,利用数码相机进行作物叶面积指数(LAI)测量不断得以应用。由于数码相机成像时会对入射光辐射强度进行Gamma编码变换,输出的相片DN(Digital Number)值与入射光辐射强度呈非线性关系,会造成在确定相片中植被叶片与背景的分割阈值时出现误差,并最终导致LAI估算存在较大不确定性。以水稻为研究对象,获取不同生长期水稻冠层相片并结合同步LAI 2000测量的LAI数据,基于相片Gamma校正原理,对水稻不同生长期冠层相片进行Gamma校正,在此基础上利用冠层孔隙率方法,估算不同生长期水稻LAI。结果表明,经过Gamma校正相片估算的水稻LAI总体精度有显著提高,相片估算的IMAGE LAI与LAI-2000测量值比较的决定系数达到0.71(P0.05)。在整个观测期内,两种方法观测的LAI值在时间变化趋势上表现一致,但在不同生长期内存在差别,在水稻分蘖期和拔节期相片估算的IMAGE LAI要高于LAI-2000测量值,孕穗期到抽穗期期间IMAGE LAI低于LAI-2000测量值,乳熟期到成熟期IMAGE LAI又高于LAI-2000的观测结果。

Estimation of paddy rice leaf area index based on photo gamma correction

The automatic compensation effect in a digital camera can cause a bias of the digital number (DN) of the acquired images. This study carried out DN correction using gamma transformation theory combined with a look-up table method. The DN of the acquired image generally bears a nonlinear relationship with the intensity of incident light, which is an inherent characteristic in common commercial digital cameras. The gamma method is a nonlinear operation used to code and decode luminance or tri-stimulus values in video or still image systems. It has been widely used in photography and video productions, but is rarely used in image processing and applications to vegetation parameter determination. The gamma transformation is mainly used to compensate for the properties of human vision by maximizing the use of the bits or bandwidth relative to the perception of light and color. If images are not gamma encoded, they allocate too many bits or too much bandwidth to the darker areas that humans cannot differentiate and too few bits to the brighter areas that humans are sensitive to, and would thus require more bits to maintain the same visual quality. Although the visual quality of RAW image is generally not very appropriate in commercial use, the RAW image is precisely required in the retrieval of vegetation parameters to obtain a better fit with field measurements. Apart from the gamma correction, for the various annual paddy rice conditions we used the vertical gap fraction to obtain the leaf area index (LAI), which we named image LAI, and compared with the field LAI from LAI-2000. The results indicated that the correlation coefficients after the correction procedure were remarkably similar with an R² of 0.71 (P < 0.05), better than for the uncorrected retrieved figures. In the tilling and jointing stages, there was a close approximation between the vertical gap fraction derived LAI and field LAI, but in the booting and heading stages the differences become obvious, reaching a maximum of 0.38. However, in the milk-ripened and maturation stages, the reverse occurred; the field LAI was higher than the vertical gap fraction derived-LAI mainly because the foliage of the rice paddy went yellow. In general, there was a good fit between the retrieved LAI and field LAI, but the estimation accuracy varied depending on the phenology of paddy rice. A better fit between image LAI and LAI 2000 was observed in the tilling and jointing stages, when a larger bias between image LAI and field LAI was observed in the booting and heading stages, while an overestimation in image LAI was observed in the milk-ripened and maturation stages. The commercial digital camera can carry out easy and low cost research given its characteristic properties (ease of use, less costly, and lack of atmospheric effects). These features enable anyone to carry out good estimation work related to vegetation. These factors and many related articles make the digital camera a useful alternative method for assessing vegetation parameters such as vegetation fraction, leaf color, plant type and leaf area index.