A fuzzy machine learning approach for identification of paddy stubble burnt fields

Abstract

Stubble burning is the most common practice in Northern region of India and is considered as the main reason for pollution in nearby NCR (National Capital Region) New Delhi, India. Therefore, in this research work, paddy stubble burnt fields were identified by using fuzzy based machine learning algorithms for a test site in Patiala, Punjab, India. An Euclidean Distance (ED) and Gaussian Kernel based Modified Possibilistic c-Means (MPCM) algorithms were used for this purpose. The main objective of this research work was to test the effectiveness of machine learning approach for identification of paddy stubble burnt filed and also to find out the better classifier amongst Euclidean Distance (ED) and Gaussian Kernel MPCM classifier. The Sentinel 2A/2B cloud free temporal data having a high frequency of repetitiveness in the interval of 5-days were used. To reduce spectral dimensionality of data Class Based Sensor Independent Indices (CBSII) database were generated. The burning was mainly observed from the last week of October to the first week of November and has been visualized using variation of CBSII-NDVI temporal plot. Normalized Burnt Ratio (NBR) was computed and compared with CBSII-NDVI outputs for both the ED and Kernel based MPCM classifiers. Delta Normalized Burnt Ratio (ΔNBR) was computed for cross-validation of burnt patches and is found to be in between 0.45 and 0.66. Thus, it can be concluded that kernel based MPCM classifiers are more efficient in terms of accuracy and robustness to noise in comparison to distance based MPCM classifiers, for the mapping of paddy burnt fields.