A decision tree model (KTNI) based on the K-T transformation and Normalized Difference Vegetation Index (NDVI) was developed to extract the blue-green algal blooms information in the lakes using 9 Landsat TM/ETM+images. This paper compared the KTNI with the single-band threshold value method and the multi-band threshold value methods (RVI, DVI, NDVI) in accuracy of extracting the blue-green algal blooms information, and then validated the KTNI using two observed images of the Lake Taihu and one image of the Lake Chaohu. The results showed that the KTNI can mask the water body automatically and separate the blue-green algal blooms from the land vegetation and aquatic plants effectively. Model thresholds and their ranges were determined based on the TM images;the lower limits of the Brightness, Greenness and NDVI were 0.191, -0.007 and -0.054, respectively, and Wetness ranged from 0.07 to 0.15. For the upper limits, the Brightness was from 0.3 to 0.7, Greenness was from 0.2 to 0.5, and Wetness was from 0.1 to 0.3. The decision tree model and threshold values can be used as the reference for remote sensing monitoring on the blue-green algal blooms in lakes.