Transformation Caused in the Consistency of Landcover Due to Stubble Burning in the Northern India State of Punjab from Suomi NPP Satellite Data

Abstract

The Indian state of Punjab is known as India’s breadbasket. The size is Punjab is relatively small as compared to other Indian states like Uttar Pradesh, Madhya Pradesh, Maharashtra. It ranks among the nation’s top wheat and rice producers. The dark part of this achievement is for a few weeks in October and November, Punjab also becomes a major producer of air pollution. The main reason for the air pollution during this period is uncontrolled Stubble burning. Stubble burning is among one of the major problems caused by the farmers of Punjab and Haryana which is affecting entire Northern India. Punjab is having two crops growing season i.e. May to September for rice production and November to April for wheat production. Since rice leave behind a significant amount of plant debris which is not even consumed by cattle’s so for quickly preparing the agricultural fields for the next crop, the fields are burnt intentionally. This situation becomes extremely hazardous for the nearby states and even for the people of Punjab. The deadly combination of smoke and fog gets converted in smog which is very dangerous for babies, elder peoples and for patients suffering from Asthma. Since the fires are active only for a short period of time and burnt at relatively at low temperatures but the smoke generally stays in the atmosphere. On November 2, 2016, winds carried over smoke, soil particles, dust residue and partially burnt plants towards the National Capital New Delhi. The smoke from the Punjab field combined from industrial pollutants of NCR region and heavy vehicle pollutants of the New Delhi push levels of particulate matter to an extremely high level. Here we are making an investigation in the change that occurred in the texture features of the landcover due to the burning of the crop fields. We have computed the changes in the texture features for different orientations and distances through the Grey level co-occurrence matrix (GLCM). Texture visual features which are the combination of contrast, correlation, energy, and homogeneity are computed. Finally, through the GLCM technique, we are able to identify the number of areas under fire and thus an estimate is performed for the total number of the area under fire. During this research work, three objectives have been achieved firstly change in the texture features is computed secondly the visual representation of the changes along with identification of the areas under fire and finally, a comparison of the original and subset image is presented. We have also developed a theoretical approach for texture quantification through GLCM. This approach can be further extended to study various other natural disasters like a landslide, flood, droughts, etc.