Journal of Forests 2413-8398 2409-3807 10.18488/journal.101.2020.71.18.31 Journal of Forests Color-Based Forest Cover Type Image Segmentation using K-Means Clustering Approach Journal of Forests Journal of Forests 06-2020 2020 06-2020 06-2020 7 1 18 31 25 Mar 2020 03 Jun 2020 In order to understand forest composition, classifying forest cover type can help research regarding forest resilience, carbon sequestration, and climate change concerns. The purposes of this study were to develop and implement some image processing functions based on the histogram of forest cover type color image, and to classify forest cover type using its color feature sets of image pixels. Color-based image segmentation that is based on the color feature of image pixels assumes that homogeneous colors in the image correspond to separate clusters and hence meaningful objects in the image. The Image Processing Toolbox of MATLAB R2019a was used to convert the original forest cover type image to the enhance contrast image, including histogram of enhance contrast image. Furthermore, It was also used to analyze color-based forest cover type image segmentation using the enhance contrast image for this study. Using K-Means clustering analysis, a three-cluster solution was developed, labeled as Hardwoods (Yellow Color) Cover Type, Hardwoods (Gray Color) Cover Type, and Loblolly Pines Cover Type. There was a significant difference among three different forest cover type clusters in terms of histograms and L*a*b* color space features visually.