A Comprehensive Analysis of Advanced Change Detection Technique for Planning and Monitoring Sustainable Mango Crop Areas using Landsat-8 Images

An efficient and accurate mango (Mangifera Indica L.) crop planning is a very important factor for sustainable mango crop production. Mango is a perennial fruit crop and alternate bearer, which is a major challenge for production sustainability. The main objective of this study is to detect the land cover changes using satellite images for sustainable land cover planning in three main mango-producing districts, namely Lucknow, Bulandshahr, and Meerut of Uttar Pradesh State, India. In this study, Landsat-8 OLI images from 2013–14 to 2020–21 have been used to detect the changes by implementing the log ratio (LR) and principal component analysis (PCA) fusion-based change detection method. In order to extract meaningful information from Landsat-8 images, Red and NIR band images were used to create soil-adjusted vegetation index (SAVI) images. Thereafter, the fusion-based change detection method using LR-PCA has been applied to retrieve the enhanced image. To obtain the final change images with the detected temporal changes in the area under the mango crop, image thresholding has been used. After conducting a detailed analysis, it is observed that the change detection accuracy of the LR-PCA fusion-based change detection technique is retrieved as 92%, which is quite high. In the future, proper mango monitoring techniques may play a very important role in developing sustainable mango orchards and maintaining environmental health. Future applications of this study will include change detection in mango crop areas in all major mango-producing districts in Uttar Pradesh.