In sustainable forest management, information on the extent and types of degraded forest sites is essential and crucial. It enables planning of appropriate remedial strategies. This study was carried out to detect and quantify degraded forest land in Tanah Merah District, Kelantan using remotely sensed data. Spot-5 satellite data (Path/ Row: 269/339) was acquired from MACRES, which covered part of three forest reserves ie. Sungai Sator, Gunung Basor and Gunung Stong. The ERDAS IMAGINE software version 8.7 was used to enhance the image for better visualization using band combination and spatial filtering techniques. This was followed by “Supervised Classification” of the image using “Maximum Likelihood Classifier” to detect and classify degraded forest features into pre-determined classes. The four classes detected were primary forest, degraded forest, gap and water bodies. Results showed that the degraded forest class constituted the largest area (57,878 ha), followed by primary forest gap (20,686 ha) and gap (3,488 ha). Degraded forest types were represented by road, agriculture, plantation areas. Based on the accuracy assessment, the overall classification accuracy obtained was 89% and showed that the remote sensing technique was able to detect and map degraded forest sites.