Asone of the important vegetation parameters, vegetation fractional coverage (VFC) is more difficult to measure accurately among a good many parameters of plant communities. The temperate typical steppe in the north of China was chosen for investigation in the present study and a digital camera was used to measure herb community coverage in the field, adopting methods of ocular estimation, gridding measurement, visual interpretation, supervised classification, and information extraction of colorspatial transformation to calculate the VFC of images captured by the digital camera. In addition VFC calculated by various methods was analyzed and compared VFC, enabling us to propose an effective method for measuring VFC using a digital camera. The results of the present study indicate that: (i) as two common useful and effective methods of measuring VFC with a digital camera, not only does the error of estimated values of visual estimation and supervised classification vary considerably, but the degree of automatization is very low and depends, to a great extent, on the manipulator; (ii) although the method of visual interpretation may assure the precision of the calculated VFC and enable the precision of results obtained using other methods to be determined, as far as large quantities of data are concerned, this method has the disadvantages of wasting time and energy, and the applications of this method are limited; (iii) the precision and stability of VFC calculated using the grid and node method are superior to those of visual estimation and supervised classification and inferior to those of visual interpretation, but, as for visual interpretation and supervised classification, gridding measurements are difficult to apply in practice because they are not time efficient; and (iv) in terms of the precision of calculation of the VFC, an information-extracting model based on an intensity, hue, saturation (IHS) color space-multi-component series segmentation strategy is superior to methods of ocular estimation, gridding measurement, and supervised classification. In terms of practical efficiency, the information-extracting model is superior to visual interpretation, supervised classification, and gridding measurement. It has been proven that estimating the VFC of the north temperate typical steppe using this model is feasible. This is very fundamental research work in grassland ecology.

One of the study objectives of global change is land use/cover change (LUCC) by using multiscale remotely sensed data on global and regional scale. In this paper, field sample, digital camera, Landsat-ETM+ (ETM+, Enhanced Thematic Mapper) image and the National Oceanic and Atmospheric Administration/the advanced very high resolution radiometer (NOAA/AVHRR) image were integrated to detect, simulate and analyze the vegetation fractional coverage of typical steppe in northern China. The results show: (1) Vegetation fractional coverage measured by digital camera is more precise than results measured by other methods. It can be used to validate other measuring results. (2) Vegetation fractional coverage measured by 1 m2 field sample change fluctuantly for different observers and for different sample areas. In this experiment, the coverage is generally high compared with the result measured by digital camera, and the average absolute error is 9.92%, but two groups measure results, correlation coefficient r2=0.89. (3) Three kinds of methods using remotely sensed data were adopted to simulate the vegetation fractional coverage. Average absolute errors of the vegetation fractional coverage, measured by ETM + and NOAA, are respectively 7.03% and 7.83% compared with the result measured by digital camera. When NOAA pixel was decomposed by ETM+ pixels after geometrical registry, the average absolute errors measured by this method is 5.68% compared with the digital camera result. Correction coefficients of three results with digital camera result r2 are respectively 0.78, 0.61and 0.76. (4) The result of statistic model established by NOAA-NDVI (NDVI, Normalized Difference Vegetation Index) and the vegetation fractional coverage measured by digital camera show lower precision (r2=0.65) than the result of statistic model established by ETM+-NDVI and digital camera coverage then converted to NOAA image (r2=0.80). Pixel decomposability method improves the precision of measuring the vegetation fractional coverage on a large scale. This is a significant practice on scaling by using remotely sensed data. Integrated application of multi-scale remotely sensed data in earth observation will be an important approach to promoting measuring precision of ecological parameters.