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Change detection (CD) is the process of detecting changes from multi-temporal satellite images that have undergone spatial changes due to natural phenomena and/or human-induced activities. Mango is a major fruit crop in India, but detection of changes in mango crops remains a challenging task because the reason that many perennial crops have similar reflectance profiles. Therefore, a potent change detection technique is required for different applications such as the rate of deforestation, urban developments, damage evaluation, and resource monitoring. Compared to annual and seasonal crops, relatively few studies have been conducted on change detection in perennial fruit crops. In this study, a novel log ratio change detection technique (i.e., LR-PCA) is developed using the fusion of log-ratio (LR) and principal component analysis (PCA) images derived from bi-temporal soil adjusted vegetation index (SAVI) images to extract meaningful information and detect temporal changes in the mango fruit crop areas with high change detection accuracy. The proposed approach comprised two steps: (1) SAVI images from 2015 and 2019 were used to retrieve the log-ratio (LR) and principal component (PC) images, respectively, and both the images were fused by applying the pixel-by-pixel fusion approach. (2) Fused images were classified into three classes: &#x201C;positive change&#x201D;, &#x201C;no change&#x201D;, and &#x201C;negative change&#x201D; using a derived threshold value. The results show that the LR-PCA method of change detection yields a high change detection accuracy of 92&#x0025; in comparison with the other change detection methods viz. vegetation image differencing, image ratioing, PCA, and log-ratio. To validate the adaptability of the proposed algorithm, experiments with two sets of bi-temporal SAVI indices images belonging to the Sitapur district of Uttar Pradesh State determine that the proposed change detection method performs well as compared to the existing individual methods for detection of changes in mango fruit crop. The proposed method is expected to be useful for detecting changes in the area of perennial crops. In the future, an accurate and efficient change detection analysis may be helpful for developing a real-time mango fruit crop monitoring system at the national level.

Development of LR-PCA Based Fusion Approach to Detect the Changes in Mango Fruit Crop by Using Landsat 8 OLI Images