In the event of a natural disaster, such as a flood or earthquake, using fast and efficient methods for estimating the extent of the damage is critical. Automatic change mapping and estimating are important in order to monitor environmental changes, e.g., deforestation. Traditional change detection (CD) approaches are time consuming, user dependent, and strongly influenced by noise and/or complex spectral classes in a region. Change maps obtained by these methods usually suffer from isolated changed pixels and have low accuracy. To deal with this, an automatic CD framework—which is based on the integration of change vector analysis (CVA) technique, kernel-based C-means clustering (KCMC), and kernel-based minimum distance (KBMD) classifier—is proposed. In parallel with the proposed algorithm, a support vector machine (SVM) CD method is presented and analyzed. In the first step, a differential image is generated via two approaches in high dimensional Hilbert space. Next, by using CVA and automatically determining a threshold, the pseudo-training samples of the change and no-change classes are extracted. These training samples are used for determining the initial value of KCMC parameters and training the SVM-based CD method. Then optimizing a cost function with the nature of geometrical and spectral similarity in the kernel space is employed in order to estimate the KCMC parameters and to select the precise training samples. These training samples are used to train the KBMD classifier. Last, the class label of each unknown pixel is determined using the KBMD classifier and SVM-based CD method. In order to evaluate the efficiency of the proposed algorithm for various remote sensing images and applications, two different datasets acquired by Quickbird and Landsat TM/ETM+ are used. The results show a good flexibility and effectiveness of this automatic CD method for environmental change monitoring. In addition, the comparative analysis of results from the proposed method (O. A.: 95.75, kappa: 0.91) and the classical CD techniques, namely, the principal component analysis-based CD method [overall accuracy (O. A.): 69.41, kappa: 0.55], the independent component analysis-based CD method (O. A.: 85.48, kappa: 0.68), spectral angle mapper (O. A.: 75.38, kappa: 0.60), image subtraction CD (O. A.: 86.57, kappa: 0.69), and image rationing CD (O. A.: 76.45, kappa: 0.61) methods shows that the accuracy of the obtained change map can be considerably improved. Moreover, the results demonstrate that the proposed kernel-based CD methods do not depend on the instrument or the nature of change classes.