Abstract: Abstract: Unmanned Aerial Vehicle (UAV) Remote Sensing has great advantages over traditional methods, such as lower cost, simpler operation, faster access speed and higher resolution. In this paper, after analyzing the spectral characteristics of vegetation and non-vegetation in UAV images, which only contains red, green, and blue bands, we found that the vegetation spectral had the feature of green band>red band>blue bands, which means vegetation had the biggest reflection in the green band and had the smallest reflection in the blue band. However, non-vegetation region had the reflection feature of red band>green band>blue band and blue band>green band>red band. The pixels value of the vegetation region was smaller than the non-vegetation region. For overall consideration of the above characteristics and the features of the healthy green vegetation's spectral profile, and in order to enhance the vegetation information and minimize the vegetation signal, we referenced the form of NDVI and put forward a new vegetation index--VDVI (visible-band difference vegetation index). Then we calculated the vegetation index of VDVI, EXG, NGRDI, NGBDI, and RGRI. After calculation of the vegetation index, we used the same AOI region of the prior analysis in a typical spectral characteristic and we made the line charts to analyze the feasibility of each index. After observation of the line charts, we found that NGRDI was not suitable to extract the vegetation from a UAV image because the index values were overlapping with each other, except for bare soil. On the contrary, the NGBDI and VDVI was suitable for the extraction of vegetation from the image, because there was little overlapping. And the vegetation's EXG index value was greater than twenty and the value of non-vegetation was lesser than twenty except for the area of a building. However, there was also some overlapping of a building and the field, which could cause some mistake in the extraction of the result. After that, we determined the threshold value of each index. The threshold is most important in the extraction of vegetation from an image, but the value is very difficult to determine. After observation of the histogram of the vegetation index, we decided to use the bimodal histogram and the histogram entropy threshold method to determine the threshold value of each vegetation index, and compared each extraction result of the two methods and chose the value which had the higher extraction accuracy and used it as the final value to extract the vegetation from the UAV image. After comparison of the VDVI extraction results with EXG, NGRDI, NGBDI, and RGRI, we found that whatever method was used to determine threshold value, the accuracy of VDVI extraction result was always most precise, and the EXG must use the histogram entropy threshold method to determine the threshold. It can also extract the most vegetation information. The NGRDI and RGRI have bad extraction results. In conclusion, in the VDVI overall consideration of the vegetation characteristic of reflection in the green band and the absorbent in the red and blue bands, and its value ranged from -1 to 1. And the extraction accuracy of VDVI is higher than the others, and its threshold is easier to determine which is near to zero. In order to verify that the VDVI was suitable for another UAV image, we chose another UAV image to calculate its VDVI and extracted vegetation from it. Then we generated 200 random points to evaluate the vegetation extraction accuracy from the UAV image. From an accuracy report, we found the total extraction precision was 91.5%, and the kappa coefficient was 0.8256. We concluded that the VDVI was broadly suitable to extract vegetation from N UAV image which only contains a visible band.