China is actively implementing the fast-growing and high-yielding timber base construction program in the past few years. There is an increasing number of poplar woodland in the North China Plain over the past decade, and the North China Plain is one of the major grain-producing areas in China. So the expansion of poplar woodland may influence food security, which attracts more and more attention. The area of poplar woodland is a key to the attention. We used TM band 1, band 2, band 3, band 4, band 5, and band 7 of Landsat 5 as fundamental datum and preprocessed them, then produced a new data set of 13 bands by generating vegetation index and principal component analysis. At last, we chose band combination of visual interpretation by using optimum index factor (OIF) and extracted information of area of poplar woodland by using maximum likelihood classification (MLC) and linear spectral unmixing (LSU). The results showed that: (1) Visible red light, near-infrared, mid-infrared, canopy vegetation index, and the first three principal components were dominant to the information extraction. (2) The 13 bands were separated according to correlation coefficient of the bands and then OIF was computed, which can save work time. (3) The area of extracted pixels of poplar woodland by MLC was 11 259.84ha, which accounted for 10.95% of the research area. Through outdoor authentication, the precision of producer and user was 84.07% and 93.14%, respectively, the outcome of classification and mapping was good. (4) LSU is a sub-pixel technology. So the area of extracted pixels of poplar woodland by LSU was better than that by MLC in the area of surface fragmentation. The extracted area by LSU reached 11 259.84ha, which increased largely in the research area. And it accounted for 13.33% of the research area. In a word, the two ways complemented each other, which can supply a sample for the quickly investigation of poplar woodland and the research on land-use conflict between woodland and arable land.