Yes, the Total Operating Characteristic (TOC) curve can absolutely be used to validate the accuracy of a Land Use/Land Cover (LULC) classification model, and it's often a better choice than traditional Receiver Operating Characteristic (ROC) curves when dealing with multi-class classification problems, which LULC typically is.

Here's why and how: ROC curves are fundamentally designed for binary classification (two classes). While you can create ROC curves for multi-class problems using a "one-vs-all" approach (treating each class as "positive" and all others as "negative" in turn), this doesn't fully capture the complexities of a multi-class scenario in a single, unified visualization. It gives you multiple curves, one for each class, but not a holistic view.

TOC, on the other hand, is specifically designed for multi-class classification. It considers all classes simultaneously. Unlike ROC, which focuses on True Positive Rate (TPR) and False Positive Rate (FPR), TOC incorporates the concept of "Wrong Hits." A Wrong Hit occurs when a pixel is classified as class A, but it actually belongs to class B (where B is not the "all other" class, but a specific, incorrect class). This is crucial in LULC classification because misclassifications are rarely between just two categories; they often involve confusion between multiple, distinct classes. For example, misclassifying "forest" as "urban" is different from misclassifying "forest" as "grassland." TOC accounts for these nuances.

To use a TOC curve, you'll need to calculate, for each class and across a range of probability thresholds, the following: Hits (True Positives), Misses (False Negatives), False Alarms (False Positives), and Wrong Hits. These values are derived from the confusion matrices generated at each threshold. You would then plot the proportion of Hits on Y-axis and Proportion of (False Alarms + Wrong Hits) on the X-axis, creating one curve per class. Alternatively you can plot the threshold versus a TOC area. The resulting curves, and critically, the area under the TOC curve (analogous to AUC in ROC, but more comprehensive), provide a robust measure of your model's ability to correctly classify pixels across all LULC categories, taking into account the specific types of misclassifications. A curve hugging the top-left corner indicate better performance. The area of the TOC gives an overall idea of the model performance. Therefore, TOC is a suitable and informative validation method for LULC classification models.