Integrating Mahalanobis typicalities with a neural network for rubber distribution mapping

Accurate rubber distribution mapping is critical to the study of its expansion and to provide a better understanding of the consequences of land-cover and land-use change on carbon and water cycles. Employing Mahalanobis typicalities as inputs to a hard classifier to enhance the capability of generalization has not previously been explored. This letter presents a novel approach by integrating Mahalanobis typicalities with the multi-layer perceptron (MLP) neural network for mapping of rubber. A case study from the Thai–Lao and Sino–Lao borders was conducted using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. Different combinations of the nine ASTER bands including Visible and Near Infrared (VNIR) and Short-wave Infrared (SWIR), Normalized Difference Vegetation Index (NDVI) and Mahalanobis typicalities were used as input variables to the MLP. Results indicate that including Mahalanobis typicalities as input variables can improve the MLP's performance and increase the user's accuracy of rubber mapping.